{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "R",
      "language": "R",
      "name": "ir"
    },
    "language_info": {
      "codemirror_mode": "r",
      "file_extension": ".r",
      "mimetype": "text/x-r-source",
      "name": "R",
      "pygments_lexer": "r",
      "version": "3.6.0"
    },
    "colab": {
      "name": "CS112 Final Project.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mahmud-nobe/Projects/blob/main/Causal_Inference.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sU1mAi8wBSUy"
      },
      "source": [
        "## CS112 Final Project Notebook: Effect of empowerment of women on repoting crime rate"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iCBJH-4frJVT"
      },
      "source": [
        "## Load and Preapre the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "waT661Q1TY2w"
      },
      "source": [
        " This is what I did in my Rstudio to get the dataset in csv :\n",
        "\n",
        "\n",
        "\n",
        "> `dataset = load(\"tables1to5.RData\")`\n",
        " \n",
        " > `dataset`\n",
        "\n",
        "> `# 'x'`\n",
        "\n",
        "> `final.data = x`\n",
        "\n",
        "> `head(final.data)`\n",
        "\n",
        "> `write.csv(final.data, \"tables1to5.csv\")`\n",
        "\n",
        "\n",
        "\n",
        "Then I upload it in github."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3v2IMck7KTVm"
      },
      "source": [
        "url = \"https://raw.githubusercontent.com/mahmud-nobe/CS112-Final-Project/master/tables1to5.csv\"\n",
        "data = read.csv(url)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CnRsdbO1Q_2X",
        "outputId": "941424b3-3b52-42d2-e675-c394740d2198",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "head(data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  X year stateuni       ipop     imale    pcr_prop  pcr_order  pcr_econ  \n",
              "1 1 1985 ANDHRA PRADESH 58733007 29755186 0.3701837 0.01159484 0.12194166\n",
              "2 2 1986 ANDHRA PRADESH 60028840 30416752 0.3218286 0.05244146 0.03141823\n",
              "3 3 1987 ANDHRA PRADESH 61324674 31078317 0.3477393 0.01025688 0.14506397\n",
              "4 4 1988 ANDHRA PRADESH 62620508 31739883 0.4270326 0.08220949 0.04052985\n",
              "5 5 1989 ANDHRA PRADESH 63916341 32401449 0.4614939 0.08195087 0.04596633\n",
              "6 6 1990 ANDHRA PRADESH 65212174 33063015 0.4658639 0.08265328 0.04235406\n",
              "  pmurder    pcr_womtot ⋯ lpstrape lpstpoa lpstpcr lparrest_womcrime\n",
              "1 0.02943830 0.06718931 ⋯ NA       NA      NA             NA        \n",
              "2 0.02663720 0.06629049 ⋯ NA       NA      NA             NA        \n",
              "3 0.02868340 0.05174177 ⋯ NA       NA      NA             NA        \n",
              "4 0.03265704 0.06434455 ⋯ NA       NA      NA      -2.295828        \n",
              "5 0.03595325 0.07145828 ⋯ NA       NA      NA      -2.153817        \n",
              "6 0.04003854 0.05710880 ⋯ NA       NA      NA      -2.437914        \n",
              "  lparrest_rape lparrest_womgirl lparrest_nonwomen lparrest_kidmen lpmurder_m\n",
              "1        NA            NA               NA                NA       NA        \n",
              "2        NA            NA               NA                NA       NA        \n",
              "3        NA            NA               NA                NA       NA        \n",
              "4 -4.422112     -4.599128        -4.339221         -5.081269       NA        \n",
              "5 -4.284273     -4.445257        -4.302086         -4.809580       NA        \n",
              "6 -4.500610     -4.538616        -4.560318         -4.442914       NA        \n",
              "  lpmurder_f\n",
              "1 NA        \n",
              "2 NA        \n",
              "3 NA        \n",
              "4 NA        \n",
              "5 NA        \n",
              "6 NA        "
            ],
            "text/latex": "A data.frame: 6 × 81\n\\begin{tabular}{r|lllllllllllllllllllll}\n  & X & year & stateuni & ipop & imale & pcr\\_prop & pcr\\_order & pcr\\_econ & pmurder & pcr\\_womtot & ⋯ & lpstrape & lpstpoa & lpstpcr & lparrest\\_womcrime & lparrest\\_rape & lparrest\\_womgirl & lparrest\\_nonwomen & lparrest\\_kidmen & lpmurder\\_m & lpmurder\\_f\\\\\n  & <int> & <int> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t1 & 1 & 1985 & ANDHRA PRADESH & 58733007 & 29755186 & 0.3701837 & 0.01159484 & 0.12194166 & 0.02943830 & 0.06718931 & ⋯ & NA & NA & NA &        NA &        NA &        NA &        NA &        NA & NA & NA\\\\\n\t2 & 2 & 1986 & ANDHRA PRADESH & 60028840 & 30416752 & 0.3218286 & 0.05244146 & 0.03141823 & 0.02663720 & 0.06629049 & ⋯ & NA & NA & NA &        NA &        NA &        NA &        NA &        NA & NA & NA\\\\\n\t3 & 3 & 1987 & ANDHRA PRADESH & 61324674 & 31078317 & 0.3477393 & 0.01025688 & 0.14506397 & 0.02868340 & 0.05174177 & ⋯ & NA & NA & NA &        NA &        NA &        NA &        NA &        NA & NA & NA\\\\\n\t4 & 4 & 1988 & ANDHRA PRADESH & 62620508 & 31739883 & 0.4270326 & 0.08220949 & 0.04052985 & 0.03265704 & 0.06434455 & ⋯ & NA & NA & NA & -2.295828 & -4.422112 & -4.599128 & -4.339221 & -5.081269 & NA & NA\\\\\n\t5 & 5 & 1989 & ANDHRA PRADESH & 63916341 & 32401449 & 0.4614939 & 0.08195087 & 0.04596633 & 0.03595325 & 0.07145828 & ⋯ & NA & NA & NA & -2.153817 & -4.284273 & -4.445257 & -4.302086 & -4.809580 & NA & NA\\\\\n\t6 & 6 & 1990 & ANDHRA PRADESH & 65212174 & 33063015 & 0.4658639 & 0.08265328 & 0.04235406 & 0.04003854 & 0.05710880 & ⋯ & NA & NA & NA & -2.437914 & -4.500610 & -4.538616 & -4.560318 & -4.442914 & NA & NA\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 6 × 81\n\n| <!--/--> | X &lt;int&gt; | year &lt;int&gt; | stateuni &lt;fct&gt; | ipop &lt;dbl&gt; | imale &lt;dbl&gt; | pcr_prop &lt;dbl&gt; | pcr_order &lt;dbl&gt; | pcr_econ &lt;dbl&gt; | pmurder &lt;dbl&gt; | pcr_womtot &lt;dbl&gt; | ⋯ ⋯ | lpstrape &lt;dbl&gt; | lpstpoa &lt;dbl&gt; | lpstpcr &lt;dbl&gt; | lparrest_womcrime &lt;dbl&gt; | lparrest_rape &lt;dbl&gt; | lparrest_womgirl &lt;dbl&gt; | lparrest_nonwomen &lt;dbl&gt; | lparrest_kidmen &lt;dbl&gt; | lpmurder_m &lt;dbl&gt; | lpmurder_f &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 1 | 1985 | ANDHRA PRADESH | 58733007 | 29755186 | 0.3701837 | 0.01159484 | 0.12194166 | 0.02943830 | 0.06718931 | ⋯ | NA | NA | NA |        NA |        NA |        NA |        NA |        NA | NA | NA |\n| 2 | 2 | 1986 | ANDHRA PRADESH | 60028840 | 30416752 | 0.3218286 | 0.05244146 | 0.03141823 | 0.02663720 | 0.06629049 | ⋯ | NA | NA | NA |        NA |        NA |        NA |        NA |        NA | NA | NA |\n| 3 | 3 | 1987 | ANDHRA PRADESH | 61324674 | 31078317 | 0.3477393 | 0.01025688 | 0.14506397 | 0.02868340 | 0.05174177 | ⋯ | NA | NA | NA |        NA |        NA |        NA |        NA |        NA | NA | NA |\n| 4 | 4 | 1988 | ANDHRA PRADESH | 62620508 | 31739883 | 0.4270326 | 0.08220949 | 0.04052985 | 0.03265704 | 0.06434455 | ⋯ | NA | NA | NA | -2.295828 | -4.422112 | -4.599128 | -4.339221 | -5.081269 | NA | NA |\n| 5 | 5 | 1989 | ANDHRA PRADESH | 63916341 | 32401449 | 0.4614939 | 0.08195087 | 0.04596633 | 0.03595325 | 0.07145828 | ⋯ | NA | NA | NA | -2.153817 | -4.284273 | -4.445257 | -4.302086 | -4.809580 | NA | NA |\n| 6 | 6 | 1990 | ANDHRA PRADESH | 65212174 | 33063015 | 0.4658639 | 0.08265328 | 0.04235406 | 0.04003854 | 0.05710880 | ⋯ | NA | NA | NA | -2.437914 | -4.500610 | -4.538616 | -4.560318 | -4.442914 | NA | NA |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 6 × 81</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>X</th><th scope=col>year</th><th scope=col>stateuni</th><th scope=col>ipop</th><th scope=col>imale</th><th scope=col>pcr_prop</th><th scope=col>pcr_order</th><th scope=col>pcr_econ</th><th scope=col>pmurder</th><th scope=col>pcr_womtot</th><th scope=col>⋯</th><th scope=col>lpstrape</th><th scope=col>lpstpoa</th><th scope=col>lpstpcr</th><th scope=col>lparrest_womcrime</th><th scope=col>lparrest_rape</th><th scope=col>lparrest_womgirl</th><th scope=col>lparrest_nonwomen</th><th scope=col>lparrest_kidmen</th><th scope=col>lpmurder_m</th><th scope=col>lpmurder_f</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>1</td><td>1985</td><td>ANDHRA PRADESH</td><td>58733007</td><td>29755186</td><td>0.3701837</td><td>0.01159484</td><td>0.12194166</td><td>0.02943830</td><td>0.06718931</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>NA</td><td>NA</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>2</td><td>1986</td><td>ANDHRA PRADESH</td><td>60028840</td><td>30416752</td><td>0.3218286</td><td>0.05244146</td><td>0.03141823</td><td>0.02663720</td><td>0.06629049</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>NA</td><td>NA</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>3</td><td>1987</td><td>ANDHRA PRADESH</td><td>61324674</td><td>31078317</td><td>0.3477393</td><td>0.01025688</td><td>0.14506397</td><td>0.02868340</td><td>0.05174177</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>NA</td><td>NA</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>4</td><td>1988</td><td>ANDHRA PRADESH</td><td>62620508</td><td>31739883</td><td>0.4270326</td><td>0.08220949</td><td>0.04052985</td><td>0.03265704</td><td>0.06434455</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>-2.295828</td><td>-4.422112</td><td>-4.599128</td><td>-4.339221</td><td>-5.081269</td><td>NA</td><td>NA</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>5</td><td>1989</td><td>ANDHRA PRADESH</td><td>63916341</td><td>32401449</td><td>0.4614939</td><td>0.08195087</td><td>0.04596633</td><td>0.03595325</td><td>0.07145828</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>-2.153817</td><td>-4.284273</td><td>-4.445257</td><td>-4.302086</td><td>-4.809580</td><td>NA</td><td>NA</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>6</td><td>1990</td><td>ANDHRA PRADESH</td><td>65212174</td><td>33063015</td><td>0.4658639</td><td>0.08265328</td><td>0.04235406</td><td>0.04003854</td><td>0.05710880</td><td>⋯</td><td>NA</td><td>NA</td><td>NA</td><td>-2.437914</td><td>-4.500610</td><td>-4.538616</td><td>-4.560318</td><td>-4.442914</td><td>NA</td><td>NA</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lliXdfSSZIf8"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPxsj0iOU_zX",
        "outputId": "eb69bfb1-85c3-48aa-927d-fd0d098b7e22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "str(colnames(data))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " chr [1:81] \"X\" \"year\" \"stateuni\" \"ipop\" \"imale\" \"pcr_prop\" \"pcr_order\" ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwqwgfQJTVsy",
        "outputId": "6b8edc77-5482-4d48-e289-f66d790b7c0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "data$newstateid<-as.character(data$stateid)\n",
        "head(data$stateid)\n",
        "head(data$newstateid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] ANDHRA PRADESH ANDHRA PRADESH ANDHRA PRADESH ANDHRA PRADESH ANDHRA PRADESH\n",
              "[6] ANDHRA PRADESH\n",
              "17 Levels: ANDHRA PRADESH ASSAM BIHAR GUJARAT HARYANA ... WEST BENGAL"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item ANDHRA PRADESH\n\\item ANDHRA PRADESH\n\\item ANDHRA PRADESH\n\\item ANDHRA PRADESH\n\\item ANDHRA PRADESH\n\\item ANDHRA PRADESH\n\\end{enumerate*}\n\n\\emph{Levels}: \\begin{enumerate*}\n\\item 'ANDHRA PRADESH'\n\\item 'ASSAM'\n\\item 'BIHAR'\n\\item 'GUJARAT'\n\\item 'HARYANA'\n\\item 'HIMACHAL PRADESH'\n\\item 'JAMMU \\& KASHMIR'\n\\item 'KARNATAKA'\n\\item 'KERALA'\n\\item 'MADHYA PRADESH'\n\\item 'MAHARASHTRA'\n\\item 'ORISSA'\n\\item 'PUNJAB'\n\\item 'RAJASTHAN'\n\\item 'TAMIL NADU'\n\\item 'UTTAR PRADESH'\n\\item 'WEST BENGAL'\n\\end{enumerate*}\n",
            "text/markdown": "1. ANDHRA PRADESH\n2. ANDHRA PRADESH\n3. ANDHRA PRADESH\n4. ANDHRA PRADESH\n5. ANDHRA PRADESH\n6. ANDHRA PRADESH\n\n\n\n**Levels**: 1. 'ANDHRA PRADESH'\n2. 'ASSAM'\n3. 'BIHAR'\n4. 'GUJARAT'\n5. 'HARYANA'\n6. 'HIMACHAL PRADESH'\n7. 'JAMMU &amp; KASHMIR'\n8. 'KARNATAKA'\n9. 'KERALA'\n10. 'MADHYA PRADESH'\n11. 'MAHARASHTRA'\n12. 'ORISSA'\n13. 'PUNJAB'\n14. 'RAJASTHAN'\n15. 'TAMIL NADU'\n16. 'UTTAR PRADESH'\n17. 'WEST BENGAL'\n\n\n",
            "text/html": [
              "<ol class=list-inline>\n",
              "\t<li>ANDHRA PRADESH</li>\n",
              "\t<li>ANDHRA PRADESH</li>\n",
              "\t<li>ANDHRA PRADESH</li>\n",
              "\t<li>ANDHRA PRADESH</li>\n",
              "\t<li>ANDHRA PRADESH</li>\n",
              "\t<li>ANDHRA PRADESH</li>\n",
              "</ol>\n",
              "\n",
              "<details>\n",
              "\t<summary style=display:list-item;cursor:pointer>\n",
              "\t\t<strong>Levels</strong>:\n",
              "\t</summary>\n",
              "\t<ol class=list-inline>\n",
              "\t\t<li>'ANDHRA PRADESH'</li>\n",
              "\t\t<li>'ASSAM'</li>\n",
              "\t\t<li>'BIHAR'</li>\n",
              "\t\t<li>'GUJARAT'</li>\n",
              "\t\t<li>'HARYANA'</li>\n",
              "\t\t<li>'HIMACHAL PRADESH'</li>\n",
              "\t\t<li>'JAMMU &amp; KASHMIR'</li>\n",
              "\t\t<li>'KARNATAKA'</li>\n",
              "\t\t<li>'KERALA'</li>\n",
              "\t\t<li>'MADHYA PRADESH'</li>\n",
              "\t\t<li>'MAHARASHTRA'</li>\n",
              "\t\t<li>'ORISSA'</li>\n",
              "\t\t<li>'PUNJAB'</li>\n",
              "\t\t<li>'RAJASTHAN'</li>\n",
              "\t\t<li>'TAMIL NADU'</li>\n",
              "\t\t<li>'UTTAR PRADESH'</li>\n",
              "\t\t<li>'WEST BENGAL'</li>\n",
              "\t</ol>\n",
              "</details>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] \"ANDHRA PRADESH\" \"ANDHRA PRADESH\" \"ANDHRA PRADESH\" \"ANDHRA PRADESH\"\n",
              "[5] \"ANDHRA PRADESH\" \"ANDHRA PRADESH\""
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 'ANDHRA PRADESH'\n\\item 'ANDHRA PRADESH'\n\\item 'ANDHRA PRADESH'\n\\item 'ANDHRA PRADESH'\n\\item 'ANDHRA PRADESH'\n\\item 'ANDHRA PRADESH'\n\\end{enumerate*}\n",
            "text/markdown": "1. 'ANDHRA PRADESH'\n2. 'ANDHRA PRADESH'\n3. 'ANDHRA PRADESH'\n4. 'ANDHRA PRADESH'\n5. 'ANDHRA PRADESH'\n6. 'ANDHRA PRADESH'\n\n\n",
            "text/html": [
              "<ol class=list-inline>\n",
              "\t<li>'ANDHRA PRADESH'</li>\n",
              "\t<li>'ANDHRA PRADESH'</li>\n",
              "\t<li>'ANDHRA PRADESH'</li>\n",
              "\t<li>'ANDHRA PRADESH'</li>\n",
              "\t<li>'ANDHRA PRADESH'</li>\n",
              "\t<li>'ANDHRA PRADESH'</li>\n",
              "</ol>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KoSXGl-tM5vb",
        "outputId": "1a890fd5-2c1a-4dfa-8d79-80cc669167d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        }
      },
      "source": [
        "summary(data$stateid)\n",
        "summary(data$newstateid)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  ANDHRA PRADESH            ASSAM            BIHAR          GUJARAT \n",
              "              23               23               23               23 \n",
              "         HARYANA HIMACHAL PRADESH  JAMMU & KASHMIR        KARNATAKA \n",
              "              23               23               23               23 \n",
              "          KERALA   MADHYA PRADESH      MAHARASHTRA           ORISSA \n",
              "              23               23               23               23 \n",
              "          PUNJAB        RAJASTHAN       TAMIL NADU    UTTAR PRADESH \n",
              "              23               23               23               23 \n",
              "     WEST BENGAL \n",
              "              23 "
            ],
            "text/latex": "\\begin{description*}\n\\item[ANDHRA PRADESH] 23\n\\item[ASSAM] 23\n\\item[BIHAR] 23\n\\item[GUJARAT] 23\n\\item[HARYANA] 23\n\\item[HIMACHAL PRADESH] 23\n\\item[JAMMU \\textbackslash{}\\& KASHMIR] 23\n\\item[KARNATAKA] 23\n\\item[KERALA] 23\n\\item[MADHYA PRADESH] 23\n\\item[MAHARASHTRA] 23\n\\item[ORISSA] 23\n\\item[PUNJAB] 23\n\\item[RAJASTHAN] 23\n\\item[TAMIL NADU] 23\n\\item[UTTAR PRADESH] 23\n\\item[WEST BENGAL] 23\n\\end{description*}\n",
            "text/markdown": "ANDHRA PRADESH\n:   23ASSAM\n:   23BIHAR\n:   23GUJARAT\n:   23HARYANA\n:   23HIMACHAL PRADESH\n:   23JAMMU &amp;amp; KASHMIR\n:   23KARNATAKA\n:   23KERALA\n:   23MADHYA PRADESH\n:   23MAHARASHTRA\n:   23ORISSA\n:   23PUNJAB\n:   23RAJASTHAN\n:   23TAMIL NADU\n:   23UTTAR PRADESH\n:   23WEST BENGAL\n:   23\n\n",
            "text/html": [
              "<dl class=dl-horizontal>\n",
              "\t<dt>ANDHRA PRADESH</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>ASSAM</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>BIHAR</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>GUJARAT</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>HARYANA</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>HIMACHAL PRADESH</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>JAMMU &amp; KASHMIR</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>KARNATAKA</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>KERALA</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>MADHYA PRADESH</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>MAHARASHTRA</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>ORISSA</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>PUNJAB</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>RAJASTHAN</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>TAMIL NADU</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>UTTAR PRADESH</dt>\n",
              "\t\t<dd>23</dd>\n",
              "\t<dt>WEST BENGAL</dt>\n",
              "\t\t<dd>23</dd>\n",
              "</dl>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Length     Class      Mode \n",
              "      391 character character "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wrL0cBXWMr7H"
      },
      "source": [
        "#Creating a reservation year variable\n",
        "data$year.res<-NULL\n",
        "data$year.res[data$stateid == 'ANDHRA PRADESH']<-1995\n",
        "data$year.res[data$stateid == 'ASSAM']<-2002\n",
        "data$year.res[data$stateid == 'BIHAR']<-2001\n",
        "data$year.res[data$stateid == 'GUJARAT']<-1995\n",
        "data$year.res[data$stateid == 'HARYANA']<-1995\n",
        "data$year.res[data$stateid == 'HIMACHAL PRADESH']<-1995\n",
        "data$year.res[data$stateid == 'JAMMU & KASHMIR']<-2001\n",
        "data$year.res[data$stateid == 'KARNATAKA']<-1987\n",
        "data$year.res[data$stateid == 'KERALA']<-1991\n",
        "data$year.res[data$stateid == 'MADHYA PRADESH']<-1994\n",
        "data$year.res[data$stateid == 'MAHARASHTRA']<-1992\n",
        "data$year.res[data$stateid == 'ORISSA']<-1992\n",
        "data$year.res[data$stateid == 'PUNJAB']<-1994\n",
        "data$year.res[data$stateid == 'RAJASTHAN']<-1995\n",
        "data$year.res[data$stateid == 'TAMIL NADU']<-1996\n",
        "data$year.res[data$stateid == 'UTTAR PRADESH']<-2006\n",
        "data$year.res[data$stateid == 'WEST BENGAL']<-1993"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXbc9EcZZ_rg"
      },
      "source": [
        "data$treat <- NULL\n",
        "data$treat <- as.integer(data$year >= data$year.res)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jvKblt5lkUCr"
      },
      "source": [
        "data$state <- as.integer(data$stateid)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkuwYUywLJPZ",
        "outputId": "6c4922b2-aded-444f-db51-5abe3a7c3fe2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "#Subsetting data to years post-1994 and reservation post-1994\n",
        "after.data <- data[which(data$year.res>=1995),]\n",
        "after.data <- after.data[which(after.data$year>=1995),]\n",
        "# View(better.data)\n",
        "head(after.data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   X  year stateuni       ipop     imale    pcr_prop  pcr_order  pcr_econ  \n",
              "11 11 1995 ANDHRA PRADESH 70388808 35645714 0.3257336 0.08722978 0.04314606\n",
              "12 12 1996 ANDHRA PRADESH 71359008 36125997 0.3057217 0.07088103 0.04721198\n",
              "13 13 1997 ANDHRA PRADESH 72329207 36606280 0.3230230 0.06644619 0.06015550\n",
              "14 14 1998 ANDHRA PRADESH 73299407 37086563 0.3350914 0.07862274 0.06476178\n",
              "15 15 1999 ANDHRA PRADESH 74269607 37566847 0.3073532 0.05264603 0.07184635\n",
              "16 16 2000 ANDHRA PRADESH 75239807 38047130 0.3260641 0.04444456 0.07518627\n",
              "   pmurder    pcr_womtot ⋯ lparrest_rape lparrest_womgirl lparrest_nonwomen\n",
              "11 0.03511922 0.2424079  ⋯ -4.185441     -4.740167        -4.340173        \n",
              "12 0.03656161 0.2523769  ⋯ -4.191689     -4.060709        -4.282272        \n",
              "13 0.03970733 0.2805201  ⋯ -3.962047     -4.418194        -4.405653        \n",
              "14 0.04054603 0.2887097  ⋯ -4.166159     -4.553323        -5.081213        \n",
              "15 0.03650214 0.3282587  ⋯ -4.060061     -4.375981        -4.902295        \n",
              "16 0.03593842 0.3652063  ⋯ -4.099138     -4.536352        -4.708059        \n",
              "   lparrest_kidmen lpmurder_m lpmurder_f newstateid     year.res treat state\n",
              "11 -4.385282             NA          NA  ANDHRA PRADESH 1995     1     1    \n",
              "12 -4.906383             NA          NA  ANDHRA PRADESH 1995     1     1    \n",
              "13 -4.719733             NA          NA  ANDHRA PRADESH 1995     1     1    \n",
              "14 -4.785575             NA          NA  ANDHRA PRADESH 1995     1     1    \n",
              "15 -4.361103       -2.87896   -3.994414  ANDHRA PRADESH 1995     1     1    \n",
              "16 -4.693194             NA          NA  ANDHRA PRADESH 1995     1     1    "
            ],
            "text/latex": "A data.frame: 6 × 85\n\\begin{tabular}{r|lllllllllllllllllllll}\n  & X & year & stateuni & ipop & imale & pcr\\_prop & pcr\\_order & pcr\\_econ & pmurder & pcr\\_womtot & ⋯ & lparrest\\_rape & lparrest\\_womgirl & lparrest\\_nonwomen & lparrest\\_kidmen & lpmurder\\_m & lpmurder\\_f & newstateid & year.res & treat & state\\\\\n  & <int> & <int> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <int> & <int>\\\\\n\\hline\n\t11 & 11 & 1995 & ANDHRA PRADESH & 70388808 & 35645714 & 0.3257336 & 0.08722978 & 0.04314606 & 0.03511922 & 0.2424079 & ⋯ & -4.185441 & -4.740167 & -4.340173 & -4.385282 &       NA &        NA & ANDHRA PRADESH & 1995 & 1 & 1\\\\\n\t12 & 12 & 1996 & ANDHRA PRADESH & 71359008 & 36125997 & 0.3057217 & 0.07088103 & 0.04721198 & 0.03656161 & 0.2523769 & ⋯ & -4.191689 & -4.060709 & -4.282272 & -4.906383 &       NA &        NA & ANDHRA PRADESH & 1995 & 1 & 1\\\\\n\t13 & 13 & 1997 & ANDHRA PRADESH & 72329207 & 36606280 & 0.3230230 & 0.06644619 & 0.06015550 & 0.03970733 & 0.2805201 & ⋯ & -3.962047 & -4.418194 & -4.405653 & -4.719733 &       NA &        NA & ANDHRA PRADESH & 1995 & 1 & 1\\\\\n\t14 & 14 & 1998 & ANDHRA PRADESH & 73299407 & 37086563 & 0.3350914 & 0.07862274 & 0.06476178 & 0.04054603 & 0.2887097 & ⋯ & -4.166159 & -4.553323 & -5.081213 & -4.785575 &       NA &        NA & ANDHRA PRADESH & 1995 & 1 & 1\\\\\n\t15 & 15 & 1999 & ANDHRA PRADESH & 74269607 & 37566847 & 0.3073532 & 0.05264603 & 0.07184635 & 0.03650214 & 0.3282587 & ⋯ & -4.060061 & -4.375981 & -4.902295 & -4.361103 & -2.87896 & -3.994414 & ANDHRA PRADESH & 1995 & 1 & 1\\\\\n\t16 & 16 & 2000 & ANDHRA PRADESH & 75239807 & 38047130 & 0.3260641 & 0.04444456 & 0.07518627 & 0.03593842 & 0.3652063 & ⋯ & -4.099138 & -4.536352 & -4.708059 & -4.693194 &       NA &        NA & ANDHRA PRADESH & 1995 & 1 & 1\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 6 × 85\n\n| <!--/--> | X &lt;int&gt; | year &lt;int&gt; | stateuni &lt;fct&gt; | ipop &lt;dbl&gt; | imale &lt;dbl&gt; | pcr_prop &lt;dbl&gt; | pcr_order &lt;dbl&gt; | pcr_econ &lt;dbl&gt; | pmurder &lt;dbl&gt; | pcr_womtot &lt;dbl&gt; | ⋯ ⋯ | lparrest_rape &lt;dbl&gt; | lparrest_womgirl &lt;dbl&gt; | lparrest_nonwomen &lt;dbl&gt; | lparrest_kidmen &lt;dbl&gt; | lpmurder_m &lt;dbl&gt; | lpmurder_f &lt;dbl&gt; | newstateid &lt;chr&gt; | year.res &lt;dbl&gt; | treat &lt;int&gt; | state &lt;int&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 11 | 11 | 1995 | ANDHRA PRADESH | 70388808 | 35645714 | 0.3257336 | 0.08722978 | 0.04314606 | 0.03511922 | 0.2424079 | ⋯ | -4.185441 | -4.740167 | -4.340173 | -4.385282 |       NA |        NA | ANDHRA PRADESH | 1995 | 1 | 1 |\n| 12 | 12 | 1996 | ANDHRA PRADESH | 71359008 | 36125997 | 0.3057217 | 0.07088103 | 0.04721198 | 0.03656161 | 0.2523769 | ⋯ | -4.191689 | -4.060709 | -4.282272 | -4.906383 |       NA |        NA | ANDHRA PRADESH | 1995 | 1 | 1 |\n| 13 | 13 | 1997 | ANDHRA PRADESH | 72329207 | 36606280 | 0.3230230 | 0.06644619 | 0.06015550 | 0.03970733 | 0.2805201 | ⋯ | -3.962047 | -4.418194 | -4.405653 | -4.719733 |       NA |        NA | ANDHRA PRADESH | 1995 | 1 | 1 |\n| 14 | 14 | 1998 | ANDHRA PRADESH | 73299407 | 37086563 | 0.3350914 | 0.07862274 | 0.06476178 | 0.04054603 | 0.2887097 | ⋯ | -4.166159 | -4.553323 | -5.081213 | -4.785575 |       NA |        NA | ANDHRA PRADESH | 1995 | 1 | 1 |\n| 15 | 15 | 1999 | ANDHRA PRADESH | 74269607 | 37566847 | 0.3073532 | 0.05264603 | 0.07184635 | 0.03650214 | 0.3282587 | ⋯ | -4.060061 | -4.375981 | -4.902295 | -4.361103 | -2.87896 | -3.994414 | ANDHRA PRADESH | 1995 | 1 | 1 |\n| 16 | 16 | 2000 | ANDHRA PRADESH | 75239807 | 38047130 | 0.3260641 | 0.04444456 | 0.07518627 | 0.03593842 | 0.3652063 | ⋯ | -4.099138 | -4.536352 | -4.708059 | -4.693194 |       NA |        NA | ANDHRA PRADESH | 1995 | 1 | 1 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 6 × 85</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>X</th><th scope=col>year</th><th scope=col>stateuni</th><th scope=col>ipop</th><th scope=col>imale</th><th scope=col>pcr_prop</th><th scope=col>pcr_order</th><th scope=col>pcr_econ</th><th scope=col>pmurder</th><th scope=col>pcr_womtot</th><th scope=col>⋯</th><th scope=col>lparrest_rape</th><th scope=col>lparrest_womgirl</th><th scope=col>lparrest_nonwomen</th><th scope=col>lparrest_kidmen</th><th scope=col>lpmurder_m</th><th scope=col>lpmurder_f</th><th scope=col>newstateid</th><th scope=col>year.res</th><th scope=col>treat</th><th scope=col>state</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>11</th><td>11</td><td>1995</td><td>ANDHRA PRADESH</td><td>70388808</td><td>35645714</td><td>0.3257336</td><td>0.08722978</td><td>0.04314606</td><td>0.03511922</td><td>0.2424079</td><td>⋯</td><td>-4.185441</td><td>-4.740167</td><td>-4.340173</td><td>-4.385282</td><td>      NA</td><td>       NA</td><td>ANDHRA PRADESH</td><td>1995</td><td>1</td><td>1</td></tr>\n",
              "\t<tr><th scope=row>12</th><td>12</td><td>1996</td><td>ANDHRA PRADESH</td><td>71359008</td><td>36125997</td><td>0.3057217</td><td>0.07088103</td><td>0.04721198</td><td>0.03656161</td><td>0.2523769</td><td>⋯</td><td>-4.191689</td><td>-4.060709</td><td>-4.282272</td><td>-4.906383</td><td>      NA</td><td>       NA</td><td>ANDHRA PRADESH</td><td>1995</td><td>1</td><td>1</td></tr>\n",
              "\t<tr><th scope=row>13</th><td>13</td><td>1997</td><td>ANDHRA PRADESH</td><td>72329207</td><td>36606280</td><td>0.3230230</td><td>0.06644619</td><td>0.06015550</td><td>0.03970733</td><td>0.2805201</td><td>⋯</td><td>-3.962047</td><td>-4.418194</td><td>-4.405653</td><td>-4.719733</td><td>      NA</td><td>       NA</td><td>ANDHRA PRADESH</td><td>1995</td><td>1</td><td>1</td></tr>\n",
              "\t<tr><th scope=row>14</th><td>14</td><td>1998</td><td>ANDHRA PRADESH</td><td>73299407</td><td>37086563</td><td>0.3350914</td><td>0.07862274</td><td>0.06476178</td><td>0.04054603</td><td>0.2887097</td><td>⋯</td><td>-4.166159</td><td>-4.553323</td><td>-5.081213</td><td>-4.785575</td><td>      NA</td><td>       NA</td><td>ANDHRA PRADESH</td><td>1995</td><td>1</td><td>1</td></tr>\n",
              "\t<tr><th scope=row>15</th><td>15</td><td>1999</td><td>ANDHRA PRADESH</td><td>74269607</td><td>37566847</td><td>0.3073532</td><td>0.05264603</td><td>0.07184635</td><td>0.03650214</td><td>0.3282587</td><td>⋯</td><td>-4.060061</td><td>-4.375981</td><td>-4.902295</td><td>-4.361103</td><td>-2.87896</td><td>-3.994414</td><td>ANDHRA PRADESH</td><td>1995</td><td>1</td><td>1</td></tr>\n",
              "\t<tr><th scope=row>16</th><td>16</td><td>2000</td><td>ANDHRA PRADESH</td><td>75239807</td><td>38047130</td><td>0.3260641</td><td>0.04444456</td><td>0.07518627</td><td>0.03593842</td><td>0.3652063</td><td>⋯</td><td>-4.099138</td><td>-4.536352</td><td>-4.708059</td><td>-4.693194</td><td>      NA</td><td>       NA</td><td>ANDHRA PRADESH</td><td>1995</td><td>1</td><td>1</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wb6RV1bdNHNK",
        "outputId": "1eea29d2-b48e-4f75-c630-f31a1920edce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "#Subsetting data to years pre-1994 and reservation pre-1994\n",
        "before.data <- data[which(data$year.res<1995),]\n",
        "before.data <- before.data[which(before.data$year<1995),]\n",
        "# View(better.data)\n",
        "head(before.data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    X   year stateuni  ipop     imale    pcr_prop  pcr_order pcr_econ  \n",
              "162 162 1985 KARNATAKA 40272309 20534343 0.6110650 0.1448390 0.07300302\n",
              "163 163 1986 KARNATAKA 41056458 20937272 0.5563802 0.1464082 0.06396071\n",
              "164 164 1987 KARNATAKA 41840606 21340201 0.5894035 0.1856570 0.06644263\n",
              "165 165 1988 KARNATAKA 42624755 21743130 0.5734696 0.1559188 0.06737868\n",
              "166 166 1989 KARNATAKA 43408904 22146059 0.5705742 0.1552216 0.07337204\n",
              "167 167 1990 KARNATAKA 44193052 22548988 0.6182420 0.1819064 0.07483077\n",
              "    pmurder    pcr_womtot ⋯ lparrest_rape lparrest_womgirl lparrest_nonwomen\n",
              "162 0.02515376 0.11753997 ⋯        NA            NA               NA        \n",
              "163 0.02601296 0.09900998 ⋯        NA            NA               NA        \n",
              "164 0.02650535 0.08170570 ⋯        NA            NA               NA        \n",
              "165 0.02981835 0.14668399 ⋯ -5.043419     -4.605751        -4.734059        \n",
              "166 0.02796661 0.14932151 ⋯ -5.076301     -4.694484        -4.408559        \n",
              "167 0.03045728 0.12640879 ⋯ -5.166894     -5.086851        -4.320787        \n",
              "    lparrest_kidmen lpmurder_m lpmurder_f newstateid year.res treat state\n",
              "162        NA       NA         NA         KARNATAKA  1987     0     8    \n",
              "163        NA       NA         NA         KARNATAKA  1987     0     8    \n",
              "164        NA       NA         NA         KARNATAKA  1987     1     8    \n",
              "165 -6.768370       NA         NA         KARNATAKA  1987     1     8    \n",
              "166 -5.818607       NA         NA         KARNATAKA  1987     1     8    \n",
              "167 -5.754681       NA         NA         KARNATAKA  1987     1     8    "
            ],
            "text/latex": "A data.frame: 6 × 85\n\\begin{tabular}{r|lllllllllllllllllllll}\n  & X & year & stateuni & ipop & imale & pcr\\_prop & pcr\\_order & pcr\\_econ & pmurder & pcr\\_womtot & ⋯ & lparrest\\_rape & lparrest\\_womgirl & lparrest\\_nonwomen & lparrest\\_kidmen & lpmurder\\_m & lpmurder\\_f & newstateid & year.res & treat & state\\\\\n  & <int> & <int> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <int> & <int>\\\\\n\\hline\n\t162 & 162 & 1985 & KARNATAKA & 40272309 & 20534343 & 0.6110650 & 0.1448390 & 0.07300302 & 0.02515376 & 0.11753997 & ⋯ &        NA &        NA &        NA &        NA & NA & NA & KARNATAKA & 1987 & 0 & 8\\\\\n\t163 & 163 & 1986 & KARNATAKA & 41056458 & 20937272 & 0.5563802 & 0.1464082 & 0.06396071 & 0.02601296 & 0.09900998 & ⋯ &        NA &        NA &        NA &        NA & NA & NA & KARNATAKA & 1987 & 0 & 8\\\\\n\t164 & 164 & 1987 & KARNATAKA & 41840606 & 21340201 & 0.5894035 & 0.1856570 & 0.06644263 & 0.02650535 & 0.08170570 & ⋯ &        NA &        NA &        NA &        NA & NA & NA & KARNATAKA & 1987 & 1 & 8\\\\\n\t165 & 165 & 1988 & KARNATAKA & 42624755 & 21743130 & 0.5734696 & 0.1559188 & 0.06737868 & 0.02981835 & 0.14668399 & ⋯ & -5.043419 & -4.605751 & -4.734059 & -6.768370 & NA & NA & KARNATAKA & 1987 & 1 & 8\\\\\n\t166 & 166 & 1989 & KARNATAKA & 43408904 & 22146059 & 0.5705742 & 0.1552216 & 0.07337204 & 0.02796661 & 0.14932151 & ⋯ & -5.076301 & -4.694484 & -4.408559 & -5.818607 & NA & NA & KARNATAKA & 1987 & 1 & 8\\\\\n\t167 & 167 & 1990 & KARNATAKA & 44193052 & 22548988 & 0.6182420 & 0.1819064 & 0.07483077 & 0.03045728 & 0.12640879 & ⋯ & -5.166894 & -5.086851 & -4.320787 & -5.754681 & NA & NA & KARNATAKA & 1987 & 1 & 8\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 6 × 85\n\n| <!--/--> | X &lt;int&gt; | year &lt;int&gt; | stateuni &lt;fct&gt; | ipop &lt;dbl&gt; | imale &lt;dbl&gt; | pcr_prop &lt;dbl&gt; | pcr_order &lt;dbl&gt; | pcr_econ &lt;dbl&gt; | pmurder &lt;dbl&gt; | pcr_womtot &lt;dbl&gt; | ⋯ ⋯ | lparrest_rape &lt;dbl&gt; | lparrest_womgirl &lt;dbl&gt; | lparrest_nonwomen &lt;dbl&gt; | lparrest_kidmen &lt;dbl&gt; | lpmurder_m &lt;dbl&gt; | lpmurder_f &lt;dbl&gt; | newstateid &lt;chr&gt; | year.res &lt;dbl&gt; | treat &lt;int&gt; | state &lt;int&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 162 | 162 | 1985 | KARNATAKA | 40272309 | 20534343 | 0.6110650 | 0.1448390 | 0.07300302 | 0.02515376 | 0.11753997 | ⋯ |        NA |        NA |        NA |        NA | NA | NA | KARNATAKA | 1987 | 0 | 8 |\n| 163 | 163 | 1986 | KARNATAKA | 41056458 | 20937272 | 0.5563802 | 0.1464082 | 0.06396071 | 0.02601296 | 0.09900998 | ⋯ |        NA |        NA |        NA |        NA | NA | NA | KARNATAKA | 1987 | 0 | 8 |\n| 164 | 164 | 1987 | KARNATAKA | 41840606 | 21340201 | 0.5894035 | 0.1856570 | 0.06644263 | 0.02650535 | 0.08170570 | ⋯ |        NA |        NA |        NA |        NA | NA | NA | KARNATAKA | 1987 | 1 | 8 |\n| 165 | 165 | 1988 | KARNATAKA | 42624755 | 21743130 | 0.5734696 | 0.1559188 | 0.06737868 | 0.02981835 | 0.14668399 | ⋯ | -5.043419 | -4.605751 | -4.734059 | -6.768370 | NA | NA | KARNATAKA | 1987 | 1 | 8 |\n| 166 | 166 | 1989 | KARNATAKA | 43408904 | 22146059 | 0.5705742 | 0.1552216 | 0.07337204 | 0.02796661 | 0.14932151 | ⋯ | -5.076301 | -4.694484 | -4.408559 | -5.818607 | NA | NA | KARNATAKA | 1987 | 1 | 8 |\n| 167 | 167 | 1990 | KARNATAKA | 44193052 | 22548988 | 0.6182420 | 0.1819064 | 0.07483077 | 0.03045728 | 0.12640879 | ⋯ | -5.166894 | -5.086851 | -4.320787 | -5.754681 | NA | NA | KARNATAKA | 1987 | 1 | 8 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 6 × 85</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>X</th><th scope=col>year</th><th scope=col>stateuni</th><th scope=col>ipop</th><th scope=col>imale</th><th scope=col>pcr_prop</th><th scope=col>pcr_order</th><th scope=col>pcr_econ</th><th scope=col>pmurder</th><th scope=col>pcr_womtot</th><th scope=col>⋯</th><th scope=col>lparrest_rape</th><th scope=col>lparrest_womgirl</th><th scope=col>lparrest_nonwomen</th><th scope=col>lparrest_kidmen</th><th scope=col>lpmurder_m</th><th scope=col>lpmurder_f</th><th scope=col>newstateid</th><th scope=col>year.res</th><th scope=col>treat</th><th scope=col>state</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>162</th><td>162</td><td>1985</td><td>KARNATAKA</td><td>40272309</td><td>20534343</td><td>0.6110650</td><td>0.1448390</td><td>0.07300302</td><td>0.02515376</td><td>0.11753997</td><td>⋯</td><td>       NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>NA</td><td>NA</td><td>KARNATAKA</td><td>1987</td><td>0</td><td>8</td></tr>\n",
              "\t<tr><th scope=row>163</th><td>163</td><td>1986</td><td>KARNATAKA</td><td>41056458</td><td>20937272</td><td>0.5563802</td><td>0.1464082</td><td>0.06396071</td><td>0.02601296</td><td>0.09900998</td><td>⋯</td><td>       NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>NA</td><td>NA</td><td>KARNATAKA</td><td>1987</td><td>0</td><td>8</td></tr>\n",
              "\t<tr><th scope=row>164</th><td>164</td><td>1987</td><td>KARNATAKA</td><td>41840606</td><td>21340201</td><td>0.5894035</td><td>0.1856570</td><td>0.06644263</td><td>0.02650535</td><td>0.08170570</td><td>⋯</td><td>       NA</td><td>       NA</td><td>       NA</td><td>       NA</td><td>NA</td><td>NA</td><td>KARNATAKA</td><td>1987</td><td>1</td><td>8</td></tr>\n",
              "\t<tr><th scope=row>165</th><td>165</td><td>1988</td><td>KARNATAKA</td><td>42624755</td><td>21743130</td><td>0.5734696</td><td>0.1559188</td><td>0.06737868</td><td>0.02981835</td><td>0.14668399</td><td>⋯</td><td>-5.043419</td><td>-4.605751</td><td>-4.734059</td><td>-6.768370</td><td>NA</td><td>NA</td><td>KARNATAKA</td><td>1987</td><td>1</td><td>8</td></tr>\n",
              "\t<tr><th scope=row>166</th><td>166</td><td>1989</td><td>KARNATAKA</td><td>43408904</td><td>22146059</td><td>0.5705742</td><td>0.1552216</td><td>0.07337204</td><td>0.02796661</td><td>0.14932151</td><td>⋯</td><td>-5.076301</td><td>-4.694484</td><td>-4.408559</td><td>-5.818607</td><td>NA</td><td>NA</td><td>KARNATAKA</td><td>1987</td><td>1</td><td>8</td></tr>\n",
              "\t<tr><th scope=row>167</th><td>167</td><td>1990</td><td>KARNATAKA</td><td>44193052</td><td>22548988</td><td>0.6182420</td><td>0.1819064</td><td>0.07483077</td><td>0.03045728</td><td>0.12640879</td><td>⋯</td><td>-5.166894</td><td>-5.086851</td><td>-4.320787</td><td>-5.754681</td><td>NA</td><td>NA</td><td>KARNATAKA</td><td>1987</td><td>1</td><td>8</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqTHn-_bNHVx"
      },
      "source": [
        "## Outcome Variable\n",
        "# pcr_womtat:Percent of total crime against women\n",
        "\n",
        "## Treatment Variable\n",
        "# treat : 0 if the year is before the law passed in the state, 1 otherwise\n",
        "\n",
        "## Control Variables to Match on\n",
        "# newstateid: State Name \n",
        "# pcgsdp\t  : GDP per capita\n",
        "# pfemale\t  : Female-male Ratio\n",
        "# plit  \t  : Percent of Literacy\n",
        "# pwlit\t    : Prcent of Women's Literacy\n",
        "# prural\t  : Percent of rural"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7wvD5-VFZp8n",
        "outputId": "6595b1e2-6692-4fc1-85c9-e6532f3955fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "dim(data)\n",
        "dim(after.data)\n",
        "dim(before.data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 391  85"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 391\n\\item 85\n\\end{enumerate*}\n",
            "text/markdown": "1. 391\n2. 85\n\n\n",
            "text/html": [
              "<ol class=list-inline>\n",
              "\t<li>391</li>\n",
              "\t<li>85</li>\n",
              "</ol>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 130  85"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 130\n\\item 85\n\\end{enumerate*}\n",
            "text/markdown": "1. 130\n2. 85\n\n\n",
            "text/html": [
              "<ol class=list-inline>\n",
              "\t<li>130</li>\n",
              "\t<li>85</li>\n",
              "</ol>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 70 85"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 70\n\\item 85\n\\end{enumerate*}\n",
            "text/markdown": "1. 70\n2. 85\n\n\n",
            "text/html": [
              "<ol class=list-inline>\n",
              "\t<li>70</li>\n",
              "\t<li>85</li>\n",
              "</ol>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wlPikHwIkKvC",
        "outputId": "23bd45bb-ec7e-414f-817b-440feb08a3b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        }
      },
      "source": [
        "summary(as.factor(data$treat))\n",
        "summary(as.factor(after.data$treat))\n",
        "summary(as.factor(before.data$treat))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0   1 \n",
              "179 212 "
            ],
            "text/latex": "\\begin{description*}\n\\item[0] 179\n\\item[1] 212\n\\end{description*}\n",
            "text/markdown": "0\n:   1791\n:   212\n\n",
            "text/html": [
              "<dl class=dl-horizontal>\n",
              "\t<dt>0</dt>\n",
              "\t\t<dd>179</dd>\n",
              "\t<dt>1</dt>\n",
              "\t\t<dd>212</dd>\n",
              "</dl>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              " 0  1 \n",
              "31 99 "
            ],
            "text/latex": "\\begin{description*}\n\\item[0] 31\n\\item[1] 99\n\\end{description*}\n",
            "text/markdown": "0\n:   311\n:   99\n\n",
            "text/html": [
              "<dl class=dl-horizontal>\n",
              "\t<dt>0</dt>\n",
              "\t\t<dd>31</dd>\n",
              "\t<dt>1</dt>\n",
              "\t\t<dd>99</dd>\n",
              "</dl>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              " 0  1 \n",
              "48 22 "
            ],
            "text/latex": "\\begin{description*}\n\\item[0] 48\n\\item[1] 22\n\\end{description*}\n",
            "text/markdown": "0\n:   481\n:   22\n\n",
            "text/html": [
              "<dl class=dl-horizontal>\n",
              "\t<dt>0</dt>\n",
              "\t\t<dd>48</dd>\n",
              "\t<dt>1</dt>\n",
              "\t\t<dd>22</dd>\n",
              "</dl>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xC61EAwz74b1"
      },
      "source": [
        "## Replication Figure and Table"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CvIIieV2efm9"
      },
      "source": [
        "### Figure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bFKMJ2L072ET",
        "outputId": "f7c4a84b-4a07-4c40-a8c5-7fa86fc993b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "#install.packages('ggplot2')\n",
        "require('ggplot2')\n",
        "\n",
        "############\n",
        "# Figure 2 #\n",
        "############\n",
        "\n",
        "data$res<-\"Post\"\n",
        "data$res[data$postwres==0]<-\"Pre\"\n",
        "\n",
        "p<-ggplot(data, aes(y=pcr_womtot, x=year, group=newstateid, colour = factor(res))) + \n",
        "  geom_point() +facet_wrap( ~ newstateid, ncol = 3) +geom_vline(xintercept = 1993) +\n",
        "  xlab(\"Year\")+ ylab(\"Total Crimes against women per 1000 women\") +  \n",
        "  scale_colour_discrete(name = \"Reservation Implementation\")+theme(legend.position=\"bottom\")\n",
        "\n",
        "print(p)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LWrIWLVdaRTddiyeyd51gSSJInDB+JXP1CHDlgoSuE4SDg7AoBhWVqTMe4sOHU0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19ampqb///e/bt2+fmZk5a9Ysl8tVXFyc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\nSktLfWvYitRrMRi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nrqqacuXry4YMGC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cbR7VVjKzyPpgR11SRtMvnGqx+KdewX32CdFs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",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RRyry3cx8y57"
      },
      "source": [
        "### Table"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XfL3GfJv81JR",
        "outputId": "47f1e9c6-a02c-4abc-d007-be065720216a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# these two packages are necessary to run the next lines of code\n",
        "\n",
        "install.packages(\"sandwich\")\n",
        "library(sandwich)\n",
        "\n",
        "install.packages(\"lmtest\")\n",
        "library(lmtest)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependency ‘zoo’\n",
            "\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Loading required package: zoo\n",
            "\n",
            "\n",
            "Attaching package: ‘zoo’\n",
            "\n",
            "\n",
            "The following objects are masked from ‘package:base’:\n",
            "\n",
            "    as.Date, as.Date.numeric\n",
            "\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZJHNXJw9IeU"
      },
      "source": [
        "# define clustered standard errors function\n",
        "cl <- function(dat,fm, cluster) {\n",
        "  require(sandwich, quietly = TRUE)\n",
        "  require(lmtest, quietly = TRUE)\n",
        "  M <- length(unique(cluster))\n",
        "  N <- length(cluster)\n",
        "  K <- fm$rank\n",
        "  dfc <- (M/(M-1))*((N-1)/(N-K))\n",
        "  uj  <- apply(estfun(fm),2, function(x) tapply(x, cluster, sum))\n",
        "  vcovCL <- dfc*sandwich(fm, meat=crossprod(uj)/N)\n",
        "  return(list(coeftest(fm, vcovCL), n=round(length(fm$fitted.values),0), rsq=round(summary(fm)$r.squared,2)))\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPc5oips81JH",
        "outputId": "b80f7c76-a0e0-4256-8069-1ba6e0242b53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "##################################################################\n",
        "##TABLE 1\n",
        "#Analysis of the data in Iyer et al. on and after 1995 in states where the reservation policy was implemented in 1995 and after\n",
        "\n",
        "#Subsetting data to years post-1994 and reservation post-1994\n",
        "better.data <- data[which(data$year.res>=1995),]\n",
        "better.data <- data[which(better.data$year>=1995),]\n",
        "# View(better.data)\n",
        "head(better.data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   X  year stateuni       ipop     imale    pcr_prop  pcr_order  pcr_econ  \n",
              "11 11 1995 ANDHRA PRADESH 70388808 35645714 0.3257336 0.08722978 0.04314606\n",
              "12 12 1996 ANDHRA PRADESH 71359008 36125997 0.3057217 0.07088103 0.04721198\n",
              "13 13 1997 ANDHRA PRADESH 72329207 36606280 0.3230230 0.06644619 0.06015550\n",
              "14 14 1998 ANDHRA PRADESH 73299407 37086563 0.3350914 0.07862274 0.06476178\n",
              "15 15 1999 ANDHRA PRADESH 74269607 37566847 0.3073532 0.05264603 0.07184635\n",
              "16 16 2000 ANDHRA PRADESH 75239807 38047130 0.3260641 0.04444456 0.07518627\n",
              "   pmurder    pcr_womtot ⋯ lparrest_womgirl lparrest_nonwomen lparrest_kidmen\n",
              "11 0.03511922 0.2424079  ⋯ -4.740167        -4.340173         -4.385282      \n",
              "12 0.03656161 0.2523769  ⋯ -4.060709        -4.282272         -4.906383      \n",
              "13 0.03970733 0.2805201  ⋯ -4.418194        -4.405653         -4.719733      \n",
              "14 0.04054603 0.2887097  ⋯ -4.553323        -5.081213         -4.785575      \n",
              "15 0.03650214 0.3282587  ⋯ -4.375981        -4.902295         -4.361103      \n",
              "16 0.03593842 0.3652063  ⋯ -4.536352        -4.708059         -4.693194      \n",
              "   lpmurder_m lpmurder_f newstateid     year.res treat state res \n",
              "11       NA          NA  ANDHRA PRADESH 1995     1     1     Post\n",
              "12       NA          NA  ANDHRA PRADESH 1995     1     1     Post\n",
              "13       NA          NA  ANDHRA PRADESH 1995     1     1     Post\n",
              "14       NA          NA  ANDHRA PRADESH 1995     1     1     Post\n",
              "15 -2.87896   -3.994414  ANDHRA PRADESH 1995     1     1     Post\n",
              "16       NA          NA  ANDHRA PRADESH 1995     1     1     Post"
            ],
            "text/latex": "A data.frame: 6 × 86\n\\begin{tabular}{r|lllllllllllllllllllll}\n  & X & year & stateuni & ipop & imale & pcr\\_prop & pcr\\_order & pcr\\_econ & pmurder & pcr\\_womtot & ⋯ & lparrest\\_womgirl & lparrest\\_nonwomen & lparrest\\_kidmen & lpmurder\\_m & lpmurder\\_f & newstateid & year.res & treat & state & res\\\\\n  & <int> & <int> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <dbl> & <int> & <dbl> & <chr>\\\\\n\\hline\n\t11 & 11 & 1995 & ANDHRA PRADESH & 70388808 & 35645714 & 0.3257336 & 0.08722978 & 0.04314606 & 0.03511922 & 0.2424079 & ⋯ & -4.740167 & -4.340173 & -4.385282 &       NA &        NA & ANDHRA PRADESH & 1995 & 1 & 1 & Post\\\\\n\t12 & 12 & 1996 & ANDHRA PRADESH & 71359008 & 36125997 & 0.3057217 & 0.07088103 & 0.04721198 & 0.03656161 & 0.2523769 & ⋯ & -4.060709 & -4.282272 & -4.906383 &       NA &        NA & ANDHRA PRADESH & 1995 & 1 & 1 & Post\\\\\n\t13 & 13 & 1997 & ANDHRA PRADESH & 72329207 & 36606280 & 0.3230230 & 0.06644619 & 0.06015550 & 0.03970733 & 0.2805201 & ⋯ & -4.418194 & -4.405653 & -4.719733 &       NA &        NA & ANDHRA PRADESH & 1995 & 1 & 1 & Post\\\\\n\t14 & 14 & 1998 & ANDHRA PRADESH & 73299407 & 37086563 & 0.3350914 & 0.07862274 & 0.06476178 & 0.04054603 & 0.2887097 & ⋯ & -4.553323 & -5.081213 & -4.785575 &       NA &        NA & ANDHRA PRADESH & 1995 & 1 & 1 & Post\\\\\n\t15 & 15 & 1999 & ANDHRA PRADESH & 74269607 & 37566847 & 0.3073532 & 0.05264603 & 0.07184635 & 0.03650214 & 0.3282587 & ⋯ & -4.375981 & -4.902295 & -4.361103 & -2.87896 & -3.994414 & ANDHRA PRADESH & 1995 & 1 & 1 & Post\\\\\n\t16 & 16 & 2000 & ANDHRA PRADESH & 75239807 & 38047130 & 0.3260641 & 0.04444456 & 0.07518627 & 0.03593842 & 0.3652063 & ⋯ & -4.536352 & -4.708059 & -4.693194 &       NA &        NA & ANDHRA PRADESH & 1995 & 1 & 1 & Post\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 6 × 86\n\n| <!--/--> | X &lt;int&gt; | year &lt;int&gt; | stateuni &lt;chr&gt; | ipop &lt;dbl&gt; | imale &lt;dbl&gt; | pcr_prop &lt;dbl&gt; | pcr_order &lt;dbl&gt; | pcr_econ &lt;dbl&gt; | pmurder &lt;dbl&gt; | pcr_womtot &lt;dbl&gt; | ⋯ ⋯ | lparrest_womgirl &lt;dbl&gt; | lparrest_nonwomen &lt;dbl&gt; | lparrest_kidmen &lt;dbl&gt; | lpmurder_m &lt;dbl&gt; | lpmurder_f &lt;dbl&gt; | newstateid &lt;chr&gt; | year.res &lt;dbl&gt; | treat &lt;int&gt; | state &lt;dbl&gt; | res &lt;chr&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 11 | 11 | 1995 | ANDHRA PRADESH | 70388808 | 35645714 | 0.3257336 | 0.08722978 | 0.04314606 | 0.03511922 | 0.2424079 | ⋯ | -4.740167 | -4.340173 | -4.385282 |       NA |        NA | ANDHRA PRADESH | 1995 | 1 | 1 | Post |\n| 12 | 12 | 1996 | ANDHRA PRADESH | 71359008 | 36125997 | 0.3057217 | 0.07088103 | 0.04721198 | 0.03656161 | 0.2523769 | ⋯ | -4.060709 | -4.282272 | -4.906383 |       NA |        NA | ANDHRA PRADESH | 1995 | 1 | 1 | Post |\n| 13 | 13 | 1997 | ANDHRA PRADESH | 72329207 | 36606280 | 0.3230230 | 0.06644619 | 0.06015550 | 0.03970733 | 0.2805201 | ⋯ | -4.418194 | -4.405653 | -4.719733 |       NA |        NA | ANDHRA PRADESH | 1995 | 1 | 1 | Post |\n| 14 | 14 | 1998 | ANDHRA PRADESH | 73299407 | 37086563 | 0.3350914 | 0.07862274 | 0.06476178 | 0.04054603 | 0.2887097 | ⋯ | -4.553323 | -5.081213 | -4.785575 |       NA |        NA | ANDHRA PRADESH | 1995 | 1 | 1 | Post |\n| 15 | 15 | 1999 | ANDHRA PRADESH | 74269607 | 37566847 | 0.3073532 | 0.05264603 | 0.07184635 | 0.03650214 | 0.3282587 | ⋯ | -4.375981 | -4.902295 | -4.361103 | -2.87896 | -3.994414 | ANDHRA PRADESH | 1995 | 1 | 1 | Post |\n| 16 | 16 | 2000 | ANDHRA PRADESH | 75239807 | 38047130 | 0.3260641 | 0.04444456 | 0.07518627 | 0.03593842 | 0.3652063 | ⋯ | -4.536352 | -4.708059 | -4.693194 |       NA |        NA | ANDHRA PRADESH | 1995 | 1 | 1 | Post |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 6 × 86</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>X</th><th scope=col>year</th><th scope=col>stateuni</th><th scope=col>ipop</th><th scope=col>imale</th><th scope=col>pcr_prop</th><th scope=col>pcr_order</th><th scope=col>pcr_econ</th><th scope=col>pmurder</th><th scope=col>pcr_womtot</th><th scope=col>⋯</th><th scope=col>lparrest_womgirl</th><th scope=col>lparrest_nonwomen</th><th scope=col>lparrest_kidmen</th><th scope=col>lpmurder_m</th><th scope=col>lpmurder_f</th><th scope=col>newstateid</th><th scope=col>year.res</th><th scope=col>treat</th><th scope=col>state</th><th scope=col>res</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>11</th><td>11</td><td>1995</td><td>ANDHRA PRADESH</td><td>70388808</td><td>35645714</td><td>0.3257336</td><td>0.08722978</td><td>0.04314606</td><td>0.03511922</td><td>0.2424079</td><td>⋯</td><td>-4.740167</td><td>-4.340173</td><td>-4.385282</td><td>      NA</td><td>       NA</td><td>ANDHRA PRADESH</td><td>1995</td><td>1</td><td>1</td><td>Post</td></tr>\n",
              "\t<tr><th scope=row>12</th><td>12</td><td>1996</td><td>ANDHRA PRADESH</td><td>71359008</td><td>36125997</td><td>0.3057217</td><td>0.07088103</td><td>0.04721198</td><td>0.03656161</td><td>0.2523769</td><td>⋯</td><td>-4.060709</td><td>-4.282272</td><td>-4.906383</td><td>      NA</td><td>       NA</td><td>ANDHRA PRADESH</td><td>1995</td><td>1</td><td>1</td><td>Post</td></tr>\n",
              "\t<tr><th scope=row>13</th><td>13</td><td>1997</td><td>ANDHRA PRADESH</td><td>72329207</td><td>36606280</td><td>0.3230230</td><td>0.06644619</td><td>0.06015550</td><td>0.03970733</td><td>0.2805201</td><td>⋯</td><td>-4.418194</td><td>-4.405653</td><td>-4.719733</td><td>      NA</td><td>       NA</td><td>ANDHRA PRADESH</td><td>1995</td><td>1</td><td>1</td><td>Post</td></tr>\n",
              "\t<tr><th scope=row>14</th><td>14</td><td>1998</td><td>ANDHRA PRADESH</td><td>73299407</td><td>37086563</td><td>0.3350914</td><td>0.07862274</td><td>0.06476178</td><td>0.04054603</td><td>0.2887097</td><td>⋯</td><td>-4.553323</td><td>-5.081213</td><td>-4.785575</td><td>      NA</td><td>       NA</td><td>ANDHRA PRADESH</td><td>1995</td><td>1</td><td>1</td><td>Post</td></tr>\n",
              "\t<tr><th scope=row>15</th><td>15</td><td>1999</td><td>ANDHRA PRADESH</td><td>74269607</td><td>37566847</td><td>0.3073532</td><td>0.05264603</td><td>0.07184635</td><td>0.03650214</td><td>0.3282587</td><td>⋯</td><td>-4.375981</td><td>-4.902295</td><td>-4.361103</td><td>-2.87896</td><td>-3.994414</td><td>ANDHRA PRADESH</td><td>1995</td><td>1</td><td>1</td><td>Post</td></tr>\n",
              "\t<tr><th scope=row>16</th><td>16</td><td>2000</td><td>ANDHRA PRADESH</td><td>75239807</td><td>38047130</td><td>0.3260641</td><td>0.04444456</td><td>0.07518627</td><td>0.03593842</td><td>0.3652063</td><td>⋯</td><td>-4.536352</td><td>-4.708059</td><td>-4.693194</td><td>      NA</td><td>       NA</td><td>ANDHRA PRADESH</td><td>1995</td><td>1</td><td>1</td><td>Post</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7JatvSAI81JN",
        "outputId": "e20318d8-4b0a-4b76-c7bd-ba3f534f586a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Running the initial model with post-1995 data, column 1\n",
        "better.data$newstateid<-as.character(better.data$stateid)\n",
        "new.column1<-lm(better.data$lpcr_womtot ~ better.data$postwres + factor(better.data$year) + factor(better.data$newstateid), subset=(better.data$majstate==1))\n",
        "summary(new.column1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = better.data$lpcr_womtot ~ better.data$postwres + \n",
              "    factor(better.data$year) + factor(better.data$newstateid), \n",
              "    subset = (better.data$majstate == 1))\n",
              "\n",
              "Residuals:\n",
              "     Min       1Q   Median       3Q      Max \n",
              "-1.91519 -0.08505  0.00088  0.10176  0.69992 \n",
              "\n",
              "Coefficients:\n",
              "                                               Estimate Std. Error t value\n",
              "(Intercept)                                    -1.75246    0.13425 -13.054\n",
              "better.data$postwres                            0.20821    0.09751   2.135\n",
              "factor(better.data$year)1996                    0.38270    0.11513   3.324\n",
              "factor(better.data$year)1997                    0.49516    0.11513   4.301\n",
              "factor(better.data$year)1998                    0.56664    0.11513   4.922\n",
              "factor(better.data$year)1999                    0.58801    0.11513   5.107\n",
              "factor(better.data$year)2000                    0.60838    0.11513   5.284\n",
              "factor(better.data$year)2001                    0.62751    0.11677   5.374\n",
              "factor(better.data$year)2002                    0.69596    0.11879   5.859\n",
              "factor(better.data$year)2003                    0.64287    0.11879   5.412\n",
              "factor(better.data$year)2004                    0.69375    0.11879   5.840\n",
              "factor(better.data$year)2005                    0.67132    0.11879   5.652\n",
              "factor(better.data$year)2006                    0.73013    0.11879   6.147\n",
              "factor(better.data$year)2007                    0.79735    0.11879   6.713\n",
              "factor(better.data$newstateid)ASSAM            -0.33524    0.11381  -2.946\n",
              "factor(better.data$newstateid)BIHAR            -1.08180    0.11055  -9.786\n",
              "factor(better.data$newstateid)GUJARAT          -0.77246    0.10097  -7.650\n",
              "factor(better.data$newstateid)HARYANA          -0.18966    0.10097  -1.878\n",
              "factor(better.data$newstateid)HIMACHAL PRADESH -0.47336    0.10097  -4.688\n",
              "factor(better.data$newstateid)JAMMU & KASHMIR  -0.33610    0.11055  -3.040\n",
              "factor(better.data$newstateid)KARNATAKA        -0.57365    0.10097  -5.681\n",
              "factor(better.data$newstateid)KERALA           -0.23843    0.10097  -2.361\n",
              "factor(better.data$newstateid)MADHYA PRADESH    0.12182    0.10097   1.206\n",
              "                                               Pr(>|t|)    \n",
              "(Intercept)                                     < 2e-16 ***\n",
              "better.data$postwres                            0.03503 *  \n",
              "factor(better.data$year)1996                    0.00122 ** \n",
              "factor(better.data$year)1997                   3.77e-05 ***\n",
              "factor(better.data$year)1998                   3.13e-06 ***\n",
              "factor(better.data$year)1999                   1.43e-06 ***\n",
              "factor(better.data$year)2000                   6.70e-07 ***\n",
              "factor(better.data$year)2001                   4.54e-07 ***\n",
              "factor(better.data$year)2002                   5.21e-08 ***\n",
              "factor(better.data$year)2003                   3.84e-07 ***\n",
              "factor(better.data$year)2004                   5.67e-08 ***\n",
              "factor(better.data$year)2005                   1.33e-07 ***\n",
              "factor(better.data$year)2006                   1.38e-08 ***\n",
              "factor(better.data$year)2007                   9.46e-10 ***\n",
              "factor(better.data$newstateid)ASSAM             0.00396 ** \n",
              "factor(better.data$newstateid)BIHAR             < 2e-16 ***\n",
              "factor(better.data$newstateid)GUJARAT          9.29e-12 ***\n",
              "factor(better.data$newstateid)HARYANA           0.06305 .  \n",
              "factor(better.data$newstateid)HIMACHAL PRADESH 8.18e-06 ***\n",
              "factor(better.data$newstateid)JAMMU & KASHMIR   0.00297 ** \n",
              "factor(better.data$newstateid)KARNATAKA        1.16e-07 ***\n",
              "factor(better.data$newstateid)KERALA            0.02002 *  \n",
              "factor(better.data$newstateid)MADHYA PRADESH    0.23029    \n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 0.2574 on 107 degrees of freedom\n",
              "Multiple R-squared:  0.7646,\tAdjusted R-squared:  0.7162 \n",
              "F-statistic:  15.8 on 22 and 107 DF,  p-value: < 2.2e-16\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYIbGjYG81JU",
        "outputId": "3baa5645-d5b3-4ea1-88c8-88d38f902812",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "column1.cl<- cl(better.data, new.column1, better.data$newstateid)\n",
        "column1.cl"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[[1]]\n",
              "\n",
              "t test of coefficients:\n",
              "\n",
              "                                                  Estimate  Std. Error\n",
              "(Intercept)                                    -1.7525e+00  2.1831e-01\n",
              "better.data$postwres                            2.0821e-01  1.1064e-01\n",
              "factor(better.data$year)1996                    3.8270e-01  2.7883e-01\n",
              "factor(better.data$year)1997                    4.9516e-01  2.8622e-01\n",
              "factor(better.data$year)1998                    5.6664e-01  2.9932e-01\n",
              "factor(better.data$year)1999                    5.8801e-01  2.9469e-01\n",
              "factor(better.data$year)2000                    6.0838e-01  2.8010e-01\n",
              "factor(better.data$year)2001                    6.2751e-01  2.8098e-01\n",
              "factor(better.data$year)2002                    6.9596e-01  2.7692e-01\n",
              "factor(better.data$year)2003                    6.4287e-01  2.9687e-01\n",
              "factor(better.data$year)2004                    6.9375e-01  3.0588e-01\n",
              "factor(better.data$year)2005                    6.7132e-01  3.0401e-01\n",
              "factor(better.data$year)2006                    7.3013e-01  3.2049e-01\n",
              "factor(better.data$year)2007                    7.9735e-01  3.2009e-01\n",
              "factor(better.data$newstateid)ASSAM            -3.3524e-01  5.9576e-02\n",
              "factor(better.data$newstateid)BIHAR            -1.0818e+00  5.1065e-02\n",
              "factor(better.data$newstateid)GUJARAT          -7.7246e-01  1.2504e-16\n",
              "factor(better.data$newstateid)HARYANA          -1.8966e-01  1.3999e-16\n",
              "factor(better.data$newstateid)HIMACHAL PRADESH -4.7336e-01  1.3638e-16\n",
              "factor(better.data$newstateid)JAMMU & KASHMIR  -3.3610e-01  5.1065e-02\n",
              "factor(better.data$newstateid)KARNATAKA        -5.7365e-01  1.3589e-16\n",
              "factor(better.data$newstateid)KERALA           -2.3843e-01  1.3681e-16\n",
              "factor(better.data$newstateid)MADHYA PRADESH    1.2182e-01  1.2705e-16\n",
              "                                                   t value  Pr(>|t|)    \n",
              "(Intercept)                                    -8.0275e+00 1.378e-12 ***\n",
              "better.data$postwres                            1.8819e+00   0.06257 .  \n",
              "factor(better.data$year)1996                    1.3725e+00   0.17276    \n",
              "factor(better.data$year)1997                    1.7300e+00   0.08651 .  \n",
              "factor(better.data$year)1998                    1.8931e+00   0.06105 .  \n",
              "factor(better.data$year)1999                    1.9954e+00   0.04855 *  \n",
              "factor(better.data$year)2000                    2.1720e+00   0.03206 *  \n",
              "factor(better.data$year)2001                    2.2333e+00   0.02761 *  \n",
              "factor(better.data$year)2002                    2.5132e+00   0.01345 *  \n",
              "factor(better.data$year)2003                    2.1655e+00   0.03257 *  \n",
              "factor(better.data$year)2004                    2.2681e+00   0.02533 *  \n",
              "factor(better.data$year)2005                    2.2083e+00   0.02936 *  \n",
              "factor(better.data$year)2006                    2.2782e+00   0.02470 *  \n",
              "factor(better.data$year)2007                    2.4910e+00   0.01427 *  \n",
              "factor(better.data$newstateid)ASSAM            -5.6272e+00 1.483e-07 ***\n",
              "factor(better.data$newstateid)BIHAR            -2.1185e+01 < 2.2e-16 ***\n",
              "factor(better.data$newstateid)GUJARAT          -6.1777e+15 < 2.2e-16 ***\n",
              "factor(better.data$newstateid)HARYANA          -1.3549e+15 < 2.2e-16 ***\n",
              "factor(better.data$newstateid)HIMACHAL PRADESH -3.4710e+15 < 2.2e-16 ***\n",
              "factor(better.data$newstateid)JAMMU & KASHMIR  -6.5818e+00 1.773e-09 ***\n",
              "factor(better.data$newstateid)KARNATAKA        -4.2215e+15 < 2.2e-16 ***\n",
              "factor(better.data$newstateid)KERALA           -1.7427e+15 < 2.2e-16 ***\n",
              "factor(better.data$newstateid)MADHYA PRADESH    9.5886e+14 < 2.2e-16 ***\n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "\n",
              "$n\n",
              "[1] 130\n",
              "\n",
              "$rsq\n",
              "[1] 0.76\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SpYu4Z2u81JZ",
        "outputId": "97ad6da9-3ea6-4206-fdfd-a51bbbcd031f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Running the initial model with post-1995 data, column 6\n",
        "better.data$newstateid<-as.character(better.data$stateid)\n",
        "new.total.crimes<-lm(better.data$lpcr_womtot ~ better.data$postwres + better.data$newstateid:better.data$year + better.data$pfemale + better.data$prural + better.data$plit + better.data$pfarm + better.data$womancm + better.data$pcgsdp + better.data$ppol_strengt +\n",
        "              factor(better.data$year) + factor(better.data$newstateid), subset=(better.data$year>=1985 & better.data$majstate==1))\n",
        "summary(new.total.crimes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = better.data$lpcr_womtot ~ better.data$postwres + \n",
              "    better.data$newstateid:better.data$year + better.data$pfemale + \n",
              "    better.data$prural + better.data$plit + better.data$pfarm + \n",
              "    better.data$womancm + better.data$pcgsdp + better.data$ppol_strengt + \n",
              "    factor(better.data$year) + factor(better.data$newstateid), \n",
              "    subset = (better.data$year >= 1985 & better.data$majstate == \n",
              "        1))\n",
              "\n",
              "Residuals:\n",
              "     Min       1Q   Median       3Q      Max \n",
              "-1.05213 -0.09026 -0.00074  0.07835  0.65888 \n",
              "\n",
              "Coefficients: (1 not defined because of singularities)\n",
              "                                                          Estimate Std. Error\n",
              "(Intercept)                                             1402.59546  394.19170\n",
              "better.data$postwres                                      -0.04511    0.16137\n",
              "better.data$pfemale                                     -925.37030  199.53770\n",
              "better.data$prural                                      -103.07092   91.62905\n",
              "better.data$plit                                        -154.68056   38.33032\n",
              "better.data$pfarm                                       -368.73463  125.55800\n",
              "better.data$womancm                                        0.10381    0.13282\n",
              "better.data$pcgsdp                                         1.08961    0.34508\n",
              "better.data$ppol_strengt                                  -0.02230    0.08910\n",
              "factor(better.data$year)1996                               0.78392    0.51814\n",
              "factor(better.data$year)1997                               1.36218    1.00877\n",
              "factor(better.data$year)1998                               1.85387    1.48980\n",
              "factor(better.data$year)1999                               2.33152    1.95761\n",
              "factor(better.data$year)2000                               2.82645    2.41332\n",
              "factor(better.data$year)2001                               3.35930    2.86016\n",
              "factor(better.data$year)2002                               3.87345    3.29167\n",
              "factor(better.data$year)2003                               4.22034    3.71064\n",
              "factor(better.data$year)2004                               4.65562    4.11573\n",
              "factor(better.data$year)2005                               5.02753    4.51801\n",
              "factor(better.data$year)2006                               5.45279    4.91004\n",
              "factor(better.data$year)2007                               5.87489    5.28973\n",
              "factor(better.data$newstateid)ASSAM                      881.22589  524.69170\n",
              "factor(better.data$newstateid)BIHAR                      121.23641  402.29880\n",
              "factor(better.data$newstateid)GUJARAT                   3691.56500  762.45487\n",
              "factor(better.data$newstateid)HARYANA                   1056.97989  747.89419\n",
              "factor(better.data$newstateid)HIMACHAL PRADESH          1678.79433  403.66560\n",
              "factor(better.data$newstateid)JAMMU & KASHMIR            556.43231  606.58089\n",
              "factor(better.data$newstateid)KARNATAKA                 1152.50512  546.99557\n",
              "factor(better.data$newstateid)KERALA                     289.60837  706.18029\n",
              "factor(better.data$newstateid)MADHYA PRADESH            -301.43522  232.36055\n",
              "better.data$newstateidANDHRA PRADESH:better.data$year     -0.13220    0.11251\n",
              "better.data$newstateidASSAM:better.data$year              -0.60376    0.26451\n",
              "better.data$newstateidBIHAR:better.data$year              -0.22972    0.19414\n",
              "better.data$newstateidGUJARAT:better.data$year            -2.01779    0.39686\n",
              "better.data$newstateidHARYANA:better.data$year            -0.72937    0.29355\n",
              "better.data$newstateidHIMACHAL PRADESH:better.data$year   -0.96387    0.21265\n",
              "better.data$newstateidJAMMU & KASHMIR:better.data$year    -0.47512    0.24249\n",
              "better.data$newstateidKARNATAKA:better.data$year          -0.72089    0.22959\n",
              "better.data$newstateidKERALA:better.data$year             -0.25040    0.35292\n",
              "better.data$newstateidMADHYA PRADESH:better.data$year           NA         NA\n",
              "                                                        t value Pr(>|t|)    \n",
              "(Intercept)                                               3.558 0.000596 ***\n",
              "better.data$postwres                                     -0.280 0.780482    \n",
              "better.data$pfemale                                      -4.638 1.18e-05 ***\n",
              "better.data$prural                                       -1.125 0.263602    \n",
              "better.data$plit                                         -4.035 0.000113 ***\n",
              "better.data$pfarm                                        -2.937 0.004200 ** \n",
              "better.data$womancm                                       0.782 0.436497    \n",
              "better.data$pcgsdp                                        3.158 0.002159 ** \n",
              "better.data$ppol_strengt                                 -0.250 0.802919    \n",
              "factor(better.data$year)1996                              1.513 0.133762    \n",
              "factor(better.data$year)1997                              1.350 0.180258    \n",
              "factor(better.data$year)1998                              1.244 0.216557    \n",
              "factor(better.data$year)1999                              1.191 0.236751    \n",
              "factor(better.data$year)2000                              1.171 0.244581    \n",
              "factor(better.data$year)2001                              1.175 0.243252    \n",
              "factor(better.data$year)2002                              1.177 0.242367    \n",
              "factor(better.data$year)2003                              1.137 0.258373    \n",
              "factor(better.data$year)2004                              1.131 0.260953    \n",
              "factor(better.data$year)2005                              1.113 0.268736    \n",
              "factor(better.data$year)2006                              1.111 0.269693    \n",
              "factor(better.data$year)2007                              1.111 0.269658    \n",
              "factor(better.data$newstateid)ASSAM                       1.680 0.096483 .  \n",
              "factor(better.data$newstateid)BIHAR                       0.301 0.763828    \n",
              "factor(better.data$newstateid)GUJARAT                     4.842 5.24e-06 ***\n",
              "factor(better.data$newstateid)HARYANA                     1.413 0.160987    \n",
              "factor(better.data$newstateid)HIMACHAL PRADESH            4.159 7.23e-05 ***\n",
              "factor(better.data$newstateid)JAMMU & KASHMIR             0.917 0.361397    \n",
              "factor(better.data$newstateid)KARNATAKA                   2.107 0.037871 *  \n",
              "factor(better.data$newstateid)KERALA                      0.410 0.682693    \n",
              "factor(better.data$newstateid)MADHYA PRADESH             -1.297 0.197816    \n",
              "better.data$newstateidANDHRA PRADESH:better.data$year    -1.175 0.243051    \n",
              "better.data$newstateidASSAM:better.data$year             -2.283 0.024784 *  \n",
              "better.data$newstateidBIHAR:better.data$year             -1.183 0.239780    \n",
              "better.data$newstateidGUJARAT:better.data$year           -5.084 1.96e-06 ***\n",
              "better.data$newstateidHARYANA:better.data$year           -2.485 0.014797 *  \n",
              "better.data$newstateidHIMACHAL PRADESH:better.data$year  -4.533 1.77e-05 ***\n",
              "better.data$newstateidJAMMU & KASHMIR:better.data$year   -1.959 0.053139 .  \n",
              "better.data$newstateidKARNATAKA:better.data$year         -3.140 0.002280 ** \n",
              "better.data$newstateidKERALA:better.data$year            -0.709 0.479829    \n",
              "better.data$newstateidMADHYA PRADESH:better.data$year        NA       NA    \n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 0.2013 on 91 degrees of freedom\n",
              "Multiple R-squared:  0.8776,\tAdjusted R-squared:  0.8265 \n",
              "F-statistic: 17.18 on 38 and 91 DF,  p-value: < 2.2e-16\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OrCJNhhi81Jd",
        "outputId": "c6f20545-2c1f-4012-9d9d-4726564f5aa4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "column2.cl<- cl(better.data, new.total.crimes, better.data$newstateid)\n",
        "column2.cl"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[[1]]\n",
              "\n",
              "t test of coefficients:\n",
              "\n",
              "                                                           Estimate  Std. Error\n",
              "(Intercept)                                             1402.595459  878.371843\n",
              "better.data$postwres                                      -0.045107    0.109138\n",
              "better.data$pfemale                                     -925.370299  483.936102\n",
              "better.data$prural                                      -103.070923  191.481139\n",
              "better.data$plit                                        -154.680564   87.696643\n",
              "better.data$pfarm                                       -368.734630  250.140219\n",
              "better.data$womancm                                        0.103807    0.119418\n",
              "better.data$pcgsdp                                         1.089607    1.016926\n",
              "better.data$ppol_strengt                                  -0.022301    0.079896\n",
              "factor(better.data$year)1996                               0.783919    0.841481\n",
              "factor(better.data$year)1997                               1.362176    1.619231\n",
              "factor(better.data$year)1998                               1.853869    2.364886\n",
              "factor(better.data$year)1999                               2.331517    3.072889\n",
              "factor(better.data$year)2000                               2.826450    3.775966\n",
              "factor(better.data$year)2001                               3.359302    4.457001\n",
              "factor(better.data$year)2002                               3.873448    5.088305\n",
              "factor(better.data$year)2003                               4.220339    5.755277\n",
              "factor(better.data$year)2004                               4.655625    6.394183\n",
              "factor(better.data$year)2005                               5.027526    7.022628\n",
              "factor(better.data$year)2006                               5.452792    7.639588\n",
              "factor(better.data$year)2007                               5.874888    8.221773\n",
              "factor(better.data$newstateid)ASSAM                      881.225888 1103.350707\n",
              "factor(better.data$newstateid)BIHAR                      121.236406  739.060190\n",
              "factor(better.data$newstateid)GUJARAT                   3691.564999 1919.604408\n",
              "factor(better.data$newstateid)HARYANA                   1056.979886 1240.729137\n",
              "factor(better.data$newstateid)HIMACHAL PRADESH          1678.794334  810.658962\n",
              "factor(better.data$newstateid)JAMMU & KASHMIR            556.432309  902.648720\n",
              "factor(better.data$newstateid)KARNATAKA                 1152.505120 1123.226758\n",
              "factor(better.data$newstateid)KERALA                     289.608366 1308.160362\n",
              "factor(better.data$newstateid)MADHYA PRADESH            -301.435218  383.634395\n",
              "better.data$newstateidANDHRA PRADESH:better.data$year     -0.132204    0.182955\n",
              "better.data$newstateidASSAM:better.data$year              -0.603763    0.561789\n",
              "better.data$newstateidBIHAR:better.data$year              -0.229721    0.327434\n",
              "better.data$newstateidGUJARAT:better.data$year            -2.017794    1.056857\n",
              "better.data$newstateidHARYANA:better.data$year            -0.729374    0.584778\n",
              "better.data$newstateidHIMACHAL PRADESH:better.data$year   -0.963872    0.496586\n",
              "better.data$newstateidJAMMU & KASHMIR:better.data$year    -0.475116    0.396343\n",
              "better.data$newstateidKARNATAKA:better.data$year          -0.720891    0.548655\n",
              "better.data$newstateidKERALA:better.data$year             -0.250397    0.626894\n",
              "                                                        t value Pr(>|t|)  \n",
              "(Intercept)                                              1.5968  0.11377  \n",
              "better.data$postwres                                    -0.4133  0.68036  \n",
              "better.data$pfemale                                     -1.9122  0.05900 .\n",
              "better.data$prural                                      -0.5383  0.59170  \n",
              "better.data$plit                                        -1.7638  0.08112 .\n",
              "better.data$pfarm                                       -1.4741  0.14390  \n",
              "better.data$womancm                                      0.8693  0.38698  \n",
              "better.data$pcgsdp                                       1.0715  0.28679  \n",
              "better.data$ppol_strengt                                -0.2791  0.78078  \n",
              "factor(better.data$year)1996                             0.9316  0.35401  \n",
              "factor(better.data$year)1997                             0.8412  0.40241  \n",
              "factor(better.data$year)1998                             0.7839  0.43513  \n",
              "factor(better.data$year)1999                             0.7587  0.44997  \n",
              "factor(better.data$year)2000                             0.7485  0.45607  \n",
              "factor(better.data$year)2001                             0.7537  0.45297  \n",
              "factor(better.data$year)2002                             0.7612  0.44848  \n",
              "factor(better.data$year)2003                             0.7333  0.46526  \n",
              "factor(better.data$year)2004                             0.7281  0.46842  \n",
              "factor(better.data$year)2005                             0.7159  0.47588  \n",
              "factor(better.data$year)2006                             0.7138  0.47721  \n",
              "factor(better.data$year)2007                             0.7146  0.47671  \n",
              "factor(better.data$newstateid)ASSAM                      0.7987  0.42655  \n",
              "factor(better.data$newstateid)BIHAR                      0.1640  0.87006  \n",
              "factor(better.data$newstateid)GUJARAT                    1.9231  0.05760 .\n",
              "factor(better.data$newstateid)HARYANA                    0.8519  0.39650  \n",
              "factor(better.data$newstateid)HIMACHAL PRADESH           2.0709  0.04120 *\n",
              "factor(better.data$newstateid)JAMMU & KASHMIR            0.6164  0.53914  \n",
              "factor(better.data$newstateid)KARNATAKA                  1.0261  0.30758  \n",
              "factor(better.data$newstateid)KERALA                     0.2214  0.82529  \n",
              "factor(better.data$newstateid)MADHYA PRADESH            -0.7857  0.43406  \n",
              "better.data$newstateidANDHRA PRADESH:better.data$year   -0.7226  0.47178  \n",
              "better.data$newstateidASSAM:better.data$year            -1.0747  0.28534  \n",
              "better.data$newstateidBIHAR:better.data$year            -0.7016  0.48473  \n",
              "better.data$newstateidGUJARAT:better.data$year          -1.9092  0.05938 .\n",
              "better.data$newstateidHARYANA:better.data$year          -1.2473  0.21550  \n",
              "better.data$newstateidHIMACHAL PRADESH:better.data$year -1.9410  0.05535 .\n",
              "better.data$newstateidJAMMU & KASHMIR:better.data$year  -1.1988  0.23374  \n",
              "better.data$newstateidKARNATAKA:better.data$year        -1.3139  0.19217  \n",
              "better.data$newstateidKERALA:better.data$year           -0.3994  0.69052  \n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "\n",
              "$n\n",
              "[1] 130\n",
              "\n",
              "$rsq\n",
              "[1] 0.88\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AyMUWk7n81Jl",
        "outputId": "7fe3bf33-3a58-4d9e-a608-e46b0538eef4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Table 3 column 1 code for total crimes against women in Iyer\n",
        "model1w<-lm(data$lpcr_womtot ~ data$postwres +  \n",
        "              factor(data$year) + factor(data$newstateid), subset=(data$year>=1985 & data$majstate==1))\n",
        "summary(model1w)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = data$lpcr_womtot ~ data$postwres + factor(data$year) + \n",
              "    factor(data$newstateid), subset = (data$year >= 1985 & data$majstate == \n",
              "    1))\n",
              "\n",
              "Residuals:\n",
              "     Min       1Q   Median       3Q      Max \n",
              "-2.82621 -0.21707  0.00651  0.25783  1.56950 \n",
              "\n",
              "Coefficients:\n",
              "                                        Estimate Std. Error t value Pr(>|t|)\n",
              "(Intercept)                             -3.14521    0.15331 -20.515  < 2e-16\n",
              "data$postwres                            0.36511    0.09573   3.814 0.000162\n",
              "factor(data$year)1986                    0.15913    0.16649   0.956 0.339817\n",
              "factor(data$year)1987                    0.12377    0.16658   0.743 0.457995\n",
              "factor(data$year)1988                    0.27887    0.16658   1.674 0.095012\n",
              "factor(data$year)1989                    0.36170    0.16658   2.171 0.030579\n",
              "factor(data$year)1990                    0.30469    0.16658   1.829 0.068237\n",
              "factor(data$year)1991                    0.25821    0.16687   1.547 0.122674\n",
              "factor(data$year)1992                    0.28438    0.16800   1.693 0.091402\n",
              "factor(data$year)1993                    0.32019    0.16885   1.896 0.058747\n",
              "factor(data$year)1994                    0.27934    0.17109   1.633 0.103431\n",
              "factor(data$year)1995                    1.47607    0.17968   8.215 4.12e-15\n",
              "factor(data$year)1996                    1.72608    0.18187   9.491  < 2e-16\n",
              "factor(data$year)1997                    1.78819    0.18187   9.832  < 2e-16\n",
              "factor(data$year)1998                    1.87549    0.18187  10.312  < 2e-16\n",
              "factor(data$year)1999                    1.90626    0.18187  10.481  < 2e-16\n",
              "factor(data$year)2000                    1.94920    0.18187  10.717  < 2e-16\n",
              "factor(data$year)2001                    1.92627    0.18669  10.318  < 2e-16\n",
              "factor(data$year)2002                    1.91942    0.18930  10.139  < 2e-16\n",
              "factor(data$year)2003                    1.88723    0.18930   9.969  < 2e-16\n",
              "factor(data$year)2004                    1.93699    0.18930  10.232  < 2e-16\n",
              "factor(data$year)2005                    1.92218    0.18930  10.154  < 2e-16\n",
              "factor(data$year)2006                    1.94353    0.19205  10.120  < 2e-16\n",
              "factor(data$year)2007                    2.03515    0.19205  10.597  < 2e-16\n",
              "factor(data$newstateid)ASSAM            -0.22726    0.14607  -1.556 0.120645\n",
              "factor(data$newstateid)BIHAR            -0.81945    0.14530  -5.640 3.51e-08\n",
              "factor(data$newstateid)GUJARAT          -1.12832    0.14313  -7.883 4.07e-14\n",
              "factor(data$newstateid)HARYANA          -0.52207    0.14313  -3.647 0.000305\n",
              "factor(data$newstateid)HIMACHAL PRADESH -0.63358    0.14313  -4.426 1.28e-05\n",
              "factor(data$newstateid)JAMMU & KASHMIR  -0.29658    0.14530  -2.041 0.041978\n",
              "factor(data$newstateid)KARNATAKA        -0.14696    0.14696  -1.000 0.317982\n",
              "factor(data$newstateid)KERALA           -0.71922    0.14410  -4.991 9.47e-07\n",
              "factor(data$newstateid)MADHYA PRADESH    0.13818    0.14319   0.965 0.335208\n",
              "factor(data$newstateid)MAHARASHTRA      -0.31758    0.14368  -2.210 0.027726\n",
              "factor(data$newstateid)ORISSA           -0.80810    0.14368  -5.624 3.81e-08\n",
              "factor(data$newstateid)PUNJAB           -1.56178    0.14319 -10.907  < 2e-16\n",
              "factor(data$newstateid)RAJASTHAN        -0.18979    0.14313  -1.326 0.185710\n",
              "factor(data$newstateid)TAMIL NADU        0.60502    0.14319   4.225 3.05e-05\n",
              "factor(data$newstateid)UTTAR PRADESH    -0.53413    0.15028  -3.554 0.000431\n",
              "factor(data$newstateid)WEST BENGAL      -0.82391    0.14338  -5.746 1.98e-08\n",
              "                                           \n",
              "(Intercept)                             ***\n",
              "data$postwres                           ***\n",
              "factor(data$year)1986                      \n",
              "factor(data$year)1987                      \n",
              "factor(data$year)1988                   .  \n",
              "factor(data$year)1989                   *  \n",
              "factor(data$year)1990                   .  \n",
              "factor(data$year)1991                      \n",
              "factor(data$year)1992                   .  \n",
              "factor(data$year)1993                   .  \n",
              "factor(data$year)1994                      \n",
              "factor(data$year)1995                   ***\n",
              "factor(data$year)1996                   ***\n",
              "factor(data$year)1997                   ***\n",
              "factor(data$year)1998                   ***\n",
              "factor(data$year)1999                   ***\n",
              "factor(data$year)2000                   ***\n",
              "factor(data$year)2001                   ***\n",
              "factor(data$year)2002                   ***\n",
              "factor(data$year)2003                   ***\n",
              "factor(data$year)2004                   ***\n",
              "factor(data$year)2005                   ***\n",
              "factor(data$year)2006                   ***\n",
              "factor(data$year)2007                   ***\n",
              "factor(data$newstateid)ASSAM               \n",
              "factor(data$newstateid)BIHAR            ***\n",
              "factor(data$newstateid)GUJARAT          ***\n",
              "factor(data$newstateid)HARYANA          ***\n",
              "factor(data$newstateid)HIMACHAL PRADESH ***\n",
              "factor(data$newstateid)JAMMU & KASHMIR  *  \n",
              "factor(data$newstateid)KARNATAKA           \n",
              "factor(data$newstateid)KERALA           ***\n",
              "factor(data$newstateid)MADHYA PRADESH      \n",
              "factor(data$newstateid)MAHARASHTRA      *  \n",
              "factor(data$newstateid)ORISSA           ***\n",
              "factor(data$newstateid)PUNJAB           ***\n",
              "factor(data$newstateid)RAJASTHAN           \n",
              "factor(data$newstateid)TAMIL NADU       ***\n",
              "factor(data$newstateid)UTTAR PRADESH    ***\n",
              "factor(data$newstateid)WEST BENGAL      ***\n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 0.4854 on 351 degrees of freedom\n",
              "Multiple R-squared:  0.846,\tAdjusted R-squared:  0.8289 \n",
              "F-statistic: 49.46 on 39 and 351 DF,  p-value: < 2.2e-16\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuLR66kA81Jp",
        "outputId": "3774003b-65cc-4ed5-daad-9efba49372d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model1cw<- cl(data, model1w, data$newstateid)\n",
        "model1cw"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[[1]]\n",
              "\n",
              "t test of coefficients:\n",
              "\n",
              "                                           Estimate  Std. Error     t value\n",
              "(Intercept)                             -3.1452e+00  2.3829e-01 -1.3199e+01\n",
              "data$postwres                            3.6511e-01  1.9031e-01  1.9185e+00\n",
              "factor(data$year)1986                    1.5913e-01  1.5517e-01  1.0255e+00\n",
              "factor(data$year)1987                    1.2377e-01  1.7864e-01  6.9280e-01\n",
              "factor(data$year)1988                    2.7887e-01  1.8055e-01  1.5446e+00\n",
              "factor(data$year)1989                    3.6170e-01  2.0170e-01  1.7933e+00\n",
              "factor(data$year)1990                    3.0469e-01  2.2600e-01  1.3482e+00\n",
              "factor(data$year)1991                    2.5821e-01  2.0731e-01  1.2455e+00\n",
              "factor(data$year)1992                    2.8438e-01  2.0276e-01  1.4026e+00\n",
              "factor(data$year)1993                    3.2019e-01  2.1805e-01  1.4684e+00\n",
              "factor(data$year)1994                    2.7934e-01  2.5292e-01  1.1045e+00\n",
              "factor(data$year)1995                    1.4761e+00  3.0806e-01  4.7914e+00\n",
              "factor(data$year)1996                    1.7261e+00  3.1538e-01  5.4731e+00\n",
              "factor(data$year)1997                    1.7882e+00  3.2142e-01  5.5634e+00\n",
              "factor(data$year)1998                    1.8755e+00  3.2260e-01  5.8136e+00\n",
              "factor(data$year)1999                    1.9063e+00  3.2196e-01  5.9208e+00\n",
              "factor(data$year)2000                    1.9492e+00  3.1932e-01  6.1042e+00\n",
              "factor(data$year)2001                    1.9263e+00  3.3976e-01  5.6695e+00\n",
              "factor(data$year)2002                    1.9194e+00  3.4023e-01  5.6415e+00\n",
              "factor(data$year)2003                    1.8872e+00  3.4327e-01  5.4978e+00\n",
              "factor(data$year)2004                    1.9370e+00  3.5190e-01  5.5044e+00\n",
              "factor(data$year)2005                    1.9222e+00  3.5589e-01  5.4010e+00\n",
              "factor(data$year)2006                    1.9435e+00  3.7136e-01  5.2336e+00\n",
              "factor(data$year)2007                    2.0351e+00  3.7053e-01  5.4925e+00\n",
              "factor(data$newstateid)ASSAM            -2.2726e-01  5.7920e-02 -3.9237e+00\n",
              "factor(data$newstateid)BIHAR            -8.1945e-01  4.9645e-02 -1.6506e+01\n",
              "factor(data$newstateid)GUJARAT          -1.1283e+00  2.1138e-16 -5.3378e+15\n",
              "factor(data$newstateid)HARYANA          -5.2207e-01  1.9020e-16 -2.7448e+15\n",
              "factor(data$newstateid)HIMACHAL PRADESH -6.3358e-01  1.8893e-16 -3.3534e+15\n",
              "factor(data$newstateid)JAMMU & KASHMIR  -2.9658e-01  4.9645e-02 -5.9739e+00\n",
              "factor(data$newstateid)KARNATAKA        -1.4696e-01  6.6194e-02 -2.2202e+00\n",
              "factor(data$newstateid)KERALA           -7.1922e-01  3.3097e-02 -2.1731e+01\n",
              "factor(data$newstateid)MADHYA PRADESH    1.3818e-01  8.2742e-03  1.6700e+01\n",
              "factor(data$newstateid)MAHARASHTRA      -3.1758e-01  2.4823e-02 -1.2794e+01\n",
              "factor(data$newstateid)ORISSA           -8.0810e-01  2.4823e-02 -3.2555e+01\n",
              "factor(data$newstateid)PUNJAB           -1.5618e+00  8.2742e-03 -1.8875e+02\n",
              "factor(data$newstateid)RAJASTHAN        -1.8979e-01  1.7297e-16 -1.0973e+15\n",
              "factor(data$newstateid)TAMIL NADU        6.0502e-01  8.2742e-03  7.3121e+01\n",
              "factor(data$newstateid)UTTAR PRADESH    -5.3413e-01  9.1017e-02 -5.8685e+00\n",
              "factor(data$newstateid)WEST BENGAL      -8.2391e-01  1.6548e-02 -4.9788e+01\n",
              "                                         Pr(>|t|)    \n",
              "(Intercept)                             < 2.2e-16 ***\n",
              "data$postwres                           0.0558573 .  \n",
              "factor(data$year)1986                   0.3058137    \n",
              "factor(data$year)1987                   0.4888786    \n",
              "factor(data$year)1988                   0.1233482    \n",
              "factor(data$year)1989                   0.0737913 .  \n",
              "factor(data$year)1990                   0.1784722    \n",
              "factor(data$year)1991                   0.2137738    \n",
              "factor(data$year)1992                   0.1616317    \n",
              "factor(data$year)1993                   0.1428953    \n",
              "factor(data$year)1994                   0.2701486    \n",
              "factor(data$year)1995                   2.448e-06 ***\n",
              "factor(data$year)1996                   8.434e-08 ***\n",
              "factor(data$year)1997                   5.256e-08 ***\n",
              "factor(data$year)1998                   1.375e-08 ***\n",
              "factor(data$year)1999                   7.631e-09 ***\n",
              "factor(data$year)2000                   2.737e-09 ***\n",
              "factor(data$year)2001                   2.993e-08 ***\n",
              "factor(data$year)2002                   3.475e-08 ***\n",
              "factor(data$year)2003                   7.415e-08 ***\n",
              "factor(data$year)2004                   7.163e-08 ***\n",
              "factor(data$year)2005                   1.224e-07 ***\n",
              "factor(data$year)2006                   2.868e-07 ***\n",
              "factor(data$year)2007                   7.619e-08 ***\n",
              "factor(data$newstateid)ASSAM            0.0001049 ***\n",
              "factor(data$newstateid)BIHAR            < 2.2e-16 ***\n",
              "factor(data$newstateid)GUJARAT          < 2.2e-16 ***\n",
              "factor(data$newstateid)HARYANA          < 2.2e-16 ***\n",
              "factor(data$newstateid)HIMACHAL PRADESH < 2.2e-16 ***\n",
              "factor(data$newstateid)JAMMU & KASHMIR  5.684e-09 ***\n",
              "factor(data$newstateid)KARNATAKA        0.0270460 *  \n",
              "factor(data$newstateid)KERALA           < 2.2e-16 ***\n",
              "factor(data$newstateid)MADHYA PRADESH   < 2.2e-16 ***\n",
              "factor(data$newstateid)MAHARASHTRA      < 2.2e-16 ***\n",
              "factor(data$newstateid)ORISSA           < 2.2e-16 ***\n",
              "factor(data$newstateid)PUNJAB           < 2.2e-16 ***\n",
              "factor(data$newstateid)RAJASTHAN        < 2.2e-16 ***\n",
              "factor(data$newstateid)TAMIL NADU       < 2.2e-16 ***\n",
              "factor(data$newstateid)UTTAR PRADESH    1.018e-08 ***\n",
              "factor(data$newstateid)WEST BENGAL      < 2.2e-16 ***\n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "\n",
              "$n\n",
              "[1] 391\n",
              "\n",
              "$rsq\n",
              "[1] 0.85\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "outputId": "87aff4ab-3352-4e2e-d0e6-afc962a0f815",
        "id": "uCwhlAmI81J4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Table 3 column 6 code for total crimes against women in Iyer\n",
        "model6w<-lm(data$lpcr_womtot ~ data$postwres + data$newstateid:data$year + data$pfemale + data$prural + data$plit + data$pfarm + data$womancm + data$pcgsdp + data$ppol_strengt +\n",
        "              factor(data$year) + factor(data$newstateid), subset=(data$year>=1985 & data$majstate==1))\n",
        "summary(model6w)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Call:\n",
              "lm(formula = data$lpcr_womtot ~ data$postwres + data$newstateid:data$year + \n",
              "    data$pfemale + data$prural + data$plit + data$pfarm + data$womancm + \n",
              "    data$pcgsdp + data$ppol_strengt + factor(data$year) + factor(data$newstateid), \n",
              "    subset = (data$year >= 1985 & data$majstate == 1))\n",
              "\n",
              "Residuals:\n",
              "     Min       1Q   Median       3Q      Max \n",
              "-1.87459 -0.11660 -0.00157  0.12497  0.70362 \n",
              "\n",
              "Coefficients: (1 not defined because of singularities)\n",
              "                                            Estimate Std. Error t value\n",
              "(Intercept)                               -1.815e+01  5.308e+01  -0.342\n",
              "data$postwres                              2.288e-01  6.158e-02   3.716\n",
              "data$pfemale                              -2.046e+01  1.045e+01  -1.958\n",
              "data$prural                                1.184e+01  4.436e+00   2.668\n",
              "data$plit                                 -2.896e+00  3.615e+00  -0.801\n",
              "data$pfarm                                 1.615e+01  6.929e+00   2.331\n",
              "data$womancm                              -6.440e-02  7.653e-02  -0.842\n",
              "data$pcgsdp                               -1.893e-01  9.523e-02  -1.987\n",
              "data$ppol_strengt                         -9.182e-02  7.322e-02  -1.254\n",
              "factor(data$year)1986                      2.072e-01  9.911e-02   2.091\n",
              "factor(data$year)1987                      2.465e-01  1.112e-01   2.217\n",
              "factor(data$year)1988                      4.498e-01  1.266e-01   3.552\n",
              "factor(data$year)1989                      5.886e-01  1.456e-01   4.044\n",
              "factor(data$year)1990                      5.824e-01  1.660e-01   3.509\n",
              "factor(data$year)1991                      5.851e-01  1.858e-01   3.148\n",
              "factor(data$year)1992                      8.224e-01  2.124e-01   3.872\n",
              "factor(data$year)1993                      1.061e+00  2.437e-01   4.355\n",
              "factor(data$year)1994                      1.221e+00  2.766e-01   4.413\n",
              "factor(data$year)1995                      2.635e+00  3.104e-01   8.488\n",
              "factor(data$year)1996                      3.049e+00  3.437e-01   8.871\n",
              "factor(data$year)1997                      3.284e+00  3.769e-01   8.713\n",
              "factor(data$year)1998                      3.533e+00  4.100e-01   8.617\n",
              "factor(data$year)1999                      3.719e+00  4.421e-01   8.413\n",
              "factor(data$year)2000                      3.905e+00  4.728e-01   8.259\n",
              "factor(data$year)2001                      4.053e+00  5.040e-01   8.041\n",
              "factor(data$year)2002                      4.196e+00  5.337e-01   7.861\n",
              "factor(data$year)2003                      4.311e+00  5.635e-01   7.650\n",
              "factor(data$year)2004                      4.509e+00  5.927e-01   7.608\n",
              "factor(data$year)2005                      4.633e+00  6.215e-01   7.455\n",
              "factor(data$year)2006                      4.803e+00  6.504e-01   7.385\n",
              "factor(data$year)2007                      5.008e+00  6.758e-01   7.411\n",
              "factor(data$newstateid)ASSAM               5.440e-01  4.651e+01   0.012\n",
              "factor(data$newstateid)BIHAR               1.346e+02  4.915e+01   2.738\n",
              "factor(data$newstateid)GUJARAT            -1.011e+01  5.396e+01  -0.187\n",
              "factor(data$newstateid)HARYANA            -4.356e+01  4.932e+01  -0.883\n",
              "factor(data$newstateid)HIMACHAL PRADESH    4.042e+01  3.281e+01   1.232\n",
              "factor(data$newstateid)JAMMU & KASHMIR     1.206e+02  4.661e+01   2.588\n",
              "factor(data$newstateid)KARNATAKA           1.843e+02  4.021e+01   4.584\n",
              "factor(data$newstateid)KERALA             -1.243e+02  8.320e+01  -1.495\n",
              "factor(data$newstateid)MADHYA PRADESH      1.548e+01  2.730e+01   0.567\n",
              "factor(data$newstateid)MAHARASHTRA         9.390e+01  4.294e+01   2.187\n",
              "factor(data$newstateid)ORISSA             -1.754e+02  4.356e+01  -4.027\n",
              "factor(data$newstateid)PUNJAB             -9.378e+01  4.293e+01  -2.184\n",
              "factor(data$newstateid)RAJASTHAN           5.888e+01  5.628e+01   1.046\n",
              "factor(data$newstateid)TAMIL NADU          1.306e+02  7.848e+01   1.664\n",
              "factor(data$newstateid)UTTAR PRADESH       3.518e+00  4.137e+01   0.085\n",
              "factor(data$newstateid)WEST BENGAL         2.312e+01  4.692e+01   0.493\n",
              "data$newstateidANDHRA PRADESH:data$year    1.136e-02  2.310e-02   0.492\n",
              "data$newstateidASSAM:data$year             1.047e-02  2.016e-02   0.519\n",
              "data$newstateidBIHAR:data$year            -5.768e-02  2.272e-02  -2.539\n",
              "data$newstateidGUJARAT:data$year           1.677e-02  2.942e-02   0.570\n",
              "data$newstateidHARYANA:data$year           3.282e-02  2.484e-02   1.321\n",
              "data$newstateidHIMACHAL PRADESH:data$year -9.590e-03  2.517e-02  -0.381\n",
              "data$newstateidJAMMU & KASHMIR:data$year  -4.906e-02  2.146e-02  -2.287\n",
              "data$newstateidKARNATAKA:data$year        -8.037e-02  1.912e-02  -4.204\n",
              "data$newstateidKERALA:data$year            7.595e-02  3.076e-02   2.469\n",
              "data$newstateidMADHYA PRADESH:data$year    3.093e-03  2.473e-02   0.125\n",
              "data$newstateidMAHARASHTRA:data$year      -3.482e-02  2.870e-02  -1.213\n",
              "data$newstateidORISSA:data$year            9.857e-02  3.299e-02   2.988\n",
              "data$newstateidPUNJAB:data$year            5.810e-02  2.428e-02   2.393\n",
              "data$newstateidRAJASTHAN:data$year        -1.870e-02  2.730e-02  -0.685\n",
              "data$newstateidTAMIL NADU:data$year       -5.223e-02  3.663e-02  -1.426\n",
              "data$newstateidUTTAR PRADESH:data$year     8.551e-03  1.568e-02   0.545\n",
              "data$newstateidWEST BENGAL:data$year              NA         NA      NA\n",
              "                                          Pr(>|t|)    \n",
              "(Intercept)                               0.732594    \n",
              "data$postwres                             0.000237 ***\n",
              "data$pfemale                              0.051086 .  \n",
              "data$prural                               0.008015 ** \n",
              "data$plit                                 0.423777    \n",
              "data$pfarm                                0.020382 *  \n",
              "data$womancm                              0.400676    \n",
              "data$pcgsdp                               0.047701 *  \n",
              "data$ppol_strengt                         0.210733    \n",
              "factor(data$year)1986                     0.037305 *  \n",
              "factor(data$year)1987                     0.027310 *  \n",
              "factor(data$year)1988                     0.000439 ***\n",
              "factor(data$year)1989                     6.57e-05 ***\n",
              "factor(data$year)1990                     0.000512 ***\n",
              "factor(data$year)1991                     0.001793 ** \n",
              "factor(data$year)1992                     0.000131 ***\n",
              "factor(data$year)1993                     1.78e-05 ***\n",
              "factor(data$year)1994                     1.39e-05 ***\n",
              "factor(data$year)1995                     7.36e-16 ***\n",
              "factor(data$year)1996                      < 2e-16 ***\n",
              "factor(data$year)1997                      < 2e-16 ***\n",
              "factor(data$year)1998                     2.96e-16 ***\n",
              "factor(data$year)1999                     1.25e-15 ***\n",
              "factor(data$year)2000                     3.66e-15 ***\n",
              "factor(data$year)2001                     1.63e-14 ***\n",
              "factor(data$year)2002                     5.53e-14 ***\n",
              "factor(data$year)2003                     2.26e-13 ***\n",
              "factor(data$year)2004                     2.98e-13 ***\n",
              "factor(data$year)2005                     8.08e-13 ***\n",
              "factor(data$year)2006                     1.27e-12 ***\n",
              "factor(data$year)2007                     1.07e-12 ***\n",
              "factor(data$newstateid)ASSAM              0.990674    \n",
              "factor(data$newstateid)BIHAR              0.006511 ** \n",
              "factor(data$newstateid)GUJARAT            0.851513    \n",
              "factor(data$newstateid)HARYANA            0.377729    \n",
              "factor(data$newstateid)HIMACHAL PRADESH   0.218846    \n",
              "factor(data$newstateid)JAMMU & KASHMIR    0.010094 *  \n",
              "factor(data$newstateid)KARNATAKA          6.49e-06 ***\n",
              "factor(data$newstateid)KERALA             0.135997    \n",
              "factor(data$newstateid)MADHYA PRADESH     0.571001    \n",
              "factor(data$newstateid)MAHARASHTRA        0.029465 *  \n",
              "factor(data$newstateid)ORISSA             7.01e-05 ***\n",
              "factor(data$newstateid)PUNJAB             0.029654 *  \n",
              "factor(data$newstateid)RAJASTHAN          0.296297    \n",
              "factor(data$newstateid)TAMIL NADU         0.097087 .  \n",
              "factor(data$newstateid)UTTAR PRADESH      0.932279    \n",
              "factor(data$newstateid)WEST BENGAL        0.622464    \n",
              "data$newstateidANDHRA PRADESH:data$year   0.623349    \n",
              "data$newstateidASSAM:data$year            0.604047    \n",
              "data$newstateidBIHAR:data$year            0.011578 *  \n",
              "data$newstateidGUJARAT:data$year          0.568989    \n",
              "data$newstateidHARYANA:data$year          0.187269    \n",
              "data$newstateidHIMACHAL PRADESH:data$year 0.703490    \n",
              "data$newstateidJAMMU & KASHMIR:data$year  0.022848 *  \n",
              "data$newstateidKARNATAKA:data$year        3.39e-05 ***\n",
              "data$newstateidKERALA:data$year           0.014054 *  \n",
              "data$newstateidMADHYA PRADESH:data$year   0.900538    \n",
              "data$newstateidMAHARASHTRA:data$year      0.225878    \n",
              "data$newstateidORISSA:data$year           0.003021 ** \n",
              "data$newstateidPUNJAB:data$year           0.017285 *  \n",
              "data$newstateidRAJASTHAN:data$year        0.493751    \n",
              "data$newstateidTAMIL NADU:data$year       0.154847    \n",
              "data$newstateidUTTAR PRADESH:data$year    0.585919    \n",
              "data$newstateidWEST BENGAL:data$year            NA    \n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "Residual standard error: 0.2766 on 328 degrees of freedom\n",
              "Multiple R-squared:  0.9533,\tAdjusted R-squared:  0.9445 \n",
              "F-statistic:   108 on 62 and 328 DF,  p-value: < 2.2e-16\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZ7F16qF81J-",
        "outputId": "02860b0f-d42d-459d-fddd-992c380ac899",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model6cw<- cl(data, model6w, data$newstateid)\n",
        "model6cw"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[[1]]\n",
              "\n",
              "t test of coefficients:\n",
              "\n",
              "                                             Estimate  Std. Error t value\n",
              "(Intercept)                               -1.8151e+01  9.9215e+01 -0.1829\n",
              "data$postwres                              2.2885e-01  8.4441e-02  2.7102\n",
              "data$pfemale                              -2.0461e+01  1.8347e+01 -1.1153\n",
              "data$prural                                1.1835e+01  7.0727e+00  1.6734\n",
              "data$plit                                 -2.8955e+00  5.1406e+00 -0.5633\n",
              "data$pfarm                                 1.6149e+01  1.3363e+01  1.2085\n",
              "data$womancm                              -6.4402e-02  7.0851e-02 -0.9090\n",
              "data$pcgsdp                               -1.8927e-01  1.0245e-01 -1.8474\n",
              "data$ppol_strengt                         -9.1816e-02  5.3947e-02 -1.7020\n",
              "factor(data$year)1986                      2.0723e-01  1.5085e-01  1.3737\n",
              "factor(data$year)1987                      2.4650e-01  1.8910e-01  1.3036\n",
              "factor(data$year)1988                      4.4976e-01  1.9259e-01  2.3352\n",
              "factor(data$year)1989                      5.8862e-01  2.2554e-01  2.6099\n",
              "factor(data$year)1990                      5.8241e-01  2.4776e-01  2.3507\n",
              "factor(data$year)1991                      5.8509e-01  2.4574e-01  2.3809\n",
              "factor(data$year)1992                      8.2236e-01  3.1523e-01  2.6087\n",
              "factor(data$year)1993                      1.0613e+00  3.9733e-01  2.6710\n",
              "factor(data$year)1994                      1.2205e+00  4.9610e-01  2.4602\n",
              "factor(data$year)1995                      2.6348e+00  6.3049e-01  4.1789\n",
              "factor(data$year)1996                      3.0489e+00  6.9951e-01  4.3586\n",
              "factor(data$year)1997                      3.2843e+00  7.8334e-01  4.1926\n",
              "factor(data$year)1998                      3.5329e+00  8.4998e-01  4.1564\n",
              "factor(data$year)1999                      3.7192e+00  9.1609e-01  4.0599\n",
              "factor(data$year)2000                      3.9051e+00  9.7249e-01  4.0156\n",
              "factor(data$year)2001                      4.0531e+00  1.0410e+00  3.8936\n",
              "factor(data$year)2002                      4.1956e+00  1.1034e+00  3.8024\n",
              "factor(data$year)2003                      4.3107e+00  1.1676e+00  3.6921\n",
              "factor(data$year)2004                      4.5091e+00  1.2255e+00  3.6795\n",
              "factor(data$year)2005                      4.6331e+00  1.2838e+00  3.6088\n",
              "factor(data$year)2006                      4.8031e+00  1.3483e+00  3.5622\n",
              "factor(data$year)2007                      5.0083e+00  1.3872e+00  3.6104\n",
              "factor(data$newstateid)ASSAM               5.4398e-01  6.4401e+01  0.0084\n",
              "factor(data$newstateid)BIHAR               1.3458e+02  6.9888e+01  1.9257\n",
              "factor(data$newstateid)GUJARAT            -1.0109e+01  9.6203e+01 -0.1051\n",
              "factor(data$newstateid)HARYANA            -4.3560e+01  9.1519e+01 -0.4760\n",
              "factor(data$newstateid)HIMACHAL PRADESH    4.0421e+01  4.7944e+01  0.8431\n",
              "factor(data$newstateid)JAMMU & KASHMIR     1.2061e+02  8.2609e+01  1.4600\n",
              "factor(data$newstateid)KARNATAKA           1.8431e+02  6.2237e+01  2.9615\n",
              "factor(data$newstateid)KERALA             -1.2434e+02  1.3421e+02 -0.9265\n",
              "factor(data$newstateid)MADHYA PRADESH      1.5481e+01  1.6889e+01  0.9167\n",
              "factor(data$newstateid)MAHARASHTRA         9.3901e+01  6.8029e+01  1.3803\n",
              "factor(data$newstateid)ORISSA             -1.7542e+02  5.1033e+01 -3.4374\n",
              "factor(data$newstateid)PUNJAB             -9.3779e+01  7.2151e+01 -1.2998\n",
              "factor(data$newstateid)RAJASTHAN           5.8875e+01  8.7908e+01  0.6697\n",
              "factor(data$newstateid)TAMIL NADU          1.3058e+02  1.1907e+02  1.0966\n",
              "factor(data$newstateid)UTTAR PRADESH       3.5180e+00  6.4685e+01  0.0544\n",
              "factor(data$newstateid)WEST BENGAL         2.3121e+01  8.6980e+01  0.2658\n",
              "data$newstateidANDHRA PRADESH:data$year    1.1357e-02  4.2534e-02  0.2670\n",
              "data$newstateidASSAM:data$year             1.0467e-02  2.6676e-02  0.3924\n",
              "data$newstateidBIHAR:data$year            -5.7683e-02  2.5423e-02 -2.2689\n",
              "data$newstateidGUJARAT:data$year           1.6773e-02  4.8548e-02  0.3455\n",
              "data$newstateidHARYANA:data$year           3.2824e-02  4.0823e-02  0.8041\n",
              "data$newstateidHIMACHAL PRADESH:data$year -9.5901e-03  4.2791e-02 -0.2241\n",
              "data$newstateidJAMMU & KASHMIR:data$year  -4.9064e-02  2.3478e-02 -2.0898\n",
              "data$newstateidKARNATAKA:data$year        -8.0371e-02  2.6277e-02 -3.0586\n",
              "data$newstateidKERALA:data$year            7.5954e-02  3.7837e-02  2.0074\n",
              "data$newstateidMADHYA PRADESH:data$year    3.0926e-03  4.4367e-02  0.0697\n",
              "data$newstateidMAHARASHTRA:data$year      -3.4822e-02  4.9630e-02 -0.7016\n",
              "data$newstateidORISSA:data$year            9.8567e-02  6.1115e-02  1.6128\n",
              "data$newstateidPUNJAB:data$year            5.8099e-02  3.8890e-02  1.4939\n",
              "data$newstateidRAJASTHAN:data$year        -1.8703e-02  3.3731e-02 -0.5545\n",
              "data$newstateidTAMIL NADU:data$year       -5.2228e-02  5.7214e-02 -0.9128\n",
              "data$newstateidUTTAR PRADESH:data$year     8.5512e-03  2.2513e-02  0.3798\n",
              "                                           Pr(>|t|)    \n",
              "(Intercept)                               0.8549503    \n",
              "data$postwres                             0.0070789 ** \n",
              "data$pfemale                              0.2655569    \n",
              "data$prural                               0.0952026 .  \n",
              "data$plit                                 0.5736407    \n",
              "data$pfarm                                0.2277393    \n",
              "data$womancm                              0.3640278    \n",
              "data$pcgsdp                               0.0655931 .  \n",
              "data$ppol_strengt                         0.0897055 .  \n",
              "factor(data$year)1986                     0.1704616    \n",
              "factor(data$year)1987                     0.1933005    \n",
              "factor(data$year)1988                     0.0201332 *  \n",
              "factor(data$year)1989                     0.0094734 ** \n",
              "factor(data$year)1990                     0.0193275 *  \n",
              "factor(data$year)1991                     0.0178388 *  \n",
              "factor(data$year)1992                     0.0095035 ** \n",
              "factor(data$year)1993                     0.0079395 ** \n",
              "factor(data$year)1994                     0.0143994 *  \n",
              "factor(data$year)1995                     3.761e-05 ***\n",
              "factor(data$year)1996                     1.754e-05 ***\n",
              "factor(data$year)1997                     3.552e-05 ***\n",
              "factor(data$year)1998                     4.130e-05 ***\n",
              "factor(data$year)1999                     6.145e-05 ***\n",
              "factor(data$year)2000                     7.355e-05 ***\n",
              "factor(data$year)2001                     0.0001197 ***\n",
              "factor(data$year)2002                     0.0001708 ***\n",
              "factor(data$year)2003                     0.0002604 ***\n",
              "factor(data$year)2004                     0.0002731 ***\n",
              "factor(data$year)2005                     0.0003557 ***\n",
              "factor(data$year)2006                     0.0004222 ***\n",
              "factor(data$year)2007                     0.0003535 ***\n",
              "factor(data$newstateid)ASSAM              0.9932657    \n",
              "factor(data$newstateid)BIHAR              0.0550058 .  \n",
              "factor(data$newstateid)GUJARAT            0.9163750    \n",
              "factor(data$newstateid)HARYANA            0.6344151    \n",
              "factor(data$newstateid)HIMACHAL PRADESH   0.3997883    \n",
              "factor(data$newstateid)JAMMU & KASHMIR    0.1452450    \n",
              "factor(data$newstateid)KARNATAKA          0.0032853 ** \n",
              "factor(data$newstateid)KERALA             0.3548866    \n",
              "factor(data$newstateid)MADHYA PRADESH     0.3599973    \n",
              "factor(data$newstateid)MAHARASHTRA        0.1684313    \n",
              "factor(data$newstateid)ORISSA             0.0006630 ***\n",
              "factor(data$newstateid)PUNJAB             0.1945951    \n",
              "factor(data$newstateid)RAJASTHAN          0.5034949    \n",
              "factor(data$newstateid)TAMIL NADU         0.2736008    \n",
              "factor(data$newstateid)UTTAR PRADESH      0.9566603    \n",
              "factor(data$newstateid)WEST BENGAL        0.7905415    \n",
              "data$newstateidANDHRA PRADESH:data$year   0.7896286    \n",
              "data$newstateidASSAM:data$year            0.6950320    \n",
              "data$newstateidBIHAR:data$year            0.0239212 *  \n",
              "data$newstateidGUJARAT:data$year          0.7299406    \n",
              "data$newstateidHARYANA:data$year          0.4219485    \n",
              "data$newstateidHIMACHAL PRADESH:data$year 0.8228080    \n",
              "data$newstateidJAMMU & KASHMIR:data$year  0.0374047 *  \n",
              "data$newstateidKARNATAKA:data$year        0.0024070 ** \n",
              "data$newstateidKERALA:data$year           0.0455275 *  \n",
              "data$newstateidMADHYA PRADESH:data$year   0.9444708    \n",
              "data$newstateidMAHARASHTRA:data$year      0.4834063    \n",
              "data$newstateidORISSA:data$year           0.1077498    \n",
              "data$newstateidPUNJAB:data$year           0.1361502    \n",
              "data$newstateidRAJASTHAN:data$year        0.5796355    \n",
              "data$newstateidTAMIL NADU:data$year       0.3619959    \n",
              "data$newstateidUTTAR PRADESH:data$year    0.7043099    \n",
              "---\n",
              "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
              "\n",
              "\n",
              "$n\n",
              "[1] 391\n",
              "\n",
              "$rsq\n",
              "[1] 0.95\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7awC6MJM81KB"
      },
      "source": [
        "# Outputting the results from the model restricted to post-1995\n",
        "table2.1<-as.matrix(c(round(column1.cl[[1]][\"better.data$postwres\",1],3),paste(\"[\",round(column1.cl[[1]][\"better.data$postwres\",2],3),\"]\"),round(column1.cl[[3]],2), round(column1.cl[[2]],0) ))\n",
        "table2.2<-as.matrix(c(round(column2.cl[[1]][\"better.data$postwres\",1],3),paste(\"[\",round(column2.cl[[1]][\"better.data$postwres\",2],3),\"]\"),round(column2.cl[[3]],2), round(column2.cl[[2]],0) ))\n",
        "table2.3<-as.matrix(c(round(model1cw[[1]][\"data$postwres\",1],3),paste(\"[\",round(model1cw[[1]][\"data$postwres\",2],3),\"]\"),round(model1cw[[3]],2), round(model1cw[[2]],0) ))\n",
        "table2.4<-as.matrix(c(round(model6cw[[1]][\"data$postwres\",1],3),paste(\"[\",round(model6cw[[1]][\"data$postwres\",2],3),\"]\"),round(model6cw[[3]],2), round(model6cw[[2]],0) ))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lzDms4l81KE"
      },
      "source": [
        "table2_out <- matrix(NA,nrow=8,ncol=2)\n",
        "table2_out[1:4,1] <- table2.1\n",
        "table2_out[1:4,2] <- table2.2\n",
        "table2_out[5:8,1] <- table2.3\n",
        "table2_out[5:8,2] <- table2.4"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tea6_OPx81KI",
        "outputId": "5991e766-1dca-4c46-cf98-d3e47ab355cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "colnames(table2_out) <- c(\"No controls (1)\", \"Control for state-specific time trends + other controls (2)\")\n",
        "rownames(table2_out)<- c(\"Total crimes against women per 1,000 women\", \"\", \"R2\", \"Observations\",\n",
        "                         \"Total crimes against women per 1,000 women\", \"\", \"R2\", \"Observations\")\n",
        "\n",
        "(table2_out)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                           No controls (1)\n",
              "Total crimes against women per 1,000 women 0.208          \n",
              "                                           [ 0.111 ]      \n",
              "R2                                         0.76           \n",
              "Observations                               130            \n",
              "Total crimes against women per 1,000 women 0.365          \n",
              "                                           [ 0.19 ]       \n",
              "R2                                         0.85           \n",
              "Observations                               391            \n",
              "                                           Control for state-specific time trends + other controls (2)\n",
              "Total crimes against women per 1,000 women -0.045                                                     \n",
              "                                           [ 0.109 ]                                                  \n",
              "R2                                         0.88                                                       \n",
              "Observations                               130                                                        \n",
              "Total crimes against women per 1,000 women 0.229                                                      \n",
              "                                           [ 0.084 ]                                                  \n",
              "R2                                         0.95                                                       \n",
              "Observations                               391                                                        "
            ],
            "text/latex": "A matrix: 8 × 2 of type chr\n\\begin{tabular}{r|ll}\n  & No controls (1) & Control for state-specific time trends + other controls (2)\\\\\n\\hline\n\tTotal crimes against women per 1,000 women & 0.208     & -0.045   \\\\\n\t & {[} 0.111 {]} & {[} 0.109 {]}\\\\\n\tR2 & 0.76      & 0.88     \\\\\n\tObservations & 130       & 130      \\\\\n\tTotal crimes against women per 1,000 women & 0.365     & 0.229    \\\\\n\t & {[} 0.19 {]}  & {[} 0.084 {]}\\\\\n\tR2 & 0.85      & 0.95     \\\\\n\tObservations & 391       & 391      \\\\\n\\end{tabular}\n",
            "text/markdown": "\nA matrix: 8 × 2 of type chr\n\n| <!--/--> | No controls (1) | Control for state-specific time trends + other controls (2) |\n|---|---|---|\n| Total crimes against women per 1,000 women | 0.208     | -0.045    |\n| <!----> | [ 0.111 ] | [ 0.109 ] |\n| R2 | 0.76      | 0.88      |\n| Observations | 130       | 130       |\n| Total crimes against women per 1,000 women | 0.365     | 0.229     |\n| <!----> | [ 0.19 ]  | [ 0.084 ] |\n| R2 | 0.85      | 0.95      |\n| Observations | 391       | 391       |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A matrix: 8 × 2 of type chr</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>No controls (1)</th><th scope=col>Control for state-specific time trends + other controls (2)</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>Total crimes against women per 1,000 women</th><td>0.208    </td><td>-0.045   </td></tr>\n",
              "\t<tr><th scope=row></th><td>[ 0.111 ]</td><td>[ 0.109 ]</td></tr>\n",
              "\t<tr><th scope=row>R2</th><td>0.76     </td><td>0.88     </td></tr>\n",
              "\t<tr><th scope=row>Observations</th><td>130      </td><td>130      </td></tr>\n",
              "\t<tr><th scope=row>Total crimes against women per 1,000 women</th><td>0.365    </td><td>0.229    </td></tr>\n",
              "\t<tr><th scope=row></th><td>[ 0.19 ] </td><td>[ 0.084 ]</td></tr>\n",
              "\t<tr><th scope=row>R2</th><td>0.85     </td><td>0.95     </td></tr>\n",
              "\t<tr><th scope=row>Observations</th><td>391      </td><td>391      </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "miM2R0q08FmV",
        "outputId": "319c407b-4c0b-4b17-aabe-c9c8ec1ab6fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        }
      },
      "source": [
        "install.packages('stargazer')\n",
        "library(stargazer)\n",
        "stargazer(table2_out,title=\"Women's Political Representation and Crimes against Women\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu\n",
            "% Date and time: Fri, Dec 20, 2019 - 06:27:25 AM\n",
            "\\begin{table}[!htbp] \\centering \n",
            "  \\caption{Women's Political Representation and Crimes against Women} \n",
            "  \\label{} \n",
            "\\begin{tabular}{@{\\extracolsep{5pt}} ccc} \n",
            "\\\\[-1.8ex]\\hline \n",
            "\\hline \\\\[-1.8ex] \n",
            " & No controls (1) & Control for state-specific time trends + other controls (2) \\\\ \n",
            "\\hline \\\\[-1.8ex] \n",
            "Total.crimes.against.women.per.1.000.women & 0.208 & -0.045 \\\\ \n",
            "X & [ 0.111 ] & [ 0.109 ] \\\\ \n",
            "R2 & 0.76 & 0.88 \\\\ \n",
            "Observations & 130 & 130 \\\\ \n",
            "Total.crimes.against.women.per.1.000.women.1 & 0.365 & 0.229 \\\\ \n",
            "X.1 & [ 0.19 ] & [ 0.084 ] \\\\ \n",
            "R2.1 & 0.85 & 0.95 \\\\ \n",
            "Observations.1 & 391 & 391 \\\\ \n",
            "\\hline \\\\[-1.8ex] \n",
            "\\end{tabular} \n",
            "\\end{table} \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6nxwR_qe_sXQ"
      },
      "source": [
        "**Table 2:**\n",
        "\n",
        "I copy paste the above latex code to a latex editor and copy the picture to the github and add the link here.\n",
        "\n",
        "![Table 2](https://github.com/mahmud-nobe/CS112-Final-Project/raw/master/table%202.PNG)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "POmm5ifTdsq7"
      },
      "source": [
        "**Table 2.2:**\n",
        "\n",
        "Disregarding Panel A, this is the replication of the table from original paper:\n",
        "\n",
        "![Table 2](https://github.com/mahmud-nobe/CS112-Final-Project/raw/master/table%202.2.PNG)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SgDsJrzYrdyF"
      },
      "source": [
        "## GenMatch "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7Ppuh2iNHZ_",
        "outputId": "fb46b427-3158-46ed-8c6e-de7917c5b96a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        }
      },
      "source": [
        "# installing library for genmatch\n",
        "install.packages('Matching')\n",
        "install.packages('rgenoud')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lViLKGSNHd1"
      },
      "source": [
        "library(Matching)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LOjafLCQPBea",
        "outputId": "dce6908e-7a33-4104-9273-c1403ea70031",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "attach(after.data)\n",
        "X = cbind(state, pcgsdp, pfemale, plit, pwlit, prural)\n",
        "X.all = cbind(state, pcgsdp, pfemale, plit, pwlit, prural, state^2, pcgsdp^2, pfemale^2, plit^2, pwlit^2, prural^2, \n",
        "          state*pcgsdp, state*pfemale, state*plit, state*pwlit, state*prural,\n",
        "          pcgsdp*pfemale, pcgsdp*plit, pcgsdp*pwlit, pcgsdp*prural,\n",
        "          pfemale*plit, pfemale*pwlit, pfemale*prural, plit*pwlit, plit*prural, pwlit*prural)\n",
        "Tr = treat\n",
        "Y = pcr_womtot\n",
        "detach(after.data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The following object is masked _by_ .GlobalEnv:\n",
            "\n",
            "    X\n",
            "\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I1AOS3GByauB",
        "outputId": "d0222ea4-b97c-4dfa-e0eb-f81be2d86bc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "genout = GenMatch(Tr = Tr, X = X, M=1, pop.size = 250,\n",
        "                  max.generations = 20, wait.generations = 25,\n",
        "                  exact = c(T, F, F, F, F, F))\n",
        "mout = Match(Tr = Tr, X = X, M=1, Weight.matrix = genout,\n",
        "                  exact = c(T, F, F, F, F, F))\n",
        "summary(mout)\n",
        "mb <- MatchBalance(treat ~ state + pcgsdp + pfemale + plit + pwlit + prural, data=after.data, match.out=mout, nboots=500)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Fri Dec 20 10:15:31 2019\n",
            "Domains:\n",
            " 0.000000e+00   <=  X1   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X2   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X3   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X4   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X5   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X6   <=    1.000000e+03 \n",
            "\n",
            "Data Type: Floating Point\n",
            "Operators (code number, name, population) \n",
            "\t(1) Cloning........................... \t30\n",
            "\t(2) Uniform Mutation.................. \t31\n",
            "\t(3) Boundary Mutation................. \t31\n",
            "\t(4) Non-Uniform Mutation.............. \t31\n",
            "\t(5) Polytope Crossover................ \t31\n",
            "\t(6) Simple Crossover.................. \t32\n",
            "\t(7) Whole Non-Uniform Mutation........ \t31\n",
            "\t(8) Heuristic Crossover............... \t32\n",
            "\t(9) Local-Minimum Crossover........... \t0\n",
            "\n",
            "SOFT Maximum Number of Generations: 20\n",
            "Maximum Nonchanging Generations: 25\n",
            "Population size       : 250\n",
            "Convergence Tolerance: 1.000000e-03\n",
            "\n",
            "Not Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation.\n",
            "Not Checking Gradients before Stopping.\n",
            "Using Out of Bounds Individuals.\n",
            "\n",
            "Maximization Problem.\n",
            "GENERATION: 0 (initializing the population)\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 250, #Total UniqueCount: 250\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.869624e+02\n",
            "variance........ 8.084156e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.839293e+02\n",
            "variance........ 8.471762e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.120986e+02\n",
            "variance........ 8.837688e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.805688e+02\n",
            "variance........ 8.008272e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.007292e+02\n",
            "variance........ 9.040462e+04\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.641186e+02\n",
            "variance........ 8.570719e+04\n",
            "\n",
            "GENERATION: 1\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 177, #Total UniqueCount: 427\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.067249e+02\n",
            "variance........ 5.493086e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.357338e+02\n",
            "variance........ 1.098860e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.614042e+02\n",
            "variance........ 8.870857e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.543288e+02\n",
            "variance........ 5.839917e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.534290e+02\n",
            "variance........ 1.014301e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.578133e+02\n",
            "variance........ 1.116520e+05\n",
            "\n",
            "GENERATION: 2\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 164, #Total UniqueCount: 591\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.994536e+02\n",
            "variance........ 5.200102e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.788272e+02\n",
            "variance........ 9.019044e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.559352e+02\n",
            "variance........ 7.747169e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.572351e+02\n",
            "variance........ 5.866570e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.704266e+02\n",
            "variance........ 1.102362e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.206100e+02\n",
            "variance........ 1.401245e+05\n",
            "\n",
            "GENERATION: 3\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 162, #Total UniqueCount: 753\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.268730e+02\n",
            "variance........ 5.453758e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.401008e+02\n",
            "variance........ 1.023198e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.084607e+02\n",
            "variance........ 8.716244e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.753605e+02\n",
            "variance........ 5.534352e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.241352e+02\n",
            "variance........ 1.073301e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.455492e+02\n",
            "variance........ 1.237564e+05\n",
            "\n",
            "GENERATION: 4\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 154, #Total UniqueCount: 907\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.084203e+02\n",
            "variance........ 5.462835e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.184203e+02\n",
            "variance........ 9.890479e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.836734e+02\n",
            "variance........ 8.938773e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.619593e+02\n",
            "variance........ 6.005827e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.813070e+02\n",
            "variance........ 1.021858e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.002430e+02\n",
            "variance........ 1.164179e+05\n",
            "\n",
            "GENERATION: 5\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 170, #Total UniqueCount: 1077\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.076459e+02\n",
            "variance........ 5.569905e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.129720e+02\n",
            "variance........ 1.047148e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.776420e+02\n",
            "variance........ 8.277077e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.721222e+02\n",
            "variance........ 6.389149e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.838032e+02\n",
            "variance........ 1.140802e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.301203e+02\n",
            "variance........ 1.381721e+05\n",
            "\n",
            "GENERATION: 6\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 154, #Total UniqueCount: 1231\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.942233e+02\n",
            "variance........ 4.989629e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.237191e+02\n",
            "variance........ 1.092290e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.666690e+02\n",
            "variance........ 8.856326e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.623851e+02\n",
            "variance........ 5.829072e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.751511e+02\n",
            "variance........ 1.114314e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.039160e+02\n",
            "variance........ 1.297595e+05\n",
            "\n",
            "GENERATION: 7\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 161, #Total UniqueCount: 1392\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.837207e+02\n",
            "variance........ 5.477880e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.691805e+02\n",
            "variance........ 9.902492e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.382289e+02\n",
            "variance........ 8.143733e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.304691e+02\n",
            "variance........ 6.086561e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.144773e+02\n",
            "variance........ 1.062251e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.570007e+02\n",
            "variance........ 1.284794e+05\n",
            "\n",
            "GENERATION: 8\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 159, #Total UniqueCount: 1551\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.903505e+02\n",
            "variance........ 5.967128e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.938140e+02\n",
            "variance........ 1.063389e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.445971e+02\n",
            "variance........ 8.810372e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.277297e+02\n",
            "variance........ 6.258419e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.144160e+02\n",
            "variance........ 1.048731e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.445338e+02\n",
            "variance........ 1.109111e+05\n",
            "\n",
            "GENERATION: 9\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 162, #Total UniqueCount: 1713\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.023027e+02\n",
            "variance........ 5.638079e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.180537e+02\n",
            "variance........ 1.031398e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.418609e+02\n",
            "variance........ 7.228735e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.386364e+02\n",
            "variance........ 5.583064e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.527736e+02\n",
            "variance........ 1.058426e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.028022e+02\n",
            "variance........ 1.238470e+05\n",
            "\n",
            "GENERATION: 10\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 158, #Total UniqueCount: 1871\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.001690e+02\n",
            "variance........ 5.538045e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.191838e+02\n",
            "variance........ 1.105278e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.494640e+02\n",
            "variance........ 7.691773e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.406349e+02\n",
            "variance........ 5.613031e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.683129e+02\n",
            "variance........ 1.122084e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.917663e+02\n",
            "variance........ 1.275593e+05\n",
            "\n",
            "GENERATION: 11\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 152, #Total UniqueCount: 2023\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.013667e+02\n",
            "variance........ 5.889774e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.238917e+02\n",
            "variance........ 1.062029e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.540243e+02\n",
            "variance........ 8.278003e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.562011e+02\n",
            "variance........ 6.023430e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.675100e+02\n",
            "variance........ 1.171977e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.826311e+02\n",
            "variance........ 1.305029e+05\n",
            "\n",
            "GENERATION: 12\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 163, #Total UniqueCount: 2186\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.958114e+02\n",
            "variance........ 4.758129e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.950702e+02\n",
            "variance........ 9.228156e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.833182e+02\n",
            "variance........ 8.171594e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.675138e+02\n",
            "variance........ 5.533521e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.300685e+02\n",
            "variance........ 1.221966e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.738344e+02\n",
            "variance........ 1.502409e+05\n",
            "\n",
            "GENERATION: 13\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 161, #Total UniqueCount: 2347\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.943046e+02\n",
            "variance........ 4.704871e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.212490e+02\n",
            "variance........ 9.909819e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.705518e+02\n",
            "variance........ 7.170864e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.714113e+02\n",
            "variance........ 5.077226e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.078594e+02\n",
            "variance........ 1.116341e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.676769e+02\n",
            "variance........ 1.376910e+05\n",
            "\n",
            "GENERATION: 14\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 164, #Total UniqueCount: 2511\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.012485e+02\n",
            "variance........ 4.954427e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.377469e+02\n",
            "variance........ 1.069995e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.677215e+02\n",
            "variance........ 8.305993e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.580200e+02\n",
            "variance........ 5.475477e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.136056e+02\n",
            "variance........ 1.173366e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.391369e+02\n",
            "variance........ 1.373090e+05\n",
            "\n",
            "GENERATION: 15\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 151, #Total UniqueCount: 2662\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.965637e+02\n",
            "variance........ 4.538670e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.288374e+02\n",
            "variance........ 1.027069e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.846472e+02\n",
            "variance........ 8.364597e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.639190e+02\n",
            "variance........ 5.582352e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.002579e+02\n",
            "variance........ 1.111046e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.578894e+02\n",
            "variance........ 1.311778e+05\n",
            "\n",
            "GENERATION: 16\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 158, #Total UniqueCount: 2820\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.942062e+02\n",
            "variance........ 5.305476e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.905828e+02\n",
            "variance........ 9.864715e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.689397e+02\n",
            "variance........ 8.548512e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.531729e+02\n",
            "variance........ 5.550607e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.988732e+02\n",
            "variance........ 1.208279e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.293242e+02\n",
            "variance........ 1.430052e+05\n",
            "\n",
            "GENERATION: 17\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 148, #Total UniqueCount: 2968\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.143480e+02\n",
            "variance........ 5.931058e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.197709e+02\n",
            "variance........ 1.122269e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.402866e+02\n",
            "variance........ 6.960078e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.505911e+02\n",
            "variance........ 5.481377e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.055188e+02\n",
            "variance........ 1.200994e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.058763e+02\n",
            "variance........ 1.318117e+05\n",
            "\n",
            "GENERATION: 18\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 151, #Total UniqueCount: 3119\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.954253e+02\n",
            "variance........ 5.569896e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.044475e+02\n",
            "variance........ 1.087581e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.498452e+02\n",
            "variance........ 7.943949e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.391911e+02\n",
            "variance........ 5.589177e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.526786e+02\n",
            "variance........ 1.135026e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.956704e+02\n",
            "variance........ 1.333540e+05\n",
            "\n",
            "GENERATION: 19\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 151, #Total UniqueCount: 3270\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.003663e+02\n",
            "variance........ 5.391044e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.179106e+02\n",
            "variance........ 1.069114e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.530954e+02\n",
            "variance........ 8.033255e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.594295e+02\n",
            "variance........ 6.004887e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.779002e+02\n",
            "variance........ 1.172377e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.086634e+02\n",
            "variance........ 1.308589e+05\n",
            "\n",
            "GENERATION: 20\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 149, #Total UniqueCount: 3419\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.923237e+02\n",
            "variance........ 4.931169e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.177500e+02\n",
            "variance........ 9.936288e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.552078e+02\n",
            "variance........ 7.592238e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.559744e+02\n",
            "variance........ 5.587061e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.654223e+02\n",
            "variance........ 1.075130e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.157585e+02\n",
            "variance........ 1.262211e+05\n",
            "\n",
            "Increasing 'max.generations' limit by 25 generations to 45 because the fitness is still impoving.\n",
            "\n",
            "\n",
            "GENERATION: 21\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 165, #Total UniqueCount: 3584\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.117621e+02\n",
            "variance........ 5.704886e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.507969e+02\n",
            "variance........ 1.160030e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.777568e+02\n",
            "variance........ 9.063593e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.692659e+02\n",
            "variance........ 6.221478e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.084472e+02\n",
            "variance........ 1.202905e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.977886e+02\n",
            "variance........ 1.280544e+05\n",
            "\n",
            "GENERATION: 22\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 140, #Total UniqueCount: 3724\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.933690e+02\n",
            "variance........ 5.110886e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.220379e+02\n",
            "variance........ 1.121888e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.468595e+02\n",
            "variance........ 8.061371e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.507874e+02\n",
            "variance........ 5.953572e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.687719e+02\n",
            "variance........ 1.140645e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.939964e+02\n",
            "variance........ 1.224475e+05\n",
            "\n",
            "GENERATION: 23\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 157, #Total UniqueCount: 3881\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.803034e+02\n",
            "variance........ 5.455288e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.731252e+02\n",
            "variance........ 1.049311e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.411679e+02\n",
            "variance........ 8.186236e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.350011e+02\n",
            "variance........ 6.157529e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.376392e+02\n",
            "variance........ 1.135073e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.601811e+02\n",
            "variance........ 1.290032e+05\n",
            "\n",
            "GENERATION: 24\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 154, #Total UniqueCount: 4035\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.771536e+02\n",
            "variance........ 4.705948e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.846423e+02\n",
            "variance........ 1.004132e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.498303e+02\n",
            "variance........ 7.676543e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.338603e+02\n",
            "variance........ 5.474791e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.566962e+02\n",
            "variance........ 1.090304e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.045162e+02\n",
            "variance........ 1.343969e+05\n",
            "\n",
            "GENERATION: 25\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 160, #Total UniqueCount: 4195\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.101181e+02\n",
            "variance........ 6.207225e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.046734e+02\n",
            "variance........ 1.108346e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.431538e+02\n",
            "variance........ 7.600884e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.483739e+02\n",
            "variance........ 6.052008e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.690396e+02\n",
            "variance........ 1.207061e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.895274e+02\n",
            "variance........ 1.369338e+05\n",
            "\n",
            "GENERATION: 26\n",
            "Lexical Fit..... 7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "#unique......... 156, #Total UniqueCount: 4351\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.147517e+02\n",
            "variance........ 5.044245e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.310971e+02\n",
            "variance........ 1.006291e+05\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.036269e+02\n",
            "variance........ 8.598458e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.713838e+02\n",
            "variance........ 5.266361e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.404465e+02\n",
            "variance........ 1.090858e+05\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.646261e+02\n",
            "variance........ 1.325930e+05\n",
            "\n",
            "'wait.generations' limit reached.\n",
            "No significant improvement in 25 generations.\n",
            "\n",
            "Solution Lexical Fitness Value:\n",
            "7.091676e-09  2.152124e-08  1.978875e-07  3.632187e-07  2.408537e-04  1.074108e-03  1.387813e-02  2.895102e-02  2.895102e-02  2.895102e-02  1.000000e+00  1.000000e+00  \n",
            "\n",
            "Parameters at the Solution:\n",
            "\n",
            " X[ 1] :\t1.000000e+00\n",
            " X[ 2] :\t1.000000e+00\n",
            " X[ 3] :\t1.000000e+00\n",
            " X[ 4] :\t1.000000e+00\n",
            " X[ 5] :\t1.000000e+00\n",
            " X[ 6] :\t1.000000e+00\n",
            "\n",
            "Solution Found Generation 1\n",
            "Number of Generations Run 26\n",
            "\n",
            "Fri Dec 20 10:15:54 2019\n",
            "Total run time : 0 hours 0 minutes and 23 seconds\n",
            "\n",
            "Estimate...  0 \n",
            "SE.........  0 \n",
            "T-stat.....  NaN \n",
            "p.val......  NA \n",
            "\n",
            "Original number of observations..............  130 \n",
            "Original number of treated obs...............  99 \n",
            "Matched number of observations...............  34 \n",
            "Matched number of observations  (unweighted).  34 \n",
            "\n",
            "Number of obs dropped by 'exact' or 'caliper'  65 \n",
            "\n",
            "\n",
            "***** (V1) state *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........     6.9091 \t \t     8.6471 \n",
            "mean control..........     8.5484 \t \t     8.6471 \n",
            "std mean diff.........    -32.599 \t \t          0 \n",
            "\n",
            "mean raw eQQ diff.....     2.1613 \t \t          0 \n",
            "med  raw eQQ diff.....          1 \t \t          0 \n",
            "max  raw eQQ diff.....          9 \t \t          0 \n",
            "\n",
            "mean eCDF diff........    0.13851 \t \t          0 \n",
            "med  eCDF diff........    0.12284 \t \t          0 \n",
            "max  eCDF diff........    0.33464 \t \t          0 \n",
            "\n",
            "var ratio (Tr/Co).....    0.65871 \t \t          1 \n",
            "T-test p-value........    0.18686 \t \t          1 \n",
            "KS Bootstrap p-value..      0.004 \t \t          1 \n",
            "KS Naive p-value......   0.010111 \t \t          1 \n",
            "KS Statistic..........    0.33464 \t \t          0 \n",
            "\n",
            "\n",
            "***** (V2) pcgsdp *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........     2.1787 \t \t     1.8473 \n",
            "mean control..........      1.164 \t \t     1.5029 \n",
            "std mean diff.........     143.57 \t \t     57.045 \n",
            "\n",
            "mean raw eQQ diff.....     1.0135 \t \t    0.34582 \n",
            "med  raw eQQ diff.....    0.93248 \t \t    0.30473 \n",
            "max  raw eQQ diff.....     2.3154 \t \t     1.2072 \n",
            "\n",
            "mean eCDF diff........    0.42017 \t \t    0.18929 \n",
            "med  eCDF diff........    0.42929 \t \t    0.17647 \n",
            "max  eCDF diff........    0.75562 \t \t    0.47059 \n",
            "\n",
            "var ratio (Tr/Co).....     4.1453 \t \t     2.1406 \n",
            "T-test p-value........ < 2.22e-16 \t \t 1.9789e-07 \n",
            "KS Bootstrap p-value.. < 2.22e-16 \t \t < 2.22e-16 \n",
            "KS Naive p-value...... 5.9952e-14 \t \t  0.0010741 \n",
            "KS Statistic..........    0.75562 \t \t    0.47059 \n",
            "\n",
            "\n",
            "***** (V3) pfemale *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.93402 \t \t    0.94236 \n",
            "mean control..........    0.91168 \t \t    0.93789 \n",
            "std mean diff.........     54.573 \t \t     11.913 \n",
            "\n",
            "mean raw eQQ diff.....   0.029935 \t \t  0.0044713 \n",
            "med  raw eQQ diff.....   0.022884 \t \t  0.0035205 \n",
            "max  raw eQQ diff.....   0.054991 \t \t    0.01465 \n",
            "\n",
            "mean eCDF diff........    0.20641 \t \t    0.11916 \n",
            "med  eCDF diff........    0.24373 \t \t    0.11765 \n",
            "max  eCDF diff........    0.41219 \t \t    0.35294 \n",
            "\n",
            "var ratio (Tr/Co).....      4.068 \t \t     1.1947 \n",
            "T-test p-value........ 9.4019e-05 \t \t 3.6322e-07 \n",
            "KS Bootstrap p-value.. < 2.22e-16 \t \t      0.016 \n",
            "KS Naive p-value...... 0.00039652 \t \t   0.028951 \n",
            "KS Statistic..........    0.41219 \t \t    0.35294 \n",
            "\n",
            "\n",
            "***** (V4) plit *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.55776 \t \t    0.54351 \n",
            "mean control..........    0.44882 \t \t    0.50517 \n",
            "std mean diff.........     131.34 \t \t     39.621 \n",
            "\n",
            "mean raw eQQ diff.....    0.10727 \t \t   0.038343 \n",
            "med  raw eQQ diff.....    0.10429 \t \t   0.031325 \n",
            "max  raw eQQ diff.....    0.17097 \t \t    0.11462 \n",
            "\n",
            "mean eCDF diff........    0.35533 \t \t    0.12368 \n",
            "med  eCDF diff........    0.35761 \t \t    0.11765 \n",
            "max  eCDF diff........    0.63245 \t \t    0.35294 \n",
            "\n",
            "var ratio (Tr/Co).....     2.1262 \t \t     1.5482 \n",
            "T-test p-value........ 4.4098e-12 \t \t 7.0917e-09 \n",
            "KS Bootstrap p-value.. < 2.22e-16 \t \t      0.026 \n",
            "KS Naive p-value...... 1.7559e-09 \t \t   0.028951 \n",
            "KS Statistic..........    0.63245 \t \t    0.35294 \n",
            "\n",
            "\n",
            "***** (V5) pwlit *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.46168 \t \t     0.4519 \n",
            "mean control..........    0.34436 \t \t    0.40848 \n",
            "std mean diff.........     119.33 \t \t     38.661 \n",
            "\n",
            "mean raw eQQ diff.....    0.11511 \t \t   0.043416 \n",
            "med  raw eQQ diff.....    0.12534 \t \t   0.034081 \n",
            "max  raw eQQ diff.....    0.17462 \t \t    0.13454 \n",
            "\n",
            "mean eCDF diff........    0.32959 \t \t    0.13348 \n",
            "med  eCDF diff........    0.35761 \t \t    0.11765 \n",
            "max  eCDF diff........     0.5334 \t \t    0.38235 \n",
            "\n",
            "var ratio (Tr/Co).....     1.9776 \t \t     1.5666 \n",
            "T-test p-value........ 2.8749e-10 \t \t 2.1521e-08 \n",
            "KS Bootstrap p-value.. < 2.22e-16 \t \t      0.012 \n",
            "KS Naive p-value......  1.009e-06 \t \t   0.013878 \n",
            "KS Statistic..........     0.5334 \t \t    0.38235 \n",
            "\n",
            "\n",
            "***** (V6) prural *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.74033 \t \t    0.72783 \n",
            "mean control..........    0.81251 \t \t    0.75093 \n",
            "std mean diff.........    -66.339 \t \t    -16.824 \n",
            "\n",
            "mean raw eQQ diff.....   0.081253 \t \t   0.023102 \n",
            "med  raw eQQ diff.....   0.074682 \t \t  0.0045347 \n",
            "max  raw eQQ diff.....    0.21918 \t \t    0.10663 \n",
            "\n",
            "mean eCDF diff........    0.25724 \t \t    0.14329 \n",
            "med  eCDF diff........    0.23656 \t \t    0.14706 \n",
            "max  eCDF diff........     0.5536 \t \t    0.35294 \n",
            "\n",
            "var ratio (Tr/Co).....     3.2615 \t \t      1.554 \n",
            "T-test p-value........ 9.2926e-06 \t \t 0.00024085 \n",
            "KS Bootstrap p-value.. < 2.22e-16 \t \t       0.02 \n",
            "KS Naive p-value...... 3.1028e-07 \t \t   0.028951 \n",
            "KS Statistic..........     0.5536 \t \t    0.35294 \n",
            "\n",
            "\n",
            "Before Matching Minimum p.value: < 2.22e-16 \n",
            "Variable Name(s): pcgsdp pfemale plit pwlit prural  Number(s): 2 3 4 5 6 \n",
            "\n",
            "After Matching Minimum p.value: < 2.22e-16 \n",
            "Variable Name(s): pcgsdp  Number(s): 2 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h4_PAzjTyg6p",
        "outputId": "2d030603-4de2-4787-f35e-bf308bfde752",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "attach(before.data)\n",
        "X = cbind(state, pcgsdp, pfemale, plit, pwlit, prural)\n",
        "X.all = cbind(state, pcgsdp, pfemale, plit, pwlit, prural, state^2, pcgsdp^2, pfemale^2, plit^2, pwlit^2, prural^2, \n",
        "          state*pcgsdp, state*pfemale, state*plit, state*pwlit, state*prural,\n",
        "          pcgsdp*pfemale, pcgsdp*plit, pcgsdp*pwlit, pcgsdp*prural,\n",
        "          pfemale*plit, pfemale*pwlit, pfemale*prural, plit*pwlit, plit*prural, pwlit*prural)\n",
        "Tr = treat\n",
        "Y = pcr_womtot\n",
        "detach(before.data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The following object is masked _by_ .GlobalEnv:\n",
            "\n",
            "    X\n",
            "\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FRM8dFP9zMJ3",
        "outputId": "a1f95ce6-77cb-4fe9-a6f0-fe6214fc97f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "genout = GenMatch(Tr = Tr, X = X, M=1, pop.size = 250,\n",
        "                  max.generations = 20, wait.generations = 25,\n",
        "                  exact = c(T, F, F, F, F, F))\n",
        "mout = Match(Tr = Tr, X = X, M=1, Weight.matrix = genout,\n",
        "                  exact = c(T, F, F, F, F, F))\n",
        "summary(mout)\n",
        "mb <- MatchBalance(treat ~ state + pcgsdp + pfemale + plit + pwlit + prural, data=before.data, match.out=mout, nboots=500)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Fri Dec 20 10:15:55 2019\n",
            "Domains:\n",
            " 0.000000e+00   <=  X1   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X2   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X3   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X4   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X5   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X6   <=    1.000000e+03 \n",
            "\n",
            "Data Type: Floating Point\n",
            "Operators (code number, name, population) \n",
            "\t(1) Cloning........................... \t30\n",
            "\t(2) Uniform Mutation.................. \t31\n",
            "\t(3) Boundary Mutation................. \t31\n",
            "\t(4) Non-Uniform Mutation.............. \t31\n",
            "\t(5) Polytope Crossover................ \t31\n",
            "\t(6) Simple Crossover.................. \t32\n",
            "\t(7) Whole Non-Uniform Mutation........ \t31\n",
            "\t(8) Heuristic Crossover............... \t32\n",
            "\t(9) Local-Minimum Crossover........... \t0\n",
            "\n",
            "SOFT Maximum Number of Generations: 20\n",
            "Maximum Nonchanging Generations: 25\n",
            "Population size       : 250\n",
            "Convergence Tolerance: 1.000000e-03\n",
            "\n",
            "Not Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation.\n",
            "Not Checking Gradients before Stopping.\n",
            "Using Out of Bounds Individuals.\n",
            "\n",
            "Maximization Problem.\n",
            "GENERATION: 0 (initializing the population)\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 250, #Total UniqueCount: 250\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 4.909806e+02\n",
            "variance........ 7.784093e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 5.258769e+02\n",
            "variance........ 8.721967e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 4.967395e+02\n",
            "variance........ 8.414962e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 5.224222e+02\n",
            "variance........ 7.806885e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 5.326944e+02\n",
            "variance........ 7.761485e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 5.092808e+02\n",
            "variance........ 9.450652e+04\n",
            "\n",
            "GENERATION: 1\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 186, #Total UniqueCount: 436\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.590886e+02\n",
            "variance........ 5.243539e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.407733e+02\n",
            "variance........ 2.013411e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.058430e+02\n",
            "variance........ 4.031019e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.592884e+02\n",
            "variance........ 3.616959e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.804238e+02\n",
            "variance........ 8.334957e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.614263e+02\n",
            "variance........ 4.336604e+04\n",
            "\n",
            "GENERATION: 2\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 178, #Total UniqueCount: 614\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.604761e+02\n",
            "variance........ 5.731195e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.222245e+02\n",
            "variance........ 1.884533e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.148562e+02\n",
            "variance........ 3.864256e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.504772e+02\n",
            "variance........ 3.081858e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.919682e+02\n",
            "variance........ 7.718485e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.518995e+02\n",
            "variance........ 4.833318e+04\n",
            "\n",
            "GENERATION: 3\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 174, #Total UniqueCount: 788\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.696878e+02\n",
            "variance........ 5.366355e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.356209e+02\n",
            "variance........ 1.711843e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.031577e+02\n",
            "variance........ 3.652508e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.655973e+02\n",
            "variance........ 3.413413e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.336753e+02\n",
            "variance........ 7.228644e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.689049e+02\n",
            "variance........ 3.982166e+04\n",
            "\n",
            "GENERATION: 4\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 160, #Total UniqueCount: 948\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.538115e+02\n",
            "variance........ 5.700729e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.398456e+02\n",
            "variance........ 2.307843e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.146023e+02\n",
            "variance........ 3.753382e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.627637e+02\n",
            "variance........ 3.222735e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.895853e+02\n",
            "variance........ 7.975268e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.760671e+02\n",
            "variance........ 3.857018e+04\n",
            "\n",
            "GENERATION: 5\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 159, #Total UniqueCount: 1107\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.672189e+02\n",
            "variance........ 5.677971e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.337610e+02\n",
            "variance........ 1.787736e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.101685e+02\n",
            "variance........ 3.616847e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.699949e+02\n",
            "variance........ 3.011871e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.642800e+02\n",
            "variance........ 7.675012e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.818659e+02\n",
            "variance........ 3.662726e+04\n",
            "\n",
            "GENERATION: 6\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 174, #Total UniqueCount: 1281\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.459780e+02\n",
            "variance........ 5.743055e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.447678e+02\n",
            "variance........ 1.847089e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 2.927697e+02\n",
            "variance........ 3.600444e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.815540e+02\n",
            "variance........ 3.051584e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.688461e+02\n",
            "variance........ 9.183910e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.551814e+02\n",
            "variance........ 4.377960e+04\n",
            "\n",
            "GENERATION: 7\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 169, #Total UniqueCount: 1450\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.613539e+02\n",
            "variance........ 5.902825e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.336930e+02\n",
            "variance........ 1.871719e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.042399e+02\n",
            "variance........ 3.907653e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.703360e+02\n",
            "variance........ 2.948157e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.840103e+02\n",
            "variance........ 8.455122e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.710845e+02\n",
            "variance........ 4.101206e+04\n",
            "\n",
            "GENERATION: 8\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 158, #Total UniqueCount: 1608\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.624532e+02\n",
            "variance........ 6.420413e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.287584e+02\n",
            "variance........ 2.189431e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.190032e+02\n",
            "variance........ 4.318240e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.561437e+02\n",
            "variance........ 3.028936e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.884947e+02\n",
            "variance........ 7.679204e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.787708e+02\n",
            "variance........ 4.154573e+04\n",
            "\n",
            "GENERATION: 9\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 165, #Total UniqueCount: 1773\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.626961e+02\n",
            "variance........ 5.896894e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.549946e+02\n",
            "variance........ 2.766307e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.151362e+02\n",
            "variance........ 4.083493e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.647228e+02\n",
            "variance........ 3.315809e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.747990e+02\n",
            "variance........ 8.193194e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.615045e+02\n",
            "variance........ 4.473977e+04\n",
            "\n",
            "GENERATION: 10\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 174, #Total UniqueCount: 1947\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.750929e+02\n",
            "variance........ 6.232763e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.242162e+02\n",
            "variance........ 2.287161e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.132812e+02\n",
            "variance........ 4.085516e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.469368e+02\n",
            "variance........ 3.286276e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 4.022864e+02\n",
            "variance........ 8.043515e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.752508e+02\n",
            "variance........ 4.867932e+04\n",
            "\n",
            "GENERATION: 11\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 161, #Total UniqueCount: 2108\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.833534e+02\n",
            "variance........ 6.065161e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.222997e+02\n",
            "variance........ 2.077661e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.187772e+02\n",
            "variance........ 4.088590e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.575395e+02\n",
            "variance........ 3.441098e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.773274e+02\n",
            "variance........ 7.550167e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.830284e+02\n",
            "variance........ 4.192486e+04\n",
            "\n",
            "GENERATION: 12\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 163, #Total UniqueCount: 2271\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.301394e+02\n",
            "variance........ 5.645018e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.391058e+02\n",
            "variance........ 2.002088e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 2.981591e+02\n",
            "variance........ 3.936803e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.648763e+02\n",
            "variance........ 3.172409e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 4.230420e+02\n",
            "variance........ 9.443394e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.658389e+02\n",
            "variance........ 4.184454e+04\n",
            "\n",
            "GENERATION: 13\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 163, #Total UniqueCount: 2434\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.637148e+02\n",
            "variance........ 6.481022e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.272837e+02\n",
            "variance........ 1.954588e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.240901e+02\n",
            "variance........ 4.164470e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.595362e+02\n",
            "variance........ 3.187203e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.879228e+02\n",
            "variance........ 7.998485e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.735871e+02\n",
            "variance........ 4.394647e+04\n",
            "\n",
            "GENERATION: 14\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 170, #Total UniqueCount: 2604\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.686147e+02\n",
            "variance........ 5.410645e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.333709e+02\n",
            "variance........ 2.278598e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.076248e+02\n",
            "variance........ 3.965905e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.568825e+02\n",
            "variance........ 3.483874e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.652502e+02\n",
            "variance........ 7.930420e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.748576e+02\n",
            "variance........ 4.278602e+04\n",
            "\n",
            "GENERATION: 15\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 167, #Total UniqueCount: 2771\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.766635e+02\n",
            "variance........ 5.727944e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.204165e+02\n",
            "variance........ 2.122576e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.159570e+02\n",
            "variance........ 3.967685e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.591430e+02\n",
            "variance........ 2.994513e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.975550e+02\n",
            "variance........ 8.551085e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.787561e+02\n",
            "variance........ 4.475716e+04\n",
            "\n",
            "GENERATION: 16\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 163, #Total UniqueCount: 2934\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.794228e+02\n",
            "variance........ 5.974849e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.270486e+02\n",
            "variance........ 2.163209e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.239272e+02\n",
            "variance........ 4.188718e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.645814e+02\n",
            "variance........ 2.955000e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.891124e+02\n",
            "variance........ 8.372103e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.830266e+02\n",
            "variance........ 4.033989e+04\n",
            "\n",
            "GENERATION: 17\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 162, #Total UniqueCount: 3096\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.366154e+02\n",
            "variance........ 6.603224e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.349139e+02\n",
            "variance........ 2.059811e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 2.986010e+02\n",
            "variance........ 4.055164e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.655876e+02\n",
            "variance........ 3.131696e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.881378e+02\n",
            "variance........ 8.501450e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.512953e+02\n",
            "variance........ 4.935137e+04\n",
            "\n",
            "GENERATION: 18\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 164, #Total UniqueCount: 3260\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 7.030267e+02\n",
            "variance........ 5.297231e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.216912e+02\n",
            "variance........ 2.573633e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.321767e+02\n",
            "variance........ 4.135022e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.508065e+02\n",
            "variance........ 3.570684e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.755232e+02\n",
            "variance........ 7.745007e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.918886e+02\n",
            "variance........ 4.074717e+04\n",
            "\n",
            "GENERATION: 19\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 161, #Total UniqueCount: 3421\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.620666e+02\n",
            "variance........ 5.647818e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.386403e+02\n",
            "variance........ 2.326338e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.098352e+02\n",
            "variance........ 3.789291e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.705636e+02\n",
            "variance........ 2.814224e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.941566e+02\n",
            "variance........ 8.228592e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.759632e+02\n",
            "variance........ 3.690275e+04\n",
            "\n",
            "GENERATION: 20\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 176, #Total UniqueCount: 3597\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.513805e+02\n",
            "variance........ 5.458437e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.451954e+02\n",
            "variance........ 2.008711e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 2.984037e+02\n",
            "variance........ 3.996016e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.838034e+02\n",
            "variance........ 2.828457e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.860212e+02\n",
            "variance........ 9.528657e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.637063e+02\n",
            "variance........ 3.772533e+04\n",
            "\n",
            "Increasing 'max.generations' limit by 25 generations to 45 because the fitness is still impoving.\n",
            "\n",
            "\n",
            "GENERATION: 21\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 162, #Total UniqueCount: 3759\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.663867e+02\n",
            "variance........ 6.649460e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.270272e+02\n",
            "variance........ 2.138234e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.184293e+02\n",
            "variance........ 4.110122e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.564155e+02\n",
            "variance........ 3.082945e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 4.041183e+02\n",
            "variance........ 7.875737e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.983679e+02\n",
            "variance........ 3.457462e+04\n",
            "\n",
            "GENERATION: 22\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 158, #Total UniqueCount: 3917\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.589007e+02\n",
            "variance........ 5.434039e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.427935e+02\n",
            "variance........ 2.055828e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 2.870378e+02\n",
            "variance........ 3.932059e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.825743e+02\n",
            "variance........ 2.885959e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.929032e+02\n",
            "variance........ 8.706678e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.858223e+02\n",
            "variance........ 3.663371e+04\n",
            "\n",
            "GENERATION: 23\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 170, #Total UniqueCount: 4087\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.622683e+02\n",
            "variance........ 5.882352e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.320252e+02\n",
            "variance........ 2.153107e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 2.968838e+02\n",
            "variance........ 3.781825e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.654496e+02\n",
            "variance........ 3.059723e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 4.037713e+02\n",
            "variance........ 9.130898e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.819414e+02\n",
            "variance........ 3.973774e+04\n",
            "\n",
            "GENERATION: 24\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 165, #Total UniqueCount: 4252\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.583948e+02\n",
            "variance........ 5.725195e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.479233e+02\n",
            "variance........ 2.212222e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 2.966315e+02\n",
            "variance........ 3.783655e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.707093e+02\n",
            "variance........ 2.824863e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.737454e+02\n",
            "variance........ 8.426233e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.575351e+02\n",
            "variance........ 4.324100e+04\n",
            "\n",
            "GENERATION: 25\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 171, #Total UniqueCount: 4423\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.842619e+02\n",
            "variance........ 6.546609e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.215692e+02\n",
            "variance........ 2.483260e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.203411e+02\n",
            "variance........ 4.023322e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.475078e+02\n",
            "variance........ 3.153429e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 4.120610e+02\n",
            "variance........ 8.211786e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 7.013180e+02\n",
            "variance........ 3.748728e+04\n",
            "\n",
            "GENERATION: 26\n",
            "Lexical Fit..... 5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "#unique......... 164, #Total UniqueCount: 4587\n",
            "var 1:\n",
            "best............ 7.241435e+02\n",
            "mean............ 6.951975e+02\n",
            "variance........ 5.672273e+04\n",
            "var 2:\n",
            "best............ 2.952383e+02\n",
            "mean............ 2.119854e+02\n",
            "variance........ 2.129900e+04\n",
            "var 3:\n",
            "best............ 2.034740e+02\n",
            "mean............ 3.252190e+02\n",
            "variance........ 3.959972e+04\n",
            "var 4:\n",
            "best............ 7.858773e+02\n",
            "mean............ 6.518055e+02\n",
            "variance........ 2.935928e+04\n",
            "var 5:\n",
            "best............ 1.240093e+02\n",
            "mean............ 3.996956e+02\n",
            "variance........ 7.900184e+04\n",
            "var 6:\n",
            "best............ 6.755669e+02\n",
            "mean............ 6.923418e+02\n",
            "variance........ 4.425540e+04\n",
            "\n",
            "'wait.generations' limit reached.\n",
            "No significant improvement in 25 generations.\n",
            "\n",
            "Solution Lexical Fitness Value:\n",
            "5.681661e-07  7.457105e-07  2.005396e-06  1.117432e-05  2.123067e-02  5.035351e-02  1.090329e-01  1.090329e-01  2.153739e-01  4.067936e-01  1.000000e+00  1.000000e+00  \n",
            "\n",
            "Parameters at the Solution:\n",
            "\n",
            " X[ 1] :\t7.241435e+02\n",
            " X[ 2] :\t2.952383e+02\n",
            " X[ 3] :\t2.034740e+02\n",
            " X[ 4] :\t7.858773e+02\n",
            " X[ 5] :\t1.240093e+02\n",
            " X[ 6] :\t6.755669e+02\n",
            "\n",
            "Solution Found Generation 1\n",
            "Number of Generations Run 26\n",
            "\n",
            "Fri Dec 20 10:16:19 2019\n",
            "Total run time : 0 hours 0 minutes and 24 seconds\n",
            "\n",
            "Estimate...  0 \n",
            "SE.........  0 \n",
            "T-stat.....  NaN \n",
            "p.val......  NA \n",
            "\n",
            "Original number of observations..............  70 \n",
            "Original number of treated obs...............  22 \n",
            "Matched number of observations...............  22 \n",
            "Matched number of observations  (unweighted).  22 \n",
            "\n",
            "Number of obs dropped by 'exact' or 'caliper'  0 \n",
            "\n",
            "\n",
            "***** (V1) state *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........     10.273 \t \t     10.273 \n",
            "mean control..........     11.958 \t \t     10.273 \n",
            "std mean diff.........    -61.773 \t \t          0 \n",
            "\n",
            "mean raw eQQ diff.....     1.7273 \t \t          0 \n",
            "med  raw eQQ diff.....          2 \t \t          0 \n",
            "max  raw eQQ diff.....          5 \t \t          0 \n",
            "\n",
            "mean eCDF diff........    0.20833 \t \t          0 \n",
            "med  eCDF diff........    0.22727 \t \t          0 \n",
            "max  eCDF diff........    0.37879 \t \t          0 \n",
            "\n",
            "var ratio (Tr/Co).....     1.0356 \t \t          1 \n",
            "T-test p-value........   0.020505 \t \t          1 \n",
            "KS Bootstrap p-value..      0.012 \t \t          1 \n",
            "KS Naive p-value......   0.026361 \t \t          1 \n",
            "KS Statistic..........    0.37879 \t \t          0 \n",
            "\n",
            "\n",
            "***** (V2) pcgsdp *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........     1.3631 \t \t     1.3631 \n",
            "mean control..........     1.2199 \t \t     1.1971 \n",
            "std mean diff.........     29.002 \t \t     33.602 \n",
            "\n",
            "mean raw eQQ diff.....    0.17316 \t \t    0.16722 \n",
            "med  raw eQQ diff.....    0.13723 \t \t    0.15606 \n",
            "max  raw eQQ diff.....    0.48856 \t \t    0.37295 \n",
            "\n",
            "mean eCDF diff........    0.10181 \t \t     0.1395 \n",
            "med  eCDF diff........    0.07197 \t \t    0.13636 \n",
            "max  eCDF diff........    0.27652 \t \t    0.36364 \n",
            "\n",
            "var ratio (Tr/Co).....     0.7376 \t \t      1.235 \n",
            "T-test p-value........    0.29083 \t \t 1.1174e-05 \n",
            "KS Bootstrap p-value..      0.154 \t \t       0.07 \n",
            "KS Naive p-value......    0.16118 \t \t    0.10903 \n",
            "KS Statistic..........    0.27652 \t \t    0.36364 \n",
            "\n",
            "\n",
            "***** (V3) pfemale *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.96387 \t \t    0.96387 \n",
            "mean control..........    0.94051 \t \t    0.96343 \n",
            "std mean diff.........     54.736 \t \t     1.0303 \n",
            "\n",
            "mean raw eQQ diff.....   0.024367 \t \t  0.0016081 \n",
            "med  raw eQQ diff.....   0.026087 \t \t  0.0007568 \n",
            "max  raw eQQ diff.....   0.060867 \t \t  0.0073917 \n",
            "\n",
            "mean eCDF diff........    0.14713 \t \t   0.094044 \n",
            "med  eCDF diff........    0.14205 \t \t   0.090909 \n",
            "max  eCDF diff........    0.36932 \t \t    0.31818 \n",
            "\n",
            "var ratio (Tr/Co).....    0.85977 \t \t     1.0702 \n",
            "T-test p-value........    0.04404 \t \t    0.40679 \n",
            "KS Bootstrap p-value..      0.016 \t \t      0.134 \n",
            "KS Naive p-value......   0.023517 \t \t    0.21537 \n",
            "KS Statistic..........    0.36932 \t \t    0.31818 \n",
            "\n",
            "\n",
            "***** (V4) plit *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.53846 \t \t    0.53846 \n",
            "mean control..........     0.4768 \t \t    0.51202 \n",
            "std mean diff.........     48.866 \t \t     20.956 \n",
            "\n",
            "mean raw eQQ diff.....   0.062687 \t \t   0.026445 \n",
            "med  raw eQQ diff.....    0.05157 \t \t   0.022452 \n",
            "max  raw eQQ diff.....    0.24671 \t \t   0.073748 \n",
            "\n",
            "mean eCDF diff........    0.17045 \t \t    0.14263 \n",
            "med  eCDF diff........     0.1714 \t \t    0.13636 \n",
            "max  eCDF diff........    0.33333 \t \t    0.45455 \n",
            "\n",
            "var ratio (Tr/Co).....     1.0596 \t \t    0.89063 \n",
            "T-test p-value........   0.062707 \t \t 7.4571e-07 \n",
            "KS Bootstrap p-value..      0.048 \t \t       0.01 \n",
            "KS Naive p-value......    0.05266 \t \t   0.021231 \n",
            "KS Statistic..........    0.33333 \t \t    0.45455 \n",
            "\n",
            "\n",
            "***** (V5) pwlit *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.45226 \t \t    0.45226 \n",
            "mean control..........     0.3824 \t \t    0.42222 \n",
            "std mean diff.........     44.828 \t \t     19.273 \n",
            "\n",
            "mean raw eQQ diff.....   0.071041 \t \t   0.030033 \n",
            "med  raw eQQ diff.....   0.056334 \t \t   0.026898 \n",
            "max  raw eQQ diff.....    0.31462 \t \t   0.083025 \n",
            "\n",
            "mean eCDF diff........    0.15341 \t \t    0.13009 \n",
            "med  eCDF diff........    0.14773 \t \t   0.090909 \n",
            "max  eCDF diff........    0.33333 \t \t    0.40909 \n",
            "\n",
            "var ratio (Tr/Co).....     1.0848 \t \t    0.90467 \n",
            "T-test p-value........   0.085679 \t \t 5.6817e-07 \n",
            "KS Bootstrap p-value..      0.046 \t \t      0.024 \n",
            "KS Naive p-value......    0.05266 \t \t   0.050354 \n",
            "KS Statistic..........    0.33333 \t \t    0.40909 \n",
            "\n",
            "\n",
            "***** (V6) prural *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.71686 \t \t    0.71686 \n",
            "mean control..........    0.74083 \t \t    0.72349 \n",
            "std mean diff.........    -32.806 \t \t    -9.0771 \n",
            "\n",
            "mean raw eQQ diff.....   0.024061 \t \t  0.0066332 \n",
            "med  raw eQQ diff.....   0.020843 \t \t   0.005777 \n",
            "max  raw eQQ diff.....   0.047551 \t \t   0.016132 \n",
            "\n",
            "mean eCDF diff........     0.1622 \t \t    0.11755 \n",
            "med  eCDF diff........    0.15625 \t \t   0.090909 \n",
            "max  eCDF diff........    0.37879 \t \t    0.36364 \n",
            "\n",
            "var ratio (Tr/Co).....     1.0261 \t \t     1.0561 \n",
            "T-test p-value........    0.20809 \t \t 2.0054e-06 \n",
            "KS Bootstrap p-value..      0.024 \t \t       0.06 \n",
            "KS Naive p-value......   0.018529 \t \t    0.10903 \n",
            "KS Statistic..........    0.37879 \t \t    0.36364 \n",
            "\n",
            "\n",
            "Before Matching Minimum p.value: 0.012 \n",
            "Variable Name(s): state  Number(s): 1 \n",
            "\n",
            "After Matching Minimum p.value: 5.6817e-07 \n",
            "Variable Name(s): pwlit  Number(s): 5 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZEtqJwyzj5l",
        "outputId": "0c82ac4d-0023-42f2-8efd-072b15cfd7d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "attach(after.data)\n",
        "X = cbind(state, pcgsdp, pfemale, plit, pwlit, prural)\n",
        "X.all = cbind(state, pcgsdp, pfemale, plit, pwlit, prural, state^2, pcgsdp^2, pfemale^2, plit^2, pwlit^2, prural^2, \n",
        "          state*pcgsdp, state*pfemale, state*plit, state*pwlit, state*prural,\n",
        "          pcgsdp*pfemale, pcgsdp*plit, pcgsdp*pwlit, pcgsdp*prural,\n",
        "          pfemale*plit, pfemale*pwlit, pfemale*prural, plit*pwlit, plit*prural, pwlit*prural)\n",
        "Tr = treat\n",
        "Y = pcr_womtot\n",
        "detach(after.data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The following object is masked _by_ .GlobalEnv:\n",
            "\n",
            "    X\n",
            "\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TRZc3S5rgLD9",
        "outputId": "0062af0e-3168-4c1a-cc49-8da6b7b4d5e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "genout = GenMatch(Tr = Tr, X = X.all, M=1, pop.size = 250,\n",
        "                  max.generations = 20, wait.generations = 25)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Fri Dec 20 10:16:20 2019\n",
            "Domains:\n",
            " 0.000000e+00   <=  X1   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X2   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X3   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X4   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X5   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X6   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X7   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X8   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X9   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X10  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X11  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X12  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X13  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X14  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X15  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X16  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X17  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X18  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X19  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X20  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X21  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X22  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X23  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X24  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X25  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X26  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X27  <=    1.000000e+03 \n",
            "\n",
            "Data Type: Floating Point\n",
            "Operators (code number, name, population) \n",
            "\t(1) Cloning........................... \t30\n",
            "\t(2) Uniform Mutation.................. \t31\n",
            "\t(3) Boundary Mutation................. \t31\n",
            "\t(4) Non-Uniform Mutation.............. \t31\n",
            "\t(5) Polytope Crossover................ \t31\n",
            "\t(6) Simple Crossover.................. \t32\n",
            "\t(7) Whole Non-Uniform Mutation........ \t31\n",
            "\t(8) Heuristic Crossover............... \t32\n",
            "\t(9) Local-Minimum Crossover........... \t0\n",
            "\n",
            "SOFT Maximum Number of Generations: 20\n",
            "Maximum Nonchanging Generations: 25\n",
            "Population size       : 250\n",
            "Convergence Tolerance: 1.000000e-03\n",
            "\n",
            "Not Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation.\n",
            "Not Checking Gradients before Stopping.\n",
            "Using Out of Bounds Individuals.\n",
            "\n",
            "Maximization Problem.\n",
            "GENERATION: 0 (initializing the population)\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  6.661338e-16  6.661338e-16  2.220446e-15  3.552714e-15  1.121325e-14  2.531308e-14  3.497203e-14  6.772360e-13  1.073586e-12  8.583578e-12  1.354405e-10  1.387955e-09  3.374435e-08  1.985566e-07  4.252923e-07  4.814471e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.575224e-06  1.974618e-06  4.127817e-06  1.035784e-05  1.597501e-05  1.697752e-05  1.697752e-05  1.697752e-05  6.440707e-05  2.253712e-04  3.060400e-04  4.089972e-04  7.273922e-04  7.273922e-04  9.521151e-04  1.267778e-03  2.165431e-03  3.624699e-03  5.356821e-02  7.382733e-02  7.580170e-02  7.580170e-02  7.580170e-02  1.039896e-01  6.607681e-01  7.826396e-01  8.257729e-01  \n",
            "#unique......... 250, #Total UniqueCount: 250\n",
            "var 1:\n",
            "best............ 3.660089e+02\n",
            "mean............ 4.574686e+02\n",
            "variance........ 8.253670e+04\n",
            "var 2:\n",
            "best............ 9.293239e+02\n",
            "mean............ 4.905441e+02\n",
            "variance........ 9.210357e+04\n",
            "var 3:\n",
            "best............ 1.202280e+02\n",
            "mean............ 4.860566e+02\n",
            "variance........ 8.070108e+04\n",
            "var 4:\n",
            "best............ 7.923539e+02\n",
            "mean............ 4.988409e+02\n",
            "variance........ 8.597252e+04\n",
            "var 5:\n",
            "best............ 4.913383e+02\n",
            "mean............ 4.901057e+02\n",
            "variance........ 8.822684e+04\n",
            "var 6:\n",
            "best............ 3.865060e+02\n",
            "mean............ 4.679510e+02\n",
            "variance........ 9.060059e+04\n",
            "var 7:\n",
            "best............ 4.432836e+01\n",
            "mean............ 4.861251e+02\n",
            "variance........ 8.162457e+04\n",
            "var 8:\n",
            "best............ 1.878812e+02\n",
            "mean............ 5.052935e+02\n",
            "variance........ 9.016133e+04\n",
            "var 9:\n",
            "best............ 1.277082e+02\n",
            "mean............ 5.149091e+02\n",
            "variance........ 8.148916e+04\n",
            "var 10:\n",
            "best............ 1.491775e+02\n",
            "mean............ 5.157701e+02\n",
            "variance........ 8.322283e+04\n",
            "var 11:\n",
            "best............ 9.024858e+02\n",
            "mean............ 4.900504e+02\n",
            "variance........ 8.743633e+04\n",
            "var 12:\n",
            "best............ 7.782969e+02\n",
            "mean............ 4.964674e+02\n",
            "variance........ 8.529086e+04\n",
            "var 13:\n",
            "best............ 7.784723e+02\n",
            "mean............ 4.799689e+02\n",
            "variance........ 7.476432e+04\n",
            "var 14:\n",
            "best............ 2.819536e+01\n",
            "mean............ 4.804005e+02\n",
            "variance........ 7.613543e+04\n",
            "var 15:\n",
            "best............ 1.253656e+02\n",
            "mean............ 4.987383e+02\n",
            "variance........ 8.382448e+04\n",
            "var 16:\n",
            "best............ 2.171270e+01\n",
            "mean............ 4.781416e+02\n",
            "variance........ 8.464087e+04\n",
            "var 17:\n",
            "best............ 5.700509e+02\n",
            "mean............ 5.300420e+02\n",
            "variance........ 7.610789e+04\n",
            "var 18:\n",
            "best............ 9.928578e+02\n",
            "mean............ 4.908752e+02\n",
            "variance........ 7.948992e+04\n",
            "var 19:\n",
            "best............ 5.083637e+02\n",
            "mean............ 5.185900e+02\n",
            "variance........ 8.070847e+04\n",
            "var 20:\n",
            "best............ 4.897722e+02\n",
            "mean............ 5.300550e+02\n",
            "variance........ 9.133915e+04\n",
            "var 21:\n",
            "best............ 9.739131e+02\n",
            "mean............ 4.853462e+02\n",
            "variance........ 9.237946e+04\n",
            "var 22:\n",
            "best............ 6.593869e+02\n",
            "mean............ 4.911900e+02\n",
            "variance........ 8.220499e+04\n",
            "var 23:\n",
            "best............ 1.228153e+02\n",
            "mean............ 5.106356e+02\n",
            "variance........ 8.323077e+04\n",
            "var 24:\n",
            "best............ 7.207056e+02\n",
            "mean............ 5.083510e+02\n",
            "variance........ 8.951388e+04\n",
            "var 25:\n",
            "best............ 9.417408e+02\n",
            "mean............ 4.970107e+02\n",
            "variance........ 8.864270e+04\n",
            "var 26:\n",
            "best............ 6.092895e+02\n",
            "mean............ 4.947803e+02\n",
            "variance........ 8.605374e+04\n",
            "var 27:\n",
            "best............ 4.387013e+02\n",
            "mean............ 4.953023e+02\n",
            "variance........ 7.772989e+04\n",
            "\n",
            "GENERATION: 1\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  8.881784e-16  1.110223e-15  3.552714e-15  7.993606e-15  1.121325e-14  3.497203e-14  5.773160e-14  1.419309e-12  3.876677e-12  1.604450e-11  2.744680e-10  3.029662e-09  3.374435e-08  2.005139e-07  4.252923e-07  4.814471e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.554429e-06  1.974618e-06  4.127817e-06  1.617441e-05  1.697752e-05  1.697752e-05  1.697752e-05  2.259650e-05  6.440707e-05  2.253712e-04  2.786973e-04  4.089972e-04  7.273922e-04  7.273922e-04  8.532785e-04  1.267778e-03  1.267778e-03  2.165431e-03  5.430745e-02  7.485525e-02  7.580170e-02  7.580170e-02  7.580170e-02  1.030776e-01  6.097649e-01  7.954970e-01  8.393559e-01  \n",
            "#unique......... 192, #Total UniqueCount: 442\n",
            "var 1:\n",
            "best............ 3.660089e+02\n",
            "mean............ 2.897814e+02\n",
            "variance........ 3.705490e+04\n",
            "var 2:\n",
            "best............ 9.293239e+02\n",
            "mean............ 7.154884e+02\n",
            "variance........ 5.831407e+04\n",
            "var 3:\n",
            "best............ 1.202280e+02\n",
            "mean............ 2.656153e+02\n",
            "variance........ 4.338019e+04\n",
            "var 4:\n",
            "best............ 7.923539e+02\n",
            "mean............ 7.570531e+02\n",
            "variance........ 3.032220e+04\n",
            "var 5:\n",
            "best............ 4.913383e+02\n",
            "mean............ 4.668744e+02\n",
            "variance........ 3.556404e+04\n",
            "var 6:\n",
            "best............ 3.865060e+02\n",
            "mean............ 3.779978e+02\n",
            "variance........ 3.916436e+04\n",
            "var 7:\n",
            "best............ 4.432836e+01\n",
            "mean............ 2.672014e+02\n",
            "variance........ 7.746227e+04\n",
            "var 8:\n",
            "best............ 1.878812e+02\n",
            "mean............ 4.630416e+02\n",
            "variance........ 8.218963e+04\n",
            "var 9:\n",
            "best............ 1.277082e+02\n",
            "mean............ 2.578739e+02\n",
            "variance........ 3.773728e+04\n",
            "var 10:\n",
            "best............ 1.491775e+02\n",
            "mean............ 3.306990e+02\n",
            "variance........ 6.402559e+04\n",
            "var 11:\n",
            "best............ 9.024858e+02\n",
            "mean............ 6.883894e+02\n",
            "variance........ 5.908710e+04\n",
            "var 12:\n",
            "best............ 7.782969e+02\n",
            "mean............ 5.984261e+02\n",
            "variance........ 5.269298e+04\n",
            "var 13:\n",
            "best............ 7.784723e+02\n",
            "mean............ 5.625045e+02\n",
            "variance........ 6.936394e+04\n",
            "var 14:\n",
            "best............ 2.819536e+01\n",
            "mean............ 1.992087e+02\n",
            "variance........ 4.621086e+04\n",
            "var 15:\n",
            "best............ 1.253656e+02\n",
            "mean............ 2.229806e+02\n",
            "variance........ 4.643956e+04\n",
            "var 16:\n",
            "best............ 2.171270e+01\n",
            "mean............ 1.613754e+02\n",
            "variance........ 3.696767e+04\n",
            "var 17:\n",
            "best............ 5.700509e+02\n",
            "mean............ 5.550519e+02\n",
            "variance........ 4.285627e+04\n",
            "var 18:\n",
            "best............ 9.928578e+02\n",
            "mean............ 6.308551e+02\n",
            "variance........ 1.203393e+05\n",
            "var 19:\n",
            "best............ 5.083637e+02\n",
            "mean............ 5.351961e+02\n",
            "variance........ 3.037122e+04\n",
            "var 20:\n",
            "best............ 7.780427e+02\n",
            "mean............ 6.307604e+02\n",
            "variance........ 4.057538e+04\n",
            "var 21:\n",
            "best............ 6.191127e+02\n",
            "mean............ 7.321703e+02\n",
            "variance........ 7.088536e+04\n",
            "var 22:\n",
            "best............ 5.316980e+02\n",
            "mean............ 5.871999e+02\n",
            "variance........ 2.760165e+04\n",
            "var 23:\n",
            "best............ 1.945403e+02\n",
            "mean............ 4.441472e+02\n",
            "variance........ 1.027444e+05\n",
            "var 24:\n",
            "best............ 9.680908e+02\n",
            "mean............ 6.584325e+02\n",
            "variance........ 4.236766e+04\n",
            "var 25:\n",
            "best............ 1.719816e+02\n",
            "mean............ 6.892998e+02\n",
            "variance........ 8.033946e+04\n",
            "var 26:\n",
            "best............ 2.666130e+02\n",
            "mean............ 4.759400e+02\n",
            "variance........ 4.434528e+04\n",
            "var 27:\n",
            "best............ 4.936656e+02\n",
            "mean............ 4.726110e+02\n",
            "variance........ 3.146903e+04\n",
            "\n",
            "GENERATION: 2\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  1.110223e-15  1.776357e-15  3.552714e-15  1.121325e-14  2.753353e-14  3.497203e-14  2.158274e-13  3.946399e-12  7.877254e-12  3.627543e-11  1.575883e-09  1.159063e-08  3.938530e-08  3.967538e-07  4.252923e-07  7.089656e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.974618e-06  2.230901e-06  4.127817e-06  4.168633e-06  5.688693e-06  1.697752e-05  1.697752e-05  1.697752e-05  6.440707e-05  2.253712e-04  3.709957e-04  4.089972e-04  7.273922e-04  7.273922e-04  1.111089e-03  1.267778e-03  1.267778e-03  2.165431e-03  3.167165e-02  4.441303e-02  6.387269e-02  7.580170e-02  7.580170e-02  7.580170e-02  6.223789e-01  8.041329e-01  8.462771e-01  \n",
            "#unique......... 181, #Total UniqueCount: 623\n",
            "var 1:\n",
            "best............ 3.551944e+02\n",
            "mean............ 3.628820e+02\n",
            "variance........ 3.651357e+03\n",
            "var 2:\n",
            "best............ 8.506254e+02\n",
            "mean............ 9.041559e+02\n",
            "variance........ 5.786639e+03\n",
            "var 3:\n",
            "best............ 7.507560e+01\n",
            "mean............ 1.375377e+02\n",
            "variance........ 7.791306e+03\n",
            "var 4:\n",
            "best............ 7.201089e+02\n",
            "mean............ 7.697387e+02\n",
            "variance........ 6.579987e+03\n",
            "var 5:\n",
            "best............ 4.969453e+02\n",
            "mean............ 4.978304e+02\n",
            "variance........ 4.629083e+03\n",
            "var 6:\n",
            "best............ 5.476728e+02\n",
            "mean............ 4.056372e+02\n",
            "variance........ 4.271448e+03\n",
            "var 7:\n",
            "best............ 4.055478e+01\n",
            "mean............ 6.941193e+01\n",
            "variance........ 1.262913e+04\n",
            "var 8:\n",
            "best............ 8.872319e+01\n",
            "mean............ 2.027119e+02\n",
            "variance........ 7.154206e+03\n",
            "var 9:\n",
            "best............ 5.384960e+01\n",
            "mean............ 1.415594e+02\n",
            "variance........ 6.574821e+03\n",
            "var 10:\n",
            "best............ 1.618133e+02\n",
            "mean............ 1.703412e+02\n",
            "variance........ 7.335124e+03\n",
            "var 11:\n",
            "best............ 6.453827e+02\n",
            "mean............ 8.669847e+02\n",
            "variance........ 9.659908e+03\n",
            "var 12:\n",
            "best............ 6.876431e+02\n",
            "mean............ 7.576974e+02\n",
            "variance........ 7.106862e+03\n",
            "var 13:\n",
            "best............ 7.831241e+02\n",
            "mean............ 7.626083e+02\n",
            "variance........ 4.980661e+03\n",
            "var 14:\n",
            "best............ 2.016812e+01\n",
            "mean............ 5.529718e+01\n",
            "variance........ 1.097648e+04\n",
            "var 15:\n",
            "best............ 1.228302e+02\n",
            "mean............ 1.443280e+02\n",
            "variance........ 9.299882e+03\n",
            "var 16:\n",
            "best............ 2.087983e+01\n",
            "mean............ 3.851948e+01\n",
            "variance........ 4.920835e+03\n",
            "var 17:\n",
            "best............ 5.851313e+02\n",
            "mean............ 5.697103e+02\n",
            "variance........ 8.470289e+03\n",
            "var 18:\n",
            "best............ 9.470211e+02\n",
            "mean............ 9.327140e+02\n",
            "variance........ 1.976117e+04\n",
            "var 19:\n",
            "best............ 5.076942e+02\n",
            "mean............ 4.923138e+02\n",
            "variance........ 8.273384e+03\n",
            "var 20:\n",
            "best............ 7.566603e+02\n",
            "mean............ 6.052139e+02\n",
            "variance........ 1.998866e+04\n",
            "var 21:\n",
            "best............ 6.556506e+02\n",
            "mean............ 8.186254e+02\n",
            "variance........ 3.350742e+04\n",
            "var 22:\n",
            "best............ 5.468898e+02\n",
            "mean............ 5.725852e+02\n",
            "variance........ 1.874430e+04\n",
            "var 23:\n",
            "best............ 1.863820e+02\n",
            "mean............ 1.656390e+02\n",
            "variance........ 7.140298e+03\n",
            "var 24:\n",
            "best............ 9.434444e+02\n",
            "mean............ 8.604425e+02\n",
            "variance........ 1.579085e+04\n",
            "var 25:\n",
            "best............ 1.898540e+02\n",
            "mean............ 4.395366e+02\n",
            "variance........ 1.114918e+05\n",
            "var 26:\n",
            "best............ 3.061104e+02\n",
            "mean............ 4.086952e+02\n",
            "variance........ 2.711849e+04\n",
            "var 27:\n",
            "best............ 5.110440e+02\n",
            "mean............ 4.906413e+02\n",
            "variance........ 7.526513e+03\n",
            "\n",
            "GENERATION: 3\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  1.332268e-15  2.220446e-15  3.552714e-15  8.881784e-15  1.121325e-14  3.497203e-14  6.927792e-14  1.025846e-12  1.759481e-12  8.166356e-12  1.009228e-10  9.146965e-10  2.581907e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.637724e-06  1.974618e-06  1.974618e-06  2.839498e-06  3.142434e-06  5.078842e-06  8.456373e-06  8.456373e-06  1.553669e-05  3.340340e-05  3.340340e-05  3.340340e-05  1.217034e-04  1.217034e-04  4.089972e-04  6.779445e-04  1.226157e-03  1.267778e-03  1.267778e-03  2.322518e-03  4.327922e-03  5.946011e-03  6.528728e-03  9.558856e-03  3.518054e-02  3.518054e-02  5.216543e-02  4.674646e-01  5.058205e-01  8.668635e-01  \n",
            "#unique......... 174, #Total UniqueCount: 797\n",
            "var 1:\n",
            "best............ 3.551944e+02\n",
            "mean............ 3.613538e+02\n",
            "variance........ 2.734915e+03\n",
            "var 2:\n",
            "best............ 8.506254e+02\n",
            "mean............ 8.652406e+02\n",
            "variance........ 3.958740e+03\n",
            "var 3:\n",
            "best............ 7.507560e+01\n",
            "mean............ 1.024346e+02\n",
            "variance........ 5.401695e+03\n",
            "var 4:\n",
            "best............ 7.201089e+02\n",
            "mean............ 7.240654e+02\n",
            "variance........ 7.315486e+03\n",
            "var 5:\n",
            "best............ 4.969453e+02\n",
            "mean............ 4.926647e+02\n",
            "variance........ 1.776828e+03\n",
            "var 6:\n",
            "best............ 5.476728e+02\n",
            "mean............ 4.927173e+02\n",
            "variance........ 7.093449e+03\n",
            "var 7:\n",
            "best............ 4.055478e+01\n",
            "mean............ 5.746528e+01\n",
            "variance........ 6.261654e+03\n",
            "var 8:\n",
            "best............ 8.872319e+01\n",
            "mean............ 1.386806e+02\n",
            "variance........ 6.230851e+03\n",
            "var 9:\n",
            "best............ 5.384960e+01\n",
            "mean............ 9.092575e+01\n",
            "variance........ 4.770269e+03\n",
            "var 10:\n",
            "best............ 1.618133e+02\n",
            "mean............ 1.655067e+02\n",
            "variance........ 2.851934e+03\n",
            "var 11:\n",
            "best............ 1.894312e+02\n",
            "mean............ 7.201214e+02\n",
            "variance........ 1.888249e+04\n",
            "var 12:\n",
            "best............ 6.876431e+02\n",
            "mean............ 7.171783e+02\n",
            "variance........ 3.871064e+03\n",
            "var 13:\n",
            "best............ 7.789375e+02\n",
            "mean............ 7.706509e+02\n",
            "variance........ 3.106888e+03\n",
            "var 14:\n",
            "best............ 2.739263e+01\n",
            "mean............ 4.161389e+01\n",
            "variance........ 4.788076e+03\n",
            "var 15:\n",
            "best............ 1.228302e+02\n",
            "mean............ 1.391813e+02\n",
            "variance........ 5.779986e+03\n",
            "var 16:\n",
            "best............ 2.087983e+01\n",
            "mean............ 3.319644e+01\n",
            "variance........ 3.725881e+03\n",
            "var 17:\n",
            "best............ 5.851313e+02\n",
            "mean............ 5.461061e+02\n",
            "variance........ 1.011565e+04\n",
            "var 18:\n",
            "best............ 9.470211e+02\n",
            "mean............ 9.291460e+02\n",
            "variance........ 7.764298e+03\n",
            "var 19:\n",
            "best............ 5.076942e+02\n",
            "mean............ 5.089788e+02\n",
            "variance........ 1.637194e+03\n",
            "var 20:\n",
            "best............ 4.972168e+02\n",
            "mean............ 6.404998e+02\n",
            "variance........ 2.042628e+04\n",
            "var 21:\n",
            "best............ 9.749710e+02\n",
            "mean............ 7.691185e+02\n",
            "variance........ 3.277013e+04\n",
            "var 22:\n",
            "best............ 6.618099e+02\n",
            "mean............ 5.744819e+02\n",
            "variance........ 9.247807e+03\n",
            "var 23:\n",
            "best............ 1.218294e+02\n",
            "mean............ 1.705375e+02\n",
            "variance........ 5.777574e+03\n",
            "var 24:\n",
            "best............ 9.434444e+02\n",
            "mean............ 9.249702e+02\n",
            "variance........ 1.001898e+04\n",
            "var 25:\n",
            "best............ 1.898540e+02\n",
            "mean............ 2.029456e+02\n",
            "variance........ 9.646147e+03\n",
            "var 26:\n",
            "best............ 3.061104e+02\n",
            "mean............ 3.003308e+02\n",
            "variance........ 4.431530e+03\n",
            "var 27:\n",
            "best............ 5.110440e+02\n",
            "mean............ 5.049076e+02\n",
            "variance........ 1.313058e+03\n",
            "\n",
            "GENERATION: 4\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  1.332268e-15  2.220446e-15  3.552714e-15  8.881784e-15  1.121325e-14  3.497203e-14  6.927792e-14  1.025846e-12  1.759481e-12  8.166356e-12  1.009228e-10  9.146965e-10  2.581907e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.637724e-06  1.974618e-06  1.974618e-06  2.839498e-06  3.142434e-06  5.078842e-06  8.456373e-06  8.456373e-06  1.553669e-05  3.340340e-05  3.340340e-05  3.340340e-05  1.217034e-04  1.217034e-04  4.089972e-04  6.779445e-04  1.226157e-03  1.267778e-03  1.267778e-03  2.322518e-03  4.327922e-03  5.946011e-03  6.528728e-03  9.558856e-03  3.518054e-02  3.518054e-02  5.216543e-02  4.674646e-01  5.058205e-01  8.668635e-01  \n",
            "#unique......... 193, #Total UniqueCount: 990\n",
            "var 1:\n",
            "best............ 3.551944e+02\n",
            "mean............ 3.582172e+02\n",
            "variance........ 2.676596e+03\n",
            "var 2:\n",
            "best............ 8.506254e+02\n",
            "mean............ 8.331991e+02\n",
            "variance........ 3.717904e+03\n",
            "var 3:\n",
            "best............ 7.507560e+01\n",
            "mean............ 9.237985e+01\n",
            "variance........ 6.221144e+03\n",
            "var 4:\n",
            "best............ 7.201089e+02\n",
            "mean............ 6.873263e+02\n",
            "variance........ 5.333085e+03\n",
            "var 5:\n",
            "best............ 4.969453e+02\n",
            "mean............ 4.938952e+02\n",
            "variance........ 4.047131e+03\n",
            "var 6:\n",
            "best............ 5.476728e+02\n",
            "mean............ 5.444619e+02\n",
            "variance........ 4.040652e+03\n",
            "var 7:\n",
            "best............ 4.055478e+01\n",
            "mean............ 5.891208e+01\n",
            "variance........ 3.273648e+03\n",
            "var 8:\n",
            "best............ 8.872319e+01\n",
            "mean............ 1.053553e+02\n",
            "variance........ 5.573677e+03\n",
            "var 9:\n",
            "best............ 5.384960e+01\n",
            "mean............ 7.810919e+01\n",
            "variance........ 8.429505e+03\n",
            "var 10:\n",
            "best............ 1.618133e+02\n",
            "mean............ 1.726482e+02\n",
            "variance........ 4.488133e+03\n",
            "var 11:\n",
            "best............ 1.894312e+02\n",
            "mean............ 4.036726e+02\n",
            "variance........ 4.482253e+04\n",
            "var 12:\n",
            "best............ 6.876431e+02\n",
            "mean............ 6.869964e+02\n",
            "variance........ 1.853409e+03\n",
            "var 13:\n",
            "best............ 7.789375e+02\n",
            "mean............ 7.786813e+02\n",
            "variance........ 5.729798e+03\n",
            "var 14:\n",
            "best............ 2.739263e+01\n",
            "mean............ 5.851259e+01\n",
            "variance........ 6.709332e+03\n",
            "var 15:\n",
            "best............ 1.228302e+02\n",
            "mean............ 1.470850e+02\n",
            "variance........ 6.839067e+03\n",
            "var 16:\n",
            "best............ 2.087983e+01\n",
            "mean............ 4.330673e+01\n",
            "variance........ 8.576359e+03\n",
            "var 17:\n",
            "best............ 5.851313e+02\n",
            "mean............ 5.712632e+02\n",
            "variance........ 3.168245e+03\n",
            "var 18:\n",
            "best............ 9.470211e+02\n",
            "mean............ 9.290210e+02\n",
            "variance........ 5.413747e+03\n",
            "var 19:\n",
            "best............ 5.076942e+02\n",
            "mean............ 5.217254e+02\n",
            "variance........ 3.315462e+03\n",
            "var 20:\n",
            "best............ 4.972168e+02\n",
            "mean............ 5.665428e+02\n",
            "variance........ 1.398040e+04\n",
            "var 21:\n",
            "best............ 9.749710e+02\n",
            "mean............ 8.363396e+02\n",
            "variance........ 2.906282e+04\n",
            "var 22:\n",
            "best............ 6.618099e+02\n",
            "mean............ 6.118600e+02\n",
            "variance........ 7.262127e+03\n",
            "var 23:\n",
            "best............ 1.218294e+02\n",
            "mean............ 1.496912e+02\n",
            "variance........ 4.874476e+03\n",
            "var 24:\n",
            "best............ 9.434444e+02\n",
            "mean............ 9.117992e+02\n",
            "variance........ 5.732855e+03\n",
            "var 25:\n",
            "best............ 1.898540e+02\n",
            "mean............ 2.079309e+02\n",
            "variance........ 4.311647e+03\n",
            "var 26:\n",
            "best............ 3.061104e+02\n",
            "mean............ 2.998288e+02\n",
            "variance........ 4.388075e+03\n",
            "var 27:\n",
            "best............ 5.110440e+02\n",
            "mean............ 5.002087e+02\n",
            "variance........ 3.589570e+03\n",
            "\n",
            "GENERATION: 5\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  1.332268e-15  3.330669e-15  3.552714e-15  1.121325e-14  1.754152e-14  3.497203e-14  1.363354e-13  1.813216e-12  2.867484e-12  1.331513e-11  2.978044e-10  2.201241e-09  1.257855e-07  6.213603e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.055250e-06  1.499214e-06  1.974618e-06  2.422631e-06  4.127817e-06  7.443816e-06  1.697752e-05  1.697752e-05  1.697752e-05  1.697752e-05  2.253712e-04  2.253712e-04  4.089972e-04  7.273922e-04  1.197433e-03  1.267778e-03  2.013003e-03  3.489649e-03  3.505622e-03  3.624699e-03  5.946011e-03  1.323579e-02  3.518054e-02  3.518054e-02  7.580170e-02  5.383379e-01  5.767612e-01  9.047933e-01  \n",
            "#unique......... 162, #Total UniqueCount: 1152\n",
            "var 1:\n",
            "best............ 3.551944e+02\n",
            "mean............ 3.556317e+02\n",
            "variance........ 1.211115e+03\n",
            "var 2:\n",
            "best............ 8.506254e+02\n",
            "mean............ 8.423693e+02\n",
            "variance........ 2.892056e+03\n",
            "var 3:\n",
            "best............ 7.507560e+01\n",
            "mean............ 8.389349e+01\n",
            "variance........ 2.164145e+03\n",
            "var 4:\n",
            "best............ 7.201089e+02\n",
            "mean............ 7.107742e+02\n",
            "variance........ 4.470143e+03\n",
            "var 5:\n",
            "best............ 4.969453e+02\n",
            "mean............ 4.929445e+02\n",
            "variance........ 1.948889e+03\n",
            "var 6:\n",
            "best............ 5.476728e+02\n",
            "mean............ 5.466670e+02\n",
            "variance........ 1.419860e+03\n",
            "var 7:\n",
            "best............ 4.055478e+01\n",
            "mean............ 5.589695e+01\n",
            "variance........ 6.823972e+03\n",
            "var 8:\n",
            "best............ 8.872319e+01\n",
            "mean............ 1.027007e+02\n",
            "variance........ 3.476316e+03\n",
            "var 9:\n",
            "best............ 5.384960e+01\n",
            "mean............ 7.406246e+01\n",
            "variance........ 9.007070e+03\n",
            "var 10:\n",
            "best............ 1.618133e+02\n",
            "mean............ 1.748822e+02\n",
            "variance........ 3.785131e+03\n",
            "var 11:\n",
            "best............ 1.587023e+02\n",
            "mean............ 1.740530e+02\n",
            "variance........ 4.795663e+03\n",
            "var 12:\n",
            "best............ 6.876431e+02\n",
            "mean............ 6.817716e+02\n",
            "variance........ 1.587375e+03\n",
            "var 13:\n",
            "best............ 7.789375e+02\n",
            "mean............ 7.726066e+02\n",
            "variance........ 2.109403e+03\n",
            "var 14:\n",
            "best............ 2.739263e+01\n",
            "mean............ 3.919381e+01\n",
            "variance........ 5.931829e+03\n",
            "var 15:\n",
            "best............ 1.228302e+02\n",
            "mean............ 1.323954e+02\n",
            "variance........ 4.830833e+03\n",
            "var 16:\n",
            "best............ 2.087983e+01\n",
            "mean............ 3.345787e+01\n",
            "variance........ 5.117811e+03\n",
            "var 17:\n",
            "best............ 2.574940e+02\n",
            "mean............ 5.787861e+02\n",
            "variance........ 1.741674e+03\n",
            "var 18:\n",
            "best............ 9.565131e+02\n",
            "mean............ 9.267856e+02\n",
            "variance........ 1.053358e+04\n",
            "var 19:\n",
            "best............ 5.076942e+02\n",
            "mean............ 5.075610e+02\n",
            "variance........ 2.391820e+03\n",
            "var 20:\n",
            "best............ 4.972168e+02\n",
            "mean............ 4.961786e+02\n",
            "variance........ 1.434294e+03\n",
            "var 21:\n",
            "best............ 9.749710e+02\n",
            "mean............ 9.551428e+02\n",
            "variance........ 7.173458e+03\n",
            "var 22:\n",
            "best............ 6.618099e+02\n",
            "mean............ 6.580356e+02\n",
            "variance........ 1.754622e+03\n",
            "var 23:\n",
            "best............ 1.218294e+02\n",
            "mean............ 1.329209e+02\n",
            "variance........ 2.355794e+03\n",
            "var 24:\n",
            "best............ 9.434444e+02\n",
            "mean............ 9.312320e+02\n",
            "variance........ 3.387635e+03\n",
            "var 25:\n",
            "best............ 1.898540e+02\n",
            "mean............ 2.015263e+02\n",
            "variance........ 3.968503e+03\n",
            "var 26:\n",
            "best............ 3.061104e+02\n",
            "mean............ 3.113619e+02\n",
            "variance........ 1.951982e+03\n",
            "var 27:\n",
            "best............ 5.110440e+02\n",
            "mean............ 5.027460e+02\n",
            "variance........ 2.782647e+03\n",
            "\n",
            "GENERATION: 6\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  1.332268e-15  3.552714e-15  3.996803e-15  1.121325e-14  3.286260e-14  3.497203e-14  2.544631e-13  3.222089e-12  5.001999e-12  2.260347e-11  1.338196e-09  7.656424e-09  5.653575e-08  2.093416e-07  3.258486e-07  4.252923e-07  7.100270e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.049519e-06  1.974618e-06  1.974618e-06  3.358808e-06  8.456373e-06  8.456373e-06  8.456373e-06  3.340340e-05  4.089972e-04  4.089972e-04  4.089972e-04  4.089972e-04  5.829066e-04  1.267778e-03  1.791901e-03  2.165431e-03  3.624699e-03  8.279000e-03  1.285139e-02  3.518054e-02  3.518054e-02  3.907535e-02  1.079376e-01  6.194237e-01  6.569819e-01  6.936774e-01  \n",
            "#unique......... 171, #Total UniqueCount: 1323\n",
            "var 1:\n",
            "best............ 3.551944e+02\n",
            "mean............ 3.612886e+02\n",
            "variance........ 2.167190e+03\n",
            "var 2:\n",
            "best............ 8.506254e+02\n",
            "mean............ 8.406514e+02\n",
            "variance........ 4.307838e+03\n",
            "var 3:\n",
            "best............ 7.507560e+01\n",
            "mean............ 8.905365e+01\n",
            "variance........ 5.291584e+03\n",
            "var 4:\n",
            "best............ 7.201089e+02\n",
            "mean............ 7.162191e+02\n",
            "variance........ 1.283398e+03\n",
            "var 5:\n",
            "best............ 4.969453e+02\n",
            "mean............ 4.955033e+02\n",
            "variance........ 5.303542e+02\n",
            "var 6:\n",
            "best............ 5.476728e+02\n",
            "mean............ 5.955335e+02\n",
            "variance........ 2.246858e+04\n",
            "var 7:\n",
            "best............ 4.055478e+01\n",
            "mean............ 5.457715e+01\n",
            "variance........ 5.244298e+03\n",
            "var 8:\n",
            "best............ 8.872319e+01\n",
            "mean............ 1.022383e+02\n",
            "variance........ 4.697799e+03\n",
            "var 9:\n",
            "best............ 5.384960e+01\n",
            "mean............ 7.088765e+01\n",
            "variance........ 7.989746e+03\n",
            "var 10:\n",
            "best............ 1.618133e+02\n",
            "mean............ 1.681511e+02\n",
            "variance........ 2.131297e+03\n",
            "var 11:\n",
            "best............ 1.587023e+02\n",
            "mean............ 1.717733e+02\n",
            "variance........ 4.484073e+03\n",
            "var 12:\n",
            "best............ 6.876431e+02\n",
            "mean............ 6.852830e+02\n",
            "variance........ 9.373039e+02\n",
            "var 13:\n",
            "best............ 7.789375e+02\n",
            "mean............ 7.723292e+02\n",
            "variance........ 2.182975e+03\n",
            "var 14:\n",
            "best............ 2.739263e+01\n",
            "mean............ 3.992702e+01\n",
            "variance........ 5.072339e+03\n",
            "var 15:\n",
            "best............ 1.228302e+02\n",
            "mean............ 1.338972e+02\n",
            "variance........ 3.968620e+03\n",
            "var 16:\n",
            "best............ 2.087983e+01\n",
            "mean............ 3.165244e+01\n",
            "variance........ 3.590308e+03\n",
            "var 17:\n",
            "best............ 2.574940e+02\n",
            "mean............ 4.245435e+02\n",
            "variance........ 3.421582e+04\n",
            "var 18:\n",
            "best............ 9.565131e+02\n",
            "mean............ 9.318825e+02\n",
            "variance........ 1.054218e+04\n",
            "var 19:\n",
            "best............ 5.076942e+02\n",
            "mean............ 4.999229e+02\n",
            "variance........ 1.596559e+03\n",
            "var 20:\n",
            "best............ 4.972168e+02\n",
            "mean............ 4.984616e+02\n",
            "variance........ 1.806450e+03\n",
            "var 21:\n",
            "best............ 9.749710e+02\n",
            "mean............ 9.660342e+02\n",
            "variance........ 3.627421e+03\n",
            "var 22:\n",
            "best............ 2.176226e+02\n",
            "mean............ 6.595765e+02\n",
            "variance........ 1.558192e+03\n",
            "var 23:\n",
            "best............ 1.218294e+02\n",
            "mean............ 1.318615e+02\n",
            "variance........ 3.873497e+03\n",
            "var 24:\n",
            "best............ 9.434444e+02\n",
            "mean............ 9.306686e+02\n",
            "variance........ 5.360039e+03\n",
            "var 25:\n",
            "best............ 1.898540e+02\n",
            "mean............ 2.023959e+02\n",
            "variance........ 3.161540e+03\n",
            "var 26:\n",
            "best............ 3.061104e+02\n",
            "mean............ 3.126052e+02\n",
            "variance........ 2.772313e+03\n",
            "var 27:\n",
            "best............ 5.110440e+02\n",
            "mean............ 5.132099e+02\n",
            "variance........ 2.176604e+03\n",
            "\n",
            "GENERATION: 7\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  1.332268e-15  3.552714e-15  4.884981e-15  5.773160e-15  1.121325e-14  3.497203e-14  4.618528e-14  4.864997e-13  6.412648e-13  3.029577e-12  1.510825e-10  8.115300e-10  1.462365e-08  3.191265e-08  5.078766e-08  8.448847e-08  4.252923e-07  4.561268e-07  7.110591e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.974618e-06  1.974618e-06  1.974618e-06  1.974618e-06  2.502379e-06  1.697752e-05  1.217034e-04  2.253712e-04  2.253712e-04  4.089972e-04  5.718314e-04  1.267778e-03  1.957995e-03  3.624699e-03  5.946011e-03  6.849036e-03  9.558856e-03  9.558856e-03  1.229482e-02  7.580170e-02  1.057379e-01  3.947020e-01  4.233159e-01  6.924282e-01  \n",
            "#unique......... 176, #Total UniqueCount: 1499\n",
            "var 1:\n",
            "best............ 3.461008e+02\n",
            "mean............ 3.541348e+02\n",
            "variance........ 1.094559e+03\n",
            "var 2:\n",
            "best............ 8.417003e+02\n",
            "mean............ 8.447603e+02\n",
            "variance........ 1.212428e+03\n",
            "var 3:\n",
            "best............ 7.273499e+01\n",
            "mean............ 8.133447e+01\n",
            "variance........ 2.665933e+03\n",
            "var 4:\n",
            "best............ 6.454606e+02\n",
            "mean............ 7.003039e+02\n",
            "variance........ 5.263384e+03\n",
            "var 5:\n",
            "best............ 5.000424e+02\n",
            "mean............ 4.997105e+02\n",
            "variance........ 1.156378e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 6.372579e+02\n",
            "variance........ 2.736150e+04\n",
            "var 7:\n",
            "best............ 9.002112e+01\n",
            "mean............ 6.226650e+01\n",
            "variance........ 5.853869e+03\n",
            "var 8:\n",
            "best............ 8.866947e+01\n",
            "mean............ 9.091986e+01\n",
            "variance........ 2.666321e+02\n",
            "var 9:\n",
            "best............ 5.381457e+01\n",
            "mean............ 6.603749e+01\n",
            "variance........ 4.563377e+03\n",
            "var 10:\n",
            "best............ 1.652954e+02\n",
            "mean............ 1.812029e+02\n",
            "variance........ 6.159690e+03\n",
            "var 11:\n",
            "best............ 1.618248e+02\n",
            "mean............ 1.696122e+02\n",
            "variance........ 1.110902e+03\n",
            "var 12:\n",
            "best............ 6.898186e+02\n",
            "mean............ 6.832489e+02\n",
            "variance........ 4.323734e+03\n",
            "var 13:\n",
            "best............ 7.718291e+02\n",
            "mean............ 7.593603e+02\n",
            "variance........ 3.273798e+03\n",
            "var 14:\n",
            "best............ 2.763550e+01\n",
            "mean............ 3.557584e+01\n",
            "variance........ 1.880051e+03\n",
            "var 15:\n",
            "best............ 1.227104e+02\n",
            "mean............ 1.292399e+02\n",
            "variance........ 2.174162e+03\n",
            "var 16:\n",
            "best............ 2.109631e+01\n",
            "mean............ 3.658918e+01\n",
            "variance........ 7.303994e+03\n",
            "var 17:\n",
            "best............ 2.908718e+02\n",
            "mean............ 3.393595e+02\n",
            "variance........ 2.445601e+04\n",
            "var 18:\n",
            "best............ 9.553972e+02\n",
            "mean............ 9.320564e+02\n",
            "variance........ 1.051589e+04\n",
            "var 19:\n",
            "best............ 5.040680e+02\n",
            "mean............ 4.963141e+02\n",
            "variance........ 3.123827e+03\n",
            "var 20:\n",
            "best............ 5.014930e+02\n",
            "mean............ 5.079244e+02\n",
            "variance........ 2.915236e+03\n",
            "var 21:\n",
            "best............ 9.750900e+02\n",
            "mean............ 9.614712e+02\n",
            "variance........ 5.418079e+03\n",
            "var 22:\n",
            "best............ 2.620608e+02\n",
            "mean............ 4.643173e+02\n",
            "variance........ 4.950958e+04\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.263119e+02\n",
            "variance........ 5.889752e+02\n",
            "var 24:\n",
            "best............ 9.424335e+02\n",
            "mean............ 9.314548e+02\n",
            "variance........ 5.150126e+03\n",
            "var 25:\n",
            "best............ 1.904730e+02\n",
            "mean............ 1.951703e+02\n",
            "variance........ 9.014808e+02\n",
            "var 26:\n",
            "best............ 3.051213e+02\n",
            "mean............ 3.118168e+02\n",
            "variance........ 2.386174e+03\n",
            "var 27:\n",
            "best............ 5.113860e+02\n",
            "mean............ 5.092991e+02\n",
            "variance........ 1.314989e+03\n",
            "\n",
            "GENERATION: 8\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  1.332268e-15  3.552714e-15  7.105427e-15  1.121325e-14  3.497203e-14  7.460699e-14  4.711787e-13  4.740208e-12  7.671197e-12  2.735479e-11  3.404961e-09  1.019280e-08  1.336421e-08  1.426335e-08  1.459959e-08  3.653356e-08  1.643307e-07  1.914826e-07  2.128563e-07  7.504222e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.974618e-06  6.440707e-05  6.440707e-05  1.744015e-04  4.089972e-04  6.182106e-04  7.273922e-04  7.273922e-04  1.267778e-03  1.267778e-03  2.165431e-03  1.505956e-02  1.505956e-02  5.098877e-02  7.580170e-02  8.049107e-02  3.152315e-01  4.070036e-01  5.810068e-01  6.121519e-01  \n",
            "#unique......... 161, #Total UniqueCount: 1660\n",
            "var 1:\n",
            "best............ 3.461008e+02\n",
            "mean............ 3.473314e+02\n",
            "variance........ 4.374019e+02\n",
            "var 2:\n",
            "best............ 8.417003e+02\n",
            "mean............ 8.375179e+02\n",
            "variance........ 2.870733e+03\n",
            "var 3:\n",
            "best............ 7.273499e+01\n",
            "mean............ 8.416977e+01\n",
            "variance........ 3.816395e+03\n",
            "var 4:\n",
            "best............ 6.454606e+02\n",
            "mean............ 6.571637e+02\n",
            "variance........ 2.531250e+03\n",
            "var 5:\n",
            "best............ 5.000424e+02\n",
            "mean............ 5.001461e+02\n",
            "variance........ 1.022098e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 8.311269e+02\n",
            "variance........ 2.225398e+04\n",
            "var 7:\n",
            "best............ 9.002112e+01\n",
            "mean............ 7.857382e+01\n",
            "variance........ 1.957656e+03\n",
            "var 8:\n",
            "best............ 8.818593e+01\n",
            "mean............ 9.989973e+01\n",
            "variance........ 6.080876e+03\n",
            "var 9:\n",
            "best............ 5.349933e+01\n",
            "mean............ 5.972240e+01\n",
            "variance........ 2.611241e+03\n",
            "var 10:\n",
            "best............ 1.966346e+02\n",
            "mean............ 1.841492e+02\n",
            "variance........ 5.574334e+03\n",
            "var 11:\n",
            "best............ 1.899278e+02\n",
            "mean............ 1.782148e+02\n",
            "variance........ 5.307514e+03\n",
            "var 12:\n",
            "best............ 7.093979e+02\n",
            "mean............ 6.856292e+02\n",
            "variance........ 3.247720e+03\n",
            "var 13:\n",
            "best............ 7.078536e+02\n",
            "mean............ 7.553289e+02\n",
            "variance........ 2.281756e+03\n",
            "var 14:\n",
            "best............ 2.982133e+01\n",
            "mean............ 3.830744e+01\n",
            "variance........ 5.140679e+03\n",
            "var 15:\n",
            "best............ 1.216322e+02\n",
            "mean............ 1.348000e+02\n",
            "variance........ 5.578581e+03\n",
            "var 16:\n",
            "best............ 2.109631e+01\n",
            "mean............ 3.107004e+01\n",
            "variance........ 5.860816e+03\n",
            "var 17:\n",
            "best............ 8.775474e+00\n",
            "mean............ 3.003526e+02\n",
            "variance........ 5.549587e+03\n",
            "var 18:\n",
            "best............ 9.553972e+02\n",
            "mean............ 9.483810e+02\n",
            "variance........ 2.229865e+03\n",
            "var 19:\n",
            "best............ 5.040680e+02\n",
            "mean............ 5.037011e+02\n",
            "variance........ 2.271829e+03\n",
            "var 20:\n",
            "best............ 5.014930e+02\n",
            "mean............ 4.992402e+02\n",
            "variance........ 1.362240e+03\n",
            "var 21:\n",
            "best............ 9.750900e+02\n",
            "mean............ 9.655748e+02\n",
            "variance........ 2.573186e+03\n",
            "var 22:\n",
            "best............ 2.620608e+02\n",
            "mean............ 2.795732e+02\n",
            "variance........ 7.493317e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.289862e+02\n",
            "variance........ 2.330519e+03\n",
            "var 24:\n",
            "best............ 9.424335e+02\n",
            "mean............ 9.337981e+02\n",
            "variance........ 2.283852e+03\n",
            "var 25:\n",
            "best............ 1.904730e+02\n",
            "mean............ 1.953819e+02\n",
            "variance........ 2.797247e+03\n",
            "var 26:\n",
            "best............ 3.051213e+02\n",
            "mean............ 3.102575e+02\n",
            "variance........ 2.613774e+03\n",
            "var 27:\n",
            "best............ 5.113860e+02\n",
            "mean............ 5.064456e+02\n",
            "variance........ 1.960433e+03\n",
            "\n",
            "GENERATION: 9\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  1.332268e-15  3.552714e-15  7.993606e-15  1.121325e-14  3.497203e-14  1.398881e-13  8.455459e-13  7.622347e-12  1.165512e-11  4.039147e-11  4.264566e-09  5.103647e-09  5.786891e-09  7.712059e-09  1.548149e-08  2.139293e-08  8.448847e-08  1.014935e-07  1.284614e-07  4.252923e-07  4.252923e-07  4.252923e-07  4.419160e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.974618e-06  3.340340e-05  1.008852e-04  1.217034e-04  3.611111e-04  4.089972e-04  7.273922e-04  1.267778e-03  1.267778e-03  1.267778e-03  1.267778e-03  1.505956e-02  1.505956e-02  5.216543e-02  1.654459e-01  2.471588e-01  2.885671e-01  6.375701e-01  6.432204e-01  6.741069e-01  \n",
            "#unique......... 164, #Total UniqueCount: 1824\n",
            "var 1:\n",
            "best............ 3.461008e+02\n",
            "mean............ 3.510394e+02\n",
            "variance........ 1.941180e+03\n",
            "var 2:\n",
            "best............ 8.417003e+02\n",
            "mean............ 8.384359e+02\n",
            "variance........ 7.872845e+02\n",
            "var 3:\n",
            "best............ 7.273499e+01\n",
            "mean............ 7.884544e+01\n",
            "variance........ 2.506612e+03\n",
            "var 4:\n",
            "best............ 6.454606e+02\n",
            "mean............ 6.430993e+02\n",
            "variance........ 1.164103e+03\n",
            "var 5:\n",
            "best............ 5.000424e+02\n",
            "mean............ 5.013442e+02\n",
            "variance........ 1.397235e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.032276e+02\n",
            "variance........ 4.028614e+03\n",
            "var 7:\n",
            "best............ 1.708473e+01\n",
            "mean............ 9.469294e+01\n",
            "variance........ 4.702503e+03\n",
            "var 8:\n",
            "best............ 8.818593e+01\n",
            "mean............ 2.243335e+02\n",
            "variance........ 6.274540e+04\n",
            "var 9:\n",
            "best............ 5.349933e+01\n",
            "mean............ 6.072889e+01\n",
            "variance........ 1.745791e+03\n",
            "var 10:\n",
            "best............ 1.966346e+02\n",
            "mean............ 1.869071e+02\n",
            "variance........ 3.523344e+03\n",
            "var 11:\n",
            "best............ 1.899278e+02\n",
            "mean............ 1.800939e+02\n",
            "variance........ 3.287148e+03\n",
            "var 12:\n",
            "best............ 7.093979e+02\n",
            "mean............ 6.955837e+02\n",
            "variance........ 1.964529e+03\n",
            "var 13:\n",
            "best............ 7.078536e+02\n",
            "mean............ 7.348592e+02\n",
            "variance........ 4.653510e+03\n",
            "var 14:\n",
            "best............ 2.982133e+01\n",
            "mean............ 3.544754e+01\n",
            "variance........ 4.228041e+03\n",
            "var 15:\n",
            "best............ 1.216322e+02\n",
            "mean............ 1.259216e+02\n",
            "variance........ 9.612240e+02\n",
            "var 16:\n",
            "best............ 2.109631e+01\n",
            "mean............ 3.402551e+01\n",
            "variance........ 5.860820e+03\n",
            "var 17:\n",
            "best............ 8.775474e+00\n",
            "mean............ 1.860580e+02\n",
            "variance........ 1.877459e+04\n",
            "var 18:\n",
            "best............ 9.553972e+02\n",
            "mean............ 9.458309e+02\n",
            "variance........ 3.161237e+03\n",
            "var 19:\n",
            "best............ 5.040680e+02\n",
            "mean............ 5.292363e+02\n",
            "variance........ 1.090532e+04\n",
            "var 20:\n",
            "best............ 5.014930e+02\n",
            "mean............ 5.048781e+02\n",
            "variance........ 1.908264e+03\n",
            "var 21:\n",
            "best............ 9.750900e+02\n",
            "mean............ 9.631596e+02\n",
            "variance........ 6.104325e+03\n",
            "var 22:\n",
            "best............ 2.620608e+02\n",
            "mean............ 2.671149e+02\n",
            "variance........ 1.640262e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.295826e+02\n",
            "variance........ 4.075502e+03\n",
            "var 24:\n",
            "best............ 9.424335e+02\n",
            "mean............ 9.352752e+02\n",
            "variance........ 1.895770e+03\n",
            "var 25:\n",
            "best............ 1.904730e+02\n",
            "mean............ 1.944883e+02\n",
            "variance........ 8.506756e+02\n",
            "var 26:\n",
            "best............ 3.051213e+02\n",
            "mean............ 3.079769e+02\n",
            "variance........ 1.001972e+03\n",
            "var 27:\n",
            "best............ 5.113860e+02\n",
            "mean............ 5.110400e+02\n",
            "variance........ 1.267468e+03\n",
            "\n",
            "GENERATION: 10\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  2.220446e-16  8.881784e-16  3.552714e-15  1.121325e-14  1.243450e-14  3.497203e-14  3.132161e-12  1.696576e-11  1.425384e-10  2.199048e-10  7.064802e-10  1.980649e-09  4.748436e-09  6.429247e-09  7.111387e-09  4.890058e-08  5.082631e-08  8.448847e-08  1.582142e-07  1.648276e-07  4.252923e-07  4.252923e-07  4.252923e-07  4.894746e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.974618e-06  1.697752e-05  3.132464e-05  1.061433e-04  1.217034e-04  2.253712e-04  4.089972e-04  7.273922e-04  1.267778e-03  5.946011e-03  5.946011e-03  1.505956e-02  5.216543e-02  5.216543e-02  1.373304e-01  6.845883e-01  8.674598e-01  9.316348e-01  9.625828e-01  9.634943e-01  \n",
            "#unique......... 164, #Total UniqueCount: 1988\n",
            "var 1:\n",
            "best............ 3.461008e+02\n",
            "mean............ 3.483398e+02\n",
            "variance........ 3.674522e+03\n",
            "var 2:\n",
            "best............ 8.417003e+02\n",
            "mean............ 8.296023e+02\n",
            "variance........ 4.907992e+03\n",
            "var 3:\n",
            "best............ 7.273499e+01\n",
            "mean............ 8.518052e+01\n",
            "variance........ 5.548367e+03\n",
            "var 4:\n",
            "best............ 6.454606e+02\n",
            "mean............ 6.378093e+02\n",
            "variance........ 3.418077e+03\n",
            "var 5:\n",
            "best............ 5.000424e+02\n",
            "mean............ 4.978624e+02\n",
            "variance........ 1.777061e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.052439e+02\n",
            "variance........ 2.079428e+03\n",
            "var 7:\n",
            "best............ 9.002112e+01\n",
            "mean............ 6.329139e+01\n",
            "variance........ 7.244770e+03\n",
            "var 8:\n",
            "best............ 8.818593e+01\n",
            "mean............ 1.244240e+02\n",
            "variance........ 1.675407e+04\n",
            "var 9:\n",
            "best............ 5.349933e+01\n",
            "mean............ 6.147155e+01\n",
            "variance........ 3.009430e+03\n",
            "var 10:\n",
            "best............ 1.966346e+02\n",
            "mean............ 1.982128e+02\n",
            "variance........ 4.495704e+02\n",
            "var 11:\n",
            "best............ 1.899278e+02\n",
            "mean............ 2.050685e+02\n",
            "variance........ 8.352826e+03\n",
            "var 12:\n",
            "best............ 7.093979e+02\n",
            "mean............ 7.085537e+02\n",
            "variance........ 1.866124e+03\n",
            "var 13:\n",
            "best............ 2.925077e+02\n",
            "mean............ 6.989332e+02\n",
            "variance........ 3.008063e+03\n",
            "var 14:\n",
            "best............ 2.982133e+01\n",
            "mean............ 4.269892e+01\n",
            "variance........ 5.619925e+03\n",
            "var 15:\n",
            "best............ 1.216322e+02\n",
            "mean............ 1.405529e+02\n",
            "variance........ 7.942456e+03\n",
            "var 16:\n",
            "best............ 2.109631e+01\n",
            "mean............ 3.779919e+01\n",
            "variance........ 7.789046e+03\n",
            "var 17:\n",
            "best............ 8.775474e+00\n",
            "mean............ 3.412478e+01\n",
            "variance........ 8.198590e+03\n",
            "var 18:\n",
            "best............ 9.553972e+02\n",
            "mean............ 9.502375e+02\n",
            "variance........ 1.630998e+03\n",
            "var 19:\n",
            "best............ 5.040680e+02\n",
            "mean............ 5.055491e+02\n",
            "variance........ 2.961727e+03\n",
            "var 20:\n",
            "best............ 9.664172e+02\n",
            "mean............ 5.751833e+02\n",
            "variance........ 2.852137e+04\n",
            "var 21:\n",
            "best............ 9.750900e+02\n",
            "mean............ 9.645924e+02\n",
            "variance........ 5.318666e+03\n",
            "var 22:\n",
            "best............ 2.620608e+02\n",
            "mean............ 2.692511e+02\n",
            "variance........ 2.740289e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.383074e+02\n",
            "variance........ 6.732633e+03\n",
            "var 24:\n",
            "best............ 9.424335e+02\n",
            "mean............ 9.288376e+02\n",
            "variance........ 7.782128e+03\n",
            "var 25:\n",
            "best............ 1.904730e+02\n",
            "mean............ 2.021027e+02\n",
            "variance........ 4.078569e+03\n",
            "var 26:\n",
            "best............ 3.051213e+02\n",
            "mean............ 3.089174e+02\n",
            "variance........ 1.430304e+03\n",
            "var 27:\n",
            "best............ 5.113860e+02\n",
            "mean............ 5.066044e+02\n",
            "variance........ 2.021272e+03\n",
            "\n",
            "GENERATION: 11\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  8.881784e-16  8.881784e-16  3.552714e-15  1.121325e-14  2.153833e-14  3.497203e-14  4.865019e-11  2.000580e-10  8.295673e-10  1.454420e-09  2.584292e-09  6.338690e-09  6.429247e-09  6.777019e-09  1.597617e-08  3.065725e-08  3.370020e-08  8.448847e-08  8.629259e-08  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.353863e-06  1.974618e-06  3.694053e-06  4.127817e-06  1.483444e-05  3.340340e-05  4.741250e-05  6.440707e-05  4.089972e-04  1.267778e-03  2.165431e-03  5.946011e-03  1.505956e-02  1.505956e-02  2.325117e-02  2.325117e-02  8.126566e-02  6.211601e-01  6.659768e-01  6.934392e-01  6.986077e-01  8.129350e-01  \n",
            "#unique......... 163, #Total UniqueCount: 2151\n",
            "var 1:\n",
            "best............ 3.461008e+02\n",
            "mean............ 3.510611e+02\n",
            "variance........ 2.550999e+03\n",
            "var 2:\n",
            "best............ 8.417003e+02\n",
            "mean............ 8.379853e+02\n",
            "variance........ 1.608113e+03\n",
            "var 3:\n",
            "best............ 7.273499e+01\n",
            "mean............ 8.415832e+01\n",
            "variance........ 4.793179e+03\n",
            "var 4:\n",
            "best............ 6.454606e+02\n",
            "mean............ 6.436900e+02\n",
            "variance........ 1.651203e+03\n",
            "var 5:\n",
            "best............ 5.000424e+02\n",
            "mean............ 4.965077e+02\n",
            "variance........ 2.217199e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.092159e+02\n",
            "variance........ 3.543801e+02\n",
            "var 7:\n",
            "best............ 9.002112e+01\n",
            "mean............ 6.385154e+01\n",
            "variance........ 2.533208e+03\n",
            "var 8:\n",
            "best............ 8.818593e+01\n",
            "mean............ 1.244054e+02\n",
            "variance........ 1.652673e+04\n",
            "var 9:\n",
            "best............ 5.349933e+01\n",
            "mean............ 6.289937e+01\n",
            "variance........ 3.882366e+03\n",
            "var 10:\n",
            "best............ 1.966346e+02\n",
            "mean............ 2.014384e+02\n",
            "variance........ 3.232511e+03\n",
            "var 11:\n",
            "best............ 1.899278e+02\n",
            "mean............ 1.904006e+02\n",
            "variance........ 8.305761e+02\n",
            "var 12:\n",
            "best............ 7.093979e+02\n",
            "mean............ 7.073229e+02\n",
            "variance........ 5.925277e+02\n",
            "var 13:\n",
            "best............ 9.705906e+01\n",
            "mean............ 5.434971e+02\n",
            "variance........ 3.764337e+04\n",
            "var 14:\n",
            "best............ 2.982133e+01\n",
            "mean............ 3.980633e+01\n",
            "variance........ 3.132039e+03\n",
            "var 15:\n",
            "best............ 1.216322e+02\n",
            "mean............ 1.314363e+02\n",
            "variance........ 4.742152e+03\n",
            "var 16:\n",
            "best............ 2.109631e+01\n",
            "mean............ 3.083203e+01\n",
            "variance........ 3.922697e+03\n",
            "var 17:\n",
            "best............ 8.775474e+00\n",
            "mean............ 3.482287e+01\n",
            "variance........ 7.623404e+03\n",
            "var 18:\n",
            "best............ 9.553972e+02\n",
            "mean............ 9.451128e+02\n",
            "variance........ 4.169437e+03\n",
            "var 19:\n",
            "best............ 5.040680e+02\n",
            "mean............ 4.982302e+02\n",
            "variance........ 2.202603e+03\n",
            "var 20:\n",
            "best............ 9.664172e+02\n",
            "mean............ 7.363967e+02\n",
            "variance........ 4.718102e+04\n",
            "var 21:\n",
            "best............ 9.750900e+02\n",
            "mean............ 9.708636e+02\n",
            "variance........ 1.258205e+03\n",
            "var 22:\n",
            "best............ 2.620608e+02\n",
            "mean............ 2.648924e+02\n",
            "variance........ 9.968582e+02\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.258960e+02\n",
            "variance........ 1.111269e+03\n",
            "var 24:\n",
            "best............ 9.424335e+02\n",
            "mean............ 9.280198e+02\n",
            "variance........ 7.615222e+03\n",
            "var 25:\n",
            "best............ 1.904730e+02\n",
            "mean............ 1.958257e+02\n",
            "variance........ 1.818829e+03\n",
            "var 26:\n",
            "best............ 3.051213e+02\n",
            "mean............ 3.092702e+02\n",
            "variance........ 1.163161e+03\n",
            "var 27:\n",
            "best............ 5.113860e+02\n",
            "mean............ 5.110473e+02\n",
            "variance........ 7.286138e+02\n",
            "\n",
            "GENERATION: 12\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  1.332268e-15  1.554312e-15  3.552714e-15  1.121325e-14  2.797762e-14  3.497203e-14  5.623857e-11  2.266887e-10  1.727942e-09  3.419982e-09  5.646420e-09  6.191725e-09  7.666201e-09  1.548149e-08  1.726409e-08  8.448847e-08  1.085740e-07  1.653376e-07  2.578272e-07  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.447746e-06  1.974618e-06  4.270445e-06  8.456373e-06  2.728567e-05  6.440707e-05  7.328374e-05  1.217034e-04  4.089972e-04  1.267778e-03  2.165431e-03  5.946011e-03  1.505956e-02  1.505956e-02  2.325117e-02  2.325117e-02  9.437148e-02  5.565128e-01  6.419501e-01  6.687189e-01  6.960586e-01  7.516658e-01  \n",
            "#unique......... 165, #Total UniqueCount: 2316\n",
            "var 1:\n",
            "best............ 3.461008e+02\n",
            "mean............ 3.441337e+02\n",
            "variance........ 9.989267e+02\n",
            "var 2:\n",
            "best............ 8.417003e+02\n",
            "mean............ 8.359216e+02\n",
            "variance........ 2.144952e+03\n",
            "var 3:\n",
            "best............ 7.273499e+01\n",
            "mean............ 7.809129e+01\n",
            "variance........ 2.653860e+03\n",
            "var 4:\n",
            "best............ 6.454606e+02\n",
            "mean............ 6.464876e+02\n",
            "variance........ 1.973718e+03\n",
            "var 5:\n",
            "best............ 5.000424e+02\n",
            "mean............ 5.005660e+02\n",
            "variance........ 2.295269e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 8.998964e+02\n",
            "variance........ 5.781898e+03\n",
            "var 7:\n",
            "best............ 9.002112e+01\n",
            "mean............ 9.721846e+01\n",
            "variance........ 4.005662e+03\n",
            "var 8:\n",
            "best............ 7.924422e+02\n",
            "mean............ 1.002091e+02\n",
            "variance........ 4.330501e+03\n",
            "var 9:\n",
            "best............ 5.349933e+01\n",
            "mean............ 7.031737e+01\n",
            "variance........ 7.412344e+03\n",
            "var 10:\n",
            "best............ 1.966346e+02\n",
            "mean............ 2.034230e+02\n",
            "variance........ 3.884584e+03\n",
            "var 11:\n",
            "best............ 1.899278e+02\n",
            "mean............ 1.952043e+02\n",
            "variance........ 2.562941e+03\n",
            "var 12:\n",
            "best............ 7.093979e+02\n",
            "mean............ 7.069857e+02\n",
            "variance........ 8.829229e+02\n",
            "var 13:\n",
            "best............ 9.705906e+01\n",
            "mean............ 1.799402e+02\n",
            "variance........ 1.497850e+04\n",
            "var 14:\n",
            "best............ 2.982133e+01\n",
            "mean............ 4.128604e+01\n",
            "variance........ 6.311398e+03\n",
            "var 15:\n",
            "best............ 1.216322e+02\n",
            "mean............ 1.316765e+02\n",
            "variance........ 4.633819e+03\n",
            "var 16:\n",
            "best............ 2.109631e+01\n",
            "mean............ 2.440035e+01\n",
            "variance........ 8.720964e+02\n",
            "var 17:\n",
            "best............ 8.775474e+00\n",
            "mean............ 2.301576e+01\n",
            "variance........ 5.014129e+03\n",
            "var 18:\n",
            "best............ 9.553972e+02\n",
            "mean............ 9.369545e+02\n",
            "variance........ 1.068774e+04\n",
            "var 19:\n",
            "best............ 5.040680e+02\n",
            "mean............ 5.029759e+02\n",
            "variance........ 1.064666e+03\n",
            "var 20:\n",
            "best............ 9.664172e+02\n",
            "mean............ 9.439621e+02\n",
            "variance........ 7.337834e+03\n",
            "var 21:\n",
            "best............ 9.750900e+02\n",
            "mean............ 9.559240e+02\n",
            "variance........ 1.145908e+04\n",
            "var 22:\n",
            "best............ 2.620608e+02\n",
            "mean............ 2.642071e+02\n",
            "variance........ 1.082939e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.265426e+02\n",
            "variance........ 1.894189e+03\n",
            "var 24:\n",
            "best............ 9.424335e+02\n",
            "mean............ 9.309760e+02\n",
            "variance........ 4.263799e+03\n",
            "var 25:\n",
            "best............ 1.904730e+02\n",
            "mean............ 1.949583e+02\n",
            "variance........ 1.210957e+03\n",
            "var 26:\n",
            "best............ 3.051213e+02\n",
            "mean............ 3.127631e+02\n",
            "variance........ 3.209422e+03\n",
            "var 27:\n",
            "best............ 5.113860e+02\n",
            "mean............ 5.120303e+02\n",
            "variance........ 1.834668e+03\n",
            "\n",
            "GENERATION: 13\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  2.220446e-16  1.332268e-15  2.220446e-15  3.552714e-15  1.121325e-14  3.441691e-14  3.497203e-14  2.927547e-11  1.405862e-10  1.135187e-09  1.923572e-09  3.118389e-09  5.352788e-09  6.429247e-09  8.864945e-09  3.233340e-08  8.448847e-08  3.526329e-07  4.252923e-07  4.252923e-07  4.252923e-07  7.000038e-07  7.311414e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.021204e-06  1.974618e-06  3.054370e-06  1.697752e-05  5.526466e-05  1.217034e-04  1.217034e-04  1.293604e-04  4.089972e-04  1.267778e-03  2.165431e-03  5.946011e-03  1.505956e-02  1.505956e-02  2.325117e-02  2.325117e-02  1.128073e-01  5.027488e-01  5.798516e-01  6.867848e-01  6.989916e-01  7.137175e-01  \n",
            "#unique......... 167, #Total UniqueCount: 2483\n",
            "var 1:\n",
            "best............ 3.461008e+02\n",
            "mean............ 3.507649e+02\n",
            "variance........ 1.518587e+03\n",
            "var 2:\n",
            "best............ 8.417003e+02\n",
            "mean............ 8.305418e+02\n",
            "variance........ 4.956412e+03\n",
            "var 3:\n",
            "best............ 7.273499e+01\n",
            "mean............ 8.817038e+01\n",
            "variance........ 8.417648e+03\n",
            "var 4:\n",
            "best............ 6.454606e+02\n",
            "mean............ 6.421940e+02\n",
            "variance........ 1.498141e+03\n",
            "var 5:\n",
            "best............ 5.000424e+02\n",
            "mean............ 4.986345e+02\n",
            "variance........ 1.390384e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.080414e+02\n",
            "variance........ 1.258634e+03\n",
            "var 7:\n",
            "best............ 5.001815e+01\n",
            "mean............ 9.573717e+01\n",
            "variance........ 1.228223e+03\n",
            "var 8:\n",
            "best............ 8.818593e+01\n",
            "mean............ 4.079426e+02\n",
            "variance........ 1.185749e+05\n",
            "var 9:\n",
            "best............ 5.349933e+01\n",
            "mean............ 5.699421e+01\n",
            "variance........ 7.372682e+02\n",
            "var 10:\n",
            "best............ 1.966346e+02\n",
            "mean............ 2.040243e+02\n",
            "variance........ 3.607996e+03\n",
            "var 11:\n",
            "best............ 1.899278e+02\n",
            "mean............ 1.942428e+02\n",
            "variance........ 7.107259e+02\n",
            "var 12:\n",
            "best............ 7.093979e+02\n",
            "mean............ 7.053814e+02\n",
            "variance........ 2.085888e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 9.124872e+01\n",
            "variance........ 6.006776e+03\n",
            "var 14:\n",
            "best............ 2.982133e+01\n",
            "mean............ 3.544709e+01\n",
            "variance........ 3.936547e+03\n",
            "var 15:\n",
            "best............ 1.216322e+02\n",
            "mean............ 1.265063e+02\n",
            "variance........ 1.778873e+03\n",
            "var 16:\n",
            "best............ 2.109631e+01\n",
            "mean............ 3.011559e+01\n",
            "variance........ 4.363624e+03\n",
            "var 17:\n",
            "best............ 8.775474e+00\n",
            "mean............ 1.649409e+01\n",
            "variance........ 2.948393e+03\n",
            "var 18:\n",
            "best............ 9.553972e+02\n",
            "mean............ 9.423671e+02\n",
            "variance........ 6.708704e+03\n",
            "var 19:\n",
            "best............ 5.040680e+02\n",
            "mean............ 5.081250e+02\n",
            "variance........ 3.390259e+03\n",
            "var 20:\n",
            "best............ 9.664172e+02\n",
            "mean............ 9.582203e+02\n",
            "variance........ 4.477485e+03\n",
            "var 21:\n",
            "best............ 9.750900e+02\n",
            "mean............ 9.707386e+02\n",
            "variance........ 1.164626e+03\n",
            "var 22:\n",
            "best............ 2.620608e+02\n",
            "mean............ 2.707506e+02\n",
            "variance........ 5.381773e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.320449e+02\n",
            "variance........ 3.873846e+03\n",
            "var 24:\n",
            "best............ 9.424335e+02\n",
            "mean............ 9.367955e+02\n",
            "variance........ 3.485678e+03\n",
            "var 25:\n",
            "best............ 1.904730e+02\n",
            "mean............ 1.997814e+02\n",
            "variance........ 3.910319e+03\n",
            "var 26:\n",
            "best............ 3.051213e+02\n",
            "mean............ 3.079659e+02\n",
            "variance........ 1.798164e+03\n",
            "var 27:\n",
            "best............ 5.113860e+02\n",
            "mean............ 5.121395e+02\n",
            "variance........ 8.806012e+02\n",
            "\n",
            "GENERATION: 14\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  4.440892e-16  3.552714e-15  4.440892e-15  4.440892e-15  1.121325e-14  3.497203e-14  7.238654e-14  6.191092e-11  3.119374e-10  1.573602e-09  2.534470e-09  4.165291e-09  6.429247e-09  6.441021e-09  1.192650e-08  8.448847e-08  4.252923e-07  4.252923e-07  4.252923e-07  4.483517e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.873829e-06  1.974618e-06  2.392606e-06  3.731448e-06  5.806709e-06  7.515771e-06  3.340340e-05  6.440707e-05  6.440707e-05  1.329733e-04  2.328933e-04  4.089972e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.200042e-01  2.510115e-01  3.879032e-01  4.029181e-01  5.786642e-01  6.026297e-01  \n",
            "#unique......... 166, #Total UniqueCount: 2649\n",
            "var 1:\n",
            "best............ 3.461008e+02\n",
            "mean............ 3.463794e+02\n",
            "variance........ 3.339828e+02\n",
            "var 2:\n",
            "best............ 8.417003e+02\n",
            "mean............ 8.406203e+02\n",
            "variance........ 2.289305e+02\n",
            "var 3:\n",
            "best............ 7.273499e+01\n",
            "mean............ 1.168756e+02\n",
            "variance........ 1.897210e+04\n",
            "var 4:\n",
            "best............ 6.454606e+02\n",
            "mean............ 6.424652e+02\n",
            "variance........ 2.305587e+03\n",
            "var 5:\n",
            "best............ 5.000424e+02\n",
            "mean............ 4.979297e+02\n",
            "variance........ 1.593612e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.032402e+02\n",
            "variance........ 5.398727e+03\n",
            "var 7:\n",
            "best............ 5.001815e+01\n",
            "mean............ 7.904585e+01\n",
            "variance........ 5.057292e+03\n",
            "var 8:\n",
            "best............ 8.818593e+01\n",
            "mean............ 4.100609e+02\n",
            "variance........ 1.189527e+05\n",
            "var 9:\n",
            "best............ 5.349933e+01\n",
            "mean............ 6.186623e+01\n",
            "variance........ 5.707310e+03\n",
            "var 10:\n",
            "best............ 1.966346e+02\n",
            "mean............ 1.991725e+02\n",
            "variance........ 2.241659e+03\n",
            "var 11:\n",
            "best............ 1.899278e+02\n",
            "mean............ 1.920704e+02\n",
            "variance........ 5.475343e+02\n",
            "var 12:\n",
            "best............ 7.093979e+02\n",
            "mean............ 7.006694e+02\n",
            "variance........ 4.796589e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 6.656824e+01\n",
            "variance........ 6.274058e+03\n",
            "var 14:\n",
            "best............ 2.982133e+01\n",
            "mean............ 4.198931e+01\n",
            "variance........ 7.240815e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 1.234145e+02\n",
            "variance........ 1.706968e+03\n",
            "var 16:\n",
            "best............ 2.109631e+01\n",
            "mean............ 3.180048e+01\n",
            "variance........ 6.976855e+03\n",
            "var 17:\n",
            "best............ 8.775474e+00\n",
            "mean............ 1.364604e+01\n",
            "variance........ 1.289295e+03\n",
            "var 18:\n",
            "best............ 9.553972e+02\n",
            "mean............ 9.514019e+02\n",
            "variance........ 2.718735e+03\n",
            "var 19:\n",
            "best............ 5.040680e+02\n",
            "mean............ 5.035797e+02\n",
            "variance........ 2.340896e+03\n",
            "var 20:\n",
            "best............ 9.664172e+02\n",
            "mean............ 9.640033e+02\n",
            "variance........ 2.219874e+02\n",
            "var 21:\n",
            "best............ 9.750900e+02\n",
            "mean............ 9.712847e+02\n",
            "variance........ 9.784716e+02\n",
            "var 22:\n",
            "best............ 2.620608e+02\n",
            "mean............ 3.737097e+02\n",
            "variance........ 5.894847e+04\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.248154e+02\n",
            "variance........ 9.006998e+02\n",
            "var 24:\n",
            "best............ 9.424335e+02\n",
            "mean............ 9.366827e+02\n",
            "variance........ 2.404404e+03\n",
            "var 25:\n",
            "best............ 1.904730e+02\n",
            "mean............ 1.954749e+02\n",
            "variance........ 1.831318e+03\n",
            "var 26:\n",
            "best............ 3.051213e+02\n",
            "mean............ 3.107532e+02\n",
            "variance........ 2.940926e+03\n",
            "var 27:\n",
            "best............ 5.113860e+02\n",
            "mean............ 5.090600e+02\n",
            "variance........ 1.606424e+03\n",
            "\n",
            "GENERATION: 15\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  8.881784e-16  3.552714e-15  4.440892e-15  7.105427e-15  1.121325e-14  3.497203e-14  8.304468e-14  2.336220e-10  1.025385e-09  1.725347e-09  6.429247e-09  8.325448e-09  9.120154e-09  1.605893e-08  3.820043e-08  8.448847e-08  2.651976e-07  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.564310e-06  1.974618e-06  2.413143e-06  5.420195e-06  7.460024e-06  1.697752e-05  2.167373e-05  3.340340e-05  3.340340e-05  8.382764e-05  1.444584e-04  4.089972e-04  1.267778e-03  2.165431e-03  2.165431e-03  5.946011e-03  5.946011e-03  9.558856e-03  9.558856e-03  9.081473e-02  1.501808e-01  2.613640e-01  3.010160e-01  4.764592e-01  4.969021e-01  \n",
            "#unique......... 164, #Total UniqueCount: 2813\n",
            "var 1:\n",
            "best............ 3.461008e+02\n",
            "mean............ 3.494900e+02\n",
            "variance........ 2.264989e+03\n",
            "var 2:\n",
            "best............ 8.417003e+02\n",
            "mean............ 8.284055e+02\n",
            "variance........ 6.470920e+03\n",
            "var 3:\n",
            "best............ 7.273499e+01\n",
            "mean............ 8.514591e+01\n",
            "variance........ 5.203361e+03\n",
            "var 4:\n",
            "best............ 6.454606e+02\n",
            "mean............ 6.424919e+02\n",
            "variance........ 3.620784e+03\n",
            "var 5:\n",
            "best............ 6.807779e+02\n",
            "mean............ 4.708370e+02\n",
            "variance........ 1.512921e+04\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 8.987850e+02\n",
            "variance........ 8.356867e+03\n",
            "var 7:\n",
            "best............ 5.001815e+01\n",
            "mean............ 7.337487e+01\n",
            "variance........ 1.257460e+04\n",
            "var 8:\n",
            "best............ 8.818593e+01\n",
            "mean............ 9.853906e+01\n",
            "variance........ 4.108490e+03\n",
            "var 9:\n",
            "best............ 5.349933e+01\n",
            "mean............ 6.574421e+01\n",
            "variance........ 4.923583e+03\n",
            "var 10:\n",
            "best............ 1.966346e+02\n",
            "mean............ 1.972207e+02\n",
            "variance........ 7.006746e+02\n",
            "var 11:\n",
            "best............ 1.899278e+02\n",
            "mean............ 1.972530e+02\n",
            "variance........ 2.433584e+03\n",
            "var 12:\n",
            "best............ 7.093979e+02\n",
            "mean............ 7.045795e+02\n",
            "variance........ 3.362637e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 2.961662e+01\n",
            "variance........ 3.803065e+03\n",
            "var 14:\n",
            "best............ 2.982133e+01\n",
            "mean............ 4.460773e+01\n",
            "variance........ 6.686788e+03\n",
            "var 15:\n",
            "best............ 1.309800e-02\n",
            "mean............ 9.818416e+01\n",
            "variance........ 9.750006e+03\n",
            "var 16:\n",
            "best............ 2.109631e+01\n",
            "mean............ 3.834806e+01\n",
            "variance........ 9.469558e+03\n",
            "var 17:\n",
            "best............ 8.775474e+00\n",
            "mean............ 1.853045e+01\n",
            "variance........ 3.525501e+03\n",
            "var 18:\n",
            "best............ 9.553972e+02\n",
            "mean............ 9.415973e+02\n",
            "variance........ 7.689901e+03\n",
            "var 19:\n",
            "best............ 5.040680e+02\n",
            "mean............ 5.829577e+02\n",
            "variance........ 2.609796e+04\n",
            "var 20:\n",
            "best............ 9.664172e+02\n",
            "mean............ 9.559804e+02\n",
            "variance........ 3.988205e+03\n",
            "var 21:\n",
            "best............ 9.750900e+02\n",
            "mean............ 9.553997e+02\n",
            "variance........ 7.771810e+03\n",
            "var 22:\n",
            "best............ 2.620608e+02\n",
            "mean............ 2.652201e+02\n",
            "variance........ 2.094725e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.350004e+02\n",
            "variance........ 5.223628e+03\n",
            "var 24:\n",
            "best............ 9.424335e+02\n",
            "mean............ 9.294206e+02\n",
            "variance........ 5.905396e+03\n",
            "var 25:\n",
            "best............ 1.904730e+02\n",
            "mean............ 1.969167e+02\n",
            "variance........ 2.041505e+03\n",
            "var 26:\n",
            "best............ 3.051213e+02\n",
            "mean............ 3.114213e+02\n",
            "variance........ 2.248273e+03\n",
            "var 27:\n",
            "best............ 5.113860e+02\n",
            "mean............ 5.140999e+02\n",
            "variance........ 2.283541e+03\n",
            "\n",
            "GENERATION: 16\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  8.881784e-16  3.552714e-15  6.439294e-15  7.327472e-15  1.121325e-14  3.497203e-14  1.052491e-13  4.915512e-11  2.766476e-10  8.817658e-10  2.153524e-09  2.993797e-09  3.940703e-09  6.429247e-09  9.940028e-09  8.448847e-08  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.607548e-07  1.864471e-06  1.974618e-06  3.188408e-06  7.452061e-06  1.024305e-05  2.604031e-05  3.340340e-05  6.440707e-05  6.440707e-05  2.602644e-04  4.039378e-04  4.089972e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.442087e-01  2.218600e-01  3.635035e-01  3.675548e-01  5.722779e-01  5.968595e-01  \n",
            "#unique......... 155, #Total UniqueCount: 2968\n",
            "var 1:\n",
            "best............ 2.139305e+02\n",
            "mean............ 3.450479e+02\n",
            "variance........ 7.052465e+02\n",
            "var 2:\n",
            "best............ 8.924996e+02\n",
            "mean............ 8.334272e+02\n",
            "variance........ 3.427114e+03\n",
            "var 3:\n",
            "best............ 1.136399e+02\n",
            "mean............ 6.898636e+01\n",
            "variance........ 6.032926e+03\n",
            "var 4:\n",
            "best............ 6.179413e+02\n",
            "mean............ 6.420654e+02\n",
            "variance........ 2.669288e+03\n",
            "var 5:\n",
            "best............ 5.795375e+02\n",
            "mean............ 5.672105e+02\n",
            "variance........ 9.746007e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.040895e+02\n",
            "variance........ 4.205007e+03\n",
            "var 7:\n",
            "best............ 5.734357e+01\n",
            "mean............ 6.151698e+01\n",
            "variance........ 5.745979e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 9.624299e+01\n",
            "variance........ 3.845271e+03\n",
            "var 9:\n",
            "best............ 2.460378e+02\n",
            "mean............ 7.310903e+01\n",
            "variance........ 9.185158e+03\n",
            "var 10:\n",
            "best............ 1.966346e+02\n",
            "mean............ 2.128206e+02\n",
            "variance........ 7.251926e+03\n",
            "var 11:\n",
            "best............ 5.799510e+02\n",
            "mean............ 2.105312e+02\n",
            "variance........ 9.542864e+03\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 6.938042e+02\n",
            "variance........ 6.233386e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 3.632585e+01\n",
            "variance........ 7.901591e+03\n",
            "var 14:\n",
            "best............ 2.982133e+01\n",
            "mean............ 4.572376e+01\n",
            "variance........ 9.904454e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 3.429830e+01\n",
            "variance........ 5.121957e+03\n",
            "var 16:\n",
            "best............ 2.109631e+01\n",
            "mean............ 3.163807e+01\n",
            "variance........ 3.524100e+03\n",
            "var 17:\n",
            "best............ 8.775474e+00\n",
            "mean............ 2.336725e+01\n",
            "variance........ 6.704804e+03\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 9.441067e+02\n",
            "variance........ 4.176529e+03\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 5.028958e+02\n",
            "variance........ 9.986559e+02\n",
            "var 20:\n",
            "best............ 8.901518e+02\n",
            "mean............ 9.517267e+02\n",
            "variance........ 8.383413e+03\n",
            "var 21:\n",
            "best............ 9.750900e+02\n",
            "mean............ 9.516512e+02\n",
            "variance........ 1.302564e+04\n",
            "var 22:\n",
            "best............ 2.620608e+02\n",
            "mean............ 2.646078e+02\n",
            "variance........ 1.971705e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.329008e+02\n",
            "variance........ 5.099238e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 9.274728e+02\n",
            "variance........ 6.610238e+03\n",
            "var 25:\n",
            "best............ 4.812697e+02\n",
            "mean............ 2.019965e+02\n",
            "variance........ 5.043625e+03\n",
            "var 26:\n",
            "best............ 3.051213e+02\n",
            "mean............ 3.111901e+02\n",
            "variance........ 2.971685e+03\n",
            "var 27:\n",
            "best............ 1.554725e+02\n",
            "mean............ 5.110570e+02\n",
            "variance........ 2.448151e+03\n",
            "\n",
            "GENERATION: 17\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  4.218847e-15  1.121325e-14  1.199041e-14  3.153033e-14  3.497203e-14  2.593481e-13  3.448375e-10  3.627927e-10  1.043599e-09  1.667291e-09  2.479008e-09  1.194385e-08  1.705346e-08  5.179588e-08  8.448847e-08  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.974618e-06  6.625842e-06  7.964914e-06  1.881570e-05  2.159746e-05  2.462558e-05  3.340340e-05  6.440707e-05  6.440707e-05  1.113398e-04  4.089972e-04  4.123290e-04  5.252871e-04  1.267778e-03  1.267778e-03  2.165431e-03  3.624699e-03  3.624699e-03  5.946011e-03  5.946011e-03  5.068358e-02  1.094119e-01  1.227917e-01  1.661615e-01  3.938027e-01  4.109834e-01  \n",
            "#unique......... 182, #Total UniqueCount: 3150\n",
            "var 1:\n",
            "best............ 5.855255e+01\n",
            "mean............ 3.040512e+02\n",
            "variance........ 7.616207e+03\n",
            "var 2:\n",
            "best............ 8.924996e+02\n",
            "mean............ 8.566440e+02\n",
            "variance........ 2.054379e+03\n",
            "var 3:\n",
            "best............ 1.136399e+02\n",
            "mean............ 6.861564e+01\n",
            "variance........ 4.931301e+03\n",
            "var 4:\n",
            "best............ 6.179413e+02\n",
            "mean............ 6.321967e+02\n",
            "variance........ 2.873380e+03\n",
            "var 5:\n",
            "best............ 5.795375e+02\n",
            "mean............ 5.718088e+02\n",
            "variance........ 6.683575e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.004361e+02\n",
            "variance........ 5.387286e+03\n",
            "var 7:\n",
            "best............ 5.734357e+01\n",
            "mean............ 6.294110e+01\n",
            "variance........ 2.953130e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 8.951386e+01\n",
            "variance........ 2.995504e+03\n",
            "var 9:\n",
            "best............ 2.460378e+02\n",
            "mean............ 1.314940e+02\n",
            "variance........ 1.013345e+04\n",
            "var 10:\n",
            "best............ 1.966346e+02\n",
            "mean............ 2.068008e+02\n",
            "variance........ 3.454763e+03\n",
            "var 11:\n",
            "best............ 5.799510e+02\n",
            "mean............ 3.383259e+02\n",
            "variance........ 3.720803e+04\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 7.660706e+02\n",
            "variance........ 1.148766e+04\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 2.930341e+01\n",
            "variance........ 3.172429e+03\n",
            "var 14:\n",
            "best............ 2.982133e+01\n",
            "mean............ 4.208394e+01\n",
            "variance........ 5.346974e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 3.959998e+01\n",
            "variance........ 5.514288e+03\n",
            "var 16:\n",
            "best............ 2.109631e+01\n",
            "mean............ 3.125104e+01\n",
            "variance........ 3.842980e+03\n",
            "var 17:\n",
            "best............ 8.775474e+00\n",
            "mean............ 1.647647e+01\n",
            "variance........ 3.174363e+03\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 9.019024e+02\n",
            "variance........ 8.148077e+03\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 4.969277e+02\n",
            "variance........ 1.804703e+03\n",
            "var 20:\n",
            "best............ 8.901518e+02\n",
            "mean............ 9.306711e+02\n",
            "variance........ 4.715514e+03\n",
            "var 21:\n",
            "best............ 9.750900e+02\n",
            "mean............ 9.637668e+02\n",
            "variance........ 4.723814e+03\n",
            "var 22:\n",
            "best............ 2.620608e+02\n",
            "mean............ 2.738712e+02\n",
            "variance........ 5.741808e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.332319e+02\n",
            "variance........ 4.407258e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 9.063279e+02\n",
            "variance........ 4.709437e+03\n",
            "var 25:\n",
            "best............ 4.812697e+02\n",
            "mean............ 2.995745e+02\n",
            "variance........ 1.959566e+04\n",
            "var 26:\n",
            "best............ 3.051213e+02\n",
            "mean............ 3.968074e+02\n",
            "variance........ 2.806911e+04\n",
            "var 27:\n",
            "best............ 1.554725e+02\n",
            "mean............ 3.837157e+02\n",
            "variance........ 2.836609e+04\n",
            "\n",
            "GENERATION: 18\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  4.218847e-15  1.121325e-14  1.199041e-14  3.153033e-14  3.497203e-14  2.593481e-13  3.448375e-10  3.627927e-10  1.043599e-09  1.667291e-09  2.479008e-09  1.194385e-08  1.705346e-08  5.179588e-08  8.448847e-08  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.974618e-06  6.625842e-06  7.964914e-06  1.881570e-05  2.159746e-05  2.462558e-05  3.340340e-05  6.440707e-05  6.440707e-05  1.113398e-04  4.089972e-04  4.123290e-04  5.252871e-04  1.267778e-03  1.267778e-03  2.165431e-03  3.624699e-03  3.624699e-03  5.946011e-03  5.946011e-03  5.068358e-02  1.094119e-01  1.227917e-01  1.661615e-01  3.938027e-01  4.109834e-01  \n",
            "#unique......... 171, #Total UniqueCount: 3321\n",
            "var 1:\n",
            "best............ 5.855255e+01\n",
            "mean............ 9.466003e+01\n",
            "variance........ 9.363924e+03\n",
            "var 2:\n",
            "best............ 8.924996e+02\n",
            "mean............ 8.862218e+02\n",
            "variance........ 2.892216e+03\n",
            "var 3:\n",
            "best............ 1.136399e+02\n",
            "mean............ 1.304697e+02\n",
            "variance........ 6.633980e+03\n",
            "var 4:\n",
            "best............ 6.179413e+02\n",
            "mean............ 6.275228e+02\n",
            "variance........ 2.275438e+03\n",
            "var 5:\n",
            "best............ 5.795375e+02\n",
            "mean............ 5.737167e+02\n",
            "variance........ 3.755086e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.025074e+02\n",
            "variance........ 4.121995e+03\n",
            "var 7:\n",
            "best............ 5.734357e+01\n",
            "mean............ 7.229118e+01\n",
            "variance........ 7.855708e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 1.553723e+02\n",
            "variance........ 3.643958e+04\n",
            "var 9:\n",
            "best............ 2.460378e+02\n",
            "mean............ 2.684962e+02\n",
            "variance........ 4.240443e+03\n",
            "var 10:\n",
            "best............ 1.966346e+02\n",
            "mean............ 2.005854e+02\n",
            "variance........ 1.588946e+03\n",
            "var 11:\n",
            "best............ 5.799510e+02\n",
            "mean............ 5.887482e+02\n",
            "variance........ 4.222759e+03\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 8.857870e+02\n",
            "variance........ 5.462223e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 2.901052e+01\n",
            "variance........ 4.115707e+03\n",
            "var 14:\n",
            "best............ 2.982133e+01\n",
            "mean............ 3.505479e+01\n",
            "variance........ 1.952095e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 3.883652e+01\n",
            "variance........ 8.736273e+02\n",
            "var 16:\n",
            "best............ 2.109631e+01\n",
            "mean............ 2.746169e+01\n",
            "variance........ 1.790367e+03\n",
            "var 17:\n",
            "best............ 8.775474e+00\n",
            "mean............ 2.029807e+01\n",
            "variance........ 5.400012e+03\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 7.627232e+02\n",
            "variance........ 2.871303e+04\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 5.016632e+02\n",
            "variance........ 8.149255e+02\n",
            "var 20:\n",
            "best............ 8.901518e+02\n",
            "mean............ 8.834178e+02\n",
            "variance........ 3.360929e+03\n",
            "var 21:\n",
            "best............ 9.750900e+02\n",
            "mean............ 9.631864e+02\n",
            "variance........ 6.044041e+03\n",
            "var 22:\n",
            "best............ 2.620608e+02\n",
            "mean............ 2.716719e+02\n",
            "variance........ 4.204826e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.273168e+02\n",
            "variance........ 2.087939e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 8.667500e+02\n",
            "variance........ 1.037918e+03\n",
            "var 25:\n",
            "best............ 4.812697e+02\n",
            "mean............ 4.823980e+02\n",
            "variance........ 5.860455e+03\n",
            "var 26:\n",
            "best............ 3.051213e+02\n",
            "mean............ 3.116567e+02\n",
            "variance........ 7.383853e+03\n",
            "var 27:\n",
            "best............ 1.554725e+02\n",
            "mean............ 1.639796e+02\n",
            "variance........ 7.509379e+03\n",
            "\n",
            "GENERATION: 19\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  4.218847e-15  1.121325e-14  1.199041e-14  3.153033e-14  3.497203e-14  2.593481e-13  3.448375e-10  3.627927e-10  1.043599e-09  1.667291e-09  2.479008e-09  1.194385e-08  1.705346e-08  5.179588e-08  8.448847e-08  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.974618e-06  6.625842e-06  7.964914e-06  1.881570e-05  2.159746e-05  2.462558e-05  3.340340e-05  6.440707e-05  6.440707e-05  1.113398e-04  4.089972e-04  4.123290e-04  5.252871e-04  1.267778e-03  1.267778e-03  2.165431e-03  3.624699e-03  3.624699e-03  5.946011e-03  5.946011e-03  5.068358e-02  1.094119e-01  1.227917e-01  1.661615e-01  3.938027e-01  4.109834e-01  \n",
            "#unique......... 176, #Total UniqueCount: 3497\n",
            "var 1:\n",
            "best............ 5.855255e+01\n",
            "mean............ 5.250479e+01\n",
            "variance........ 4.689900e+03\n",
            "var 2:\n",
            "best............ 8.924996e+02\n",
            "mean............ 8.881334e+02\n",
            "variance........ 1.647647e+03\n",
            "var 3:\n",
            "best............ 1.136399e+02\n",
            "mean............ 1.236242e+02\n",
            "variance........ 5.047417e+03\n",
            "var 4:\n",
            "best............ 6.179413e+02\n",
            "mean............ 6.150550e+02\n",
            "variance........ 3.189759e+03\n",
            "var 5:\n",
            "best............ 5.795375e+02\n",
            "mean............ 5.736150e+02\n",
            "variance........ 3.214250e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.058237e+02\n",
            "variance........ 2.580852e+03\n",
            "var 7:\n",
            "best............ 5.734357e+01\n",
            "mean............ 7.044847e+01\n",
            "variance........ 5.545452e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 9.222606e+01\n",
            "variance........ 9.625440e+03\n",
            "var 9:\n",
            "best............ 2.460378e+02\n",
            "mean............ 2.523209e+02\n",
            "variance........ 2.224511e+03\n",
            "var 10:\n",
            "best............ 1.966346e+02\n",
            "mean............ 2.030083e+02\n",
            "variance........ 3.672401e+03\n",
            "var 11:\n",
            "best............ 5.799510e+02\n",
            "mean............ 5.783383e+02\n",
            "variance........ 3.585215e+03\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 8.872082e+02\n",
            "variance........ 4.888227e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 3.102401e+01\n",
            "variance........ 5.562331e+03\n",
            "var 14:\n",
            "best............ 2.982133e+01\n",
            "mean............ 4.763405e+01\n",
            "variance........ 6.801398e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 5.755155e+01\n",
            "variance........ 1.031618e+04\n",
            "var 16:\n",
            "best............ 2.109631e+01\n",
            "mean............ 3.538111e+01\n",
            "variance........ 5.449582e+03\n",
            "var 17:\n",
            "best............ 8.775474e+00\n",
            "mean............ 2.833415e+01\n",
            "variance........ 9.840211e+03\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 8.156545e+02\n",
            "variance........ 5.531335e+03\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 4.975291e+02\n",
            "variance........ 1.396274e+03\n",
            "var 20:\n",
            "best............ 8.901518e+02\n",
            "mean............ 8.853990e+02\n",
            "variance........ 9.137713e+02\n",
            "var 21:\n",
            "best............ 9.750900e+02\n",
            "mean............ 9.626540e+02\n",
            "variance........ 6.119964e+03\n",
            "var 22:\n",
            "best............ 2.620608e+02\n",
            "mean............ 2.678327e+02\n",
            "variance........ 2.970592e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.330311e+02\n",
            "variance........ 4.544628e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 8.547591e+02\n",
            "variance........ 7.291230e+03\n",
            "var 25:\n",
            "best............ 4.812697e+02\n",
            "mean............ 4.811089e+02\n",
            "variance........ 6.779170e+02\n",
            "var 26:\n",
            "best............ 3.051213e+02\n",
            "mean............ 3.119148e+02\n",
            "variance........ 4.168636e+03\n",
            "var 27:\n",
            "best............ 1.554725e+02\n",
            "mean............ 1.625763e+02\n",
            "variance........ 3.904507e+03\n",
            "\n",
            "GENERATION: 20\n",
            "Lexical Fit..... 0.000000e+00  0.000000e+00  0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  5.329071e-15  6.661338e-15  1.121325e-14  3.497203e-14  4.884981e-14  2.191580e-13  1.074070e-09  1.212682e-09  3.754238e-09  6.429247e-09  6.919756e-09  1.978169e-08  3.346159e-08  6.682283e-08  8.448847e-08  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.459377e-06  1.974618e-06  2.408828e-06  7.456604e-06  8.435402e-06  1.070355e-05  2.813214e-05  3.340340e-05  3.340340e-05  3.340340e-05  2.543777e-04  3.811547e-04  4.089972e-04  1.267778e-03  2.165431e-03  2.165431e-03  5.946011e-03  5.946011e-03  9.558856e-03  9.558856e-03  1.263876e-01  1.440181e-01  2.499032e-01  3.302518e-01  3.998716e-01  4.224374e-01  \n",
            "#unique......... 175, #Total UniqueCount: 3672\n",
            "var 1:\n",
            "best............ 1.913792e+01\n",
            "mean............ 5.365062e+01\n",
            "variance........ 5.338756e+03\n",
            "var 2:\n",
            "best............ 8.925004e+02\n",
            "mean............ 8.888577e+02\n",
            "variance........ 1.334846e+03\n",
            "var 3:\n",
            "best............ 1.136423e+02\n",
            "mean............ 1.164111e+02\n",
            "variance........ 6.578568e+02\n",
            "var 4:\n",
            "best............ 6.179373e+02\n",
            "mean............ 6.201417e+02\n",
            "variance........ 6.602152e+02\n",
            "var 5:\n",
            "best............ 5.795372e+02\n",
            "mean............ 5.801230e+02\n",
            "variance........ 1.052788e+02\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 8.980792e+02\n",
            "variance........ 6.493034e+03\n",
            "var 7:\n",
            "best............ 5.734397e+01\n",
            "mean............ 6.513856e+01\n",
            "variance........ 2.949774e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 8.019239e+01\n",
            "variance........ 2.062961e+03\n",
            "var 9:\n",
            "best............ 7.746980e+02\n",
            "mean............ 2.512306e+02\n",
            "variance........ 3.100524e+03\n",
            "var 10:\n",
            "best............ 1.966342e+02\n",
            "mean............ 2.004066e+02\n",
            "variance........ 1.949655e+03\n",
            "var 11:\n",
            "best............ 5.799542e+02\n",
            "mean............ 5.793599e+02\n",
            "variance........ 2.301804e+03\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 8.811509e+02\n",
            "variance........ 6.018458e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 2.398493e+01\n",
            "variance........ 1.478407e+03\n",
            "var 14:\n",
            "best............ 2.982995e+01\n",
            "mean............ 4.388307e+01\n",
            "variance........ 6.999691e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 4.367384e+01\n",
            "variance........ 2.844619e+03\n",
            "var 16:\n",
            "best............ 2.110198e+01\n",
            "mean............ 2.605495e+01\n",
            "variance........ 1.062222e+03\n",
            "var 17:\n",
            "best............ 8.775398e+00\n",
            "mean............ 1.758813e+01\n",
            "variance........ 4.618402e+03\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 8.194189e+02\n",
            "variance........ 4.708781e+03\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 4.980752e+02\n",
            "variance........ 2.490689e+03\n",
            "var 20:\n",
            "best............ 8.901450e+02\n",
            "mean............ 8.814380e+02\n",
            "variance........ 3.745045e+03\n",
            "var 21:\n",
            "best............ 9.750866e+02\n",
            "mean............ 9.661425e+02\n",
            "variance........ 3.625062e+03\n",
            "var 22:\n",
            "best............ 2.620642e+02\n",
            "mean............ 2.653880e+02\n",
            "variance........ 1.289567e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.303205e+02\n",
            "variance........ 3.060567e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 8.632608e+02\n",
            "variance........ 2.958035e+03\n",
            "var 25:\n",
            "best............ 4.812721e+02\n",
            "mean............ 4.849697e+02\n",
            "variance........ 2.117102e+03\n",
            "var 26:\n",
            "best............ 3.051199e+02\n",
            "mean............ 3.072794e+02\n",
            "variance........ 1.094124e+03\n",
            "var 27:\n",
            "best............ 1.554725e+02\n",
            "mean............ 1.645720e+02\n",
            "variance........ 5.113097e+03\n",
            "\n",
            "Increasing 'max.generations' limit by 25 generations to 45 because the fitness is still impoving.\n",
            "\n",
            "\n",
            "GENERATION: 21\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  2.220446e-16  2.220446e-16  1.776357e-15  3.552714e-15  1.121325e-14  3.497203e-14  4.329870e-14  3.264056e-13  3.952394e-13  5.851754e-09  1.157434e-08  1.790189e-08  2.732019e-08  3.653356e-08  6.295580e-08  8.448847e-08  1.498187e-07  1.529332e-07  4.252923e-07  4.252923e-07  4.252923e-07  8.798614e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.974618e-06  1.987690e-06  3.079479e-06  1.566364e-05  1.886744e-05  3.340340e-05  3.340340e-05  3.340340e-05  4.157610e-05  4.089972e-04  4.584860e-04  7.184279e-04  1.267778e-03  2.165431e-03  5.946011e-03  1.505956e-02  1.505956e-02  2.325117e-02  2.325117e-02  1.833246e-01  5.053685e-01  5.159243e-01  5.551105e-01  6.336746e-01  6.819733e-01  \n",
            "#unique......... 182, #Total UniqueCount: 3854\n",
            "var 1:\n",
            "best............ 1.913792e+01\n",
            "mean............ 4.137834e+01\n",
            "variance........ 5.482696e+03\n",
            "var 2:\n",
            "best............ 8.925004e+02\n",
            "mean............ 8.777134e+02\n",
            "variance........ 5.009774e+03\n",
            "var 3:\n",
            "best............ 8.535008e+02\n",
            "mean............ 1.329516e+02\n",
            "variance........ 9.355192e+03\n",
            "var 4:\n",
            "best............ 6.179373e+02\n",
            "mean............ 6.076604e+02\n",
            "variance........ 5.458251e+03\n",
            "var 5:\n",
            "best............ 5.795372e+02\n",
            "mean............ 5.791661e+02\n",
            "variance........ 1.018510e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.063092e+02\n",
            "variance........ 2.347569e+03\n",
            "var 7:\n",
            "best............ 5.734397e+01\n",
            "mean............ 6.972665e+01\n",
            "variance........ 6.061644e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 8.893540e+01\n",
            "variance........ 6.597419e+03\n",
            "var 9:\n",
            "best............ 7.746980e+02\n",
            "mean............ 4.531246e+02\n",
            "variance........ 6.563140e+04\n",
            "var 10:\n",
            "best............ 1.966342e+02\n",
            "mean............ 2.087957e+02\n",
            "variance........ 5.370098e+03\n",
            "var 11:\n",
            "best............ 5.799542e+02\n",
            "mean............ 5.775654e+02\n",
            "variance........ 1.746641e+03\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 8.787526e+02\n",
            "variance........ 7.080673e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 3.640642e+01\n",
            "variance........ 8.721724e+03\n",
            "var 14:\n",
            "best............ 2.982995e+01\n",
            "mean............ 4.474971e+01\n",
            "variance........ 7.632258e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 4.636846e+01\n",
            "variance........ 6.562053e+03\n",
            "var 16:\n",
            "best............ 2.110198e+01\n",
            "mean............ 2.995560e+01\n",
            "variance........ 2.575714e+03\n",
            "var 17:\n",
            "best............ 8.775398e+00\n",
            "mean............ 2.486507e+01\n",
            "variance........ 8.520232e+03\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 8.183053e+02\n",
            "variance........ 4.919117e+03\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 4.953795e+02\n",
            "variance........ 3.143316e+03\n",
            "var 20:\n",
            "best............ 8.901450e+02\n",
            "mean............ 8.711526e+02\n",
            "variance........ 8.028561e+03\n",
            "var 21:\n",
            "best............ 9.750866e+02\n",
            "mean............ 9.642632e+02\n",
            "variance........ 4.973032e+03\n",
            "var 22:\n",
            "best............ 2.620642e+02\n",
            "mean............ 2.697259e+02\n",
            "variance........ 3.342267e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.293070e+02\n",
            "variance........ 1.884461e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 8.600926e+02\n",
            "variance........ 5.073129e+03\n",
            "var 25:\n",
            "best............ 4.812721e+02\n",
            "mean............ 4.846261e+02\n",
            "variance........ 2.869395e+03\n",
            "var 26:\n",
            "best............ 3.051199e+02\n",
            "mean............ 3.082029e+02\n",
            "variance........ 2.792184e+03\n",
            "var 27:\n",
            "best............ 1.554725e+02\n",
            "mean............ 1.675256e+02\n",
            "variance........ 5.424736e+03\n",
            "\n",
            "GENERATION: 22\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  2.220446e-16  2.220446e-16  1.776357e-15  3.552714e-15  1.121325e-14  3.497203e-14  4.529710e-14  3.410605e-13  4.019007e-13  6.517466e-09  1.286101e-08  1.950254e-08  3.099684e-08  3.653356e-08  6.856439e-08  8.448847e-08  1.659050e-07  1.678040e-07  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.527694e-07  1.974618e-06  1.987690e-06  3.366068e-06  1.566364e-05  1.697752e-05  1.883350e-05  3.340340e-05  3.340340e-05  4.162717e-05  4.089972e-04  4.520324e-04  7.059673e-04  1.267778e-03  2.165431e-03  5.946011e-03  1.505956e-02  1.505956e-02  2.325117e-02  2.325117e-02  1.820861e-01  5.040013e-01  5.126679e-01  5.518511e-01  6.343811e-01  6.806536e-01  \n",
            "#unique......... 166, #Total UniqueCount: 4020\n",
            "var 1:\n",
            "best............ 1.913792e+01\n",
            "mean............ 4.595033e+01\n",
            "variance........ 1.474602e+04\n",
            "var 2:\n",
            "best............ 8.925004e+02\n",
            "mean............ 7.978148e+02\n",
            "variance........ 2.999623e+04\n",
            "var 3:\n",
            "best............ 8.535008e+02\n",
            "mean............ 4.647940e+02\n",
            "variance........ 1.081084e+05\n",
            "var 4:\n",
            "best............ 6.179373e+02\n",
            "mean............ 6.255227e+02\n",
            "variance........ 2.112719e+03\n",
            "var 5:\n",
            "best............ 5.795372e+02\n",
            "mean............ 5.734560e+02\n",
            "variance........ 2.265285e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.033187e+02\n",
            "variance........ 3.611276e+03\n",
            "var 7:\n",
            "best............ 5.734397e+01\n",
            "mean............ 7.634661e+01\n",
            "variance........ 8.376131e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 8.955716e+01\n",
            "variance........ 6.818878e+03\n",
            "var 9:\n",
            "best............ 7.746980e+02\n",
            "mean............ 7.654192e+02\n",
            "variance........ 5.297587e+03\n",
            "var 10:\n",
            "best............ 5.572056e+01\n",
            "mean............ 2.065002e+02\n",
            "variance........ 4.745550e+03\n",
            "var 11:\n",
            "best............ 5.799542e+02\n",
            "mean............ 5.590556e+02\n",
            "variance........ 5.174299e+03\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 8.445607e+02\n",
            "variance........ 1.002186e+04\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 3.725945e+01\n",
            "variance........ 8.783417e+03\n",
            "var 14:\n",
            "best............ 2.982995e+01\n",
            "mean............ 4.152352e+01\n",
            "variance........ 5.382183e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 5.727074e+01\n",
            "variance........ 1.216016e+04\n",
            "var 16:\n",
            "best............ 2.110198e+01\n",
            "mean............ 3.092295e+01\n",
            "variance........ 3.811476e+03\n",
            "var 17:\n",
            "best............ 8.775398e+00\n",
            "mean............ 2.460364e+01\n",
            "variance........ 6.670199e+03\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 8.191429e+02\n",
            "variance........ 5.983532e+03\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 5.011822e+02\n",
            "variance........ 9.776376e+02\n",
            "var 20:\n",
            "best............ 8.901450e+02\n",
            "mean............ 7.995604e+02\n",
            "variance........ 2.672552e+04\n",
            "var 21:\n",
            "best............ 9.750866e+02\n",
            "mean............ 9.541485e+02\n",
            "variance........ 9.947352e+03\n",
            "var 22:\n",
            "best............ 2.620642e+02\n",
            "mean............ 2.646081e+02\n",
            "variance........ 7.127108e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.632007e+02\n",
            "variance........ 7.352058e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 7.692551e+02\n",
            "variance........ 3.568265e+04\n",
            "var 25:\n",
            "best............ 4.812721e+02\n",
            "mean............ 4.832568e+02\n",
            "variance........ 5.106484e+02\n",
            "var 26:\n",
            "best............ 3.051199e+02\n",
            "mean............ 3.625338e+02\n",
            "variance........ 1.394682e+04\n",
            "var 27:\n",
            "best............ 1.554725e+02\n",
            "mean............ 1.686468e+02\n",
            "variance........ 6.946138e+03\n",
            "\n",
            "GENERATION: 23\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  4.440892e-16  4.440892e-16  1.776357e-15  3.552714e-15  1.121325e-14  3.497203e-14  7.038814e-14  5.120349e-13  5.666578e-13  5.589086e-09  7.698300e-09  1.194971e-08  2.367535e-08  3.653356e-08  4.160445e-08  8.448847e-08  9.805071e-08  1.001846e-07  3.116175e-07  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.246649e-06  1.974618e-06  3.269727e-06  2.306252e-05  2.822977e-05  3.340340e-05  5.537585e-05  6.440707e-05  6.440707e-05  4.089972e-04  7.027465e-04  1.124665e-03  1.267778e-03  1.267778e-03  9.558856e-03  9.558856e-03  9.558856e-03  3.518054e-02  3.518054e-02  2.325469e-01  6.812027e-01  7.060514e-01  7.289926e-01  7.335321e-01  9.002555e-01  \n",
            "#unique......... 170, #Total UniqueCount: 4190\n",
            "var 1:\n",
            "best............ 1.913792e+01\n",
            "mean............ 3.379337e+01\n",
            "variance........ 8.272073e+03\n",
            "var 2:\n",
            "best............ 8.925004e+02\n",
            "mean............ 8.729255e+02\n",
            "variance........ 9.725368e+03\n",
            "var 3:\n",
            "best............ 8.535008e+02\n",
            "mean............ 8.340125e+02\n",
            "variance........ 1.490268e+04\n",
            "var 4:\n",
            "best............ 6.179373e+02\n",
            "mean............ 6.193212e+02\n",
            "variance........ 1.707951e+03\n",
            "var 5:\n",
            "best............ 5.795372e+02\n",
            "mean............ 5.743848e+02\n",
            "variance........ 2.528854e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.062401e+02\n",
            "variance........ 2.621606e+03\n",
            "var 7:\n",
            "best............ 5.734397e+01\n",
            "mean............ 7.221766e+01\n",
            "variance........ 7.086491e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 8.138783e+01\n",
            "variance........ 5.134617e+03\n",
            "var 9:\n",
            "best............ 7.746980e+02\n",
            "mean............ 7.703753e+02\n",
            "variance........ 1.669554e+03\n",
            "var 10:\n",
            "best............ 5.572056e+01\n",
            "mean............ 1.309944e+02\n",
            "variance........ 5.880685e+03\n",
            "var 11:\n",
            "best............ 5.799542e+02\n",
            "mean............ 5.756200e+02\n",
            "variance........ 2.130892e+03\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 8.764158e+02\n",
            "variance........ 8.166008e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 2.546439e+01\n",
            "variance........ 2.272202e+03\n",
            "var 14:\n",
            "best............ 2.982995e+01\n",
            "mean............ 3.751088e+01\n",
            "variance........ 3.525191e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 4.426978e+01\n",
            "variance........ 3.128699e+03\n",
            "var 16:\n",
            "best............ 2.110198e+01\n",
            "mean............ 2.566290e+01\n",
            "variance........ 1.220883e+03\n",
            "var 17:\n",
            "best............ 8.775398e+00\n",
            "mean............ 1.642230e+01\n",
            "variance........ 3.392138e+03\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 8.308969e+02\n",
            "variance........ 5.259518e+02\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 5.014120e+02\n",
            "variance........ 2.040493e+03\n",
            "var 20:\n",
            "best............ 2.177022e+02\n",
            "mean............ 8.708138e+02\n",
            "variance........ 8.491231e+03\n",
            "var 21:\n",
            "best............ 9.750866e+02\n",
            "mean............ 9.700046e+02\n",
            "variance........ 2.143746e+03\n",
            "var 22:\n",
            "best............ 2.620642e+02\n",
            "mean............ 2.641942e+02\n",
            "variance........ 1.754632e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.344724e+02\n",
            "variance........ 5.733625e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 8.472810e+02\n",
            "variance........ 1.041672e+04\n",
            "var 25:\n",
            "best............ 4.812721e+02\n",
            "mean............ 4.819620e+02\n",
            "variance........ 5.717124e+02\n",
            "var 26:\n",
            "best............ 3.051199e+02\n",
            "mean............ 3.166627e+02\n",
            "variance........ 3.040744e+03\n",
            "var 27:\n",
            "best............ 1.554725e+02\n",
            "mean............ 1.614087e+02\n",
            "variance........ 2.472774e+03\n",
            "\n",
            "GENERATION: 24\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  4.440892e-16  6.661338e-16  2.442491e-15  3.552714e-15  1.121325e-14  3.497203e-14  8.393286e-14  5.782042e-13  7.196466e-13  2.316843e-09  4.576558e-09  7.967423e-09  1.255542e-08  1.548149e-08  2.996394e-08  6.782847e-08  7.617793e-08  8.448847e-08  2.165888e-07  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.386734e-07  1.974618e-06  5.021478e-06  2.957007e-05  3.340340e-05  3.690233e-05  6.933943e-05  1.217034e-04  1.217034e-04  4.089972e-04  9.215574e-04  1.267778e-03  1.267778e-03  1.468230e-03  9.558856e-03  9.558856e-03  9.558856e-03  3.518054e-02  3.518054e-02  2.575748e-01  6.475601e-01  6.761381e-01  7.215330e-01  7.680437e-01  8.465037e-01  \n",
            "#unique......... 181, #Total UniqueCount: 4371\n",
            "var 1:\n",
            "best............ 1.913792e+01\n",
            "mean............ 2.789474e+01\n",
            "variance........ 4.395885e+03\n",
            "var 2:\n",
            "best............ 8.925004e+02\n",
            "mean............ 8.832744e+02\n",
            "variance........ 5.823529e+03\n",
            "var 3:\n",
            "best............ 8.535008e+02\n",
            "mean............ 8.499633e+02\n",
            "variance........ 4.748306e+03\n",
            "var 4:\n",
            "best............ 6.179373e+02\n",
            "mean............ 6.170289e+02\n",
            "variance........ 1.039601e+03\n",
            "var 5:\n",
            "best............ 5.795372e+02\n",
            "mean............ 5.797295e+02\n",
            "variance........ 9.409009e+02\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.062749e+02\n",
            "variance........ 2.664614e+03\n",
            "var 7:\n",
            "best............ 5.734397e+01\n",
            "mean............ 6.149463e+01\n",
            "variance........ 1.324963e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 7.728778e+01\n",
            "variance........ 2.535731e+03\n",
            "var 9:\n",
            "best............ 7.746980e+02\n",
            "mean............ 7.747058e+02\n",
            "variance........ 2.703310e+02\n",
            "var 10:\n",
            "best............ 5.572056e+01\n",
            "mean............ 8.044222e+01\n",
            "variance........ 4.179319e+03\n",
            "var 11:\n",
            "best............ 5.799542e+02\n",
            "mean............ 5.764280e+02\n",
            "variance........ 2.283793e+03\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 8.892366e+02\n",
            "variance........ 2.326818e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 2.666754e+01\n",
            "variance........ 3.088832e+03\n",
            "var 14:\n",
            "best............ 2.982995e+01\n",
            "mean............ 3.560450e+01\n",
            "variance........ 1.656816e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 4.157850e+01\n",
            "variance........ 2.317372e+03\n",
            "var 16:\n",
            "best............ 2.110198e+01\n",
            "mean............ 3.083936e+01\n",
            "variance........ 5.062053e+03\n",
            "var 17:\n",
            "best............ 8.775398e+00\n",
            "mean............ 2.046315e+01\n",
            "variance........ 5.369346e+03\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 8.221055e+02\n",
            "variance........ 4.973093e+03\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 4.976295e+02\n",
            "variance........ 2.473477e+03\n",
            "var 20:\n",
            "best............ 2.177022e+02\n",
            "mean............ 5.883891e+02\n",
            "variance........ 1.100668e+05\n",
            "var 21:\n",
            "best............ 9.750866e+02\n",
            "mean............ 9.670145e+02\n",
            "variance........ 3.291779e+03\n",
            "var 22:\n",
            "best............ 2.620642e+02\n",
            "mean............ 2.726851e+02\n",
            "variance........ 4.533386e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.322231e+02\n",
            "variance........ 5.249472e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 8.666282e+02\n",
            "variance........ 3.349670e+03\n",
            "var 25:\n",
            "best............ 4.812721e+02\n",
            "mean............ 4.844846e+02\n",
            "variance........ 2.603313e+03\n",
            "var 26:\n",
            "best............ 8.520080e+02\n",
            "mean............ 3.092477e+02\n",
            "variance........ 2.937411e+03\n",
            "var 27:\n",
            "best............ 1.554725e+02\n",
            "mean............ 2.815678e+02\n",
            "variance........ 5.839775e+04\n",
            "\n",
            "GENERATION: 25\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  4.440892e-16  6.661338e-16  2.664535e-15  3.552714e-15  1.121325e-14  3.497203e-14  8.926193e-14  6.081802e-13  7.480683e-13  2.510088e-09  4.970136e-09  8.467371e-09  1.340679e-08  1.548149e-08  3.150572e-08  7.213304e-08  7.946728e-08  8.448847e-08  2.271222e-07  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.776246e-07  1.974618e-06  5.021478e-06  2.957007e-05  3.340340e-05  3.690223e-05  6.938182e-05  1.217034e-04  1.217034e-04  4.089972e-04  9.178229e-04  1.267778e-03  1.267778e-03  1.461250e-03  9.558856e-03  9.558856e-03  9.558856e-03  3.518054e-02  3.518054e-02  2.565436e-01  6.451998e-01  6.745095e-01  7.215142e-01  7.680257e-01  8.442581e-01  \n",
            "#unique......... 166, #Total UniqueCount: 4537\n",
            "var 1:\n",
            "best............ 1.913792e+01\n",
            "mean............ 2.589885e+01\n",
            "variance........ 3.032358e+03\n",
            "var 2:\n",
            "best............ 9.447400e+02\n",
            "mean............ 8.909243e+02\n",
            "variance........ 1.799974e+03\n",
            "var 3:\n",
            "best............ 8.535008e+02\n",
            "mean............ 8.397812e+02\n",
            "variance........ 5.603566e+03\n",
            "var 4:\n",
            "best............ 6.179373e+02\n",
            "mean............ 6.136006e+02\n",
            "variance........ 1.973179e+03\n",
            "var 5:\n",
            "best............ 5.795372e+02\n",
            "mean............ 5.845066e+02\n",
            "variance........ 2.914608e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.061203e+02\n",
            "variance........ 2.222847e+03\n",
            "var 7:\n",
            "best............ 5.734397e+01\n",
            "mean............ 6.693330e+01\n",
            "variance........ 3.721698e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 8.410072e+01\n",
            "variance........ 6.023029e+03\n",
            "var 9:\n",
            "best............ 7.746980e+02\n",
            "mean............ 7.692752e+02\n",
            "variance........ 1.965689e+03\n",
            "var 10:\n",
            "best............ 5.572056e+01\n",
            "mean............ 6.638100e+01\n",
            "variance........ 5.695873e+03\n",
            "var 11:\n",
            "best............ 5.799542e+02\n",
            "mean............ 5.803142e+02\n",
            "variance........ 7.406951e+02\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 8.862221e+02\n",
            "variance........ 2.714057e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 3.662913e+01\n",
            "variance........ 8.654308e+03\n",
            "var 14:\n",
            "best............ 2.982995e+01\n",
            "mean............ 4.118621e+01\n",
            "variance........ 5.326632e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 4.383296e+01\n",
            "variance........ 2.695444e+03\n",
            "var 16:\n",
            "best............ 2.110198e+01\n",
            "mean............ 3.286453e+01\n",
            "variance........ 4.315876e+03\n",
            "var 17:\n",
            "best............ 8.775398e+00\n",
            "mean............ 1.665896e+01\n",
            "variance........ 2.733452e+03\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 8.268907e+02\n",
            "variance........ 1.597558e+03\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 4.966479e+02\n",
            "variance........ 6.190531e+02\n",
            "var 20:\n",
            "best............ 2.177022e+02\n",
            "mean............ 2.107075e+02\n",
            "variance........ 3.632712e+03\n",
            "var 21:\n",
            "best............ 9.750866e+02\n",
            "mean............ 9.667790e+02\n",
            "variance........ 2.336138e+03\n",
            "var 22:\n",
            "best............ 2.620642e+02\n",
            "mean............ 2.691104e+02\n",
            "variance........ 3.758472e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.314755e+02\n",
            "variance........ 4.165402e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 8.667531e+02\n",
            "variance........ 9.203178e+02\n",
            "var 25:\n",
            "best............ 4.812721e+02\n",
            "mean............ 4.817544e+02\n",
            "variance........ 2.323020e+03\n",
            "var 26:\n",
            "best............ 7.973192e+02\n",
            "mean............ 5.167978e+02\n",
            "variance........ 6.716546e+04\n",
            "var 27:\n",
            "best............ 1.496920e+02\n",
            "mean............ 1.594828e+02\n",
            "variance........ 7.409607e+03\n",
            "\n",
            "GENERATION: 26\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  4.440892e-16  6.661338e-16  2.664535e-15  3.552714e-15  1.121325e-14  3.497203e-14  8.926193e-14  6.081802e-13  7.480683e-13  2.510088e-09  4.970136e-09  8.467371e-09  1.340679e-08  1.548149e-08  3.150572e-08  7.213304e-08  7.946728e-08  8.448847e-08  2.271222e-07  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.776246e-07  1.974618e-06  5.021478e-06  2.957007e-05  3.340340e-05  3.690223e-05  6.938182e-05  1.217034e-04  1.217034e-04  4.089972e-04  9.178229e-04  1.267778e-03  1.267778e-03  1.461250e-03  9.558856e-03  9.558856e-03  9.558856e-03  3.518054e-02  3.518054e-02  2.565436e-01  6.451998e-01  6.745095e-01  7.215142e-01  7.680257e-01  8.442581e-01  \n",
            "#unique......... 188, #Total UniqueCount: 4725\n",
            "var 1:\n",
            "best............ 1.913792e+01\n",
            "mean............ 3.419400e+01\n",
            "variance........ 7.151394e+03\n",
            "var 2:\n",
            "best............ 9.447400e+02\n",
            "mean............ 9.046374e+02\n",
            "variance........ 6.997888e+03\n",
            "var 3:\n",
            "best............ 8.535008e+02\n",
            "mean............ 8.434285e+02\n",
            "variance........ 4.605288e+03\n",
            "var 4:\n",
            "best............ 6.179373e+02\n",
            "mean............ 6.098035e+02\n",
            "variance........ 2.790597e+03\n",
            "var 5:\n",
            "best............ 5.795372e+02\n",
            "mean............ 5.774169e+02\n",
            "variance........ 2.201163e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.017498e+02\n",
            "variance........ 5.271424e+03\n",
            "var 7:\n",
            "best............ 5.734397e+01\n",
            "mean............ 7.283223e+01\n",
            "variance........ 8.329191e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 8.268837e+01\n",
            "variance........ 3.899598e+03\n",
            "var 9:\n",
            "best............ 7.746980e+02\n",
            "mean............ 7.651072e+02\n",
            "variance........ 3.150385e+03\n",
            "var 10:\n",
            "best............ 5.572056e+01\n",
            "mean............ 6.982698e+01\n",
            "variance........ 6.995371e+03\n",
            "var 11:\n",
            "best............ 5.799542e+02\n",
            "mean............ 5.795809e+02\n",
            "variance........ 6.794123e+02\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 8.826793e+02\n",
            "variance........ 5.170458e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 2.726354e+01\n",
            "variance........ 3.108394e+03\n",
            "var 14:\n",
            "best............ 2.982995e+01\n",
            "mean............ 4.139580e+01\n",
            "variance........ 4.740558e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 5.241658e+01\n",
            "variance........ 8.894817e+03\n",
            "var 16:\n",
            "best............ 2.110198e+01\n",
            "mean............ 3.497517e+01\n",
            "variance........ 5.565293e+03\n",
            "var 17:\n",
            "best............ 8.775398e+00\n",
            "mean............ 2.046841e+01\n",
            "variance........ 5.033065e+03\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 8.259565e+02\n",
            "variance........ 2.346585e+03\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 4.920303e+02\n",
            "variance........ 1.811564e+03\n",
            "var 20:\n",
            "best............ 2.177022e+02\n",
            "mean............ 2.193549e+02\n",
            "variance........ 2.421416e+03\n",
            "var 21:\n",
            "best............ 9.750866e+02\n",
            "mean............ 9.694944e+02\n",
            "variance........ 1.609915e+03\n",
            "var 22:\n",
            "best............ 2.620642e+02\n",
            "mean............ 2.639986e+02\n",
            "variance........ 9.079069e+02\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.326143e+02\n",
            "variance........ 5.643110e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 8.618368e+02\n",
            "variance........ 2.186151e+03\n",
            "var 25:\n",
            "best............ 4.812721e+02\n",
            "mean............ 4.798376e+02\n",
            "variance........ 2.454167e+03\n",
            "var 26:\n",
            "best............ 7.973192e+02\n",
            "mean............ 7.311123e+02\n",
            "variance........ 1.501780e+04\n",
            "var 27:\n",
            "best............ 1.496920e+02\n",
            "mean............ 1.506841e+02\n",
            "variance........ 1.538412e+03\n",
            "\n",
            "GENERATION: 27\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  4.440892e-16  6.661338e-16  2.664535e-15  3.552714e-15  1.121325e-14  3.497203e-14  8.926193e-14  6.081802e-13  7.480683e-13  2.510088e-09  4.970136e-09  8.467371e-09  1.340679e-08  1.548149e-08  3.150572e-08  7.213304e-08  7.946728e-08  8.448847e-08  2.271222e-07  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.776246e-07  1.974618e-06  5.021478e-06  2.957007e-05  3.340340e-05  3.690223e-05  6.938182e-05  1.217034e-04  1.217034e-04  4.089972e-04  9.178229e-04  1.267778e-03  1.267778e-03  1.461250e-03  9.558856e-03  9.558856e-03  9.558856e-03  3.518054e-02  3.518054e-02  2.565436e-01  6.451998e-01  6.745095e-01  7.215142e-01  7.680257e-01  8.442581e-01  \n",
            "#unique......... 191, #Total UniqueCount: 4916\n",
            "var 1:\n",
            "best............ 1.913792e+01\n",
            "mean............ 2.666669e+01\n",
            "variance........ 2.682458e+03\n",
            "var 2:\n",
            "best............ 9.447400e+02\n",
            "mean............ 9.323452e+02\n",
            "variance........ 4.944814e+03\n",
            "var 3:\n",
            "best............ 8.535008e+02\n",
            "mean............ 8.426290e+02\n",
            "variance........ 4.073330e+03\n",
            "var 4:\n",
            "best............ 6.179373e+02\n",
            "mean............ 6.110347e+02\n",
            "variance........ 3.430723e+03\n",
            "var 5:\n",
            "best............ 5.795372e+02\n",
            "mean............ 5.799283e+02\n",
            "variance........ 1.389807e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 8.953091e+02\n",
            "variance........ 7.484724e+03\n",
            "var 7:\n",
            "best............ 5.734397e+01\n",
            "mean............ 6.571390e+01\n",
            "variance........ 3.511737e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 7.978910e+01\n",
            "variance........ 2.771157e+03\n",
            "var 9:\n",
            "best............ 7.746980e+02\n",
            "mean............ 7.692567e+02\n",
            "variance........ 3.511980e+03\n",
            "var 10:\n",
            "best............ 5.572056e+01\n",
            "mean............ 6.905297e+01\n",
            "variance........ 6.672374e+03\n",
            "var 11:\n",
            "best............ 5.799542e+02\n",
            "mean............ 5.806512e+02\n",
            "variance........ 1.766155e+03\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 8.881335e+02\n",
            "variance........ 2.768484e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 2.996144e+01\n",
            "variance........ 4.800732e+03\n",
            "var 14:\n",
            "best............ 2.982995e+01\n",
            "mean............ 4.121177e+01\n",
            "variance........ 4.604549e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 4.604680e+01\n",
            "variance........ 3.071078e+03\n",
            "var 16:\n",
            "best............ 2.110198e+01\n",
            "mean............ 3.740230e+01\n",
            "variance........ 7.048069e+03\n",
            "var 17:\n",
            "best............ 8.775398e+00\n",
            "mean............ 1.775485e+01\n",
            "variance........ 3.306985e+03\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 8.217648e+02\n",
            "variance........ 4.646303e+03\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 4.991769e+02\n",
            "variance........ 2.407800e+03\n",
            "var 20:\n",
            "best............ 2.177022e+02\n",
            "mean............ 2.208851e+02\n",
            "variance........ 2.574756e+03\n",
            "var 21:\n",
            "best............ 9.750866e+02\n",
            "mean............ 9.558682e+02\n",
            "variance........ 8.428915e+03\n",
            "var 22:\n",
            "best............ 2.620642e+02\n",
            "mean............ 2.680032e+02\n",
            "variance........ 3.214736e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.332279e+02\n",
            "variance........ 4.445571e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 8.588483e+02\n",
            "variance........ 6.070866e+03\n",
            "var 25:\n",
            "best............ 4.812721e+02\n",
            "mean............ 4.805895e+02\n",
            "variance........ 2.638013e+03\n",
            "var 26:\n",
            "best............ 7.973192e+02\n",
            "mean............ 7.902576e+02\n",
            "variance........ 1.477741e+03\n",
            "var 27:\n",
            "best............ 1.496920e+02\n",
            "mean............ 1.574868e+02\n",
            "variance........ 2.325067e+03\n",
            "\n",
            "GENERATION: 28\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  4.440892e-16  6.661338e-16  2.664535e-15  3.552714e-15  1.121325e-14  3.497203e-14  8.926193e-14  6.081802e-13  7.480683e-13  2.510088e-09  4.970136e-09  8.467371e-09  1.340679e-08  1.548149e-08  3.150572e-08  7.213304e-08  7.946728e-08  8.448847e-08  2.271222e-07  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.776246e-07  1.974618e-06  5.021478e-06  2.957007e-05  3.340340e-05  3.690223e-05  6.938182e-05  1.217034e-04  1.217034e-04  4.089972e-04  9.178229e-04  1.267778e-03  1.267778e-03  1.461250e-03  9.558856e-03  9.558856e-03  9.558856e-03  3.518054e-02  3.518054e-02  2.565436e-01  6.451998e-01  6.745095e-01  7.215142e-01  7.680257e-01  8.442581e-01  \n",
            "#unique......... 188, #Total UniqueCount: 5104\n",
            "var 1:\n",
            "best............ 1.913792e+01\n",
            "mean............ 3.028226e+01\n",
            "variance........ 6.456374e+03\n",
            "var 2:\n",
            "best............ 9.447400e+02\n",
            "mean............ 9.327376e+02\n",
            "variance........ 5.365591e+03\n",
            "var 3:\n",
            "best............ 8.535008e+02\n",
            "mean............ 8.504875e+02\n",
            "variance........ 2.388624e+03\n",
            "var 4:\n",
            "best............ 6.179373e+02\n",
            "mean............ 6.149559e+02\n",
            "variance........ 1.394393e+03\n",
            "var 5:\n",
            "best............ 5.795372e+02\n",
            "mean............ 5.798219e+02\n",
            "variance........ 1.327560e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.038472e+02\n",
            "variance........ 2.846162e+03\n",
            "var 7:\n",
            "best............ 5.734397e+01\n",
            "mean............ 6.087369e+01\n",
            "variance........ 1.141952e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 8.122241e+01\n",
            "variance........ 4.415511e+03\n",
            "var 9:\n",
            "best............ 7.746980e+02\n",
            "mean............ 7.672848e+02\n",
            "variance........ 3.794607e+03\n",
            "var 10:\n",
            "best............ 5.572056e+01\n",
            "mean............ 6.310339e+01\n",
            "variance........ 2.549344e+03\n",
            "var 11:\n",
            "best............ 5.799542e+02\n",
            "mean............ 5.812423e+02\n",
            "variance........ 1.135260e+03\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 8.888254e+02\n",
            "variance........ 1.578163e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 2.516630e+01\n",
            "variance........ 1.336539e+03\n",
            "var 14:\n",
            "best............ 2.982995e+01\n",
            "mean............ 4.924286e+01\n",
            "variance........ 1.132922e+04\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 4.547472e+01\n",
            "variance........ 6.191902e+03\n",
            "var 16:\n",
            "best............ 2.110198e+01\n",
            "mean............ 3.806979e+01\n",
            "variance........ 9.429836e+03\n",
            "var 17:\n",
            "best............ 8.775398e+00\n",
            "mean............ 1.586590e+01\n",
            "variance........ 2.344667e+03\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 8.196901e+02\n",
            "variance........ 4.956681e+03\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 4.996871e+02\n",
            "variance........ 2.639047e+03\n",
            "var 20:\n",
            "best............ 2.177022e+02\n",
            "mean............ 2.232946e+02\n",
            "variance........ 2.880123e+03\n",
            "var 21:\n",
            "best............ 9.750866e+02\n",
            "mean............ 9.673030e+02\n",
            "variance........ 2.917379e+03\n",
            "var 22:\n",
            "best............ 2.620642e+02\n",
            "mean............ 2.668244e+02\n",
            "variance........ 2.274315e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.306286e+02\n",
            "variance........ 3.725471e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 8.646432e+02\n",
            "variance........ 1.743831e+03\n",
            "var 25:\n",
            "best............ 4.812721e+02\n",
            "mean............ 4.831013e+02\n",
            "variance........ 6.098743e+02\n",
            "var 26:\n",
            "best............ 7.973192e+02\n",
            "mean............ 7.918873e+02\n",
            "variance........ 2.490115e+03\n",
            "var 27:\n",
            "best............ 1.496920e+02\n",
            "mean............ 1.562560e+02\n",
            "variance........ 2.997153e+03\n",
            "\n",
            "GENERATION: 29\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  4.440892e-16  6.661338e-16  2.664535e-15  3.552714e-15  1.121325e-14  3.497203e-14  8.926193e-14  6.081802e-13  7.480683e-13  2.510088e-09  4.970136e-09  8.467371e-09  1.340679e-08  1.548149e-08  3.150572e-08  7.213304e-08  7.946728e-08  8.448847e-08  2.271222e-07  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.776246e-07  1.974618e-06  5.021478e-06  2.957007e-05  3.340340e-05  3.690223e-05  6.938182e-05  1.217034e-04  1.217034e-04  4.089972e-04  9.178229e-04  1.267778e-03  1.267778e-03  1.461250e-03  9.558856e-03  9.558856e-03  9.558856e-03  3.518054e-02  3.518054e-02  2.565436e-01  6.451998e-01  6.745095e-01  7.215142e-01  7.680257e-01  8.442581e-01  \n",
            "#unique......... 186, #Total UniqueCount: 5290\n",
            "var 1:\n",
            "best............ 1.913792e+01\n",
            "mean............ 2.673641e+01\n",
            "variance........ 1.759576e+03\n",
            "var 2:\n",
            "best............ 9.447400e+02\n",
            "mean............ 9.354357e+02\n",
            "variance........ 3.921320e+03\n",
            "var 3:\n",
            "best............ 8.535008e+02\n",
            "mean............ 8.484550e+02\n",
            "variance........ 1.770136e+03\n",
            "var 4:\n",
            "best............ 6.179373e+02\n",
            "mean............ 6.126372e+02\n",
            "variance........ 3.009551e+03\n",
            "var 5:\n",
            "best............ 5.795372e+02\n",
            "mean............ 5.790241e+02\n",
            "variance........ 2.110461e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 8.931109e+02\n",
            "variance........ 9.390140e+03\n",
            "var 7:\n",
            "best............ 5.734397e+01\n",
            "mean............ 6.882639e+01\n",
            "variance........ 3.633617e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 9.261629e+01\n",
            "variance........ 1.171844e+04\n",
            "var 9:\n",
            "best............ 7.746980e+02\n",
            "mean............ 7.633617e+02\n",
            "variance........ 4.693904e+03\n",
            "var 10:\n",
            "best............ 5.572056e+01\n",
            "mean............ 6.856123e+01\n",
            "variance........ 5.044068e+03\n",
            "var 11:\n",
            "best............ 5.799542e+02\n",
            "mean............ 5.804801e+02\n",
            "variance........ 1.852781e+03\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 8.889634e+02\n",
            "variance........ 1.787029e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 3.191807e+01\n",
            "variance........ 6.380541e+03\n",
            "var 14:\n",
            "best............ 2.982995e+01\n",
            "mean............ 4.537915e+01\n",
            "variance........ 6.919011e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 4.405612e+01\n",
            "variance........ 3.588378e+03\n",
            "var 16:\n",
            "best............ 2.110198e+01\n",
            "mean............ 3.181028e+01\n",
            "variance........ 3.804263e+03\n",
            "var 17:\n",
            "best............ 8.775398e+00\n",
            "mean............ 2.720680e+01\n",
            "variance........ 1.026161e+04\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 8.242073e+02\n",
            "variance........ 3.170037e+03\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 4.990509e+02\n",
            "variance........ 2.208259e+03\n",
            "var 20:\n",
            "best............ 2.177022e+02\n",
            "mean............ 2.233459e+02\n",
            "variance........ 2.383603e+03\n",
            "var 21:\n",
            "best............ 9.750866e+02\n",
            "mean............ 9.645137e+02\n",
            "variance........ 5.570720e+03\n",
            "var 22:\n",
            "best............ 2.620642e+02\n",
            "mean............ 2.730609e+02\n",
            "variance........ 4.971340e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.304382e+02\n",
            "variance........ 4.182769e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 8.611876e+02\n",
            "variance........ 4.127370e+03\n",
            "var 25:\n",
            "best............ 4.812721e+02\n",
            "mean............ 4.803450e+02\n",
            "variance........ 2.372108e+03\n",
            "var 26:\n",
            "best............ 7.973192e+02\n",
            "mean............ 7.932925e+02\n",
            "variance........ 1.182270e+03\n",
            "var 27:\n",
            "best............ 1.496920e+02\n",
            "mean............ 1.661018e+02\n",
            "variance........ 8.341942e+03\n",
            "\n",
            "GENERATION: 30\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  4.440892e-16  6.661338e-16  2.664535e-15  3.552714e-15  1.121325e-14  3.497203e-14  8.926193e-14  6.081802e-13  7.480683e-13  2.510088e-09  4.970136e-09  8.467371e-09  1.340679e-08  1.548149e-08  3.150572e-08  7.213304e-08  7.946728e-08  8.448847e-08  2.271222e-07  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.776246e-07  1.974618e-06  5.021478e-06  2.957007e-05  3.340340e-05  3.690223e-05  6.938182e-05  1.217034e-04  1.217034e-04  4.089972e-04  9.178229e-04  1.267778e-03  1.267778e-03  1.461250e-03  9.558856e-03  9.558856e-03  9.558856e-03  3.518054e-02  3.518054e-02  2.565436e-01  6.451998e-01  6.745095e-01  7.215142e-01  7.680257e-01  8.442581e-01  \n",
            "#unique......... 176, #Total UniqueCount: 5466\n",
            "var 1:\n",
            "best............ 1.913792e+01\n",
            "mean............ 3.367557e+01\n",
            "variance........ 6.073071e+03\n",
            "var 2:\n",
            "best............ 9.447400e+02\n",
            "mean............ 9.322856e+02\n",
            "variance........ 5.874344e+03\n",
            "var 3:\n",
            "best............ 8.535008e+02\n",
            "mean............ 8.487400e+02\n",
            "variance........ 1.868946e+03\n",
            "var 4:\n",
            "best............ 6.179373e+02\n",
            "mean............ 6.165548e+02\n",
            "variance........ 2.859630e+03\n",
            "var 5:\n",
            "best............ 5.795372e+02\n",
            "mean............ 5.758920e+02\n",
            "variance........ 1.706623e+03\n",
            "var 6:\n",
            "best............ 9.123595e+02\n",
            "mean............ 9.057152e+02\n",
            "variance........ 2.300674e+03\n",
            "var 7:\n",
            "best............ 5.734397e+01\n",
            "mean............ 7.138031e+01\n",
            "variance........ 6.310025e+03\n",
            "var 8:\n",
            "best............ 7.178651e+01\n",
            "mean............ 8.587328e+01\n",
            "variance........ 7.127306e+03\n",
            "var 9:\n",
            "best............ 7.746980e+02\n",
            "mean............ 7.705389e+02\n",
            "variance........ 2.567370e+03\n",
            "var 10:\n",
            "best............ 5.572056e+01\n",
            "mean............ 6.572890e+01\n",
            "variance........ 4.716563e+03\n",
            "var 11:\n",
            "best............ 5.799542e+02\n",
            "mean............ 5.759164e+02\n",
            "variance........ 2.576921e+03\n",
            "var 12:\n",
            "best............ 8.943366e+02\n",
            "mean............ 8.773585e+02\n",
            "variance........ 9.085431e+03\n",
            "var 13:\n",
            "best............ 1.959876e+01\n",
            "mean............ 3.071591e+01\n",
            "variance........ 4.606280e+03\n",
            "var 14:\n",
            "best............ 2.982995e+01\n",
            "mean............ 4.239894e+01\n",
            "variance........ 4.463699e+03\n",
            "var 15:\n",
            "best............ 3.545306e+01\n",
            "mean............ 4.680933e+01\n",
            "variance........ 4.978424e+03\n",
            "var 16:\n",
            "best............ 2.110198e+01\n",
            "mean............ 2.556901e+01\n",
            "variance........ 2.360055e+03\n",
            "var 17:\n",
            "best............ 8.775398e+00\n",
            "mean............ 1.876660e+01\n",
            "variance........ 4.216748e+03\n",
            "var 18:\n",
            "best............ 8.317360e+02\n",
            "mean............ 8.280960e+02\n",
            "variance........ 1.283233e+03\n",
            "var 19:\n",
            "best............ 4.983533e+02\n",
            "mean............ 4.977749e+02\n",
            "variance........ 3.222612e+03\n",
            "var 20:\n",
            "best............ 2.177022e+02\n",
            "mean............ 2.203133e+02\n",
            "variance........ 8.906712e+02\n",
            "var 21:\n",
            "best............ 9.750866e+02\n",
            "mean............ 9.641272e+02\n",
            "variance........ 5.541358e+03\n",
            "var 22:\n",
            "best............ 2.620642e+02\n",
            "mean............ 2.688598e+02\n",
            "variance........ 1.616623e+03\n",
            "var 23:\n",
            "best............ 1.220406e+02\n",
            "mean............ 1.316383e+02\n",
            "variance........ 3.087260e+03\n",
            "var 24:\n",
            "best............ 8.708384e+02\n",
            "mean............ 8.639412e+02\n",
            "variance........ 2.443617e+03\n",
            "var 25:\n",
            "best............ 4.812721e+02\n",
            "mean............ 4.848664e+02\n",
            "variance........ 1.537766e+03\n",
            "var 26:\n",
            "best............ 7.973192e+02\n",
            "mean............ 7.907592e+02\n",
            "variance........ 1.909960e+03\n",
            "var 27:\n",
            "best............ 1.496920e+02\n",
            "mean............ 1.533445e+02\n",
            "variance........ 1.273274e+03\n",
            "\n",
            "GENERATION: 31\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  4.440892e-16  8.881784e-16  3.552714e-15  3.996803e-15  1.121325e-14  3.497203e-14  9.148238e-14  6.625811e-13  7.909229e-13  3.798561e-09  4.567233e-09  6.821261e-09  1.548149e-08  3.040858e-08  3.057875e-08  7.989417e-08  8.605531e-08  1.914826e-07  2.096778e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.123832e-06  1.974618e-06  9.359747e-06  5.091436e-05  6.308982e-05  6.440707e-05  1.151269e-04  2.253712e-04  2.253712e-04  4.089972e-04  1.267778e-03  1.267778e-03  1.465594e-03  2.250543e-03  9.558856e-03  9.558856e-03  9.558856e-03  3.518054e-02  3.518054e-02  3.136043e-01  6.664609e-01  7.133766e-01  8.278032e-01  8.367318e-01  9.993388e-01  \n",
            "#unique......... 174, #Total UniqueCount: 5640\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 2.901073e+01\n",
            "variance........ 4.447760e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.285322e+02\n",
            "variance........ 7.345007e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.386618e+02\n",
            "variance........ 6.160903e+03\n",
            "var 4:\n",
            "best............ 6.175207e+02\n",
            "mean............ 6.136119e+02\n",
            "variance........ 2.514130e+03\n",
            "var 5:\n",
            "best............ 5.807571e+02\n",
            "mean............ 5.786902e+02\n",
            "variance........ 9.481300e+02\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.034290e+02\n",
            "variance........ 3.868860e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 7.772884e+01\n",
            "variance........ 1.036025e+04\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.892386e+01\n",
            "variance........ 8.675901e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.625606e+02\n",
            "variance........ 5.259149e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.498964e+01\n",
            "variance........ 2.646723e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.765759e+02\n",
            "variance........ 1.887419e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.875815e+02\n",
            "variance........ 3.222986e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 3.556201e+01\n",
            "variance........ 7.387413e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 3.992263e+01\n",
            "variance........ 5.570909e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 5.131619e+01\n",
            "variance........ 6.809598e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.556056e+01\n",
            "variance........ 6.839861e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.563051e+01\n",
            "variance........ 7.478414e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.236831e+02\n",
            "variance........ 4.007478e+03\n",
            "var 19:\n",
            "best............ 4.964287e+02\n",
            "mean............ 5.050009e+02\n",
            "variance........ 4.063696e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.264569e+02\n",
            "variance........ 3.399586e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.648666e+02\n",
            "variance........ 4.020095e+03\n",
            "var 22:\n",
            "best............ 2.613220e+02\n",
            "mean............ 2.658176e+02\n",
            "variance........ 1.283763e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.332117e+02\n",
            "variance........ 4.506600e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 8.503601e+02\n",
            "variance........ 1.132713e+04\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.845951e+02\n",
            "variance........ 1.631069e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.870376e+02\n",
            "variance........ 3.962720e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.628204e+02\n",
            "variance........ 5.124102e+03\n",
            "\n",
            "GENERATION: 32\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  1.776357e-15  2.220446e-15  3.552714e-15  3.996803e-15  1.121325e-14  3.497203e-14  2.708944e-13  1.391331e-12  1.782574e-12  1.666683e-09  6.429247e-09  1.625773e-08  2.328081e-08  3.266740e-08  8.448847e-08  1.168685e-07  2.796811e-07  3.185723e-07  4.252923e-07  4.252923e-07  4.252923e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.070744e-06  1.974618e-06  4.261066e-06  5.085739e-06  2.983774e-05  3.340340e-05  3.675685e-05  6.440707e-05  7.207101e-05  1.217034e-04  4.089972e-04  8.753961e-04  1.267778e-03  1.353521e-03  2.165431e-03  5.946011e-03  1.505956e-02  1.505956e-02  2.325117e-02  2.325117e-02  2.327176e-01  4.096777e-01  5.357037e-01  5.440514e-01  5.833746e-01  5.854983e-01  \n",
            "#unique......... 181, #Total UniqueCount: 5821\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 2.508032e+01\n",
            "variance........ 1.590416e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.369476e+02\n",
            "variance........ 3.851632e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.458972e+02\n",
            "variance........ 3.209715e+03\n",
            "var 4:\n",
            "best............ 6.175207e+02\n",
            "mean............ 6.143450e+02\n",
            "variance........ 1.637413e+03\n",
            "var 5:\n",
            "best............ 1.134459e+02\n",
            "mean............ 5.736050e+02\n",
            "variance........ 2.664302e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.072108e+02\n",
            "variance........ 1.185383e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 6.305010e+01\n",
            "variance........ 1.832342e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 7.554491e+01\n",
            "variance........ 1.477739e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.656334e+02\n",
            "variance........ 3.592932e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.032861e+01\n",
            "variance........ 2.248194e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.722982e+02\n",
            "variance........ 5.204278e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.872263e+02\n",
            "variance........ 2.833892e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 2.673570e+01\n",
            "variance........ 2.440944e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 3.399870e+01\n",
            "variance........ 9.855569e+02\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.349425e+01\n",
            "variance........ 3.079469e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 2.777043e+01\n",
            "variance........ 2.394943e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 1.456514e+01\n",
            "variance........ 2.348165e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.300004e+02\n",
            "variance........ 9.656227e+02\n",
            "var 19:\n",
            "best............ 4.964287e+02\n",
            "mean............ 4.991889e+02\n",
            "variance........ 2.135600e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.199080e+02\n",
            "variance........ 1.766792e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.673198e+02\n",
            "variance........ 2.301171e+03\n",
            "var 22:\n",
            "best............ 2.613220e+02\n",
            "mean............ 2.652064e+02\n",
            "variance........ 2.377799e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.244335e+02\n",
            "variance........ 2.681345e+02\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 5.582374e+02\n",
            "variance........ 1.479065e+05\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.786659e+02\n",
            "variance........ 1.005119e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.943904e+02\n",
            "variance........ 1.878833e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.542325e+02\n",
            "variance........ 1.909872e+03\n",
            "\n",
            "GENERATION: 33\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 180, #Total UniqueCount: 6001\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 2.947994e+01\n",
            "variance........ 3.571735e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.319927e+02\n",
            "variance........ 4.622395e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.484763e+02\n",
            "variance........ 1.248816e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 6.152983e+02\n",
            "variance........ 2.600442e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 2.974707e+02\n",
            "variance........ 5.718328e+04\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.054120e+02\n",
            "variance........ 3.332772e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 6.662441e+01\n",
            "variance........ 2.209182e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.173793e+01\n",
            "variance........ 3.409777e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.668021e+02\n",
            "variance........ 3.604006e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.793199e+01\n",
            "variance........ 4.844489e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.209805e+02\n",
            "variance........ 2.503992e+04\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.856552e+02\n",
            "variance........ 3.463739e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 2.758484e+01\n",
            "variance........ 3.394948e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 4.294934e+01\n",
            "variance........ 4.403988e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 3.904441e+01\n",
            "variance........ 9.056733e+02\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.240583e+01\n",
            "variance........ 5.511684e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.305435e+01\n",
            "variance........ 8.074775e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.253860e+02\n",
            "variance........ 2.960087e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 4.951710e+02\n",
            "variance........ 1.653084e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.252534e+02\n",
            "variance........ 2.224459e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.666079e+02\n",
            "variance........ 2.624141e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.674515e+02\n",
            "variance........ 2.520252e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.371275e+02\n",
            "variance........ 5.662311e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.338456e+01\n",
            "variance........ 4.030928e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.815160e+02\n",
            "variance........ 2.760801e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.890626e+02\n",
            "variance........ 4.000722e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.682586e+02\n",
            "variance........ 8.418536e+03\n",
            "\n",
            "GENERATION: 34\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 174, #Total UniqueCount: 6175\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.388075e+01\n",
            "variance........ 6.531553e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.344355e+02\n",
            "variance........ 5.870977e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.406457e+02\n",
            "variance........ 5.952504e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 4.736058e+02\n",
            "variance........ 2.817403e+04\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 7.995767e+01\n",
            "variance........ 6.927961e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.041678e+02\n",
            "variance........ 4.535335e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 7.030967e+01\n",
            "variance........ 3.336832e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.507268e+01\n",
            "variance........ 7.650103e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.734625e+02\n",
            "variance........ 5.353253e+02\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.911205e+01\n",
            "variance........ 6.268703e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.241423e+02\n",
            "variance........ 2.557409e+04\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.835060e+02\n",
            "variance........ 5.864540e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 2.591445e+01\n",
            "variance........ 1.584352e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 4.350635e+01\n",
            "variance........ 4.708568e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.621540e+01\n",
            "variance........ 6.000800e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.423771e+01\n",
            "variance........ 7.665245e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.075010e+01\n",
            "variance........ 5.616861e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.236126e+02\n",
            "variance........ 3.779093e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 5.980456e+02\n",
            "variance........ 1.416267e+04\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.213756e+02\n",
            "variance........ 1.665314e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.659159e+02\n",
            "variance........ 2.977560e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.427312e+02\n",
            "variance........ 2.515393e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.287968e+02\n",
            "variance........ 1.633975e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 2.016455e+01\n",
            "variance........ 9.037710e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.806183e+02\n",
            "variance........ 2.589137e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.969692e+02\n",
            "variance........ 2.671547e+02\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.559849e+02\n",
            "variance........ 2.095853e+03\n",
            "\n",
            "GENERATION: 35\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 187, #Total UniqueCount: 6362\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.668405e+01\n",
            "variance........ 7.859066e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.285856e+02\n",
            "variance........ 7.864154e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.421852e+02\n",
            "variance........ 5.391042e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.225112e+02\n",
            "variance........ 4.283482e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.458191e+01\n",
            "variance........ 4.293900e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.012101e+02\n",
            "variance........ 4.001213e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 7.599541e+01\n",
            "variance........ 7.200449e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.509152e+01\n",
            "variance........ 5.372270e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.691576e+02\n",
            "variance........ 2.972430e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.408717e+01\n",
            "variance........ 2.853892e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.610870e+02\n",
            "variance........ 4.433267e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.691872e+02\n",
            "variance........ 1.308655e+04\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 2.819796e+01\n",
            "variance........ 2.885240e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 3.796588e+01\n",
            "variance........ 2.024383e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.805196e+01\n",
            "variance........ 8.080984e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 4.012564e+01\n",
            "variance........ 1.141630e+04\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.871863e+01\n",
            "variance........ 9.067392e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.276451e+02\n",
            "variance........ 1.667706e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.881384e+02\n",
            "variance........ 5.657432e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.276113e+02\n",
            "variance........ 3.906533e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.637171e+02\n",
            "variance........ 4.027009e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.326982e+02\n",
            "variance........ 5.679479e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.352115e+02\n",
            "variance........ 4.341566e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.558561e+01\n",
            "variance........ 5.957099e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.792424e+02\n",
            "variance........ 7.082987e+02\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.930305e+02\n",
            "variance........ 1.469580e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.616711e+02\n",
            "variance........ 5.479367e+03\n",
            "\n",
            "GENERATION: 36\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 192, #Total UniqueCount: 6554\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 2.939613e+01\n",
            "variance........ 4.704874e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.335183e+02\n",
            "variance........ 5.198023e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.477986e+02\n",
            "variance........ 2.364330e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.271197e+02\n",
            "variance........ 4.542136e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.593165e+01\n",
            "variance........ 6.006115e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.034127e+02\n",
            "variance........ 4.132474e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 6.997249e+01\n",
            "variance........ 5.676327e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 7.519230e+01\n",
            "variance........ 1.202742e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.698254e+02\n",
            "variance........ 1.777068e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.508119e+01\n",
            "variance........ 3.487756e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.624844e+02\n",
            "variance........ 4.710153e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.910070e+02\n",
            "variance........ 9.726131e+02\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 3.115066e+01\n",
            "variance........ 4.897756e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 3.224009e+01\n",
            "variance........ 7.286946e+02\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.782510e+01\n",
            "variance........ 6.566160e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.040588e+01\n",
            "variance........ 4.514355e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 1.380385e+01\n",
            "variance........ 8.725299e+02\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.240472e+02\n",
            "variance........ 7.116735e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.935015e+02\n",
            "variance........ 4.233679e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.251848e+02\n",
            "variance........ 3.879440e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.720431e+02\n",
            "variance........ 7.823372e+02\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.207462e+02\n",
            "variance........ 1.666804e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.334222e+02\n",
            "variance........ 4.228722e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.980071e+01\n",
            "variance........ 7.071529e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.799664e+02\n",
            "variance........ 1.790563e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.928751e+02\n",
            "variance........ 1.619527e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.557895e+02\n",
            "variance........ 2.592443e+03\n",
            "\n",
            "GENERATION: 37\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 180, #Total UniqueCount: 6734\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 2.267665e+01\n",
            "variance........ 9.908418e+02\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.335656e+02\n",
            "variance........ 4.376052e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.485176e+02\n",
            "variance........ 2.435014e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.244968e+02\n",
            "variance........ 3.781847e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.107848e+01\n",
            "variance........ 4.092554e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.039986e+02\n",
            "variance........ 4.003783e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 6.581118e+01\n",
            "variance........ 2.452334e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 7.497264e+01\n",
            "variance........ 1.042194e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.701010e+02\n",
            "variance........ 3.122458e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.105490e+01\n",
            "variance........ 1.683225e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.590603e+02\n",
            "variance........ 4.194555e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.848385e+02\n",
            "variance........ 3.767448e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 3.203333e+01\n",
            "variance........ 6.496145e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 3.405760e+01\n",
            "variance........ 1.864291e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.555739e+01\n",
            "variance........ 3.797988e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 2.880390e+01\n",
            "variance........ 3.318342e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 1.854133e+01\n",
            "variance........ 5.534191e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.275161e+02\n",
            "variance........ 4.457409e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.967215e+02\n",
            "variance........ 2.340794e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.274960e+02\n",
            "variance........ 4.103466e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.676967e+02\n",
            "variance........ 3.185526e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.254765e+02\n",
            "variance........ 4.776101e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.309836e+02\n",
            "variance........ 2.794376e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.179537e+01\n",
            "variance........ 2.598521e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.817514e+02\n",
            "variance........ 1.976486e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.929119e+02\n",
            "variance........ 1.705118e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.614214e+02\n",
            "variance........ 5.817154e+03\n",
            "\n",
            "GENERATION: 38\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 182, #Total UniqueCount: 6916\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.153402e+01\n",
            "variance........ 6.402505e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.402376e+02\n",
            "variance........ 1.141408e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.468869e+02\n",
            "variance........ 2.711984e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.243629e+02\n",
            "variance........ 6.161135e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.168872e+01\n",
            "variance........ 3.739474e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 8.991043e+02\n",
            "variance........ 5.857265e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 6.484094e+01\n",
            "variance........ 1.668009e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.724788e+01\n",
            "variance........ 5.185709e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.699660e+02\n",
            "variance........ 2.094577e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.438137e+01\n",
            "variance........ 3.051881e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.662279e+02\n",
            "variance........ 4.646318e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.900311e+02\n",
            "variance........ 9.310927e+02\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 3.181329e+01\n",
            "variance........ 5.060484e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 4.480584e+01\n",
            "variance........ 9.710031e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.985373e+01\n",
            "variance........ 6.342190e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.609074e+01\n",
            "variance........ 6.771056e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 1.900952e+01\n",
            "variance........ 3.561241e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.301820e+02\n",
            "variance........ 3.136642e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 7.038396e+02\n",
            "variance........ 1.536445e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.219206e+02\n",
            "variance........ 1.594647e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.599504e+02\n",
            "variance........ 7.682900e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.225003e+02\n",
            "variance........ 2.578523e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.402560e+02\n",
            "variance........ 7.219582e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.399868e+01\n",
            "variance........ 3.634177e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.794279e+02\n",
            "variance........ 1.757799e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.933423e+02\n",
            "variance........ 1.856487e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.554572e+02\n",
            "variance........ 1.812548e+03\n",
            "\n",
            "GENERATION: 39\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 183, #Total UniqueCount: 7099\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.536848e+01\n",
            "variance........ 7.545475e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.303588e+02\n",
            "variance........ 6.942097e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.464577e+02\n",
            "variance........ 1.862481e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.225950e+02\n",
            "variance........ 3.450543e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 6.072880e+01\n",
            "variance........ 1.163032e+04\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 8.971493e+02\n",
            "variance........ 6.815582e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 6.671190e+01\n",
            "variance........ 3.260843e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 7.875595e+01\n",
            "variance........ 2.055890e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.701027e+02\n",
            "variance........ 2.005941e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 7.318307e+01\n",
            "variance........ 9.342501e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.588703e+02\n",
            "variance........ 5.544061e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.832939e+02\n",
            "variance........ 5.076817e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 2.942376e+01\n",
            "variance........ 4.565158e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 4.482769e+01\n",
            "variance........ 8.822140e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.848887e+01\n",
            "variance........ 5.658676e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.522708e+01\n",
            "variance........ 4.264509e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.999758e+01\n",
            "variance........ 9.311437e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.243597e+02\n",
            "variance........ 5.422062e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.969277e+02\n",
            "variance........ 3.770323e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.313671e+02\n",
            "variance........ 5.952616e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.639690e+02\n",
            "variance........ 3.471965e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.227809e+02\n",
            "variance........ 2.657138e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.327500e+02\n",
            "variance........ 3.479842e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 2.280243e+01\n",
            "variance........ 8.907682e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.816246e+02\n",
            "variance........ 7.451891e+02\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.896864e+02\n",
            "variance........ 3.482815e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.562033e+02\n",
            "variance........ 3.096881e+03\n",
            "\n",
            "GENERATION: 40\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 176, #Total UniqueCount: 7275\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 2.736739e+01\n",
            "variance........ 4.002877e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.340827e+02\n",
            "variance........ 4.340296e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.466981e+02\n",
            "variance........ 2.767879e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.306921e+02\n",
            "variance........ 5.196081e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.872818e+01\n",
            "variance........ 9.132680e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.009454e+02\n",
            "variance........ 4.006210e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 7.074036e+01\n",
            "variance........ 5.094436e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.214408e+01\n",
            "variance........ 6.042125e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.638750e+02\n",
            "variance........ 5.479987e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.692610e+01\n",
            "variance........ 4.413184e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.590932e+02\n",
            "variance........ 4.620945e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.879784e+02\n",
            "variance........ 2.725542e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 3.392346e+01\n",
            "variance........ 6.049203e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 4.312242e+01\n",
            "variance........ 6.974339e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.676470e+01\n",
            "variance........ 6.100093e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 2.954243e+01\n",
            "variance........ 2.535418e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.418126e+01\n",
            "variance........ 7.656082e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.258938e+02\n",
            "variance........ 5.513644e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.972206e+02\n",
            "variance........ 1.810008e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.277872e+02\n",
            "variance........ 4.447646e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.682521e+02\n",
            "variance........ 3.242443e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.227228e+02\n",
            "variance........ 1.858420e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.318102e+02\n",
            "variance........ 3.257428e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 2.169360e+01\n",
            "variance........ 9.737847e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.765279e+02\n",
            "variance........ 2.372388e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.908334e+02\n",
            "variance........ 2.780960e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.603008e+02\n",
            "variance........ 2.976501e+03\n",
            "\n",
            "GENERATION: 41\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 178, #Total UniqueCount: 7453\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.246842e+01\n",
            "variance........ 7.006656e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.289290e+02\n",
            "variance........ 9.328649e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.476560e+02\n",
            "variance........ 2.541824e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.255491e+02\n",
            "variance........ 5.647310e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.481617e+01\n",
            "variance........ 4.973245e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.049102e+02\n",
            "variance........ 3.101912e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 7.340743e+01\n",
            "variance........ 6.416018e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.547528e+01\n",
            "variance........ 7.776994e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.634002e+02\n",
            "variance........ 6.668735e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.298976e+01\n",
            "variance........ 2.326040e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.556355e+02\n",
            "variance........ 5.416259e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.868785e+02\n",
            "variance........ 3.881718e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 2.505091e+01\n",
            "variance........ 1.315764e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 4.410782e+01\n",
            "variance........ 6.578193e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 5.224265e+01\n",
            "variance........ 8.716456e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 2.895683e+01\n",
            "variance........ 2.158756e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 1.891659e+01\n",
            "variance........ 3.523747e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.219753e+02\n",
            "variance........ 8.691692e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.878985e+02\n",
            "variance........ 7.344224e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.311469e+02\n",
            "variance........ 6.189740e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.630869e+02\n",
            "variance........ 7.353873e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.244188e+02\n",
            "variance........ 2.811803e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.375136e+02\n",
            "variance........ 4.852238e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.428823e+01\n",
            "variance........ 5.262196e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.844881e+02\n",
            "variance........ 1.293509e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.947299e+02\n",
            "variance........ 7.535037e+02\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.664653e+02\n",
            "variance........ 6.387933e+03\n",
            "\n",
            "GENERATION: 42\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 171, #Total UniqueCount: 7624\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 2.794298e+01\n",
            "variance........ 2.926245e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.340535e+02\n",
            "variance........ 4.630651e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.482015e+02\n",
            "variance........ 2.828351e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.205197e+02\n",
            "variance........ 3.838895e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.200070e+01\n",
            "variance........ 2.400977e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.054528e+02\n",
            "variance........ 3.398909e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 6.695549e+01\n",
            "variance........ 1.712948e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.178162e+01\n",
            "variance........ 3.360771e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.714595e+02\n",
            "variance........ 6.630263e+02\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.988645e+01\n",
            "variance........ 5.327508e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.620138e+02\n",
            "variance........ 3.836936e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.884016e+02\n",
            "variance........ 3.043740e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 3.063628e+01\n",
            "variance........ 5.132013e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 4.474864e+01\n",
            "variance........ 8.925395e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.499130e+01\n",
            "variance........ 3.885203e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.318348e+01\n",
            "variance........ 7.820108e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 1.336459e+01\n",
            "variance........ 6.848137e+02\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.293375e+02\n",
            "variance........ 2.464765e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.981081e+02\n",
            "variance........ 2.861132e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.245540e+02\n",
            "variance........ 2.894489e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.653008e+02\n",
            "variance........ 2.925629e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.232605e+02\n",
            "variance........ 1.917971e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.384631e+02\n",
            "variance........ 6.098962e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.124994e+01\n",
            "variance........ 2.312885e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.831617e+02\n",
            "variance........ 1.447672e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.886115e+02\n",
            "variance........ 3.726522e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.615876e+02\n",
            "variance........ 5.344750e+03\n",
            "\n",
            "GENERATION: 43\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 175, #Total UniqueCount: 7799\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.599639e+01\n",
            "variance........ 7.450071e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.393721e+02\n",
            "variance........ 1.412015e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.355616e+02\n",
            "variance........ 8.729202e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.241925e+02\n",
            "variance........ 3.621576e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.702386e+01\n",
            "variance........ 6.010955e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.058726e+02\n",
            "variance........ 2.990982e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 6.796776e+01\n",
            "variance........ 2.550092e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.140393e+01\n",
            "variance........ 4.237513e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.653610e+02\n",
            "variance........ 4.766167e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 7.008527e+01\n",
            "variance........ 5.715629e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.603720e+02\n",
            "variance........ 4.050839e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.824743e+02\n",
            "variance........ 5.779559e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 2.405068e+01\n",
            "variance........ 1.196188e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 4.099168e+01\n",
            "variance........ 6.153048e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.261140e+01\n",
            "variance........ 2.505909e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 2.598929e+01\n",
            "variance........ 1.068101e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.533784e+01\n",
            "variance........ 8.649133e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.251480e+02\n",
            "variance........ 4.060337e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.908024e+02\n",
            "variance........ 3.577837e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.214832e+02\n",
            "variance........ 2.523719e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.625350e+02\n",
            "variance........ 6.863429e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.255847e+02\n",
            "variance........ 2.855163e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.360870e+02\n",
            "variance........ 4.820218e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.829095e+01\n",
            "variance........ 7.911536e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.803720e+02\n",
            "variance........ 1.020360e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.929699e+02\n",
            "variance........ 1.888000e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.636591e+02\n",
            "variance........ 5.425844e+03\n",
            "\n",
            "GENERATION: 44\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 167, #Total UniqueCount: 7966\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 2.962617e+01\n",
            "variance........ 5.229324e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.293395e+02\n",
            "variance........ 8.893824e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.460983e+02\n",
            "variance........ 3.503340e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.257886e+02\n",
            "variance........ 5.233012e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 4.572071e+01\n",
            "variance........ 1.106943e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.073881e+02\n",
            "variance........ 2.431171e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 6.488406e+01\n",
            "variance........ 1.815432e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 7.669726e+01\n",
            "variance........ 7.802995e+02\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.701564e+02\n",
            "variance........ 1.648510e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.692267e+01\n",
            "variance........ 4.584168e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.577407e+02\n",
            "variance........ 5.471093e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.849028e+02\n",
            "variance........ 3.819204e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 2.854932e+01\n",
            "variance........ 4.395269e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 3.683786e+01\n",
            "variance........ 2.859093e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 5.318467e+01\n",
            "variance........ 9.887764e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 2.974980e+01\n",
            "variance........ 5.167938e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.270672e+01\n",
            "variance........ 7.497082e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.304084e+02\n",
            "variance........ 2.623947e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.986105e+02\n",
            "variance........ 1.925956e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.239956e+02\n",
            "variance........ 2.536648e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.658658e+02\n",
            "variance........ 3.540128e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.212279e+02\n",
            "variance........ 1.420884e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.306400e+02\n",
            "variance........ 2.978128e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.506391e+01\n",
            "variance........ 6.114995e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.817998e+02\n",
            "variance........ 1.052666e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.914875e+02\n",
            "variance........ 2.727797e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.543533e+02\n",
            "variance........ 1.208038e+03\n",
            "\n",
            "GENERATION: 45\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 175, #Total UniqueCount: 8141\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.482292e+01\n",
            "variance........ 7.585119e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.219365e+02\n",
            "variance........ 1.111936e+04\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.468340e+02\n",
            "variance........ 2.115517e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.246170e+02\n",
            "variance........ 4.743844e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.328708e+01\n",
            "variance........ 4.956422e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 8.978182e+02\n",
            "variance........ 7.307202e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 6.645964e+01\n",
            "variance........ 3.141464e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.386314e+01\n",
            "variance........ 6.111785e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.716365e+02\n",
            "variance........ 1.753273e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 7.630560e+01\n",
            "variance........ 1.197959e+04\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.600830e+02\n",
            "variance........ 6.228509e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.843711e+02\n",
            "variance........ 3.774230e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 3.708032e+01\n",
            "variance........ 8.537910e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 3.809538e+01\n",
            "variance........ 2.270761e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.861105e+01\n",
            "variance........ 4.472700e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.298647e+01\n",
            "variance........ 4.202717e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 1.679593e+01\n",
            "variance........ 1.905777e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.212414e+02\n",
            "variance........ 6.277640e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.948252e+02\n",
            "variance........ 4.181088e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.284135e+02\n",
            "variance........ 6.504876e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.645843e+02\n",
            "variance........ 3.767298e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.223557e+02\n",
            "variance........ 1.363052e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.365552e+02\n",
            "variance........ 5.025289e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.493519e+01\n",
            "variance........ 5.093439e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.817736e+02\n",
            "variance........ 2.785538e+02\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.924762e+02\n",
            "variance........ 1.803750e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.656575e+02\n",
            "variance........ 8.004325e+03\n",
            "\n",
            "Increasing 'max.generations' limit by 45 generations to 90 because the fitness is still improving.\n",
            "\n",
            "\n",
            "GENERATION: 46\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 177, #Total UniqueCount: 8318\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 4.007353e+01\n",
            "variance........ 1.085822e+04\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.336442e+02\n",
            "variance........ 3.650757e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.462123e+02\n",
            "variance........ 3.219599e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.295521e+02\n",
            "variance........ 6.921790e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 6.188147e+01\n",
            "variance........ 8.142329e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.008050e+02\n",
            "variance........ 5.003809e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 8.049258e+01\n",
            "variance........ 1.258571e+04\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.562521e+01\n",
            "variance........ 6.763614e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.618512e+02\n",
            "variance........ 7.704445e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.961572e+01\n",
            "variance........ 4.302599e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.603551e+02\n",
            "variance........ 3.406285e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.810149e+02\n",
            "variance........ 5.557313e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 3.487903e+01\n",
            "variance........ 9.007364e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 4.332027e+01\n",
            "variance........ 6.269863e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 5.344123e+01\n",
            "variance........ 9.339037e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.219201e+01\n",
            "variance........ 4.337145e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.752178e+01\n",
            "variance........ 8.253033e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.284633e+02\n",
            "variance........ 6.864800e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.965474e+02\n",
            "variance........ 3.427724e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.227499e+02\n",
            "variance........ 1.356451e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.652031e+02\n",
            "variance........ 3.895735e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.229050e+02\n",
            "variance........ 3.363427e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.436551e+02\n",
            "variance........ 1.025026e+04\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.932803e+01\n",
            "variance........ 7.367946e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.847889e+02\n",
            "variance........ 1.524927e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.868727e+02\n",
            "variance........ 5.354034e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.619993e+02\n",
            "variance........ 4.804002e+03\n",
            "\n",
            "GENERATION: 47\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 167, #Total UniqueCount: 8485\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.706415e+01\n",
            "variance........ 9.927443e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.337784e+02\n",
            "variance........ 6.132059e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.432230e+02\n",
            "variance........ 4.577624e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.304300e+02\n",
            "variance........ 5.841931e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 4.777139e+01\n",
            "variance........ 1.778461e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.098976e+02\n",
            "variance........ 5.110653e+02\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 7.493223e+01\n",
            "variance........ 7.047757e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.483760e+01\n",
            "variance........ 5.213855e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.713612e+02\n",
            "variance........ 2.165730e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.125420e+01\n",
            "variance........ 1.663988e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.578688e+02\n",
            "variance........ 3.989029e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.843076e+02\n",
            "variance........ 4.382690e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 3.716578e+01\n",
            "variance........ 8.773548e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 5.077736e+01\n",
            "variance........ 1.183866e+04\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 5.560630e+01\n",
            "variance........ 9.504382e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.171527e+01\n",
            "variance........ 5.487339e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.257080e+01\n",
            "variance........ 3.938438e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.245803e+02\n",
            "variance........ 3.209888e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.894783e+02\n",
            "variance........ 3.909035e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.243435e+02\n",
            "variance........ 2.679175e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.654652e+02\n",
            "variance........ 3.167643e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.221937e+02\n",
            "variance........ 1.344342e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.442870e+02\n",
            "variance........ 8.966706e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.108083e+01\n",
            "variance........ 1.703216e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.769787e+02\n",
            "variance........ 3.194534e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.905070e+02\n",
            "variance........ 2.691935e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.549509e+02\n",
            "variance........ 1.904618e+03\n",
            "\n",
            "GENERATION: 48\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 165, #Total UniqueCount: 8650\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.079452e+01\n",
            "variance........ 4.399222e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.368707e+02\n",
            "variance........ 2.432860e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.481765e+02\n",
            "variance........ 2.645985e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.176684e+02\n",
            "variance........ 4.909529e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.825021e+01\n",
            "variance........ 7.467680e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 8.961005e+02\n",
            "variance........ 8.263771e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 7.489157e+01\n",
            "variance........ 6.814708e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.095950e+01\n",
            "variance........ 3.307810e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.677053e+02\n",
            "variance........ 3.500308e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.110879e+01\n",
            "variance........ 3.193098e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.647667e+02\n",
            "variance........ 3.138997e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.837245e+02\n",
            "variance........ 3.547337e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 3.264894e+01\n",
            "variance........ 3.903693e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 3.997403e+01\n",
            "variance........ 4.022184e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 5.254049e+01\n",
            "variance........ 8.549790e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.686462e+01\n",
            "variance........ 1.052984e+04\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.564226e+01\n",
            "variance........ 7.435993e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.319352e+02\n",
            "variance........ 4.365432e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.954651e+02\n",
            "variance........ 4.798490e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.306135e+02\n",
            "variance........ 5.947853e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.611630e+02\n",
            "variance........ 5.445059e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.251600e+02\n",
            "variance........ 2.516245e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.340088e+02\n",
            "variance........ 3.450644e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.193821e+01\n",
            "variance........ 2.850863e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.775381e+02\n",
            "variance........ 1.908104e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.924540e+02\n",
            "variance........ 1.145801e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.616170e+02\n",
            "variance........ 4.504385e+03\n",
            "\n",
            "GENERATION: 49\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 173, #Total UniqueCount: 8823\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.005311e+01\n",
            "variance........ 6.749875e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.412289e+02\n",
            "variance........ 6.459386e+02\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.455560e+02\n",
            "variance........ 3.038348e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.294453e+02\n",
            "variance........ 5.024707e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.725867e+01\n",
            "variance........ 6.842958e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.066837e+02\n",
            "variance........ 1.313367e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 7.629125e+01\n",
            "variance........ 7.850321e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.603383e+01\n",
            "variance........ 7.676825e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.704530e+02\n",
            "variance........ 2.522789e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 7.141741e+01\n",
            "variance........ 8.247365e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.560977e+02\n",
            "variance........ 4.725633e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.884423e+02\n",
            "variance........ 1.942730e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 2.800531e+01\n",
            "variance........ 2.617673e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 4.048040e+01\n",
            "variance........ 3.985624e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.497526e+01\n",
            "variance........ 4.578117e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.265940e+01\n",
            "variance........ 6.058831e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.156617e+01\n",
            "variance........ 5.455589e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.274762e+02\n",
            "variance........ 3.856771e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.932327e+02\n",
            "variance........ 2.898347e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.249799e+02\n",
            "variance........ 4.079958e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.669616e+02\n",
            "variance........ 3.466911e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.261219e+02\n",
            "variance........ 2.837587e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.279934e+02\n",
            "variance........ 1.054464e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.697283e+01\n",
            "variance........ 5.093346e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.818633e+02\n",
            "variance........ 2.676035e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.960075e+02\n",
            "variance........ 9.184828e+02\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.611928e+02\n",
            "variance........ 6.382704e+03\n",
            "\n",
            "GENERATION: 50\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 178, #Total UniqueCount: 9001\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.128325e+01\n",
            "variance........ 4.999402e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.347518e+02\n",
            "variance........ 3.909500e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.480733e+02\n",
            "variance........ 1.902998e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.253053e+02\n",
            "variance........ 4.084315e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.015786e+01\n",
            "variance........ 2.076494e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 8.907547e+02\n",
            "variance........ 1.068761e+04\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 8.045550e+01\n",
            "variance........ 1.232788e+04\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.428159e+01\n",
            "variance........ 4.428203e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.635249e+02\n",
            "variance........ 5.584714e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.115866e+01\n",
            "variance........ 1.448117e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.612131e+02\n",
            "variance........ 3.741158e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.783785e+02\n",
            "variance........ 7.290785e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 3.549312e+01\n",
            "variance........ 8.330338e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 3.547401e+01\n",
            "variance........ 2.245104e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.555696e+01\n",
            "variance........ 4.471720e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.284497e+01\n",
            "variance........ 5.693564e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.015710e+01\n",
            "variance........ 3.874445e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.255658e+02\n",
            "variance........ 4.746067e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.889910e+02\n",
            "variance........ 5.081399e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.278532e+02\n",
            "variance........ 3.960534e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.649393e+02\n",
            "variance........ 5.547406e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.206526e+02\n",
            "variance........ 1.156553e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.300577e+02\n",
            "variance........ 2.063443e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.542026e+01\n",
            "variance........ 5.708959e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.834239e+02\n",
            "variance........ 1.344323e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.849971e+02\n",
            "variance........ 4.506653e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.534985e+02\n",
            "variance........ 8.560531e+02\n",
            "\n",
            "GENERATION: 51\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 171, #Total UniqueCount: 9172\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.755096e+01\n",
            "variance........ 7.916106e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.298472e+02\n",
            "variance........ 6.174853e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.413572e+02\n",
            "variance........ 6.923393e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.294183e+02\n",
            "variance........ 5.364845e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.081527e+01\n",
            "variance........ 2.929718e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 8.948731e+02\n",
            "variance........ 9.001515e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 7.583185e+01\n",
            "variance........ 6.678299e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.039129e+01\n",
            "variance........ 3.513424e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.590879e+02\n",
            "variance........ 6.672040e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.444218e+01\n",
            "variance........ 2.617346e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.609328e+02\n",
            "variance........ 6.612200e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.765031e+02\n",
            "variance........ 8.793501e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 3.059990e+01\n",
            "variance........ 3.961581e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 3.982707e+01\n",
            "variance........ 3.794174e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.749491e+01\n",
            "variance........ 4.818489e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.843141e+01\n",
            "variance........ 7.069467e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 1.772907e+01\n",
            "variance........ 3.514177e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.235471e+02\n",
            "variance........ 4.981252e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.910505e+02\n",
            "variance........ 4.332463e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.223366e+02\n",
            "variance........ 2.139014e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.647971e+02\n",
            "variance........ 3.593278e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.229779e+02\n",
            "variance........ 1.289123e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.296164e+02\n",
            "variance........ 1.922675e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 8.430347e+00\n",
            "variance........ 1.512879e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.827982e+02\n",
            "variance........ 2.105267e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.884546e+02\n",
            "variance........ 3.623588e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.541932e+02\n",
            "variance........ 1.068564e+03\n",
            "\n",
            "GENERATION: 52\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 161, #Total UniqueCount: 9333\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.400045e+01\n",
            "variance........ 6.975781e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.332052e+02\n",
            "variance........ 6.097285e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.509356e+02\n",
            "variance........ 1.051422e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.285333e+02\n",
            "variance........ 6.318418e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.888060e+01\n",
            "variance........ 1.148984e+04\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 8.975850e+02\n",
            "variance........ 6.910954e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 6.601475e+01\n",
            "variance........ 1.469755e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 7.665135e+01\n",
            "variance........ 2.156194e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.725157e+02\n",
            "variance........ 1.991877e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.348204e+01\n",
            "variance........ 3.480713e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.638342e+02\n",
            "variance........ 4.925879e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.845871e+02\n",
            "variance........ 4.710560e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 2.947751e+01\n",
            "variance........ 3.540893e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 4.674179e+01\n",
            "variance........ 9.719781e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.664499e+01\n",
            "variance........ 5.684108e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.212379e+01\n",
            "variance........ 5.110642e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.392388e+01\n",
            "variance........ 6.399764e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.326511e+02\n",
            "variance........ 2.735963e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.893762e+02\n",
            "variance........ 5.765855e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.272927e+02\n",
            "variance........ 3.836939e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.635525e+02\n",
            "variance........ 5.256417e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.233579e+02\n",
            "variance........ 2.022022e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.294190e+02\n",
            "variance........ 1.579353e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 8.524746e+00\n",
            "variance........ 1.376919e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.846781e+02\n",
            "variance........ 3.123969e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.899682e+02\n",
            "variance........ 5.090925e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.610767e+02\n",
            "variance........ 6.270813e+03\n",
            "\n",
            "GENERATION: 53\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 163, #Total UniqueCount: 9496\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.418745e+01\n",
            "variance........ 7.481995e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.348435e+02\n",
            "variance........ 3.814168e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.446439e+02\n",
            "variance........ 3.257612e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.179599e+02\n",
            "variance........ 3.017673e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.159785e+01\n",
            "variance........ 4.774634e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.027166e+02\n",
            "variance........ 5.055912e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 7.176778e+01\n",
            "variance........ 3.963421e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 7.629110e+01\n",
            "variance........ 9.230842e+02\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.683424e+02\n",
            "variance........ 3.340394e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.282464e+01\n",
            "variance........ 3.088827e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.656451e+02\n",
            "variance........ 3.616683e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.863610e+02\n",
            "variance........ 3.663619e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 2.740478e+01\n",
            "variance........ 3.148951e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 3.728734e+01\n",
            "variance........ 2.939436e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.766338e+01\n",
            "variance........ 4.429018e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 2.883618e+01\n",
            "variance........ 2.691717e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.202326e+01\n",
            "variance........ 6.239840e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.305065e+02\n",
            "variance........ 2.003540e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 7.002874e+02\n",
            "variance........ 2.385319e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.273933e+02\n",
            "variance........ 5.156487e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.720334e+02\n",
            "variance........ 9.141640e+02\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.255792e+02\n",
            "variance........ 3.312517e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.315331e+02\n",
            "variance........ 2.418048e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 9.383230e+00\n",
            "variance........ 1.910594e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.816274e+02\n",
            "variance........ 1.235744e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.864328e+02\n",
            "variance........ 4.724484e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.561844e+02\n",
            "variance........ 1.871141e+03\n",
            "\n",
            "GENERATION: 54\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 165, #Total UniqueCount: 9661\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.783642e+01\n",
            "variance........ 8.281462e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.317197e+02\n",
            "variance........ 4.883691e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.357822e+02\n",
            "variance........ 7.023830e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.288615e+02\n",
            "variance........ 6.979532e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 6.036230e+01\n",
            "variance........ 8.127526e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 8.928680e+02\n",
            "variance........ 8.940268e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 7.009027e+01\n",
            "variance........ 2.874662e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 7.821193e+01\n",
            "variance........ 2.104746e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.658081e+02\n",
            "variance........ 5.399068e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.978256e+01\n",
            "variance........ 7.651845e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.577019e+02\n",
            "variance........ 5.270587e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.848534e+02\n",
            "variance........ 2.302036e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 4.164866e+01\n",
            "variance........ 1.192544e+04\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 3.837114e+01\n",
            "variance........ 1.672540e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.776875e+01\n",
            "variance........ 5.046552e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 4.558019e+01\n",
            "variance........ 1.140059e+04\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.384534e+01\n",
            "variance........ 5.716888e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.186903e+02\n",
            "variance........ 5.106047e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.911940e+02\n",
            "variance........ 4.063974e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.246927e+02\n",
            "variance........ 4.311813e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.619370e+02\n",
            "variance........ 7.668879e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.258889e+02\n",
            "variance........ 2.824184e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.392178e+02\n",
            "variance........ 7.696878e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.320877e+01\n",
            "variance........ 2.931581e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.817279e+02\n",
            "variance........ 6.196787e+02\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.830638e+02\n",
            "variance........ 5.332030e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.598926e+02\n",
            "variance........ 4.377871e+03\n",
            "\n",
            "GENERATION: 55\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 168, #Total UniqueCount: 9829\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.444327e+01\n",
            "variance........ 6.783458e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.271370e+02\n",
            "variance........ 8.879983e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.336099e+02\n",
            "variance........ 9.527240e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.250490e+02\n",
            "variance........ 4.670894e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.672269e+01\n",
            "variance........ 6.382457e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 8.885094e+02\n",
            "variance........ 1.306114e+04\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 6.368482e+01\n",
            "variance........ 1.134971e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.651943e+01\n",
            "variance........ 6.143431e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.705679e+02\n",
            "variance........ 1.668764e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 7.211128e+01\n",
            "variance........ 6.470392e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.615607e+02\n",
            "variance........ 4.568252e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.833413e+02\n",
            "variance........ 5.144880e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 4.468536e+01\n",
            "variance........ 1.383375e+04\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 3.720811e+01\n",
            "variance........ 1.578783e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.915209e+01\n",
            "variance........ 7.048828e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.202175e+01\n",
            "variance........ 4.463272e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.411346e+01\n",
            "variance........ 6.575942e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.212380e+02\n",
            "variance........ 4.779610e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.987437e+02\n",
            "variance........ 3.875795e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.289404e+02\n",
            "variance........ 5.463970e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.620187e+02\n",
            "variance........ 4.536410e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.214720e+02\n",
            "variance........ 1.092695e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.324774e+02\n",
            "variance........ 3.415163e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.142666e+01\n",
            "variance........ 2.260197e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.838081e+02\n",
            "variance........ 2.460330e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.897442e+02\n",
            "variance........ 3.486552e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.593654e+02\n",
            "variance........ 3.792014e+03\n",
            "\n",
            "GENERATION: 56\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 173, #Total UniqueCount: 10002\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.191328e+01\n",
            "variance........ 6.909120e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.393659e+02\n",
            "variance........ 1.772486e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.450934e+02\n",
            "variance........ 4.705762e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.362053e+02\n",
            "variance........ 3.651477e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 5.296478e+01\n",
            "variance........ 4.234258e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.084515e+02\n",
            "variance........ 1.180753e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 6.871770e+01\n",
            "variance........ 4.940410e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.202978e+01\n",
            "variance........ 4.417825e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.726735e+02\n",
            "variance........ 1.260190e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 7.145233e+01\n",
            "variance........ 8.662257e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.458494e+02\n",
            "variance........ 6.518724e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.885688e+02\n",
            "variance........ 3.452485e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 3.297219e+01\n",
            "variance........ 7.429162e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 4.011512e+01\n",
            "variance........ 3.784051e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 3.932505e+01\n",
            "variance........ 1.077942e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.377809e+01\n",
            "variance........ 7.217920e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 1.292970e+01\n",
            "variance........ 1.397208e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.262487e+02\n",
            "variance........ 3.017850e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.905161e+02\n",
            "variance........ 3.209183e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.243787e+02\n",
            "variance........ 3.357547e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.606916e+02\n",
            "variance........ 6.701887e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.266976e+02\n",
            "variance........ 2.442860e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.289491e+02\n",
            "variance........ 1.754228e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 1.216441e+01\n",
            "variance........ 3.385040e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.842805e+02\n",
            "variance........ 9.036447e+02\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.900623e+02\n",
            "variance........ 3.231697e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.593385e+02\n",
            "variance........ 4.507090e+03\n",
            "\n",
            "GENERATION: 57\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 162, #Total UniqueCount: 10164\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 2.869664e+01\n",
            "variance........ 3.432644e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.371628e+02\n",
            "variance........ 3.688034e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.470082e+02\n",
            "variance........ 1.929013e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.226435e+02\n",
            "variance........ 4.101919e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 4.737503e+01\n",
            "variance........ 1.695288e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.054319e+02\n",
            "variance........ 2.696773e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 6.501322e+01\n",
            "variance........ 1.343153e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 7.443721e+01\n",
            "variance........ 4.095881e+02\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.668155e+02\n",
            "variance........ 3.240422e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 6.886848e+01\n",
            "variance........ 7.126854e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.590918e+02\n",
            "variance........ 4.426411e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.888393e+02\n",
            "variance........ 2.496271e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 2.328296e+01\n",
            "variance........ 8.610222e+02\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 4.221815e+01\n",
            "variance........ 4.068947e+03\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.670866e+01\n",
            "variance........ 5.551886e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 2.971327e+01\n",
            "variance........ 4.075103e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 1.505089e+01\n",
            "variance........ 2.863975e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.296265e+02\n",
            "variance........ 1.615349e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 7.022830e+02\n",
            "variance........ 2.043921e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.241003e+02\n",
            "variance........ 2.166381e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.600158e+02\n",
            "variance........ 6.977112e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.252879e+02\n",
            "variance........ 2.983659e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.313803e+02\n",
            "variance........ 3.054947e+03\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 7.965773e+00\n",
            "variance........ 2.090512e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.802018e+02\n",
            "variance........ 1.589789e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.897475e+02\n",
            "variance........ 6.519736e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.525549e+02\n",
            "variance........ 2.618016e+02\n",
            "\n",
            "GENERATION: 58\n",
            "Lexical Fit..... 0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "#unique......... 161, #Total UniqueCount: 10325\n",
            "var 1:\n",
            "best............ 1.912430e+01\n",
            "mean............ 3.473427e+01\n",
            "variance........ 8.018029e+03\n",
            "var 2:\n",
            "best............ 9.447489e+02\n",
            "mean............ 9.365343e+02\n",
            "variance........ 3.850990e+03\n",
            "var 3:\n",
            "best............ 8.535139e+02\n",
            "mean............ 8.422921e+02\n",
            "variance........ 3.807471e+03\n",
            "var 4:\n",
            "best............ 3.030556e+02\n",
            "mean............ 3.270758e+02\n",
            "variance........ 4.526058e+03\n",
            "var 5:\n",
            "best............ 4.058719e+01\n",
            "mean............ 4.967017e+01\n",
            "variance........ 2.522000e+03\n",
            "var 6:\n",
            "best............ 9.123063e+02\n",
            "mean............ 9.020912e+02\n",
            "variance........ 4.642977e+03\n",
            "var 7:\n",
            "best............ 6.030979e+01\n",
            "mean............ 8.086175e+01\n",
            "variance........ 9.986970e+03\n",
            "var 8:\n",
            "best............ 7.178598e+01\n",
            "mean............ 8.137566e+01\n",
            "variance........ 4.070446e+03\n",
            "var 9:\n",
            "best............ 7.748822e+02\n",
            "mean............ 7.735449e+02\n",
            "variance........ 1.335203e+03\n",
            "var 10:\n",
            "best............ 5.572047e+01\n",
            "mean............ 7.314163e+01\n",
            "variance........ 7.864357e+03\n",
            "var 11:\n",
            "best............ 5.796664e+02\n",
            "mean............ 5.550619e+02\n",
            "variance........ 4.730622e+03\n",
            "var 12:\n",
            "best............ 8.943485e+02\n",
            "mean............ 8.788195e+02\n",
            "variance........ 8.633191e+03\n",
            "var 13:\n",
            "best............ 1.974208e+01\n",
            "mean............ 2.551350e+01\n",
            "variance........ 1.638656e+03\n",
            "var 14:\n",
            "best............ 3.116606e+01\n",
            "mean............ 3.240749e+01\n",
            "variance........ 4.213571e+02\n",
            "var 15:\n",
            "best............ 3.539334e+01\n",
            "mean............ 4.918062e+01\n",
            "variance........ 7.383937e+03\n",
            "var 16:\n",
            "best............ 2.107974e+01\n",
            "mean............ 3.108102e+01\n",
            "variance........ 4.061106e+03\n",
            "var 17:\n",
            "best............ 9.420434e+00\n",
            "mean............ 2.408360e+01\n",
            "variance........ 7.180934e+03\n",
            "var 18:\n",
            "best............ 8.316791e+02\n",
            "mean............ 8.193265e+02\n",
            "variance........ 7.492056e+03\n",
            "var 19:\n",
            "best............ 7.124023e+02\n",
            "mean............ 6.940118e+02\n",
            "variance........ 2.993173e+03\n",
            "var 20:\n",
            "best............ 2.174136e+02\n",
            "mean............ 2.236053e+02\n",
            "variance........ 3.248626e+03\n",
            "var 21:\n",
            "best............ 9.749560e+02\n",
            "mean............ 9.604548e+02\n",
            "variance........ 6.648885e+03\n",
            "var 22:\n",
            "best............ 2.151665e+02\n",
            "mean............ 2.285413e+02\n",
            "variance........ 3.267608e+03\n",
            "var 23:\n",
            "best............ 1.242981e+02\n",
            "mean............ 1.273650e+02\n",
            "variance........ 8.666112e+02\n",
            "var 24:\n",
            "best............ 3.827611e+00\n",
            "mean............ 2.138763e+01\n",
            "variance........ 7.741616e+03\n",
            "var 25:\n",
            "best............ 4.812287e+02\n",
            "mean............ 4.823194e+02\n",
            "variance........ 3.865769e+03\n",
            "var 26:\n",
            "best............ 7.975748e+02\n",
            "mean............ 7.889376e+02\n",
            "variance........ 3.657364e+03\n",
            "var 27:\n",
            "best............ 1.503871e+02\n",
            "mean............ 1.674795e+02\n",
            "variance........ 7.254472e+03\n",
            "\n",
            "'wait.generations' limit reached.\n",
            "No significant improvement in 25 generations.\n",
            "\n",
            "Solution Lexical Fitness Value:\n",
            "0.000000e+00  1.110223e-16  1.110223e-16  3.552714e-15  3.996803e-15  3.996803e-15  4.662937e-15  1.121325e-14  3.497203e-14  6.430412e-13  1.956213e-12  4.355183e-12  2.412537e-09  6.429247e-09  3.410062e-08  8.448847e-08  1.471426e-07  1.609622e-07  4.252923e-07  4.252923e-07  4.252923e-07  5.234946e-07  9.257039e-07  9.257039e-07  9.257039e-07  9.257039e-07  1.147034e-06  1.695041e-06  1.974618e-06  3.357827e-06  6.055975e-06  2.104617e-05  2.350406e-05  2.800684e-05  3.340340e-05  3.340340e-05  3.340340e-05  6.014594e-05  4.089972e-04  6.398286e-04  9.661506e-04  1.267778e-03  2.165431e-03  3.624699e-03  9.558856e-03  9.558856e-03  1.505956e-02  1.505956e-02  1.900018e-01  2.752485e-01  3.497725e-01  3.839762e-01  4.149863e-01  4.619689e-01  \n",
            "\n",
            "Parameters at the Solution:\n",
            "\n",
            " X[ 1] :\t1.912430e+01\n",
            " X[ 2] :\t9.447489e+02\n",
            " X[ 3] :\t8.535139e+02\n",
            " X[ 4] :\t3.030556e+02\n",
            " X[ 5] :\t4.058719e+01\n",
            " X[ 6] :\t9.123063e+02\n",
            " X[ 7] :\t6.030979e+01\n",
            " X[ 8] :\t7.178598e+01\n",
            " X[ 9] :\t7.748822e+02\n",
            " X[10] :\t5.572047e+01\n",
            " X[11] :\t5.796664e+02\n",
            " X[12] :\t8.943485e+02\n",
            " X[13] :\t1.974208e+01\n",
            " X[14] :\t3.116606e+01\n",
            " X[15] :\t3.539334e+01\n",
            " X[16] :\t2.107974e+01\n",
            " X[17] :\t9.420434e+00\n",
            " X[18] :\t8.316791e+02\n",
            " X[19] :\t7.124023e+02\n",
            " X[20] :\t2.174136e+02\n",
            " X[21] :\t9.749560e+02\n",
            " X[22] :\t2.151665e+02\n",
            " X[23] :\t1.242981e+02\n",
            " X[24] :\t3.827611e+00\n",
            " X[25] :\t4.812287e+02\n",
            " X[26] :\t7.975748e+02\n",
            " X[27] :\t1.503871e+02\n",
            "\n",
            "Solution Found Generation 32\n",
            "Number of Generations Run 58\n",
            "\n",
            "Fri Dec 20 10:20:42 2019\n",
            "Total run time : 0 hours 4 minutes and 22 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdSLwPfSNHl7"
      },
      "source": [
        "mout = Match(Tr = Tr, X = X.all, M=1, Weight.matrix = genout)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jN30IIfGj6cX",
        "outputId": "f6123ab0-183a-47b4-f948-6cb557c21e45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        }
      },
      "source": [
        "summary(mout)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Estimate...  0 \n",
            "SE.........  0 \n",
            "T-stat.....  NaN \n",
            "p.val......  NA \n",
            "\n",
            "Original number of observations..............  130 \n",
            "Original number of treated obs...............  99 \n",
            "Matched number of observations...............  99 \n",
            "Matched number of observations  (unweighted).  99 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zpGiES4hfvE",
        "outputId": "258b1828-2b76-4c83-fbc3-a53f1e86e716",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "mb <- MatchBalance(after.data$treat ~ after.data$state + after.data$pcgsdp + after.data$pfemale + after.data$plit + after.data$pwlit + after.data$prural, match.out=mout, nboots=500)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "***** (V1) after.data$state *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........     6.9091 \t \t     6.9091 \n",
            "mean control..........     8.5484 \t \t     9.6566 \n",
            "std mean diff.........    -32.599 \t \t    -54.636 \n",
            "\n",
            "mean raw eQQ diff.....     2.1613 \t \t     2.7475 \n",
            "med  raw eQQ diff.....          1 \t \t          1 \n",
            "max  raw eQQ diff.....          9 \t \t          9 \n",
            "\n",
            "mean eCDF diff........    0.13851 \t \t    0.15354 \n",
            "med  eCDF diff........    0.12284 \t \t    0.13131 \n",
            "max  eCDF diff........    0.33464 \t \t    0.39394 \n",
            "\n",
            "var ratio (Tr/Co).....    0.65871 \t \t    0.80819 \n",
            "T-test p-value........    0.18686 \t \t 2.3504e-05 \n",
            "KS Bootstrap p-value..      0.002 \t \t < 2.22e-16 \n",
            "KS Naive p-value......   0.010111 \t \t 4.2529e-07 \n",
            "KS Statistic..........    0.33464 \t \t    0.39394 \n",
            "\n",
            "\n",
            "***** (V2) after.data$pcgsdp *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........     2.1787 \t \t     2.1787 \n",
            "mean control..........      1.164 \t \t     1.5901 \n",
            "std mean diff.........     143.57 \t \t     83.278 \n",
            "\n",
            "mean raw eQQ diff.....     1.0135 \t \t    0.58905 \n",
            "med  raw eQQ diff.....    0.93248 \t \t    0.46903 \n",
            "max  raw eQQ diff.....     2.3154 \t \t     2.3154 \n",
            "\n",
            "mean eCDF diff........    0.42017 \t \t     0.2749 \n",
            "med  eCDF diff........    0.42929 \t \t    0.26263 \n",
            "max  eCDF diff........    0.75562 \t \t    0.61616 \n",
            "\n",
            "var ratio (Tr/Co).....     4.1453 \t \t     4.8249 \n",
            "T-test p-value........ < 2.22e-16 \t \t 3.9968e-15 \n",
            "KS Bootstrap p-value.. < 2.22e-16 \t \t < 2.22e-16 \n",
            "KS Naive p-value...... 5.9952e-14 \t \t < 2.22e-16 \n",
            "KS Statistic..........    0.75562 \t \t    0.61616 \n",
            "\n",
            "\n",
            "***** (V3) after.data$pfemale *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.93402 \t \t    0.93402 \n",
            "mean control..........    0.91168 \t \t    0.93679 \n",
            "std mean diff.........     54.573 \t \t    -6.7665 \n",
            "\n",
            "mean raw eQQ diff.....   0.029935 \t \t   0.010638 \n",
            "med  raw eQQ diff.....   0.022884 \t \t  0.0087351 \n",
            "max  raw eQQ diff.....   0.054991 \t \t   0.033243 \n",
            "\n",
            "mean eCDF diff........    0.20641 \t \t   0.077882 \n",
            "med  eCDF diff........    0.24373 \t \t   0.070707 \n",
            "max  eCDF diff........    0.41219 \t \t    0.22222 \n",
            "\n",
            "var ratio (Tr/Co).....      4.068 \t \t     1.2681 \n",
            "T-test p-value........ 9.4019e-05 \t \t    0.34977 \n",
            "KS Bootstrap p-value..      0.002 \t \t      0.002 \n",
            "KS Naive p-value...... 0.00039652 \t \t    0.01506 \n",
            "KS Statistic..........    0.41219 \t \t    0.22222 \n",
            "\n",
            "\n",
            "***** (V4) after.data$plit *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.55776 \t \t    0.55776 \n",
            "mean control..........    0.44882 \t \t    0.51977 \n",
            "std mean diff.........     131.34 \t \t     45.798 \n",
            "\n",
            "mean raw eQQ diff.....    0.10727 \t \t   0.038961 \n",
            "med  raw eQQ diff.....    0.10429 \t \t    0.03291 \n",
            "max  raw eQQ diff.....    0.17097 \t \t     0.1375 \n",
            "\n",
            "mean eCDF diff........    0.35533 \t \t    0.13943 \n",
            "med  eCDF diff........    0.35761 \t \t    0.13131 \n",
            "max  eCDF diff........    0.63245 \t \t    0.38384 \n",
            "\n",
            "var ratio (Tr/Co).....     2.1262 \t \t     1.6804 \n",
            "T-test p-value........ 4.4098e-12 \t \t 1.4714e-07 \n",
            "KS Bootstrap p-value.. < 2.22e-16 \t \t < 2.22e-16 \n",
            "KS Naive p-value...... 1.7559e-09 \t \t  9.257e-07 \n",
            "KS Statistic..........    0.63245 \t \t    0.38384 \n",
            "\n",
            "\n",
            "***** (V5) after.data$pwlit *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.46168 \t \t    0.46168 \n",
            "mean control..........    0.34436 \t \t    0.42484 \n",
            "std mean diff.........     119.33 \t \t     37.462 \n",
            "\n",
            "mean raw eQQ diff.....    0.11511 \t \t   0.039307 \n",
            "med  raw eQQ diff.....    0.12534 \t \t   0.028737 \n",
            "max  raw eQQ diff.....    0.17462 \t \t    0.16177 \n",
            "\n",
            "mean eCDF diff........    0.32959 \t \t    0.12244 \n",
            "med  eCDF diff........    0.35761 \t \t    0.11111 \n",
            "max  eCDF diff........     0.5334 \t \t    0.38384 \n",
            "\n",
            "var ratio (Tr/Co).....     1.9776 \t \t     1.7505 \n",
            "T-test p-value........ 2.8749e-10 \t \t  1.695e-06 \n",
            "KS Bootstrap p-value.. < 2.22e-16 \t \t < 2.22e-16 \n",
            "KS Naive p-value......  1.009e-06 \t \t  9.257e-07 \n",
            "KS Statistic..........     0.5334 \t \t    0.38384 \n",
            "\n",
            "\n",
            "***** (V6) after.data$prural *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.74033 \t \t    0.74033 \n",
            "mean control..........    0.81251 \t \t    0.73625 \n",
            "std mean diff.........    -66.339 \t \t     3.7538 \n",
            "\n",
            "mean raw eQQ diff.....   0.081253 \t \t   0.030271 \n",
            "med  raw eQQ diff.....   0.074682 \t \t   0.023051 \n",
            "max  raw eQQ diff.....    0.21918 \t \t    0.10901 \n",
            "\n",
            "mean eCDF diff........    0.25724 \t \t   0.080902 \n",
            "med  eCDF diff........    0.23656 \t \t   0.070707 \n",
            "max  eCDF diff........     0.5536 \t \t    0.23232 \n",
            "\n",
            "var ratio (Tr/Co).....     3.2615 \t \t     1.1261 \n",
            "T-test p-value........ 9.2926e-06 \t \t    0.41499 \n",
            "KS Bootstrap p-value.. < 2.22e-16 \t \t      0.006 \n",
            "KS Naive p-value...... 3.1028e-07 \t \t  0.0095589 \n",
            "KS Statistic..........     0.5536 \t \t    0.23232 \n",
            "\n",
            "\n",
            "Before Matching Minimum p.value: < 2.22e-16 \n",
            "Variable Name(s): after.data$pcgsdp after.data$plit after.data$pwlit after.data$prural  Number(s): 2 4 5 6 \n",
            "\n",
            "After Matching Minimum p.value: < 2.22e-16 \n",
            "Variable Name(s): after.data$state after.data$pcgsdp after.data$plit after.data$pwlit  Number(s): 1 2 4 5 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uI9pBTD4ee2G",
        "outputId": "51f6bd16-3d97-469e-b9e3-f173e05dca5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        }
      },
      "source": [
        "mout = Match(Y=Y, Tr = Tr, X = X.all, M=1, Weight.matrix = genout)\n",
        "summary(mout)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Estimate...  0.054215 \n",
            "AI SE......  0.03617 \n",
            "T-stat.....  1.4989 \n",
            "p.val......  0.1339 \n",
            "\n",
            "Original number of observations..............  130 \n",
            "Original number of treated obs...............  99 \n",
            "Matched number of observations...............  99 \n",
            "Matched number of observations  (unweighted).  99 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9CMiwkn_QI9F",
        "outputId": "6e575c20-f1fe-4a50-827e-0d231b5bb7e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "attach(before.data)\n",
        "X = cbind(state, pcgsdp, pfemale, plit, pwlit, prural)\n",
        "X.all = cbind(state, pcgsdp, pfemale, plit, pwlit, prural, state^2, pcgsdp^2, pfemale^2, plit^2, pwlit^2, prural^2, \n",
        "          state*pcgsdp, state*pfemale, state*plit, state*pwlit, state*prural,\n",
        "          pcgsdp*pfemale, pcgsdp*plit, pcgsdp*pwlit, pcgsdp*prural,\n",
        "          pfemale*plit, pfemale*pwlit, pfemale*prural, plit*pwlit, plit*prural, pwlit*prural)\n",
        "Tr = treat\n",
        "Y = pcr_womtot\n",
        "detach(before.data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The following object is masked _by_ .GlobalEnv:\n",
            "\n",
            "    X\n",
            "\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITPr_IO_QP_Y",
        "outputId": "f48c8cca-4bfa-4039-e42e-9c9cacf7630f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "genout = GenMatch(Tr = Tr, X = X.all, M=1, pop.size = 250,\n",
        "                  max.generations = 20, wait.generations = 25)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Fri Dec 20 10:20:43 2019\n",
            "Domains:\n",
            " 0.000000e+00   <=  X1   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X2   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X3   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X4   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X5   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X6   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X7   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X8   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X9   <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X10  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X11  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X12  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X13  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X14  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X15  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X16  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X17  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X18  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X19  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X20  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X21  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X22  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X23  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X24  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X25  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X26  <=    1.000000e+03 \n",
            " 0.000000e+00   <=  X27  <=    1.000000e+03 \n",
            "\n",
            "Data Type: Floating Point\n",
            "Operators (code number, name, population) \n",
            "\t(1) Cloning........................... \t30\n",
            "\t(2) Uniform Mutation.................. \t31\n",
            "\t(3) Boundary Mutation................. \t31\n",
            "\t(4) Non-Uniform Mutation.............. \t31\n",
            "\t(5) Polytope Crossover................ \t31\n",
            "\t(6) Simple Crossover.................. \t32\n",
            "\t(7) Whole Non-Uniform Mutation........ \t31\n",
            "\t(8) Heuristic Crossover............... \t32\n",
            "\t(9) Local-Minimum Crossover........... \t0\n",
            "\n",
            "SOFT Maximum Number of Generations: 20\n",
            "Maximum Nonchanging Generations: 25\n",
            "Population size       : 250\n",
            "Convergence Tolerance: 1.000000e-03\n",
            "\n",
            "Not Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation.\n",
            "Not Checking Gradients before Stopping.\n",
            "Using Out of Bounds Individuals.\n",
            "\n",
            "Maximization Problem.\n",
            "GENERATION: 0 (initializing the population)\n",
            "Lexical Fit..... 8.852150e-08  9.431144e-08  1.169723e-07  1.230700e-07  1.722511e-07  2.268592e-07  2.733605e-07  4.413216e-07  5.728545e-07  7.872736e-07  7.889980e-07  1.040951e-06  1.902085e-06  2.142962e-06  4.886798e-06  5.022626e-06  7.041035e-06  1.035134e-05  1.051435e-05  1.187738e-05  2.872238e-05  4.222110e-05  2.123067e-02  2.123067e-02  2.123067e-02  5.035351e-02  5.035351e-02  5.035351e-02  5.035351e-02  5.035351e-02  5.035351e-02  5.035351e-02  5.035351e-02  1.090329e-01  1.090329e-01  1.090329e-01  1.090329e-01  1.090329e-01  1.090329e-01  1.090329e-01  1.090329e-01  1.090329e-01  1.090329e-01  1.090329e-01  2.153739e-01  2.153739e-01  2.153739e-01  3.794570e-01  4.083897e-01  4.558007e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  \n",
            "#unique......... 250, #Total UniqueCount: 250\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.327855e+02\n",
            "variance........ 9.030723e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.780133e+02\n",
            "variance........ 8.681165e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.097220e+02\n",
            "variance........ 8.329315e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.927830e+02\n",
            "variance........ 8.227885e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.770400e+02\n",
            "variance........ 8.078711e+04\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.786488e+02\n",
            "variance........ 8.905253e+04\n",
            "var 7:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.940981e+02\n",
            "variance........ 8.093168e+04\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.046726e+02\n",
            "variance........ 8.354808e+04\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.070715e+02\n",
            "variance........ 8.229971e+04\n",
            "var 10:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.781169e+02\n",
            "variance........ 8.663116e+04\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.791785e+02\n",
            "variance........ 9.528998e+04\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.294383e+02\n",
            "variance........ 8.608798e+04\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.726052e+02\n",
            "variance........ 8.419930e+04\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.805445e+02\n",
            "variance........ 8.321999e+04\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.627961e+02\n",
            "variance........ 7.767079e+04\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.981668e+02\n",
            "variance........ 8.220074e+04\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.970537e+02\n",
            "variance........ 8.206096e+04\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.021098e+02\n",
            "variance........ 8.165988e+04\n",
            "var 19:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.853210e+02\n",
            "variance........ 8.635633e+04\n",
            "var 20:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.137982e+02\n",
            "variance........ 7.891104e+04\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.365555e+02\n",
            "variance........ 8.474674e+04\n",
            "var 22:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.820234e+02\n",
            "variance........ 8.181185e+04\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.962391e+02\n",
            "variance........ 8.385563e+04\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.026413e+02\n",
            "variance........ 8.272374e+04\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.226735e+02\n",
            "variance........ 7.607758e+04\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.187625e+02\n",
            "variance........ 8.169717e+04\n",
            "var 27:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.599821e+02\n",
            "variance........ 8.447027e+04\n",
            "\n",
            "GENERATION: 1\n",
            "Lexical Fit..... 2.873262e-03  9.362375e-03  9.362707e-03  1.015017e-02  1.125487e-02  1.203164e-02  1.276111e-02  1.713341e-02  2.244750e-02  2.333791e-02  2.432738e-02  2.458772e-02  2.648772e-02  2.958072e-02  3.689190e-02  4.003545e-02  4.664455e-02  4.805574e-02  5.035351e-02  5.035351e-02  5.579899e-02  6.245153e-02  6.651493e-02  7.201880e-02  1.090329e-01  1.090329e-01  1.090329e-01  1.090329e-01  1.090329e-01  2.136543e-01  2.153739e-01  2.153739e-01  2.180892e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.833166e-01  7.350992e-01  7.522752e-01  7.588957e-01  \n",
            "#unique......... 185, #Total UniqueCount: 435\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.937819e+02\n",
            "variance........ 1.058240e+05\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.204672e+02\n",
            "variance........ 8.765716e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.935469e+02\n",
            "variance........ 6.781148e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.525599e+02\n",
            "variance........ 5.629132e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.485351e+02\n",
            "variance........ 9.795313e+04\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.603354e+02\n",
            "variance........ 6.781277e+04\n",
            "var 7:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.492251e+02\n",
            "variance........ 5.968245e+04\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.132224e+02\n",
            "variance........ 7.293865e+04\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.815778e+02\n",
            "variance........ 7.700228e+04\n",
            "var 10:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.975100e+02\n",
            "variance........ 5.997673e+04\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.824448e+02\n",
            "variance........ 8.592386e+04\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.779212e+02\n",
            "variance........ 1.001740e+05\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.019268e+02\n",
            "variance........ 8.297027e+04\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.941728e+02\n",
            "variance........ 9.823556e+04\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.054319e+02\n",
            "variance........ 7.764611e+04\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.444973e+02\n",
            "variance........ 8.205513e+04\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.489342e+02\n",
            "variance........ 1.004237e+05\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.046139e+02\n",
            "variance........ 1.087659e+05\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 2.080046e+02\n",
            "variance........ 5.798660e+04\n",
            "var 20:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.010364e+02\n",
            "variance........ 7.865511e+04\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.929586e+02\n",
            "variance........ 6.094663e+04\n",
            "var 22:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.716133e+02\n",
            "variance........ 6.764688e+04\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.540562e+02\n",
            "variance........ 9.173700e+04\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.222731e+02\n",
            "variance........ 5.855113e+04\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.938160e+02\n",
            "variance........ 1.063512e+05\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.681507e+02\n",
            "variance........ 8.592345e+04\n",
            "var 27:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.370839e+02\n",
            "variance........ 1.399110e+05\n",
            "\n",
            "GENERATION: 2\n",
            "Lexical Fit..... 2.873262e-03  9.362375e-03  9.362707e-03  1.015017e-02  1.125487e-02  1.203164e-02  1.276111e-02  1.713341e-02  2.244750e-02  2.333791e-02  2.432738e-02  2.458772e-02  2.648772e-02  2.958072e-02  3.689190e-02  4.003545e-02  4.664455e-02  4.805574e-02  5.035351e-02  5.035351e-02  5.579899e-02  6.245153e-02  6.651493e-02  7.201880e-02  1.090329e-01  1.090329e-01  1.090329e-01  1.090329e-01  1.090329e-01  2.136543e-01  2.153739e-01  2.153739e-01  2.180892e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.833166e-01  7.350992e-01  7.522752e-01  7.588957e-01  \n",
            "#unique......... 154, #Total UniqueCount: 589\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.356641e+02\n",
            "variance........ 6.878212e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.154837e+02\n",
            "variance........ 5.470714e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.248346e+02\n",
            "variance........ 6.077459e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.035627e+02\n",
            "variance........ 4.413898e+04\n",
            "var 5:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.128216e+02\n",
            "variance........ 5.640503e+04\n",
            "var 6:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.033057e+02\n",
            "variance........ 5.032302e+04\n",
            "var 7:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.182144e+02\n",
            "variance........ 5.436732e+04\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.217056e+02\n",
            "variance........ 6.149353e+04\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.311685e+02\n",
            "variance........ 6.266986e+04\n",
            "var 10:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.293960e+02\n",
            "variance........ 6.162639e+04\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.190831e+02\n",
            "variance........ 5.787755e+04\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.385531e+02\n",
            "variance........ 7.529049e+04\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.227779e+02\n",
            "variance........ 6.066853e+04\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.360653e+02\n",
            "variance........ 6.895033e+04\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.086367e+02\n",
            "variance........ 5.948544e+04\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.094044e+02\n",
            "variance........ 5.047584e+04\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.369733e+02\n",
            "variance........ 7.144215e+04\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.397687e+02\n",
            "variance........ 6.938238e+04\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 2.697755e+02\n",
            "variance........ 6.424433e+04\n",
            "var 20:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.233591e+02\n",
            "variance........ 6.100389e+04\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.966921e+01\n",
            "variance........ 4.412248e+04\n",
            "var 22:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.047025e+02\n",
            "variance........ 4.491805e+04\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.260119e+02\n",
            "variance........ 6.461211e+04\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.129755e+02\n",
            "variance........ 5.373176e+04\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.172352e+02\n",
            "variance........ 5.860790e+04\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.242415e+02\n",
            "variance........ 6.482003e+04\n",
            "var 27:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.951008e+02\n",
            "variance........ 9.023063e+04\n",
            "\n",
            "GENERATION: 3\n",
            "Lexical Fit..... 1.797307e-02  1.955436e-02  2.123067e-02  2.203630e-02  2.315097e-02  2.398570e-02  2.547662e-02  2.746063e-02  2.925001e-02  3.597121e-02  4.135010e-02  4.257932e-02  4.428467e-02  5.035351e-02  5.337826e-02  8.227461e-02  1.034972e-01  1.090329e-01  1.604996e-01  1.820190e-01  2.103490e-01  2.143325e-01  2.153739e-01  2.432758e-01  3.803395e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.877130e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.078341e-01  8.137474e-01  8.330386e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.932581e-01  9.940639e-01  \n",
            "#unique......... 164, #Total UniqueCount: 753\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.202497e+01\n",
            "variance........ 5.434338e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.177458e+01\n",
            "variance........ 8.555222e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.874713e+01\n",
            "variance........ 4.804451e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.208265e+01\n",
            "variance........ 1.931599e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 2.197584e+01\n",
            "variance........ 7.060558e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.776851e+01\n",
            "variance........ 5.108089e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.790879e+01\n",
            "variance........ 3.678536e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.802836e+01\n",
            "variance........ 5.514631e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.394061e+01\n",
            "variance........ 8.299179e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.113319e+01\n",
            "variance........ 1.237409e+04\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.667965e+01\n",
            "variance........ 5.984122e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.625709e+01\n",
            "variance........ 4.274696e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.020915e+01\n",
            "variance........ 1.998273e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.290193e+01\n",
            "variance........ 2.189295e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.140314e+01\n",
            "variance........ 1.012902e+04\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.759658e+01\n",
            "variance........ 5.612203e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.932689e+01\n",
            "variance........ 5.235877e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.517172e+01\n",
            "variance........ 4.113490e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.661777e+02\n",
            "variance........ 1.156071e+04\n",
            "var 20:\n",
            "best............ 1.382071e+02\n",
            "mean............ 2.099795e+01\n",
            "variance........ 5.195955e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.138782e+01\n",
            "variance........ 8.272220e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 2.047873e+01\n",
            "variance........ 5.657947e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.611644e+01\n",
            "variance........ 3.512380e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.120003e+01\n",
            "variance........ 6.918699e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.170090e+00\n",
            "variance........ 6.462405e+02\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.197245e+01\n",
            "variance........ 7.452681e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 2.351275e+01\n",
            "variance........ 7.980919e+03\n",
            "\n",
            "GENERATION: 4\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 184, #Total UniqueCount: 937\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.317346e+01\n",
            "variance........ 2.817835e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.100514e+01\n",
            "variance........ 1.521126e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.214043e+01\n",
            "variance........ 5.871737e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.466157e+01\n",
            "variance........ 1.041340e+04\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.475769e+01\n",
            "variance........ 2.887929e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 2.284268e+01\n",
            "variance........ 9.079213e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.627898e+01\n",
            "variance........ 4.743682e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.055877e+01\n",
            "variance........ 8.752398e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.905544e+01\n",
            "variance........ 7.470879e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.550690e+01\n",
            "variance........ 9.640321e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.716368e+01\n",
            "variance........ 7.342643e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.753394e+01\n",
            "variance........ 4.502479e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.306745e+01\n",
            "variance........ 6.427338e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.599804e+01\n",
            "variance........ 1.340584e+04\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.526983e+01\n",
            "variance........ 1.330659e+04\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.359424e+01\n",
            "variance........ 2.985195e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.106795e+01\n",
            "variance........ 7.108108e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.383431e+01\n",
            "variance........ 8.046295e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.886239e+02\n",
            "variance........ 2.499967e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.349406e+01\n",
            "variance........ 1.032910e+04\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.194380e+01\n",
            "variance........ 8.020765e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 2.105952e+01\n",
            "variance........ 5.737114e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.435971e+01\n",
            "variance........ 3.805588e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.575136e+01\n",
            "variance........ 8.621658e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.248842e+01\n",
            "variance........ 7.720133e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.322916e+01\n",
            "variance........ 9.293406e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 2.641008e+01\n",
            "variance........ 9.344207e+03\n",
            "\n",
            "GENERATION: 5\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 182, #Total UniqueCount: 1119\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.083050e+01\n",
            "variance........ 6.412835e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.614000e+00\n",
            "variance........ 5.387519e+02\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.782318e+01\n",
            "variance........ 7.417341e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.103872e+01\n",
            "variance........ 7.558258e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.875435e+01\n",
            "variance........ 5.927269e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.456989e+01\n",
            "variance........ 5.135358e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.096255e+01\n",
            "variance........ 2.311366e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.791206e+01\n",
            "variance........ 4.459028e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.033452e+01\n",
            "variance........ 9.266788e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 2.974303e+01\n",
            "variance........ 1.776460e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.770891e+01\n",
            "variance........ 5.638487e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.918792e+01\n",
            "variance........ 7.002441e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.723329e+01\n",
            "variance........ 8.582448e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.676497e+01\n",
            "variance........ 8.126353e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.790847e+01\n",
            "variance........ 7.123597e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.058557e+01\n",
            "variance........ 6.794827e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.606209e+01\n",
            "variance........ 8.363404e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.871383e+01\n",
            "variance........ 7.016817e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.911353e+02\n",
            "variance........ 2.211467e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 1.036685e+02\n",
            "variance........ 2.878201e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.608260e+01\n",
            "variance........ 4.811835e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.464735e+01\n",
            "variance........ 5.630475e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.262937e+01\n",
            "variance........ 3.997017e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.543383e+01\n",
            "variance........ 4.557149e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.468922e+01\n",
            "variance........ 4.056208e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.425956e+00\n",
            "variance........ 1.430074e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.487693e+01\n",
            "variance........ 5.140618e+03\n",
            "\n",
            "GENERATION: 6\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 162, #Total UniqueCount: 1281\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.333749e+01\n",
            "variance........ 4.054925e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.571211e+01\n",
            "variance........ 1.350049e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.044397e+01\n",
            "variance........ 9.937022e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.759277e+01\n",
            "variance........ 8.782482e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.535326e+01\n",
            "variance........ 5.458189e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 9.452962e+00\n",
            "variance........ 2.890919e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.548683e+01\n",
            "variance........ 7.043363e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.232840e+01\n",
            "variance........ 4.113547e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.195457e+01\n",
            "variance........ 8.879510e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.880637e+01\n",
            "variance........ 7.126524e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.298003e+01\n",
            "variance........ 1.208497e+04\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.458427e+01\n",
            "variance........ 7.143222e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.690726e+01\n",
            "variance........ 6.010875e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.403446e+01\n",
            "variance........ 4.349722e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.093466e+01\n",
            "variance........ 2.413618e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.995435e+01\n",
            "variance........ 1.100934e+04\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.800215e+01\n",
            "variance........ 5.753794e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.093866e+01\n",
            "variance........ 9.529395e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.932043e+02\n",
            "variance........ 7.733186e+02\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.638552e+01\n",
            "variance........ 5.434633e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.830273e+01\n",
            "variance........ 7.166065e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.331855e+01\n",
            "variance........ 4.309293e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.306380e+01\n",
            "variance........ 4.560980e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.991389e+01\n",
            "variance........ 9.741849e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.234958e+01\n",
            "variance........ 4.719442e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.590104e+01\n",
            "variance........ 6.779804e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.054459e+01\n",
            "variance........ 1.965459e+03\n",
            "\n",
            "GENERATION: 7\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 162, #Total UniqueCount: 1443\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.590849e+01\n",
            "variance........ 5.467514e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.612822e+01\n",
            "variance........ 4.952901e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.063750e+01\n",
            "variance........ 8.442389e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.123801e+01\n",
            "variance........ 1.088388e+04\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.921703e+01\n",
            "variance........ 9.780495e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.000746e+01\n",
            "variance........ 1.825391e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.495956e+01\n",
            "variance........ 6.362966e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.394586e+01\n",
            "variance........ 3.497965e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.963804e+00\n",
            "variance........ 1.348182e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.937542e+01\n",
            "variance........ 7.572149e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.987661e+01\n",
            "variance........ 7.500076e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.630192e+01\n",
            "variance........ 7.755089e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.433114e+01\n",
            "variance........ 5.664388e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.611253e+01\n",
            "variance........ 5.478134e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.777555e+01\n",
            "variance........ 9.402083e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.892018e+01\n",
            "variance........ 8.478431e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.328800e+01\n",
            "variance........ 1.291419e+04\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.197745e+01\n",
            "variance........ 9.885193e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.894449e+02\n",
            "variance........ 7.095138e+02\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 7.828040e+01\n",
            "variance........ 2.252768e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.826190e+01\n",
            "variance........ 8.123588e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.367356e+01\n",
            "variance........ 3.891330e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.253761e+01\n",
            "variance........ 4.558609e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.287703e+01\n",
            "variance........ 5.908813e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.656536e+01\n",
            "variance........ 4.896307e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.874299e+01\n",
            "variance........ 7.845441e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.728273e+01\n",
            "variance........ 6.827377e+03\n",
            "\n",
            "GENERATION: 8\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 166, #Total UniqueCount: 1609\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.353826e+01\n",
            "variance........ 7.698319e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.525746e+01\n",
            "variance........ 8.116109e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.992904e+00\n",
            "variance........ 2.146745e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.006688e+01\n",
            "variance........ 1.016566e+04\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 7.764147e+00\n",
            "variance........ 1.243374e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.464067e+01\n",
            "variance........ 5.320795e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.077865e+01\n",
            "variance........ 4.891151e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.370451e+01\n",
            "variance........ 1.343637e+04\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.602633e+01\n",
            "variance........ 7.591509e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 2.974522e+01\n",
            "variance........ 3.196797e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.350363e+00\n",
            "variance........ 7.309578e+02\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.339350e+01\n",
            "variance........ 4.740179e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.223551e+00\n",
            "variance........ 2.961381e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.208338e+00\n",
            "variance........ 2.197690e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.534437e+01\n",
            "variance........ 6.192465e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.374644e+01\n",
            "variance........ 7.229323e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.639323e+01\n",
            "variance........ 8.871585e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.235255e+01\n",
            "variance........ 3.672622e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.905417e+02\n",
            "variance........ 1.147132e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.278786e+01\n",
            "variance........ 5.189811e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.351908e+00\n",
            "variance........ 2.976818e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.380197e+01\n",
            "variance........ 4.914413e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.208917e+01\n",
            "variance........ 4.583575e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.288787e+01\n",
            "variance........ 6.303824e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.299886e+01\n",
            "variance........ 5.955187e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.087286e+01\n",
            "variance........ 3.180955e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 9.604645e+00\n",
            "variance........ 2.655825e+03\n",
            "\n",
            "GENERATION: 9\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 163, #Total UniqueCount: 1772\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.410501e+01\n",
            "variance........ 7.550941e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.596759e+01\n",
            "variance........ 8.663768e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.479078e+01\n",
            "variance........ 4.990067e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.696292e+01\n",
            "variance........ 7.923154e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.545414e+01\n",
            "variance........ 6.488662e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.628439e+01\n",
            "variance........ 6.936912e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.700659e+01\n",
            "variance........ 8.398912e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.063529e+01\n",
            "variance........ 4.535961e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.319701e+01\n",
            "variance........ 5.788438e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.771095e+01\n",
            "variance........ 5.947144e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.613829e+01\n",
            "variance........ 7.961168e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.770710e+01\n",
            "variance........ 1.051210e+04\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.852238e+00\n",
            "variance........ 2.242535e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.140153e+01\n",
            "variance........ 3.251597e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.214622e+01\n",
            "variance........ 4.681591e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.295292e+01\n",
            "variance........ 3.802814e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.919089e+00\n",
            "variance........ 3.387487e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.416609e+01\n",
            "variance........ 6.864892e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.917017e+02\n",
            "variance........ 1.888956e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 7.780401e+01\n",
            "variance........ 3.183918e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.782169e+01\n",
            "variance........ 8.410149e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 9.297297e+00\n",
            "variance........ 2.572374e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.043403e+01\n",
            "variance........ 3.671546e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.732153e+01\n",
            "variance........ 8.731219e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.540139e+01\n",
            "variance........ 5.855225e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.303291e+01\n",
            "variance........ 5.064990e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.328015e+01\n",
            "variance........ 5.081196e+03\n",
            "\n",
            "GENERATION: 10\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 162, #Total UniqueCount: 1934\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.195771e+01\n",
            "variance........ 1.333724e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.255840e+01\n",
            "variance........ 1.060932e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.064085e+01\n",
            "variance........ 1.203229e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.510604e+00\n",
            "variance........ 1.814263e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.816021e+01\n",
            "variance........ 7.039791e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 2.000810e+01\n",
            "variance........ 1.026172e+04\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.386244e+01\n",
            "variance........ 5.174465e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.772005e+01\n",
            "variance........ 6.335807e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.317302e+01\n",
            "variance........ 4.195874e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.531536e+01\n",
            "variance........ 4.891673e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.056330e+01\n",
            "variance........ 1.126998e+04\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.177949e+01\n",
            "variance........ 4.749086e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.418776e+01\n",
            "variance........ 5.986725e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.597779e+01\n",
            "variance........ 6.351185e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.841193e+01\n",
            "variance........ 8.231557e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.411421e+00\n",
            "variance........ 4.530321e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.906803e+01\n",
            "variance........ 7.125474e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.019736e+01\n",
            "variance........ 3.271604e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.916394e+02\n",
            "variance........ 3.150878e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.493729e+01\n",
            "variance........ 6.774063e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.740656e+01\n",
            "variance........ 6.818852e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.810363e+01\n",
            "variance........ 8.544642e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.703494e+01\n",
            "variance........ 7.264116e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.097299e+01\n",
            "variance........ 4.152136e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.277396e+01\n",
            "variance........ 4.855546e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.215235e+01\n",
            "variance........ 5.875957e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.739714e+01\n",
            "variance........ 7.697058e+03\n",
            "\n",
            "GENERATION: 11\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 163, #Total UniqueCount: 2097\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.238786e+00\n",
            "variance........ 1.245825e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.304723e+01\n",
            "variance........ 5.724623e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.365466e+01\n",
            "variance........ 7.356794e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.623917e+00\n",
            "variance........ 1.854220e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 2.254615e+00\n",
            "variance........ 1.001739e+02\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 9.972419e+00\n",
            "variance........ 3.152464e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 5.827175e+00\n",
            "variance........ 1.658251e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.675072e+01\n",
            "variance........ 8.816325e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.396297e+01\n",
            "variance........ 7.319307e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 2.702626e+01\n",
            "variance........ 1.225924e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.153050e+01\n",
            "variance........ 5.734661e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.949207e+00\n",
            "variance........ 4.283337e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.137658e+00\n",
            "variance........ 1.738681e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.319002e+00\n",
            "variance........ 1.396967e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.234442e+01\n",
            "variance........ 3.951885e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.410904e+00\n",
            "variance........ 4.913823e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.040646e+01\n",
            "variance........ 3.419126e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.389733e+00\n",
            "variance........ 1.074477e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.906411e+02\n",
            "variance........ 6.039610e+02\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 7.526208e+01\n",
            "variance........ 2.557197e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.016058e+01\n",
            "variance........ 5.123983e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 6.006117e+00\n",
            "variance........ 2.329295e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.996366e+01\n",
            "variance........ 1.153507e+04\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.120488e+00\n",
            "variance........ 2.198736e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.884488e+00\n",
            "variance........ 1.834629e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.823353e+00\n",
            "variance........ 1.259657e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.112695e+01\n",
            "variance........ 4.814158e+03\n",
            "\n",
            "GENERATION: 12\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 164, #Total UniqueCount: 2261\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.562562e+01\n",
            "variance........ 5.732263e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.243742e+01\n",
            "variance........ 1.054351e+04\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.591432e+01\n",
            "variance........ 8.341035e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.064593e+01\n",
            "variance........ 3.613927e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.449800e+01\n",
            "variance........ 4.856020e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 2.036130e+01\n",
            "variance........ 9.669926e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.400656e+01\n",
            "variance........ 5.895115e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.764940e+01\n",
            "variance........ 5.857161e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.038602e+01\n",
            "variance........ 9.775345e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.785650e+01\n",
            "variance........ 8.111929e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.516779e+01\n",
            "variance........ 9.413366e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.123973e+01\n",
            "variance........ 9.859504e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.058406e+01\n",
            "variance........ 1.215159e+04\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.651998e+01\n",
            "variance........ 5.506635e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.093866e+01\n",
            "variance........ 3.708619e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.581409e+01\n",
            "variance........ 1.319384e+04\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.725676e+01\n",
            "variance........ 7.316371e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.194021e+01\n",
            "variance........ 4.922405e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.866828e+02\n",
            "variance........ 1.255769e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 7.774023e+01\n",
            "variance........ 2.930012e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.790336e+01\n",
            "variance........ 6.434593e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.981166e+01\n",
            "variance........ 9.234723e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.814671e+01\n",
            "variance........ 7.388410e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.498514e+01\n",
            "variance........ 7.474702e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.185836e+01\n",
            "variance........ 6.136833e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.499911e+01\n",
            "variance........ 5.243287e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.686740e+01\n",
            "variance........ 7.355766e+03\n",
            "\n",
            "GENERATION: 13\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 170, #Total UniqueCount: 2431\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.159584e+00\n",
            "variance........ 3.991784e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.902835e+00\n",
            "variance........ 1.365939e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.046817e+00\n",
            "variance........ 3.699818e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.812752e+01\n",
            "variance........ 8.416711e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 8.744367e+00\n",
            "variance........ 3.122565e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.898791e+01\n",
            "variance........ 7.204267e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.312088e+01\n",
            "variance........ 5.484867e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.967065e+01\n",
            "variance........ 9.130628e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.648691e+01\n",
            "variance........ 7.209483e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 4.430546e+01\n",
            "variance........ 1.305248e+04\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.762738e+01\n",
            "variance........ 7.953263e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.173097e+01\n",
            "variance........ 3.610913e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.536141e+01\n",
            "variance........ 7.147011e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.483365e+01\n",
            "variance........ 5.206720e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.063696e+01\n",
            "variance........ 5.112825e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.000096e+01\n",
            "variance........ 3.417978e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.340882e+01\n",
            "variance........ 1.049763e+04\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.703635e+01\n",
            "variance........ 8.633368e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.902962e+02\n",
            "variance........ 1.408747e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 7.586916e+01\n",
            "variance........ 1.878008e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.244276e+01\n",
            "variance........ 1.044341e+04\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.467236e+01\n",
            "variance........ 6.127181e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.685150e+01\n",
            "variance........ 8.020218e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.324196e+01\n",
            "variance........ 4.827800e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.340704e+00\n",
            "variance........ 2.816693e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.895241e+00\n",
            "variance........ 3.107048e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.420061e+01\n",
            "variance........ 6.868389e+03\n",
            "\n",
            "GENERATION: 14\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 164, #Total UniqueCount: 2595\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.213213e+00\n",
            "variance........ 2.893336e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.171927e+00\n",
            "variance........ 2.451701e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.722803e+01\n",
            "variance........ 9.762928e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.751847e+01\n",
            "variance........ 9.406749e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.362170e+01\n",
            "variance........ 4.857756e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 8.911447e+00\n",
            "variance........ 2.313417e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.716596e+01\n",
            "variance........ 8.350127e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.177834e+01\n",
            "variance........ 4.389489e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.725806e+00\n",
            "variance........ 1.125273e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.517119e+01\n",
            "variance........ 5.551869e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.509502e+01\n",
            "variance........ 7.563749e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.549336e+01\n",
            "variance........ 1.193132e+04\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.085151e+01\n",
            "variance........ 4.903668e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.290378e+01\n",
            "variance........ 6.419709e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.515032e+00\n",
            "variance........ 2.175780e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.417644e+01\n",
            "variance........ 7.184509e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.557258e+01\n",
            "variance........ 5.423928e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.007277e+01\n",
            "variance........ 2.620736e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.844726e+02\n",
            "variance........ 2.084373e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.259653e+01\n",
            "variance........ 5.957796e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.364138e+01\n",
            "variance........ 6.436982e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 2.109843e+01\n",
            "variance........ 9.776311e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.898040e+00\n",
            "variance........ 2.959764e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.961391e+01\n",
            "variance........ 1.053507e+04\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.854920e+01\n",
            "variance........ 9.142989e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.133416e+01\n",
            "variance........ 1.151074e+04\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.852468e+01\n",
            "variance........ 8.573673e+03\n",
            "\n",
            "GENERATION: 15\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 165, #Total UniqueCount: 2760\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.887694e+01\n",
            "variance........ 7.665074e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.874871e+01\n",
            "variance........ 9.676712e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.255584e+01\n",
            "variance........ 3.902398e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.792172e+00\n",
            "variance........ 5.128807e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.461987e+01\n",
            "variance........ 6.466876e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 9.483768e+00\n",
            "variance........ 2.937585e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.826210e+01\n",
            "variance........ 8.642219e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.997889e+01\n",
            "variance........ 1.265326e+04\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.468926e+01\n",
            "variance........ 7.190779e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.113637e+01\n",
            "variance........ 4.986947e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.212649e+01\n",
            "variance........ 5.543317e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.262709e+01\n",
            "variance........ 6.606104e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.217536e+01\n",
            "variance........ 4.759846e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.958758e+01\n",
            "variance........ 1.045071e+04\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.244625e+00\n",
            "variance........ 2.131369e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.528346e+01\n",
            "variance........ 9.330381e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.144443e+00\n",
            "variance........ 3.474649e+02\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.034340e+01\n",
            "variance........ 3.989771e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.951451e+02\n",
            "variance........ 2.391551e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.416287e+01\n",
            "variance........ 6.475238e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.252123e+01\n",
            "variance........ 5.798711e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.279579e+01\n",
            "variance........ 5.724820e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.233196e+01\n",
            "variance........ 4.155730e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.876305e+01\n",
            "variance........ 1.105052e+04\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.039981e+01\n",
            "variance........ 5.798012e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.313866e+01\n",
            "variance........ 4.629013e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.771202e+01\n",
            "variance........ 9.536305e+03\n",
            "\n",
            "GENERATION: 16\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 166, #Total UniqueCount: 2926\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.735768e+00\n",
            "variance........ 2.569857e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.309643e+01\n",
            "variance........ 5.447330e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.182535e+01\n",
            "variance........ 8.811251e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.225946e+01\n",
            "variance........ 6.264925e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.400093e+01\n",
            "variance........ 7.369879e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.785558e+01\n",
            "variance........ 9.995503e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 7.719096e+00\n",
            "variance........ 2.245070e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.993236e+01\n",
            "variance........ 1.082928e+04\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.618423e+00\n",
            "variance........ 2.119363e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.232233e+01\n",
            "variance........ 4.437898e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.507799e+01\n",
            "variance........ 7.281169e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.533830e+01\n",
            "variance........ 9.014081e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.624403e+00\n",
            "variance........ 2.231747e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.588610e+01\n",
            "variance........ 7.688552e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.014987e+01\n",
            "variance........ 2.835819e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.225155e+01\n",
            "variance........ 6.576285e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.192512e+00\n",
            "variance........ 1.714096e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.991919e+00\n",
            "variance........ 1.392423e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.904742e+02\n",
            "variance........ 1.400013e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.093304e+01\n",
            "variance........ 5.447310e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.548177e+01\n",
            "variance........ 8.948904e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.876860e+01\n",
            "variance........ 1.002197e+04\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.874132e+00\n",
            "variance........ 3.636169e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.057350e+01\n",
            "variance........ 3.214721e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.161928e+01\n",
            "variance........ 3.974845e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.602155e+00\n",
            "variance........ 3.164751e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.448701e+01\n",
            "variance........ 7.267616e+03\n",
            "\n",
            "GENERATION: 17\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 155, #Total UniqueCount: 3081\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.940797e+00\n",
            "variance........ 1.714547e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.197966e+01\n",
            "variance........ 7.089287e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.031244e+01\n",
            "variance........ 3.873351e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.903716e+01\n",
            "variance........ 1.071465e+04\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.039365e+01\n",
            "variance........ 4.052599e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.039217e+01\n",
            "variance........ 4.070688e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 2.037310e+01\n",
            "variance........ 1.014999e+04\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.963527e+00\n",
            "variance........ 2.649772e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.310192e+01\n",
            "variance........ 5.287140e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.919225e+01\n",
            "variance........ 8.235870e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.062575e+01\n",
            "variance........ 4.606567e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.151594e+01\n",
            "variance........ 5.085182e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.278191e+01\n",
            "variance........ 8.482753e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.334281e+01\n",
            "variance........ 5.455169e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.082102e+00\n",
            "variance........ 2.802451e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.996413e+00\n",
            "variance........ 4.491965e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.365728e+01\n",
            "variance........ 7.220498e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.940784e+01\n",
            "variance........ 8.579792e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.899920e+02\n",
            "variance........ 1.620692e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 7.297997e+01\n",
            "variance........ 7.742766e+02\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.872858e+01\n",
            "variance........ 9.377900e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 8.653597e+00\n",
            "variance........ 2.846256e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.896974e+01\n",
            "variance........ 9.352453e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.401524e+00\n",
            "variance........ 1.698374e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.088801e+01\n",
            "variance........ 5.007035e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.525067e+00\n",
            "variance........ 1.681422e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.305340e+01\n",
            "variance........ 5.712143e+03\n",
            "\n",
            "GENERATION: 18\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 162, #Total UniqueCount: 3243\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.331453e+01\n",
            "variance........ 1.368192e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.147730e+01\n",
            "variance........ 5.128344e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.424509e+01\n",
            "variance........ 7.076381e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.918708e+00\n",
            "variance........ 2.782875e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 8.868353e+00\n",
            "variance........ 5.192495e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 2.363357e+01\n",
            "variance........ 1.191724e+04\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 2.430939e+01\n",
            "variance........ 1.170949e+04\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.147565e+01\n",
            "variance........ 5.007610e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.398648e+01\n",
            "variance........ 6.723870e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 2.716203e+01\n",
            "variance........ 2.346173e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.480501e+01\n",
            "variance........ 5.377485e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.658639e+01\n",
            "variance........ 7.077989e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.365605e+01\n",
            "variance........ 7.012064e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.163181e+01\n",
            "variance........ 4.558457e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.413716e+00\n",
            "variance........ 3.089064e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.561486e+01\n",
            "variance........ 6.470769e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.508383e+01\n",
            "variance........ 8.133967e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.554471e+01\n",
            "variance........ 7.103346e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.899865e+02\n",
            "variance........ 2.345686e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.557675e+01\n",
            "variance........ 6.747891e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.418709e+01\n",
            "variance........ 5.769931e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.777245e+01\n",
            "variance........ 8.454819e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.615225e+01\n",
            "variance........ 7.476311e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.097916e+01\n",
            "variance........ 1.002046e+04\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.312431e+01\n",
            "variance........ 4.372340e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.632872e+01\n",
            "variance........ 7.378759e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 8.803424e+00\n",
            "variance........ 3.648239e+03\n",
            "\n",
            "GENERATION: 19\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 164, #Total UniqueCount: 3407\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.211680e+01\n",
            "variance........ 4.299186e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.050804e+01\n",
            "variance........ 3.246323e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.944199e+01\n",
            "variance........ 1.108967e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.827235e+01\n",
            "variance........ 9.139333e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 2.012389e+01\n",
            "variance........ 9.738385e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 7.378366e+00\n",
            "variance........ 2.343367e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.897083e+01\n",
            "variance........ 1.092713e+04\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.321934e+01\n",
            "variance........ 5.257103e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.061681e+01\n",
            "variance........ 1.359796e+04\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.376081e+01\n",
            "variance........ 4.019057e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.953721e+01\n",
            "variance........ 1.790025e+04\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.757596e+00\n",
            "variance........ 3.665010e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.472240e+01\n",
            "variance........ 8.411948e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.509944e+01\n",
            "variance........ 1.153385e+04\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.244453e+01\n",
            "variance........ 4.389383e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.139433e+01\n",
            "variance........ 1.102640e+04\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.662082e+01\n",
            "variance........ 1.492872e+04\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.311372e+01\n",
            "variance........ 1.314319e+04\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.910906e+02\n",
            "variance........ 7.373879e+02\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.029947e+01\n",
            "variance........ 5.149185e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.575489e+01\n",
            "variance........ 7.374747e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.081413e+01\n",
            "variance........ 5.313964e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.224084e+01\n",
            "variance........ 5.239498e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.939711e+01\n",
            "variance........ 9.623810e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.153112e+01\n",
            "variance........ 3.600296e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.499836e+01\n",
            "variance........ 6.499114e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.281182e+01\n",
            "variance........ 4.079223e+03\n",
            "\n",
            "GENERATION: 20\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 163, #Total UniqueCount: 3570\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.024337e+01\n",
            "variance........ 5.215953e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.477962e+00\n",
            "variance........ 2.501670e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.462270e+00\n",
            "variance........ 1.168496e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.046538e+01\n",
            "variance........ 1.126429e+04\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.320243e+01\n",
            "variance........ 5.921363e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.679764e+01\n",
            "variance........ 9.941113e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.018171e+01\n",
            "variance........ 4.392348e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.128140e+01\n",
            "variance........ 9.730201e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.102941e+01\n",
            "variance........ 4.964711e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 2.761444e+01\n",
            "variance........ 2.773020e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.280021e+01\n",
            "variance........ 5.865005e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.585419e+01\n",
            "variance........ 7.468044e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.356368e+00\n",
            "variance........ 9.844953e+02\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.741790e+01\n",
            "variance........ 9.430472e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.999024e+00\n",
            "variance........ 3.559850e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.063150e+01\n",
            "variance........ 4.448925e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.974007e+00\n",
            "variance........ 3.477395e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.123313e+01\n",
            "variance........ 3.955553e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.888701e+02\n",
            "variance........ 8.892898e+02\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.329950e+01\n",
            "variance........ 5.420125e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.443472e+01\n",
            "variance........ 5.630790e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.016393e+01\n",
            "variance........ 3.937164e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.811396e+01\n",
            "variance........ 1.029076e+04\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.218196e+01\n",
            "variance........ 5.819113e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.167486e+00\n",
            "variance........ 2.317036e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.503116e+00\n",
            "variance........ 1.769297e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.091340e+01\n",
            "variance........ 4.292694e+03\n",
            "\n",
            "Increasing 'max.generations' limit by 25 generations to 45 because the fitness is still impoving.\n",
            "\n",
            "\n",
            "GENERATION: 21\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 166, #Total UniqueCount: 3736\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.682064e+01\n",
            "variance........ 1.352891e+04\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.587121e+01\n",
            "variance........ 9.495637e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.461338e+00\n",
            "variance........ 2.424039e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.484458e+01\n",
            "variance........ 5.890874e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 7.953729e+00\n",
            "variance........ 2.056960e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 2.163902e+01\n",
            "variance........ 1.156062e+04\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.179883e+01\n",
            "variance........ 4.758196e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.815879e+01\n",
            "variance........ 7.569876e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.883112e+01\n",
            "variance........ 9.442392e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.396149e+01\n",
            "variance........ 5.364108e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.339941e+01\n",
            "variance........ 9.038968e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.665543e+01\n",
            "variance........ 1.391426e+04\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.624597e+01\n",
            "variance........ 6.872300e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.563301e+01\n",
            "variance........ 5.773675e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.893640e+01\n",
            "variance........ 7.631974e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.043376e+01\n",
            "variance........ 3.417610e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.554979e+01\n",
            "variance........ 6.754875e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.951373e+00\n",
            "variance........ 2.199756e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.867094e+02\n",
            "variance........ 1.753261e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.454188e+01\n",
            "variance........ 7.211115e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.276087e+01\n",
            "variance........ 1.025722e+04\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.560426e+01\n",
            "variance........ 6.355492e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.481099e+01\n",
            "variance........ 7.842879e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.634608e+01\n",
            "variance........ 5.796564e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.266686e+01\n",
            "variance........ 4.976529e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.158656e+01\n",
            "variance........ 5.143185e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.419647e+01\n",
            "variance........ 4.180450e+03\n",
            "\n",
            "GENERATION: 22\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 162, #Total UniqueCount: 3898\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.409135e+01\n",
            "variance........ 6.810537e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.551839e+01\n",
            "variance........ 8.622584e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.185999e+01\n",
            "variance........ 4.655356e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.745259e+01\n",
            "variance........ 7.113780e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.192194e+01\n",
            "variance........ 4.380357e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.220697e+01\n",
            "variance........ 5.313700e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.881004e+01\n",
            "variance........ 8.140314e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.318149e+01\n",
            "variance........ 7.005991e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.732281e+01\n",
            "variance........ 8.005491e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 2.826382e+01\n",
            "variance........ 1.260542e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.048595e+01\n",
            "variance........ 1.920549e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.684577e+01\n",
            "variance........ 6.030133e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.682188e+01\n",
            "variance........ 7.986758e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.662860e+01\n",
            "variance........ 6.998671e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.764798e+01\n",
            "variance........ 8.347494e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.320843e+01\n",
            "variance........ 4.397453e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.315127e+01\n",
            "variance........ 1.149567e+04\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.781971e+01\n",
            "variance........ 8.769377e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.915855e+02\n",
            "variance........ 1.001618e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.035404e+01\n",
            "variance........ 5.897138e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.331193e+01\n",
            "variance........ 5.467793e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.950314e+01\n",
            "variance........ 7.940478e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.628839e+01\n",
            "variance........ 7.686434e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.484081e+01\n",
            "variance........ 4.821989e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.707865e+00\n",
            "variance........ 3.806885e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.337297e+00\n",
            "variance........ 1.871983e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.596761e+01\n",
            "variance........ 8.420956e+03\n",
            "\n",
            "GENERATION: 23\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 156, #Total UniqueCount: 4054\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.516936e+01\n",
            "variance........ 6.676397e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.485091e+01\n",
            "variance........ 6.558294e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.445590e+01\n",
            "variance........ 5.580027e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.288013e+01\n",
            "variance........ 5.687062e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 8.512953e+00\n",
            "variance........ 2.429705e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.666245e+01\n",
            "variance........ 7.223631e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.347086e+01\n",
            "variance........ 4.892861e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.105803e+01\n",
            "variance........ 3.760580e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.036672e+01\n",
            "variance........ 3.991138e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.188503e+01\n",
            "variance........ 5.482256e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.370945e+01\n",
            "variance........ 5.139284e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.359794e+01\n",
            "variance........ 6.204491e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.539093e+01\n",
            "variance........ 6.164729e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.217395e+01\n",
            "variance........ 4.978763e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.038342e+01\n",
            "variance........ 5.772556e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.450848e+01\n",
            "variance........ 5.737919e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.453487e+01\n",
            "variance........ 4.057606e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.051358e+01\n",
            "variance........ 4.863620e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.893064e+02\n",
            "variance........ 1.976785e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 7.505137e+01\n",
            "variance........ 1.179762e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.138170e+01\n",
            "variance........ 3.579700e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.756820e+01\n",
            "variance........ 7.899982e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.560597e+01\n",
            "variance........ 7.427004e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.842270e+00\n",
            "variance........ 4.415380e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.593163e+01\n",
            "variance........ 5.407165e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.512434e+01\n",
            "variance........ 8.341148e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.135017e+01\n",
            "variance........ 4.388692e+03\n",
            "\n",
            "GENERATION: 24\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 162, #Total UniqueCount: 4216\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.204074e+01\n",
            "variance........ 6.060975e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.921814e+00\n",
            "variance........ 2.090334e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.818545e+01\n",
            "variance........ 8.762906e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.923299e+00\n",
            "variance........ 3.273006e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 2.020463e+01\n",
            "variance........ 9.809208e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 9.926274e+00\n",
            "variance........ 4.121561e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.093168e+01\n",
            "variance........ 4.188467e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.212470e+00\n",
            "variance........ 3.566173e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.237789e+01\n",
            "variance........ 6.716353e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.356234e+01\n",
            "variance........ 5.633160e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.494348e+00\n",
            "variance........ 2.898759e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.161995e+01\n",
            "variance........ 5.277463e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.416577e+01\n",
            "variance........ 7.657998e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.402852e+01\n",
            "variance........ 6.386709e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.474535e+01\n",
            "variance........ 7.283712e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.344203e+01\n",
            "variance........ 6.290384e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.904283e+00\n",
            "variance........ 1.908344e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.323725e+01\n",
            "variance........ 5.344654e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.870903e+02\n",
            "variance........ 1.400119e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 7.610925e+01\n",
            "variance........ 2.388487e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.089450e+01\n",
            "variance........ 3.485235e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 7.703463e+00\n",
            "variance........ 2.313529e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.596204e+00\n",
            "variance........ 1.832097e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.869229e+00\n",
            "variance........ 4.892920e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.016767e+01\n",
            "variance........ 3.367616e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.207485e+01\n",
            "variance........ 4.807381e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 9.229948e+00\n",
            "variance........ 4.443729e+03\n",
            "\n",
            "GENERATION: 25\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 161, #Total UniqueCount: 4377\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.308627e+01\n",
            "variance........ 4.945232e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.644123e+01\n",
            "variance........ 8.840187e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.796408e+00\n",
            "variance........ 2.200061e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.632856e+01\n",
            "variance........ 8.122819e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.468017e+01\n",
            "variance........ 4.985938e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.935877e+01\n",
            "variance........ 8.926115e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 9.508266e+00\n",
            "variance........ 2.494007e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.783097e+01\n",
            "variance........ 9.521814e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.393367e+01\n",
            "variance........ 5.292574e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 2.409018e+01\n",
            "variance........ 1.319273e+02\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.023923e+01\n",
            "variance........ 1.027260e+04\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.983836e+00\n",
            "variance........ 9.759760e+02\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.991848e+01\n",
            "variance........ 9.002281e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.237371e+01\n",
            "variance........ 4.652034e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.528451e+01\n",
            "variance........ 6.841708e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.964335e+00\n",
            "variance........ 1.970901e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.414599e+01\n",
            "variance........ 1.373439e+04\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.388944e+01\n",
            "variance........ 7.139197e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.880504e+02\n",
            "variance........ 1.396839e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.117500e+01\n",
            "variance........ 3.388781e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.019283e+01\n",
            "variance........ 3.501372e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.086847e+01\n",
            "variance........ 2.954345e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.166746e+01\n",
            "variance........ 3.650613e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.844070e+01\n",
            "variance........ 9.279416e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.617655e+01\n",
            "variance........ 8.058444e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.840906e+01\n",
            "variance........ 7.020105e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 2.734713e+01\n",
            "variance........ 1.582923e+04\n",
            "\n",
            "GENERATION: 26\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 162, #Total UniqueCount: 4539\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.262025e+01\n",
            "variance........ 4.864678e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.050493e+00\n",
            "variance........ 2.340740e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.116361e+01\n",
            "variance........ 4.416458e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.870017e+00\n",
            "variance........ 5.585341e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.342089e+01\n",
            "variance........ 4.851300e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.636313e+01\n",
            "variance........ 8.609549e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 2.920602e+00\n",
            "variance........ 3.126515e+02\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.544777e+00\n",
            "variance........ 1.614812e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.057522e+01\n",
            "variance........ 4.704375e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.551755e+01\n",
            "variance........ 4.727950e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.728490e+00\n",
            "variance........ 3.895359e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.219824e+01\n",
            "variance........ 6.396370e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.620700e+00\n",
            "variance........ 1.623566e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.029799e+01\n",
            "variance........ 2.797419e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.195256e+01\n",
            "variance........ 4.055546e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.409315e+00\n",
            "variance........ 1.026012e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.192915e+00\n",
            "variance........ 8.102529e+02\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.626151e+01\n",
            "variance........ 7.747371e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.821955e+02\n",
            "variance........ 2.737018e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 7.796825e+01\n",
            "variance........ 2.395455e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.309923e+01\n",
            "variance........ 4.099553e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.128701e+01\n",
            "variance........ 6.527221e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.339044e+00\n",
            "variance........ 8.544096e+02\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.687069e+01\n",
            "variance........ 6.719487e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.501304e+00\n",
            "variance........ 3.947204e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.627284e+01\n",
            "variance........ 9.320061e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.049076e+01\n",
            "variance........ 3.327254e+03\n",
            "\n",
            "GENERATION: 27\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 169, #Total UniqueCount: 4708\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.984173e+00\n",
            "variance........ 2.633767e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.032746e+00\n",
            "variance........ 3.675849e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.833434e+01\n",
            "variance........ 7.521316e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.063412e+01\n",
            "variance........ 2.543358e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 8.341469e+00\n",
            "variance........ 3.003630e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.027061e+01\n",
            "variance........ 3.767501e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 9.436680e+00\n",
            "variance........ 3.208338e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.677654e+00\n",
            "variance........ 9.938000e+02\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.132805e+01\n",
            "variance........ 3.759621e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.288595e+01\n",
            "variance........ 5.427221e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.292587e+01\n",
            "variance........ 6.402794e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.668022e+01\n",
            "variance........ 8.496306e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.787164e+00\n",
            "variance........ 3.962602e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.881366e+00\n",
            "variance........ 3.119238e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.638030e+01\n",
            "variance........ 8.321497e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.197302e+01\n",
            "variance........ 5.920940e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.671118e+01\n",
            "variance........ 7.132006e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.928093e+00\n",
            "variance........ 1.715383e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.881174e+02\n",
            "variance........ 1.373548e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 7.765103e+01\n",
            "variance........ 3.353522e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.361755e+01\n",
            "variance........ 1.249655e+04\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.087412e+01\n",
            "variance........ 3.629428e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.282642e+01\n",
            "variance........ 5.874144e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.136202e+01\n",
            "variance........ 3.719128e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.903262e+01\n",
            "variance........ 7.504641e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.898946e+01\n",
            "variance........ 1.027817e+04\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.829006e+01\n",
            "variance........ 7.493477e+03\n",
            "\n",
            "GENERATION: 28\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 157, #Total UniqueCount: 4865\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.148686e+01\n",
            "variance........ 5.057373e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 4.563524e+00\n",
            "variance........ 1.013676e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.903233e+01\n",
            "variance........ 1.210491e+04\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.024488e+00\n",
            "variance........ 2.122688e+02\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 7.637269e+00\n",
            "variance........ 2.487560e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 2.004010e+00\n",
            "variance........ 1.445662e+02\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.161776e+01\n",
            "variance........ 4.547473e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.307182e+00\n",
            "variance........ 1.049122e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.369753e+01\n",
            "variance........ 5.580689e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.275577e+01\n",
            "variance........ 4.379754e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.018782e+01\n",
            "variance........ 2.347302e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.511185e+01\n",
            "variance........ 7.622756e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.089152e+01\n",
            "variance........ 3.613354e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.697640e+01\n",
            "variance........ 8.976175e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.549001e+00\n",
            "variance........ 3.055819e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.049727e+01\n",
            "variance........ 3.260506e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.213017e+01\n",
            "variance........ 6.449047e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.862430e+00\n",
            "variance........ 3.794830e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.893585e+02\n",
            "variance........ 1.475140e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.876138e+01\n",
            "variance........ 9.017235e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.260898e+01\n",
            "variance........ 4.881845e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 1.206180e+01\n",
            "variance........ 4.795464e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.050549e+00\n",
            "variance........ 1.532518e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.052299e+01\n",
            "variance........ 3.734022e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.241596e+01\n",
            "variance........ 6.953306e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.042670e+01\n",
            "variance........ 4.042673e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.471852e+01\n",
            "variance........ 7.588829e+03\n",
            "\n",
            "GENERATION: 29\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 164, #Total UniqueCount: 5029\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.750079e+01\n",
            "variance........ 8.478794e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 2.014753e+01\n",
            "variance........ 8.448465e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.618796e+01\n",
            "variance........ 7.832368e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.497920e+00\n",
            "variance........ 3.615943e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.167566e+01\n",
            "variance........ 5.117875e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 7.299279e+00\n",
            "variance........ 1.761784e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 1.855074e+01\n",
            "variance........ 8.149759e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.906625e+01\n",
            "variance........ 1.082298e+04\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.097746e+01\n",
            "variance........ 3.983709e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.792016e+01\n",
            "variance........ 7.920056e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.680538e+01\n",
            "variance........ 6.955101e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.148603e+01\n",
            "variance........ 3.435755e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.523915e+01\n",
            "variance........ 8.332670e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.377503e+01\n",
            "variance........ 4.843831e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.362188e+01\n",
            "variance........ 5.963225e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.592149e+01\n",
            "variance........ 5.047007e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.718866e+00\n",
            "variance........ 2.503911e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.000736e+01\n",
            "variance........ 2.894366e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.864263e+02\n",
            "variance........ 2.906859e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 8.212401e+01\n",
            "variance........ 4.863990e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 3.224730e+01\n",
            "variance........ 1.871771e+04\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 9.201766e+00\n",
            "variance........ 2.924213e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.646439e+01\n",
            "variance........ 8.048580e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.463583e+01\n",
            "variance........ 5.098220e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.215805e+01\n",
            "variance........ 5.660056e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.576651e+01\n",
            "variance........ 7.385928e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.959941e+01\n",
            "variance........ 9.469097e+03\n",
            "\n",
            "GENERATION: 30\n",
            "Lexical Fit..... 2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "#unique......... 157, #Total UniqueCount: 5186\n",
            "var 1:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.320065e+00\n",
            "variance........ 2.194435e+03\n",
            "var 2:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.208952e+01\n",
            "variance........ 3.965698e+03\n",
            "var 3:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.551196e+00\n",
            "variance........ 1.341584e+03\n",
            "var 4:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.431163e+01\n",
            "variance........ 5.055399e+03\n",
            "var 5:\n",
            "best............ 9.986212e-01\n",
            "mean............ 1.206836e+01\n",
            "variance........ 6.054620e+03\n",
            "var 6:\n",
            "best............ 8.937201e-01\n",
            "mean............ 1.595745e+01\n",
            "variance........ 6.818895e+03\n",
            "var 7:\n",
            "best............ 9.737897e-01\n",
            "mean............ 9.320869e+00\n",
            "variance........ 4.192990e+03\n",
            "var 8:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.389218e+01\n",
            "variance........ 5.404427e+03\n",
            "var 9:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.090424e+01\n",
            "variance........ 3.766405e+03\n",
            "var 10:\n",
            "best............ 2.243829e+01\n",
            "mean............ 3.728984e+01\n",
            "variance........ 6.138056e+03\n",
            "var 11:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.440537e+01\n",
            "variance........ 8.257431e+03\n",
            "var 12:\n",
            "best............ 1.000000e+00\n",
            "mean............ 5.356514e+00\n",
            "variance........ 1.021962e+03\n",
            "var 13:\n",
            "best............ 1.000000e+00\n",
            "mean............ 9.147269e+00\n",
            "variance........ 3.869558e+03\n",
            "var 14:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.008198e+00\n",
            "variance........ 1.913059e+03\n",
            "var 15:\n",
            "best............ 1.000000e+00\n",
            "mean............ 7.623697e+00\n",
            "variance........ 2.878694e+03\n",
            "var 16:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.408870e+01\n",
            "variance........ 6.057445e+03\n",
            "var 17:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.136344e+01\n",
            "variance........ 4.782683e+03\n",
            "var 18:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.105693e+01\n",
            "variance........ 4.472298e+03\n",
            "var 19:\n",
            "best............ 4.899488e+02\n",
            "mean............ 4.861776e+02\n",
            "variance........ 2.773418e+03\n",
            "var 20:\n",
            "best............ 7.232832e+01\n",
            "mean............ 7.471611e+01\n",
            "variance........ 1.661877e+03\n",
            "var 21:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.666880e+00\n",
            "variance........ 3.249695e+03\n",
            "var 22:\n",
            "best............ 9.399122e-01\n",
            "mean............ 8.927968e+00\n",
            "variance........ 3.691870e+03\n",
            "var 23:\n",
            "best............ 1.000000e+00\n",
            "mean............ 8.006661e+00\n",
            "variance........ 2.207825e+03\n",
            "var 24:\n",
            "best............ 1.000000e+00\n",
            "mean............ 6.374561e+00\n",
            "variance........ 1.716289e+03\n",
            "var 25:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.582722e+01\n",
            "variance........ 8.541285e+03\n",
            "var 26:\n",
            "best............ 1.000000e+00\n",
            "mean............ 1.089503e+01\n",
            "variance........ 4.100767e+03\n",
            "var 27:\n",
            "best............ 9.803608e-01\n",
            "mean............ 1.324855e+01\n",
            "variance........ 5.390563e+03\n",
            "\n",
            "'wait.generations' limit reached.\n",
            "No significant improvement in 25 generations.\n",
            "\n",
            "Solution Lexical Fitness Value:\n",
            "2.003818e-02  2.123067e-02  2.357542e-02  2.562049e-02  2.661625e-02  2.733010e-02  2.911425e-02  3.456292e-02  3.905960e-02  4.245438e-02  4.510717e-02  4.547991e-02  4.746808e-02  5.035351e-02  5.750981e-02  7.911985e-02  1.034972e-01  1.090329e-01  1.619628e-01  1.823135e-01  1.914489e-01  2.143325e-01  2.153739e-01  2.328760e-01  3.500241e-01  3.735355e-01  3.865045e-01  3.865045e-01  3.865045e-01  3.865045e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  6.208099e-01  8.055631e-01  8.515892e-01  8.602552e-01  8.602552e-01  8.602552e-01  8.602552e-01  9.009411e-01  9.884424e-01  9.885595e-01  \n",
            "\n",
            "Parameters at the Solution:\n",
            "\n",
            " X[ 1] :\t1.000000e+00\n",
            " X[ 2] :\t1.000000e+00\n",
            " X[ 3] :\t1.000000e+00\n",
            " X[ 4] :\t1.000000e+00\n",
            " X[ 5] :\t9.986212e-01\n",
            " X[ 6] :\t8.937201e-01\n",
            " X[ 7] :\t9.737897e-01\n",
            " X[ 8] :\t1.000000e+00\n",
            " X[ 9] :\t1.000000e+00\n",
            " X[10] :\t2.243829e+01\n",
            " X[11] :\t1.000000e+00\n",
            " X[12] :\t1.000000e+00\n",
            " X[13] :\t1.000000e+00\n",
            " X[14] :\t1.000000e+00\n",
            " X[15] :\t1.000000e+00\n",
            " X[16] :\t1.000000e+00\n",
            " X[17] :\t1.000000e+00\n",
            " X[18] :\t1.000000e+00\n",
            " X[19] :\t4.899488e+02\n",
            " X[20] :\t7.232832e+01\n",
            " X[21] :\t1.000000e+00\n",
            " X[22] :\t9.399122e-01\n",
            " X[23] :\t1.000000e+00\n",
            " X[24] :\t1.000000e+00\n",
            " X[25] :\t1.000000e+00\n",
            " X[26] :\t1.000000e+00\n",
            " X[27] :\t9.803608e-01\n",
            "\n",
            "Solution Found Generation 4\n",
            "Number of Generations Run 30\n",
            "\n",
            "Fri Dec 20 10:22:37 2019\n",
            "Total run time : 0 hours 1 minutes and 54 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-aVCNA0QTSh"
      },
      "source": [
        "mout = Match(Tr = Tr, X = X.all, M=1, Weight.matrix = genout)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2bsFRhEQXDt",
        "outputId": "25c4071a-670f-48ef-857a-8c2b6d55b48c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "mb <- MatchBalance(treat ~ state + pcgsdp + pfemale + plit + pwlit + prural, data=before.data, match.out=mout, nboots=500)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "***** (V1) state *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........     10.273 \t \t     10.273 \n",
            "mean control..........     11.958 \t \t         11 \n",
            "std mean diff.........    -61.773 \t \t    -26.653 \n",
            "\n",
            "mean raw eQQ diff.....     1.7273 \t \t     1.0909 \n",
            "med  raw eQQ diff.....          2 \t \t          1 \n",
            "max  raw eQQ diff.....          5 \t \t          4 \n",
            "\n",
            "mean eCDF diff........    0.20833 \t \t    0.13636 \n",
            "med  eCDF diff........    0.22727 \t \t    0.13636 \n",
            "max  eCDF diff........    0.37879 \t \t    0.22727 \n",
            "\n",
            "var ratio (Tr/Co).....     1.0356 \t \t     1.4478 \n",
            "T-test p-value........   0.020505 \t \t     0.1035 \n",
            "KS Bootstrap p-value..      0.006 \t \t      0.404 \n",
            "KS Naive p-value......   0.026361 \t \t    0.62081 \n",
            "KS Statistic..........    0.37879 \t \t    0.22727 \n",
            "\n",
            "\n",
            "***** (V2) pcgsdp *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........     1.3631 \t \t     1.3631 \n",
            "mean control..........     1.2199 \t \t     1.4008 \n",
            "std mean diff.........     29.002 \t \t    -7.6342 \n",
            "\n",
            "mean raw eQQ diff.....    0.17316 \t \t    0.12067 \n",
            "med  raw eQQ diff.....    0.13723 \t \t   0.061652 \n",
            "max  raw eQQ diff.....    0.48856 \t \t    0.42141 \n",
            "\n",
            "mean eCDF diff........    0.10181 \t \t   0.075758 \n",
            "med  eCDF diff........    0.07197 \t \t   0.090909 \n",
            "max  eCDF diff........    0.27652 \t \t    0.18182 \n",
            "\n",
            "var ratio (Tr/Co).....     0.7376 \t \t    0.69065 \n",
            "T-test p-value........    0.29083 \t \t    0.35002 \n",
            "KS Bootstrap p-value..      0.146 \t \t      0.766 \n",
            "KS Naive p-value......    0.16118 \t \t    0.86026 \n",
            "KS Statistic..........    0.27652 \t \t    0.18182 \n",
            "\n",
            "\n",
            "***** (V3) pfemale *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.96387 \t \t    0.96387 \n",
            "mean control..........    0.94051 \t \t    0.94543 \n",
            "std mean diff.........     54.736 \t \t     43.213 \n",
            "\n",
            "mean raw eQQ diff.....   0.024367 \t \t   0.018661 \n",
            "med  raw eQQ diff.....   0.026087 \t \t   0.011611 \n",
            "max  raw eQQ diff.....   0.060867 \t \t   0.063806 \n",
            "\n",
            "mean eCDF diff........    0.14713 \t \t   0.088154 \n",
            "med  eCDF diff........    0.14205 \t \t   0.090909 \n",
            "max  eCDF diff........    0.36932 \t \t    0.22727 \n",
            "\n",
            "var ratio (Tr/Co).....    0.85977 \t \t    0.76563 \n",
            "T-test p-value........    0.04404 \t \t    0.02733 \n",
            "KS Bootstrap p-value..      0.018 \t \t      0.544 \n",
            "KS Naive p-value......   0.023517 \t \t    0.62081 \n",
            "KS Statistic..........    0.36932 \t \t    0.22727 \n",
            "\n",
            "\n",
            "***** (V4) plit *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.53846 \t \t    0.53846 \n",
            "mean control..........     0.4768 \t \t    0.50846 \n",
            "std mean diff.........     48.866 \t \t     23.776 \n",
            "\n",
            "mean raw eQQ diff.....   0.062687 \t \t   0.033448 \n",
            "med  raw eQQ diff.....    0.05157 \t \t   0.019613 \n",
            "max  raw eQQ diff.....    0.24671 \t \t    0.26313 \n",
            "\n",
            "mean eCDF diff........    0.17045 \t \t    0.10606 \n",
            "med  eCDF diff........     0.1714 \t \t   0.090909 \n",
            "max  eCDF diff........    0.33333 \t \t    0.27273 \n",
            "\n",
            "var ratio (Tr/Co).....     1.0596 \t \t     1.1781 \n",
            "T-test p-value........   0.062707 \t \t    0.02562 \n",
            "KS Bootstrap p-value..      0.058 \t \t      0.294 \n",
            "KS Naive p-value......    0.05266 \t \t     0.3865 \n",
            "KS Statistic..........    0.33333 \t \t    0.27273 \n",
            "\n",
            "\n",
            "***** (V5) pwlit *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.45226 \t \t    0.45226 \n",
            "mean control..........     0.3824 \t \t      0.419 \n",
            "std mean diff.........     44.828 \t \t     21.343 \n",
            "\n",
            "mean raw eQQ diff.....   0.071041 \t \t   0.035093 \n",
            "med  raw eQQ diff.....   0.056334 \t \t   0.019099 \n",
            "max  raw eQQ diff.....    0.31462 \t \t    0.29961 \n",
            "\n",
            "mean eCDF diff........    0.15341 \t \t   0.096419 \n",
            "med  eCDF diff........    0.14773 \t \t   0.090909 \n",
            "max  eCDF diff........    0.33333 \t \t    0.22727 \n",
            "\n",
            "var ratio (Tr/Co).....     1.0848 \t \t     1.1639 \n",
            "T-test p-value........   0.085679 \t \t   0.020038 \n",
            "KS Bootstrap p-value..      0.052 \t \t      0.522 \n",
            "KS Naive p-value......    0.05266 \t \t    0.62081 \n",
            "KS Statistic..........    0.33333 \t \t    0.22727 \n",
            "\n",
            "\n",
            "***** (V6) prural *****\n",
            "                       Before Matching \t \t After Matching\n",
            "mean treatment........    0.71686 \t \t    0.71686 \n",
            "mean control..........    0.74083 \t \t    0.71671 \n",
            "std mean diff.........    -32.806 \t \t    0.20191 \n",
            "\n",
            "mean raw eQQ diff.....   0.024061 \t \t   0.013619 \n",
            "med  raw eQQ diff.....   0.020843 \t \t  0.0063347 \n",
            "max  raw eQQ diff.....   0.047551 \t \t   0.050855 \n",
            "\n",
            "mean eCDF diff........     0.1622 \t \t   0.078512 \n",
            "med  eCDF diff........    0.15625 \t \t   0.090909 \n",
            "max  eCDF diff........    0.37879 \t \t    0.22727 \n",
            "\n",
            "var ratio (Tr/Co).....     1.0261 \t \t     0.9972 \n",
            "T-test p-value........    0.20809 \t \t    0.98856 \n",
            "KS Bootstrap p-value..      0.022 \t \t      0.556 \n",
            "KS Naive p-value......   0.018529 \t \t    0.62081 \n",
            "KS Statistic..........    0.37879 \t \t    0.22727 \n",
            "\n",
            "\n",
            "Before Matching Minimum p.value: 0.006 \n",
            "Variable Name(s): state  Number(s): 1 \n",
            "\n",
            "After Matching Minimum p.value: 0.020038 \n",
            "Variable Name(s): pwlit  Number(s): 5 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9e-_h_mQZae",
        "outputId": "6278dc4c-b6ee-4d6d-8697-1e0200df5725",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        }
      },
      "source": [
        "mout = Match(Y=Y, Tr = Tr, X = X.all, M=1, Weight.matrix = genout)\n",
        "summary(mout)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Estimate...  0.022826 \n",
            "AI SE......  0.00863 \n",
            "T-stat.....  2.645 \n",
            "p.val......  0.0081689 \n",
            "\n",
            "Original number of observations..............  70 \n",
            "Original number of treated obs...............  22 \n",
            "Matched number of observations...............  22 \n",
            "Matched number of observations  (unweighted).  22 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtY2yHSmQcAU",
        "outputId": "ea0c2b37-0195-4935-d5a3-2d56b5245587",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "conf.interval.95 <- function(mout){\n",
        "  up = mout$est + 1.96*mout$se\n",
        "  low = mout$est - 1.96*mout$se\n",
        "  if (is.null(mout$se)){\n",
        "    up = mout$est + 1.96*mout$se.standard\n",
        "    low = mout$est - 1.96*mout$se.standard\n",
        "  }\n",
        "  return (paste(\"[\",low,\",\",up,\"]\"))\n",
        "}\n",
        "\n",
        "conf.interval.95(mout)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] \"[ 0.00591162084618084 , 0.0397411602901828 ]\""
            ],
            "text/latex": "'{[} 0.00591162084618084 , 0.0397411602901828 {]}'",
            "text/markdown": "'[ 0.00591162084618084 , 0.0397411602901828 ]'",
            "text/html": [
              "'[ 0.00591162084618084 , 0.0397411602901828 ]'"
            ]
          },
          "metadata": {
            "tags": []
          }
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6_smflRq1-w"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0I1GOe9ubZs"
      },
      "source": [
        "## Synthetic Control\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4SZvhmPu7S0",
        "outputId": "38211b90-f40e-47d8-ab8e-f5fdc62495c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        }
      },
      "source": [
        "install.packages('Synth')\n",
        "library(Synth)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependencies ‘numDeriv’, ‘kernlab’, ‘optimx’\n",
            "\n",
            "\n",
            "##\n",
            "## Synth Package: Implements Synthetic Control Methods.\n",
            "\n",
            "\n",
            "## See http://www.mit.edu/~jhainm/software.htm for additional information.\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "300JaPNPzMLy"
      },
      "source": [
        "### ASSAM: Synthetic Control"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "guq83CuIoZPf",
        "outputId": "7c383346-a8da-4088-c3be-4650fa50e19b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        }
      },
      "source": [
        "# Mahmud: the changed that I made\n",
        "# data$state <- as.numeric(as.factor(data$stateid))\n",
        "# data$stateuni <- as.character(data$stateuni)\n",
        "# unit.names.variable = c(\"stateuni\")\n",
        "# unit.variable = \"state\",\n",
        "\n",
        "# treatment happened in 2002 in ASSAM\n",
        "\n",
        "data = data\n",
        "\n",
        "data$state <- as.numeric(as.factor(data$stateid))\n",
        "data$stateuni <- as.character(data$stateuni)\n",
        "\n",
        "dataprep.out = \n",
        "  dataprep(\n",
        "    foo = data,\n",
        "    predictors = c(\"pcgsdp\", \"pfemale\", \"plit\",\"pwlit\",\"prural\"),\n",
        "    predictors.op = \"median\", #match the median of the predictors\n",
        "    time.predictors.prior = 1985:2002, # data used to train the synthetic control unit \n",
        "    dependent = \"pcr_womtot\",\n",
        "    unit.variable = \"state\",\n",
        "    time.variable = \"year\",\n",
        "    #special.predictors = list(\n",
        "    #  list(\"pcgsdp\", seq(1985,2001,2),\"mean\"),\n",
        "    #  list(\"pfemale\", seq(1985,2001,2),\"mean\"),\n",
        "    #  list(\"plit\",  seq(1985,2001,2),\"mean\"),\n",
        "    #  list(\"pwlit\",  seq(1985,2001,2),\"mean\"),\n",
        "    #  list(\"prural\",  seq(1985,2001,2),\"mean\")\n",
        "    #),\n",
        "    treatment.identifier = 2, #point at Assam,\n",
        "    controls.identifier = c(1, 3:17),\n",
        "    time.optimize.ssr = c(1985:2002), #count the outcome variables of Assam and synthetic control units and make it as small as possible\n",
        "    unit.names.variable = c(\"stateuni\"),\n",
        "    time.plot = c(1985:2007)\n",
        "  )\n",
        "\n",
        "synth.out = synth(dataprep.out) #create synthetic controls\n",
        "\n",
        "#plot treatment vs synthetic control outcomes trend\n",
        "path.plot(synth.res = synth.out, dataprep.res = dataprep.out,\n",
        "          Ylab = \"Crime against women per 1000 women\", Xlab = 'year', tr.intake = 2002,\n",
        "          Legend = c(\"Assam\", \"Synthetic Assam\"), Legend.position = \"bottomright\")\n",
        "# abline(v = 2001, lw=2)\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0003042721 \n",
            "\n",
            "solution.v:\n",
            " 0.2300167 0.3700114 0.393585 0.006386841 0 \n",
            "\n",
            "solution.w:\n",
            " 0.05121218 0.2659888 0.03791813 0.05109935 0.02624605 0.07985753 0.04132484 0.05403987 0.03250165 0.03311749 0.03143242 0.08711503 0.03540303 0.02356482 0.06408271 0.0850961 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3EwH0c62Nf6na3ko1w/vyJIYWUc0X8VLNaOyvd+Z7+SDR6/l8j0lP8vXQVusbyG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\ngCABcIAgAXCAIAFwgCABcIAgAXDw/25NFnIZ6RiHAAAAAElFTkSuQmCC",
            "text/plain": [
              "Plot with title “NA”"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fVr3cJRxu7d1",
        "outputId": "8ae4dd61-798e-443f-88ea-30422bb62c18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "source": [
        "#gap or difference in treatment and synthetic control\n",
        "gaps <- dataprep.out$Y1plot - (dataprep.out$Y0plot %*% synth.out$solution.w)\n",
        "\n",
        "#pre built tables from synth objects\n",
        "synth.tables <- synth.tab(dataprep.res = dataprep.out, synth.res = synth.out)\n",
        "\n",
        "#comparing pre-treatment predictor values for the treated unit, the synthetic control unit , and all the units in the sample\n",
        "synth.tables$tab.pred[1:5,]\n",
        "\n",
        "#View control unit weights\n",
        "synth.tables$tab.w\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        Treated Synthetic Sample Mean\n",
              "pcgsdp  1.324   1.324     1.495      \n",
              "pfemale 0.926   0.926     0.936      \n",
              "plit    0.453   0.454     0.490      \n",
              "pwlit   0.374   0.352     0.391      \n",
              "prural  0.884   0.771     0.745      "
            ],
            "text/latex": "A matrix: 5 × 3 of type dbl\n\\begin{tabular}{r|lll}\n  & Treated & Synthetic & Sample Mean\\\\\n\\hline\n\tpcgsdp & 1.324 & 1.324 & 1.495\\\\\n\tpfemale & 0.926 & 0.926 & 0.936\\\\\n\tplit & 0.453 & 0.454 & 0.490\\\\\n\tpwlit & 0.374 & 0.352 & 0.391\\\\\n\tprural & 0.884 & 0.771 & 0.745\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA matrix: 5 × 3 of type dbl\n\n| <!--/--> | Treated | Synthetic | Sample Mean |\n|---|---|---|---|\n| pcgsdp | 1.324 | 1.324 | 1.495 |\n| pfemale | 0.926 | 0.926 | 0.936 |\n| plit | 0.453 | 0.454 | 0.490 |\n| pwlit | 0.374 | 0.352 | 0.391 |\n| prural | 0.884 | 0.771 | 0.745 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A matrix: 5 × 3 of type dbl</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Treated</th><th scope=col>Synthetic</th><th scope=col>Sample Mean</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>pcgsdp</th><td>1.324</td><td>1.324</td><td>1.495</td></tr>\n",
              "\t<tr><th scope=row>pfemale</th><td>0.926</td><td>0.926</td><td>0.936</td></tr>\n",
              "\t<tr><th scope=row>plit</th><td>0.453</td><td>0.454</td><td>0.490</td></tr>\n",
              "\t<tr><th scope=row>pwlit</th><td>0.374</td><td>0.352</td><td>0.391</td></tr>\n",
              "\t<tr><th scope=row>prural</th><td>0.884</td><td>0.771</td><td>0.745</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   w.weights unit.names       unit.numbers\n",
              "1  0.051     ANDHRA PRADESH    1          \n",
              "3  0.266     BIHAR             3          \n",
              "4  0.038     GUJARAT           4          \n",
              "5  0.051     HARYANA           5          \n",
              "6  0.026     HIMACHAL PRADESH  6          \n",
              "7  0.080     JAMMU & KASHMIR   7          \n",
              "8  0.041     KARNATAKA         8          \n",
              "9  0.054     KERALA            9          \n",
              "10 0.033     MADHYA PRADESH   10          \n",
              "11 0.033     MAHARASHTRA      11          \n",
              "12 0.031     ORISSA           12          \n",
              "13 0.087     PUNJAB           13          \n",
              "14 0.035     RAJASTHAN        14          \n",
              "15 0.024     TAMIL NADU       15          \n",
              "16 0.064     UTTAR PRADESH    16          \n",
              "17 0.085     WEST BENGAL      17          "
            ],
            "text/latex": "A data.frame: 16 × 3\n\\begin{tabular}{r|lll}\n  & w.weights & unit.names & unit.numbers\\\\\n  & <dbl> & <fct> & <dbl>\\\\\n\\hline\n\t1 & 0.051 & ANDHRA PRADESH   &  1\\\\\n\t3 & 0.266 & BIHAR            &  3\\\\\n\t4 & 0.038 & GUJARAT          &  4\\\\\n\t5 & 0.051 & HARYANA          &  5\\\\\n\t6 & 0.026 & HIMACHAL PRADESH &  6\\\\\n\t7 & 0.080 & JAMMU \\& KASHMIR  &  7\\\\\n\t8 & 0.041 & KARNATAKA        &  8\\\\\n\t9 & 0.054 & KERALA           &  9\\\\\n\t10 & 0.033 & MADHYA PRADESH   & 10\\\\\n\t11 & 0.033 & MAHARASHTRA      & 11\\\\\n\t12 & 0.031 & ORISSA           & 12\\\\\n\t13 & 0.087 & PUNJAB           & 13\\\\\n\t14 & 0.035 & RAJASTHAN        & 14\\\\\n\t15 & 0.024 & TAMIL NADU       & 15\\\\\n\t16 & 0.064 & UTTAR PRADESH    & 16\\\\\n\t17 & 0.085 & WEST BENGAL      & 17\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 16 × 3\n\n| <!--/--> | w.weights &lt;dbl&gt; | unit.names &lt;fct&gt; | unit.numbers &lt;dbl&gt; |\n|---|---|---|---|\n| 1 | 0.051 | ANDHRA PRADESH   |  1 |\n| 3 | 0.266 | BIHAR            |  3 |\n| 4 | 0.038 | GUJARAT          |  4 |\n| 5 | 0.051 | HARYANA          |  5 |\n| 6 | 0.026 | HIMACHAL PRADESH |  6 |\n| 7 | 0.080 | JAMMU &amp; KASHMIR  |  7 |\n| 8 | 0.041 | KARNATAKA        |  8 |\n| 9 | 0.054 | KERALA           |  9 |\n| 10 | 0.033 | MADHYA PRADESH   | 10 |\n| 11 | 0.033 | MAHARASHTRA      | 11 |\n| 12 | 0.031 | ORISSA           | 12 |\n| 13 | 0.087 | PUNJAB           | 13 |\n| 14 | 0.035 | RAJASTHAN        | 14 |\n| 15 | 0.024 | TAMIL NADU       | 15 |\n| 16 | 0.064 | UTTAR PRADESH    | 16 |\n| 17 | 0.085 | WEST BENGAL      | 17 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 16 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>w.weights</th><th scope=col>unit.names</th><th scope=col>unit.numbers</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>0.051</td><td>ANDHRA PRADESH  </td><td> 1</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>0.266</td><td>BIHAR           </td><td> 3</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>0.038</td><td>GUJARAT         </td><td> 4</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>0.051</td><td>HARYANA         </td><td> 5</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>0.026</td><td>HIMACHAL PRADESH</td><td> 6</td></tr>\n",
              "\t<tr><th scope=row>7</th><td>0.080</td><td>JAMMU &amp; KASHMIR </td><td> 7</td></tr>\n",
              "\t<tr><th scope=row>8</th><td>0.041</td><td>KARNATAKA       </td><td> 8</td></tr>\n",
              "\t<tr><th scope=row>9</th><td>0.054</td><td>KERALA          </td><td> 9</td></tr>\n",
              "\t<tr><th scope=row>10</th><td>0.033</td><td>MADHYA PRADESH  </td><td>10</td></tr>\n",
              "\t<tr><th scope=row>11</th><td>0.033</td><td>MAHARASHTRA     </td><td>11</td></tr>\n",
              "\t<tr><th scope=row>12</th><td>0.031</td><td>ORISSA          </td><td>12</td></tr>\n",
              "\t<tr><th scope=row>13</th><td>0.087</td><td>PUNJAB          </td><td>13</td></tr>\n",
              "\t<tr><th scope=row>14</th><td>0.035</td><td>RAJASTHAN       </td><td>14</td></tr>\n",
              "\t<tr><th scope=row>15</th><td>0.024</td><td>TAMIL NADU      </td><td>15</td></tr>\n",
              "\t<tr><th scope=row>16</th><td>0.064</td><td>UTTAR PRADESH   </td><td>16</td></tr>\n",
              "\t<tr><th scope=row>17</th><td>0.085</td><td>WEST BENGAL     </td><td>17</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m12WfRPDdJUq",
        "outputId": "9947df59-5d9c-4abe-fabd-df1eefcd698f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "gaps.plot(synth.res = synth.out,\n",
        "           dataprep.res = dataprep.out,\n",
        "           Ylab = \"Gap in crime against women (per 1000 women)\",\n",
        "           Xlab = \"year\",\n",
        "           Ylim = c(-.5,.5),\n",
        "           Main = NA,\n",
        "           tr.intake = 2002\n",
        "           )\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4pjRCimezBCgs7cyYY4RoRiQ0jQmTvZ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\nAElFTkSuQmCC",
            "text/plain": [
              "Plot with title “NA”"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hWVBePNUbRpC"
      },
      "source": [
        "#### Placebo test in space for all 17 states"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uu1TEABeej9g",
        "outputId": "d10fad75-d6d8-43ee-cffb-e96628f57d1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "### Placebo test in space\n",
        "\n",
        "store <- matrix(NA, length(1985:2007), 17)\n",
        "colnames(store) <- unique(data$stateuni)\n",
        "\n",
        "for (k in 1:17){\n",
        "  # print(k)\n",
        "  dataprep.out = dataprep(\n",
        "    foo = data,\n",
        "    predictors = c(\"pcgsdp\", \"pfemale\", \"plit\",\"pwlit\",\"prural\"),\n",
        "    predictors.op = \"median\",\n",
        "    time.predictors.prior = 1985:2002,\n",
        "    dependent = \"pcr_womtot\",\n",
        "    unit.variable = \"state\",\n",
        "    time.variable = \"year\",\n",
        "    #special.predictors = list(\n",
        "    #  list(\"pcgsdp\", seq(1985,2001,2),\"mean\"),\n",
        "    #  list(\"pfemale\", seq(1985,2001,2),\"mean\"),\n",
        "    #  list(\"plit\",  seq(1985,2001,2),\"mean\"),\n",
        "    #  list(\"pwlit\",  seq(1985,2001,2),\"mean\"),\n",
        "    #  list(\"prural\",  seq(1985,2001,2),\"mean\")\n",
        "    #),\n",
        "    treatment.identifier = k,\n",
        "    controls.identifier = c(1:17)[-k],\n",
        "    time.optimize.ssr = c(1985:2002),\n",
        "    unit.names.variable = c(\"stateuni\"),\n",
        "    time.plot = c(1985:2007)\n",
        "  )\n",
        "\n",
        "  synth.out <- synth(\n",
        "    data.prep.obj = dataprep.out,\n",
        "    method = \"BFGS\"\n",
        "  )\n",
        "\n",
        "  # store gaps\n",
        "  print(k)\n",
        "  store[, k] <- dataprep.out$Y1plot - (dataprep.out$Y0plot %*% synth.out$solution.w)\n",
        "}\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.002281638 \n",
            "\n",
            "solution.v:\n",
            " 0.1687452 0.005238963 0.5157038 0.1318055 0.1785065 \n",
            "\n",
            "solution.w:\n",
            " 9.8121e-06 6e-10 0.000225708 6.73029e-05 7.4397e-06 1.97751e-05 0.25026 8.444e-07 3.4819e-06 0.0007777076 8.33e-06 0.05677168 0.6566102 0.03520986 2.866e-06 2.49876e-05 \n",
            "\n",
            "[1] 1\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0003042721 \n",
            "\n",
            "solution.v:\n",
            " 0.2300167 0.3700114 0.393585 0.006386841 0 \n",
            "\n",
            "solution.w:\n",
            " 0.05121218 0.2659888 0.03791813 0.05109935 0.02624605 0.07985753 0.04132484 0.05403987 0.03250165 0.03311749 0.03143242 0.08711503 0.03540303 0.02356482 0.06408271 0.0850961 \n",
            "\n",
            "[1] 2\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.002169068 \n",
            "\n",
            "solution.v:\n",
            " 0.05528547 0.002649626 0.3141791 0.4514611 0.1764247 \n",
            "\n",
            "solution.w:\n",
            " 4.4e-08 4.676e-07 1.88e-08 4.73e-08 1.085e-07 6.97e-08 2.33e-08 6e-10 4.61e-08 1.34e-08 3.935e-07 2.6e-08 5e-09 1.46e-08 0.9999987 2.93e-08 \n",
            "\n",
            "[1] 3\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.007221159 \n",
            "\n",
            "solution.v:\n",
            " 0.001014589 0.02624114 0.373239 0.5995053 4e-10 \n",
            "\n",
            "solution.w:\n",
            " 0.01552747 0.01173782 0.01670935 0.1332162 0.03664353 0.01442985 0.02045348 0.0321637 0.02280408 0.5214867 0.09646981 6.13e-06 0.03046195 0.01449249 0.01827179 0.01512569 \n",
            "\n",
            "[1] 4\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.001432645 \n",
            "\n",
            "solution.v:\n",
            " 0.0005694872 0.000222546 0.01699267 0.501475 0.4807403 \n",
            "\n",
            "solution.w:\n",
            " 0.002629527 0.001293504 4.3041e-06 0.03008768 0.1329729 0.009914486 0.005721472 0.003283897 0.002988922 0.1314882 0.002017898 0.2484829 0.4073222 0.01258264 0.002090299 0.007119257 \n",
            "\n",
            "[1] 5\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0002603912 \n",
            "\n",
            "solution.v:\n",
            " 5.04e-08 0.6583224 0.01930585 0.00854398 0.3138277 \n",
            "\n",
            "solution.w:\n",
            " 1.458e-07 0.04101574 2.33268e-05 1.493e-06 9.8513e-06 7.7317e-06 6.62e-07 1.4163e-06 2.8369e-06 1.0938e-06 0.9589097 5.2126e-06 4.813e-06 2.549e-07 1.22676e-05 3.4282e-06 \n",
            "\n",
            "[1] 6\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0004331239 \n",
            "\n",
            "solution.v:\n",
            " 0.2093455 0.173046 0.06255387 0.5531136 0.001941016 \n",
            "\n",
            "solution.w:\n",
            " 0.01356235 0.01287697 0.1554817 0.01143578 0.3230693 0.006142044 0.009602002 0.0003028197 0.01317165 0.01018623 0.00748051 0.0575991 0.0614455 0.006800765 0.2995718 0.01127146 \n",
            "\n",
            "[1] 7\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.003008147 \n",
            "\n",
            "solution.v:\n",
            " 0.002636462 0.1844556 0.0846512 0.7276558 0.0006009241 \n",
            "\n",
            "solution.w:\n",
            " 0.2501449 0.02104671 0.03251453 0.03180958 0.01911742 0.006733485 0.02609233 0.08218705 0.04723486 0.02934864 0.1136328 0.01948923 0.02647704 0.2268934 0.03580363 0.03147437 \n",
            "\n",
            "[1] 8\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.02239653 \n",
            "\n",
            "solution.v:\n",
            " 0.4783424 0.01448686 0.000907648 0.003371942 0.5028911 \n",
            "\n",
            "solution.w:\n",
            " 1.81703e-05 0.0003411913 0.0002428478 0.0003823882 4.93518e-05 0.2995341 0.0002893402 0.0003102319 0.0003468769 0.0003340234 0.1273712 6.993e-07 0.0003315191 0.56958 0.0003258006 0.0005421554 \n",
            "\n",
            "[1] 9\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.007895602 \n",
            "\n",
            "solution.v:\n",
            " 1.84e-08 0.04628755 0.3321598 0.5539908 0.06756188 \n",
            "\n",
            "solution.w:\n",
            " 0.2420701 0.009648989 0.05789765 0.01526821 0.01054857 0.008282109 0.01186873 0.02061672 0.0003819452 0.01317507 0.04518543 0.008196958 0.516431 0.02038274 0.008411611 0.01163417 \n",
            "\n",
            "[1] 10\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.005609017 \n",
            "\n",
            "solution.v:\n",
            " 0.000476318 0.04355681 0.3703635 0.5851379 0.0004654575 \n",
            "\n",
            "solution.w:\n",
            " 0.002510014 0.002378307 0.002155549 0.0929607 0.4869354 0.006193566 0.003291434 0.005042277 0.2190279 0.003775956 0.004064385 0.0006443264 0.004615324 0.1584044 0.003214612 0.004785856 \n",
            "\n",
            "[1] 11\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0001150248 \n",
            "\n",
            "solution.v:\n",
            " 8.0019e-06 0.004853255 0.3866199 0.5960619 0.01245695 \n",
            "\n",
            "solution.w:\n",
            " 0.03848954 1.54542e-05 0.3192508 4.32465e-05 1.88442e-05 0.4014789 3.39623e-05 5.00811e-05 4.9876e-06 0.0001176007 3.61404e-05 1.74037e-05 0.2403275 5.25999e-05 3.01957e-05 3.26823e-05 \n",
            "\n",
            "[1] 12\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.01331187 \n",
            "\n",
            "solution.v:\n",
            " 0.1341399 0.0001096425 0.01852474 0.8434723 0.003753424 \n",
            "\n",
            "solution.w:\n",
            " 3.92196e-05 3.48522e-05 2.54411e-05 5.3932e-05 0.780545 3.40202e-05 3.8007e-05 4.13796e-05 0.2189391 3.01944e-05 2.51083e-05 2.61336e-05 3.33229e-05 6.83953e-05 2.68352e-05 3.90463e-05 \n",
            "\n",
            "[1] 13\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.004595879 \n",
            "\n",
            "solution.v:\n",
            " 0.1367132 0.2925145 0.0002309036 0.0172288 0.5533126 \n",
            "\n",
            "solution.w:\n",
            " 0.1260546 0.005090514 0.08072839 0.001978938 0.01616241 0.003114587 0.4595572 0.002029671 0.0004701508 0.2110364 0.001848925 0.004299737 0.006190218 0.001746962 0.0759201 0.003771172 \n",
            "\n",
            "[1] 14\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.04121765 \n",
            "\n",
            "solution.v:\n",
            " 4.59581e-05 0.1556958 0.2850776 0.55344 0.005740582 \n",
            "\n",
            "solution.w:\n",
            " 1.53868e-05 2.90915e-05 3.43366e-05 0.000382392 4.71453e-05 7.36502e-05 4.81695e-05 0.6958201 0.206577 0.0001623462 0.09645799 3.739e-07 1.99505e-05 0.0002151348 5.33721e-05 6.36479e-05 \n",
            "\n",
            "[1] 15\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.00035292 \n",
            "\n",
            "solution.v:\n",
            " 0.006262884 0.03988411 0.3674422 0.5744507 0.01196005 \n",
            "\n",
            "solution.w:\n",
            " 1.93868e-05 2.24504e-05 0.6630612 3.28152e-05 0.17564 1.05485e-05 0.0548646 2.40473e-05 3.735e-07 4.98448e-05 2.69648e-05 2.06527e-05 3.85486e-05 0.1061362 1.58587e-05 3.65156e-05 \n",
            "\n",
            "[1] 16\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0009140678 \n",
            "\n",
            "solution.v:\n",
            " 0.06787093 0.4585871 0.1483528 0.3168044 0.008384872 \n",
            "\n",
            "solution.w:\n",
            " 3.33319e-05 3.69084e-05 4.76055e-05 0.0002086855 3.803e-06 2.78828e-05 8.98553e-05 7.87283e-05 0.2150532 7.0616e-05 0.06857928 3.81444e-05 0.1962268 5.353e-05 7.80092e-05 0.5193736 \n",
            "\n",
            "[1] 17\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "guW81B8B_qGM"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJzjkGbxitK2",
        "outputId": "a1e8256d-57dc-49a7-d436-0d4bed47c4ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "# now the figure\n",
        "rownames(store) <- 1985:2007\n",
        "\n",
        "# set bounds in gaps data:\n",
        "gap.start <- 1\n",
        "gap.end <- nrow(store)\n",
        "years <- 1985:2007\n",
        "gap.end.pre <- which(rownames(store)=='2002')\n",
        "\n",
        "\n",
        "# Plot\n",
        "plot(c(1,2),c(1,2),\n",
        "   ylim=c(-0.5,0.5),xlab=\"year\",\n",
        "   xlim=c(1985,2007),ylab=\"gap in crime against women per 1000 women\",\n",
        "   type=\"l\",lwd=2,col=\"black\",\n",
        "   xaxs=\"i\",yaxs=\"i\")\n",
        "\n",
        "# Add lines for control states\n",
        "for (i in 1:ncol(store)) { lines(years,store[gap.start:gap.end,i],col=\"gray\") }\n",
        "\n",
        "## Add ASSAM Line\n",
        "lines(years,store[gap.start:gap.end,which(colnames(store)==\"ASSAM\")],lwd=2,col=\"black\")\n",
        "\n",
        "# Add grid\n",
        "abline(v=2002,lty=\"dotted\",lwd=2)\n",
        "abline(h=0,lty=\"dashed\",lwd=2)\n",
        "legend(\"bottomright\",legend=c(\"ASSAM\",\"control regions\"),\n",
        "       lty=c(1,1),col=c(\"black\",\"gray\"),lwd=c(2,1),cex=.8)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ecHTkV9BvOP9CfklBUCatB9iynWbubssWomaEEJezxfnrh48Qj/dmjydvJQSDuM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YJ2e8BtwRTnWOB2nOyW2Vi3ZPmZz1LRte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\nhAQAOvD/AScpa7rcgcIMAAAAAElFTkSuQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "badjSO6FRAwP"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WeZ9V8AWi6PM",
        "outputId": "f46b7fbb-1a6e-4ee2-8bf0-6f9039477b97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "# dot chart\n",
        "mse <- function(x){mean(x^2)}\n",
        "preloss <- apply(store[gap.start:gap.end.pre,],2,mse)\n",
        "postloss <- apply(store[(gap.end.pre+1):gap.end,],2,mse)\n",
        "\n",
        "#pdf(\"2ratio_post_to_preperiod_rmse2a.pdf\")\n",
        "dotchart(sort(postloss/preloss),\n",
        "         xlab=\"Post-Period MSE / Pre-Period MSE\",\n",
        "         pch=19)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VgRm8HjRlZWXjOxv3R3bsrL3SL0e38I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\n",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jPAoJ48IY3UW"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YkWHG9_ubCBC"
      },
      "source": [
        "#### Discard the states whose pre treatment MSE is respectively 20 and 5 times larger than ASSAM pre treat MSE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "outputId": "981ccbbe-3374-4067-c5de-1fcbf8ca9871",
        "id": "2WDqSJkrY86v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "# Taking out the states where pre treatment MSE is greater than (Assam pre-period MSE * 20)\n",
        "\n",
        "store.2 = store[, preloss < 20*preloss['ASSAM']]\n",
        "\n",
        "# Plot\n",
        "plot(c(1,2),c(1,2),\n",
        "   ylim=c(-0.5,0.5),xlab=\"year\",\n",
        "   xlim=c(1985,2007),ylab=\"gap in crime against women per 1000 women\",\n",
        "   type=\"l\",lwd=2,col=\"black\",\n",
        "   xaxs=\"i\",yaxs=\"i\")\n",
        "\n",
        "# Add lines for control states\n",
        "for (i in 1:ncol(store.2)) { lines(years,store.2[gap.start:gap.end,i],col=\"gray\") }\n",
        "\n",
        "## Add ASSAM Line\n",
        "lines(years,store.2[gap.start:gap.end,which(colnames(store.2)==\"ASSAM\")],lwd=2,col=\"black\")\n",
        "\n",
        "# Add grid\n",
        "abline(v=2002,lty=\"dotted\",lwd=2)\n",
        "abline(h=0,lty=\"dashed\",lwd=2)\n",
        "legend(\"bottomright\",legend=c(\"ASSAM\",\"control regions\"),\n",
        "       lty=c(1,1),col=c(\"black\",\"gray\"),lwd=c(2,1),cex=.8)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fRUzP/4hy8IxVkUn0saENLYLFK99+zxEvPGwOdAc56hNPw9DfR8BnFoeEy7GaUr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\n+qyWDHa+49pHhNV2Rv/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7CVC9n5sM9h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\nQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "outputId": "ba1fac1c-1a37-4a99-afab-ec26b33acf7c",
        "id": "icizBv2jY866",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "# dot chart\n",
        "mse <- function(x){mean(x^2)}\n",
        "preloss.2 <- apply(store.2[gap.start:gap.end.pre,],2,mse)\n",
        "postloss.2 <- apply(store.2[(gap.end.pre+1):gap.end,],2,mse)\n",
        "\n",
        "names(preloss.2) = colnames(store.2)\n",
        "\n",
        "#pdf(\"2ratio_post_to_preperiod_rmse2a.pdf\")\n",
        "dotchart(sort(postloss.2/preloss.2),\n",
        "         xlab=\"Post-Period MSE / Pre-Period MSE\",\n",
        "         pch=19)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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E8LyUdrRV1iSqM0m+k5FfJ4046hQjqX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\n2b+r8pgd8F0NA0IKoZJ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            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSK4SV42RZt3",
        "outputId": "d76b3afb-71ce-42ab-9165-d484770da7ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "# Taking out the states where pre treatment MSE is greater than (Assam pre-period MSE * 5)\n",
        "\n",
        "store.3 = store[, preloss < 5*preloss['ASSAM']]\n",
        "\n",
        "# Plot\n",
        "plot(c(1,2),c(1,2),\n",
        "   ylim=c(-0.5,0.5),xlab=\"year\",\n",
        "   xlim=c(1985,2007),ylab=\"gap in crime against women per 1000 women\",\n",
        "   type=\"l\",lwd=2,col=\"black\",\n",
        "   xaxs=\"i\",yaxs=\"i\")\n",
        "\n",
        "# Add lines for control states\n",
        "for (i in 1:ncol(store.3)) { lines(years,store.3[gap.start:gap.end,i],col=\"gray\") }\n",
        "\n",
        "## Add ASSAM Line\n",
        "lines(years,store.3[gap.start:gap.end,which(colnames(store.3)==\"ASSAM\")],lwd=2,col=\"black\")\n",
        "\n",
        "# Add grid\n",
        "abline(v=2002,lty=\"dotted\",lwd=2)\n",
        "abline(h=0,lty=\"dashed\",lwd=2)\n",
        "legend(\"bottomright\",legend=c(\"ASSAM\",\"control regions\"),\n",
        "       lty=c(1,1),col=c(\"black\",\"gray\"),lwd=c(2,1),cex=.8)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JEkpQglvT1EAkIyN7NBaopQ0ttDJCAk\nczMbpKYIJb09RAJpxpSmCCW9PUQCacaUpgglvT1EAmnGlKYIJb09RAJpxpSmCCW9PUQCImRs\nZoPUFKGkt4dIQEjmZjZITRFKenuIBIRkcIes1BShZLeHSEBIBovESk8RSmp7iASEZHBmg/QU\noeSASEBI5mY2SE4RShaIBNKMKU0RShqIBNKMqZ1FKFkgEkgzMIsQABTALEIgbcjYzAbMIgSm\nkMzNbJAxi1BiIBIQkrkdsjJmEUoMRAJCMlckGbMIJQYiASEZndmAWYTAVJHBmQ0RtFfJjiUM\nRAJpBjWREq7AJxWIBNIMiAQABSASABSQJdJ5ESyCSGCSydjMhsMOmzPGLIgEJpfMzWzYsiDc\nVIeqHZhkMrdDduSc80dCZYgEJpnMFYm1Hf7HUBEigUkmkzMbujtCJeWjVOIJAJGAkJmR2UAV\niATSDIgEAAUgEkgXBvUV/amOIS4QCaQLFblmRWHH+NulBIgE0oRhZQnbY2MMDVSHvtECIoE0\nwX4Tn9kwXKNRVw6mOhYhEAmkByOqiwMdsl6XkSnqSnU4scgV6UMrvVjCQCQQS40+nNnQZWVM\nLl9Kw4lFrkhzt9GLJQxEAjF4NM7IzIaBSpW2ZiTB9lONXJEuvcxLL5gxIBKIwaH1Rmc2jDbo\nlba+qQtguDXxyzJFal7/43fyq/xI3k98IBKIxqsTLh3ka7MozG1T8/m+1xcetvqjBLVJuSKR\nMNKjiwtEAtE0akQbvaeqOdz4Hf8pfvpzPfG2kCvSNb++aWOQiQQYB4gEovDpa+O8MhXN4a4N\nWYQsvmk+p9KCzXFqXmj+BmmASzUa97XJbg4feWoBITl397A9u7/OqRSnhkdBpB5r58QijA9E\nAlEY7fy/cedsmMzm8IPf4Oy5otpf9h68grs2kVO3Cc942SIpzyPkc5a98n8TjFMUiAQiaVXy\nc/kmmrOhv0Klc0xCDa/qKv7W6LPwE5VbjuKeOWKTLWZDuSIZZy9Yw4nUumh2/oQCFQcigUhM\nFfy/iYeajzr0CrWlytVL8crU98BsQo7aFj0jd7doDU+uSFcsa3DxV6SWZesmFKo4EAlE0MH4\nrzXjztkw1FZTZFAwRluDm0ZfrW/vEk6Y61sELwRreF+NrOHJFem4R1m/SOwjx0iPNC4QCURQ\nUOZ/SG7OhtGuBpuRUWiLatoGZH2q+SJOlvMM4i9W3j0/uoYnV6Tst4Ii7cmRvJ/4QCQQppMJ\njOeTMGeDp6uxPF+p0BbaW/onVtVrv2sWIYtej3hzzH5ianhyRTrpvqBIN54seT/xgUggTNEE\nE6N9fc12i0ahyq9wdktMZBt56khCsu8OdL8OdTbZS/JUhth8pKganlyRNh1j5kVy/5XcLnk/\n8YFIYIwehayTYbCttkTP3TiVOtp7h5K8Ov3P3+RtH3I7eYMUjKGwor6tRC1s9A7U8BZwNTy5\nIrmWZp9LVqyYQ5Y1S95PfCASGKOkSP4+RjvrbXlKhUKhyS0oqahpbO3sj98cYf8FJ8fynUZl\nwKD2/sDlzFepFLY7cDW8r/lrePvl9iO13HYct6PjbxP5jIkDkUCIfoZa1oJ3qKfN5ai0FZm0\nDCeV2mi2VtY0tHT1jaVNjHY5i2/OIWT+ZlO5o6UnpkLYwNSI7fXD1XwNb4HszAZfcxXNqxEP\nRAIhbAVjRYqrUfiG+9zNDdVlxWaDinNKpc8vLivO5a5BD36Fu75siHNCtygrRSuHpb+dR86S\nLVJTgaIo8VgNyUAkEGSQGZs3aLJWo/AOdre7HFW2qsYO3Uru2nKhKe6mneoS8WaLzn+cJ1Ok\nF5f7E8zPeFfybhIAkUCQiryx4qRPot9xN9/kvTtRC9+AIV905fFezU3yRHqezLl0w+3XXZBF\nXpe8n/hAJBBgWBmu7EyySKO7j+WbvLvHCchkEJmlsltTki1PpK+tCdwK1pz2Tcn7iQ9EAgHs\nueGbksldjcJ4BlexWjv+OG9PkUbQ+uFWVQzJbLWbrQ0Wds6RvJ/4QCTgZ0QVcds/matReP+V\nTcjpnyezqa9cFTO+vU1pl92P9JVQLtKuJZL3Ex+IBPzU6Kdm0i3XpYTkbEs209XBNEb+2MzU\nys9suOmvwcLa30neT3wgEuDxaJxT8jkHTyBkeZz0VDFcTEQzeCPTwMoXqemCaz8qc9j2XX5p\nVQOH5H2JA5EAj0M7GZO9xTLywGGEXC/pjHOrraHIHIy/e4viLEIUZxKCSIAVn4OLPhXnEHLE\nmxLf1KezBNIhaoLNinJF+uk1UUjelzgQCbDCObgoZjaEeX0+IedJn5RxKM/IjzasVAX7izGL\nEJiuxM7BNRmZDT3XEZJ1t2gf6ziMFuh6fTZ1qCkcIoHpSuwcXJPQIWs6lZCvfDb+dmJ4S5Vm\n7dh5CpHAdCUwB1cY6iL5nsohZPWEK4weg8Ix9gNEAtOUwBxcEdDObGhZQ0j21qTaBb1ue56+\nOnoOiFGzviY8rgIigWmKqTLmCcqZDV8uJOQ0cxIb9tUXqpQFdfVGRYErrN1wnnGIbVOWBTuU\nIBKYngTn4Joskuw8Gmkp1ysM5S3+u7U+u0ZdHmxdGMw18YkQ3ZrCQMsiRALTk+AcXJNE+dmE\nLBin88jX4zAzqiJnhNDeFosi18EZNGgoCLSEhMZVQCQwLeliJnMRMb7z6PyEnUcDTquaMdnd\ngly/IYdeWVSvKwpV8kbMen5chVyRfHvXrjgzgOT9xAcizXgKJ2Vx4gDd1ybuPIqqzwnxuYsU\nSvtYy0NgXIVckbYTMu+oAJL3Ex+INNPpVYgs6UUpsyGP7zyKN2KCq89ZGJXFkeh62KmyNeYp\nzM5g3oWvQtkif4LINdWS3z8+EGmmIzYHF53MBn/n0aUu0ddC9bnETeLtSr49sacy3PLQwDTI\nFSknV/LbkwAizXBE5+BK3CHbf+jhB9/OG3eprqZVhMx+UmyUUydXn9OXt8Zf0SxICxPsKPa4\nChR5Df5BTC5lWZbMK5KEURzJA5FmOBFzcIWJL1L3Z39ZmRMYf3D8RTc+8u9CkWkVAnxyPCGn\ni+2c7WaKGhPNuu8bGBge5XAyESnpA9U6pbXDx4+r+I08kf5Ec6biMSDSzCZiDq4IxDMb2v/7\n+3Nm+R3KygqP51n8/zZt/7Astj1haDO3yQ2i55YnN3bpsCj67FpFGEbNoTVwGPNyNYxSn2/J\n+7s8kXrXXHvAVuVH8n7iA5FmNhFzcEUgzGxo/mjLOYf5xZl1xqa3Diq/eH/7ptXLI3w6YdWm\nbXutodqav/PoLfGPtBni1+lGGkyK/MaBgYG+nkplXaebo4XHydHgcNSWaBldwQ+pDeyTvJ/4\nQKQZTeQcXHFxvHHz6YETL/vCLZ92s63qQmepTqErbWrOf/eh679zTPjMzDl97T27DtW/Mo+Q\n78RpG2tm4k3D5W0rVurswbpilUrsUsmyXWWq38sTaf2GjSEk7yc+EGlGEzkHlyiVL/96acCR\nuZc88D/+JPfVMHb+TQOucr1Ca23o4ep8htfu++XZ8yMHcB/2lzjzmwyo61iv2IdyVTqltS34\nis8msiBFaA/LkdkAphdRc3DF4rPuvPqEgBbzf/QPzVDg2WGLJnypiJCJw8m8+Kd135jNv2FR\nvBXDfeaCNqtSoTEWlFbWNbV3DwXav4cb8hSmhjH3vCUakc6tIBRShHqs47Y5SgUizWTizsHl\nMT+57riAREdfsd0Yvqnp1JpiMlyDMjlD/are2i93/jNmOrowNqWau+50tjbWVARn1Vfnms0G\nRl3SMjA22t1TqEvQSytbJOV5hF+x78p4sk8IiDRRBs31UzHxzmQSbw6u0YeODLZw//zpwqhf\nsoEpF/ulY2WKw2CtXmFsimxq8Ax21VuUjD4vV+1fqcJcXF7T0GI2JEpHlyuScfaCNZxIrYtm\n50veT3wg0gQZyTVp9LQX2Zli4szBVXY+L9GJ1+6KbaYeLVHFX5xrPJlGnWZFrro0+smhhjwm\nmADkHeppd9VVlVqM2ryhRFHLFemKZQ3+NWRblq2TvJ/4QKSJ4TUbRz0Olcmd6kBkID4Hl/fJ\nuZxGXxdpc+s15Mbtfg3QH2zN64tt4Pa22VSa8i6rMdJcj8vCGGolj4WSK9JxjwYXY37kGMn7\niQ9EmhC+Ej3fBTlUzljS9/jFzsHlp+6HhGSdJJbZ0KS0imwvwC+TQqkzFdns9a723iEvnyzn\nb5BzKiMOVmeZaiyDThJyRcp+KyjSnpwJfHo8INKEqNAEv5v7ihjbRKaYmgbEzsHlZ+/RhCxT\nimQ2eMuUyc/uO9Tb4aq324pMOqU/PUFlKq9pbHWpxmYwGajRM0UtE7vHlJ39fV9QpBtPntDn\niwORJkKNMvxV6s5T2cdNv5yOxM7BxdGyjvhHhQszGwbydOMsZyTKqDNfYbDW1VZaC4waXild\nXqGtqrZAYXQkvA9KhFyRNh1j5kVy/5XQTLqDSBOgiYnMB/C16DUNU7OUA1Vi5+Bi2X3HEbLw\nI7Ft29SFyS4gEcbbZlXqKsc6hCq1fX3u5vrqsiJzZfxeovGRK5Jrafa5ZMWKOWQZzbYiiCSd\n9ui1RrgzxqHOpbrW/FQgmIOr6xbucnRVu8imoWQGKfi6gjdGIToYSg0zsvuRWm7ju8iOv43q\nnwwiSaZbVSt4bsSuNE/kxjmFxM7B9eViQo4SnaRkxKIR0ysR/TUGxuKKbJsY1gougBOEQmaD\nr7mKds/Fru/VS79mz2gGtOWiT1sZa6JBNtONmDm4BrYcRsiaRrEtO7X5Epuou0wKU0PM9a7I\nRKv7eprOIvQrpcJY0ZKmDU8pYMRQFKeW012gLE+fL6XoObgMpxGyYHf4F4uYs8HBVEhUwKUs\nE/Q3OVTjdEElj2yRPLp97wagFRLLV+16+pxWjcJgc07qLIGZgicvP35nSptB7UiTtKGoObhG\nts4i5KKIYW7hORs8VqXUiVBqGIfguR6l+NQNE0GuSPnLJ288ks8v0/jJUjMeX6EhdPH2icwh\n5XNqdc60aMCLnIOr5GxC5myL/AYYG2ren5sr8ZTwFKuFjQqeXIpTfskVaeXRm59/KQC9oCIb\nG/oaSrQKva0pnWr6U41NG7pu+0oVWpFmH0+N0pRUiv6QvSN1xvk6FGN/ds+22YR8O3oqoZBI\nruSSGSIYMBpE6nBlOoo9bXJFmv8BvVjCxLTa9Tf6k6Wc1Cq0mUWVKnS0vCWaLoeySKQ6nFza\nUL9er9SUC2cXnXwGW+0FKsXYFaL6YkKyt4iuRuEtZ6QuVdypKRRRplVspqIJI1ekE5KZzl8y\nIs3fgTTeIkdPWtRRppLGsZlCPIVarsrTl69yiBykJNKGerTF3lFXEaOxTaVLo10NVq1CZa50\nheprvt3zCfm6KXZDf2bDYL5O6vnvZMpFfp0hTa3kUBMgV6S76K9GyMbtRxpqKTcq1BbIFEkL\nE7pj9lj0gQqwU2UWu4cYL22oU13qP7C8S7rKiXxde4aktWp4uupLDQplfoWrP+JP2nQ5IVmb\nxKsf7ZoCiW25Prtow4SvoIDqWSRXpP4rf/mWQuOHXlCJOmSHmstzFWpzaU2TewA+cXYoQ6MO\nRs36UJ1uqISxi5zSviadtj5uNlmrMtxCNtxgZvT25HvFfQPt9eUWnUKh0OYXVzqaOwfGM8rX\n6yzLYxijrbE7ZtO9xxCyVCH+pmqmWuIffcSiEb07rFHTbQ+WPbBvaQpmERpuqbUV6BkFoy+w\n1bo6h5I/tiN9nS0NNeV1rZkiYa86dPYP5+VGONKiNYqlc3rq9EyBU7RfqYmpi/p5qD5fkVsz\nbuvYcKfTXpzLKJR5JdWu7oHOZkdlcb6W4YzKK66oc7n7RYwaaK4yqxS6EodbeIFs/SmfoSqe\n9TYsPZlhIDdPVJguhnL2lFyRLph79X1bA1CLKckUId+gu6nGZuaHmRgsZXXNXXGu+f5Bjo4q\nW2Ee//dltEaLNV/F/eFLHW1p30k1pLMGvxGGAgtfjTFiYypF27b67FrG7BScwQ5GOMB70GFS\nGGvjNPF4+1oc5WY1Z4yl3NEW+8U02tPmtNssBsb/17HZnW09/o8cddcUabgaRaWwt9070DnA\nfrqIkIUfin9iuy52ZoZx6VAXiVZmR/WiiSAykCvS3HEWa5oYknLtvP0dzmprvpb7k+UWVjha\nuvkTytPf1dZYW1FS4B92r9TnF5VVNzS7+4aDf/DBdofNpFKo8sscHemr00huQfALf9Bg9p8x\nLabK0KHr0OvFv7/9mZtFUTlnrD3OVHIDtUZFXl3UEfL2tznKC7jDrS0oc7SJXXEiP2uoq7W+\nymrm6g8KtZGr/KnMVc1RXRmcP666iuI8fibTLzbyGaric5SM2hi91H5lR3iN12hKDRIb0MdF\n9ghZi7T3+qoP7t9/SGwwcSQTSlr19rU32ktMGoVC5R+5pc4tKKmsbWzrGoh30HwDbXWleUqF\nOr+svmPCI1FSBz+yPFDq1xXwv2RvgbLCrDA1Bp712hlrvKt0m02lto1lQfvKVPGToPtqchX5\nwVurjqoi7iKjyrPWNHdL64TxDXe3NlQ7e0MfydUTWhrsVjN/zVKbisodLvfAgRMIOSZOhkyb\nNvfPElej8NqUcepvTUrqSdFyRbr5YSnvdN+zMHA/teyhhB2ssrK/Pb1trvae5NuPfP2ttVYj\no1AXVDS40yctjR9Zrgva3+9fQG6kkika4HOc9aGRAr0mtfiMPGxgXI7G5vfHW6JJPD6ut9qg\nMDcMs61MUZXTLecrR8yfoFsN/N3RKvF4R6xMtTfxahTC9xTEG/XXrxrvm1w6ckXqvPT2g0nP\n/d10Cjn9hq2PPfa39YvJ2YnGgaRgGIWvr6WmhLtp1lgqGtNkDEdlaGR5j4a7U/I5NbnBqpzP\nbVXq/EvK+eqUhfFrrsF27lGLbvyu7u4qHZMXr6aUHL5WvkLI6PJLKutbugaj76p67z2ckPl/\nUIieFi2avJ7xlnWJpVcfb9ofr0lk8SW5yBVJ0tzfG3P2BkuenVmbE2yYsvFI3t7m6mKDIi9+\nI/H0oTY0srxLbfOxbmNUauqoMzij1ECBqj5BC+WI06xQapIb3NasVDIFdRPtxPO1GJUVAn+C\ndO7hczavqOvVMGWCQz9crKzl3yW+GsUYXcXW9vDOW1Vx84iqtJMwrECuSJLm/l50U7h8zdIE\nG6Z4YB8/CUZh80RvRz1TM9DAg4wAACAASURBVFdCExO8Ke9UlfuGbMK0hdCSci7/13lcBg3a\nXIUpiW+OAW2pt9Oep9CWNks/EX0tucryOJ/hafz0R5xGpx3gP0SpU1VHH0CX2hRohBfO2RBB\nRwFTwl2Hq4O3DHGbGfgxT+IT4ctjSscj5fwzXH5wdoINd53R0NU/PFm5/94kOp56YoYkJ01f\nuUqhNZfVtfTSbheKpj3UXN2utHuqlQVi3zzBJeWGS7gbjHj76dcVjLJ91XqmoHGEHXHHndWX\nHcm1+Hcy3FKuVRjHWx4yGq/ToLLHsW/Aztw9n5B5WwOaNarqDZqIayt3OaoZ/8/Qls9Y+/jr\nsElh4q7DHqsq7i8yoqW5ANEYFERq0x/MTW7275OvDpfXLU+w4a5b/RMmqfR5lpJyu6OptbN3\nKPnzskHxifA7xzvY3dZUWxloD1dZajrGu25wN+PcDYS0K6OvpUBhbuluri3N1wSah1sFPg27\n3U3VHPkcmoMHD365l+O13bt3v7CN4/4tWx7Y9vyrez9T5JfVuOPf3nSrgt+4LUxNs04ft3fR\nv6Rce6vOEOcv1KMp9p+0XpdFpVAqGKYizo5GI4Y88QsWK8dZsDgCb6NeXTPC5x7bGmOOOr9A\n+DOnEJK1fqyRoTDf69TqQ2lPLrV53Ps3X5tJaQtt1WfXqEty9fFDKzZOyhe0bJE0F/oXS1td\nksQbN2dtD17d+x4gWxJsyFXtvMN9XW2u+pqK0sJ8A98XpFBqjeZiW1VtY0tHj7hVzepX/vLz\nbx3Or99xzp8O9I/ZYw30JqlyzSUVtc62rv7WKrOSMZa7xhmbwd9AGB1J12OGa3Wq8jHzRrqb\na0rzFf99e8/LLzyz/aG/3PHrdT8455Tj5xBJHL1w+VkXrvrp+k1/uHfbMy/u/eigKT//0MED\nbz30r927H9/26JY7r7rm55de9ovVqy45Z8XXly8/5ujjNsZ8F/vcNqWuspQpE/vmcKtsviF3\ng82kVKjNJWaVskBV0Cn2C3stxugGTU9buV5hKBcZ/hSDp16rqRvtd1q1Cn2JVhl5HzPs0Cu/\nXMv9lt9Uhrfnk0lH7cp83vxBi1Is/zb693PlqiojK43eOkZhiPtXa6A3KDYK2SlCc2ZdvPHO\nGy/MOjKJruLOc8mC1TfceceGH8wjlyT6ro+5R/KMDPDdq5WlRQVGg1bl7yTS5xVaOco57Pb8\nfU/ctfbMI6LPwZwVNz3zpUJlCNozGC2ft8tRrFVoix1dUV9Qo+advzk9Yt3Efu52KXq+jHh0\nWRlD/Sg7XPnlO7u23Xvnb9b98JxTj58rTRuew6S/JZpjX4k99/gaT65WK+hyHa1n8iwahdJo\nc7T5f1tvW7mJ4fuJSuxOd2SzgK9EJ3J1HHBaVYzZkegOzFOn0dobSrUKna1pkL94lHD3MYFT\nuadcaah8kPvaO/LJKBtbmJ7gsA+natzLkddlUFVGS9OiLBtwGBhLi+h6R5KH1iaJXJGuPCkw\nyr5g4fpkPm3HCv9ynzkrX0x4bu66oMJeUWYtspiMBo0quHCnWmMw5luKreWV9pryAp2C0ZmK\n8/792B1rv3Vk+CzKPmnlVb9/4vHfnBk4H+f/+AlLgiv5gKvCyCjNVa3+P0X93nu+G3Hmzzpl\nzd3PfVHj6SpXqW3jtGt5nIZ3X372gV9/d3E8D2YdddLXVly0au31d9730EMPPfosxytvvv32\n+5988snnGr1ebyqvqKhoaml1u3u726pLTAc/2vviM9vu/cOm9T9ddeFZyxceLb7buQuOWr78\n9BXnfG/V6p9f9atNmzbzt+3fE1YPuBqPkjGPfXH7BtpqrEbuIl1kd/XFtENrilrrKwoNjEKZ\nW1TZ0O5PXSjTxEsUcttN/CBm8QvAaK1abdLxEoWv/SMNeQqzc9iVx53qn5zKVWaujx3vXeqv\nfPUVMEzM3ZHAAU+DTlMTc00MjCn3cX81rbBi7jUmU3OaCLIzGx4JFh48Ibk3D1aazVXj1ZZ2\nXW8qtNrK7TWOxqaWdndP38BIjHi9lvf+9ouzIs6uWSdfesdTn1aO1T+6/nv3NwKvHHfVrsrY\nD4hgtKPGovr0+T/8+ITQN/qPo9ZNnH3GT/+045V9Y6sfxuBr1Lxx/y/OOXFW1Pl92NEnf+u7\nl1296c//eHrPfw6aKkJDbYY7m6pL+StpscVPvonHaPCj4Zf4VXPXWx2fvSbIvRl015TlKz7b\nd/DLPa/lVlTXuN1mhUo4B+S+xdzXyZ+F9wjeFrNCUTLMD/8pN/M1ufLi8Ex4o4O97tam+prS\nzxzubm0Jv1PfQEdjVbGRu2/SW/KYugRtJ8MtNjVjEjY/9BQpGX7yekEFutemVChL+muu5I7U\nt4WjBkZ0Vfz4eJXJqKqJ/NT7YzIbRmo0uvqYsDzF6lBe1GhjvsLcFP16uX6ymlRlz/39RrDw\nGtW5v+efvCIe5/AsCp+zWQu/c+3DH9jEmlZdb98UTE4/6cY3413SvdZXNn0rqEH22be/Ebjb\nbtO/9terzjo8/DHzv77q5md0ERemZsO7j2669LTZkVedky7Z8ODrh2xNshrvR7pc1dY8FXcC\nF1bUt/cLKyg2rf/UHDAoisQSMXp+n03IUrHEz4FC7hqkUObZHO19/d0lyvI6e7nVYjJoAld8\nve6vi8hpWw9plYaIy9RQZ1Ohgr9L1RWU1bV09o+I1Zh8XTX5jLqkaeyvMNBk5X6DfKfIXaiv\nrVBhqig+sJG7XTzqGbETu4Pp7MtXN3G3PzptY/jjojtkh+0qgzPW3QFj1NIUfVUalS2imaWV\nob4m3lg4MkVaHPqS+PMSKvEE2PX1OBWkKBZefMOmB3Z/uO+157bv2P1hdygdVXkwBL8UqX33\n1ccHNl+67sEP+Oc8/Z3dA6P+7f798LUr5gX3tvSqh95XPf34s3ve/yT0Xr4BcNfmi5Zkhz/z\nmAtv+Oebj/925dJIgbIWXXDV7x6977Hdu59/5snt2x9/qaDc0drnFcQiFl/c57o/ef/1l557\nYvvjz+z+d31HII/Gv93rOz7k/tVXMUxTnPcW8ksJXfR27Gd88fF/3nzq8WdfePalwCTymlz9\nKy/veePt9/d98OEnOt/Qc0v8v82yu19+4vHtT+x89Z2DgZpDM1OnPPj5h3vfennn49t3HlJo\ncs3FZdVV+//z4adfjH2Gb6i90vTlk08+v+edd17b9dT2FxS6ep9IfCMOvbK0RXnwIa4CkHXR\nS1+IHoNyNVPMOzmoeHvHs/tC7w2IFNju0z1PPLvXEPveD57crYt+7st9ux9/5r/B+7uhQ+9O\n/O8xznMamSLdcMR//fWA/fNvlrQP++rVMc+4b980xiURdxrfuirAkogz96sb/vG+mbsl/VnE\ncxvVxQ18SmTkc4F+K1/RtyOeuzFwx/Xliojnlt/7X39FPfK9fwymu0Y+J8bFj79xgB+d8YOI\n5+6xmvxf4ZcKYhGLL/q5B4eFz92m5G9YqhrXRjx3i6or7v5+GvXcaG9bQ1VJZHz3dfUUamKO\n35VclZCc/PvjI3+3jbqi6haXsj5yu99yseiM+abI/d1m0PAjkPJXR75XqcvNL7SW/ygylm6b\nUl83EvW594scg9sUZuHv5l3tz2wY7/iJPXdzUauXHxT7wwm8N9nnZIpUu5AsWnXlqkXkRGkz\nUlgEKUVRIp0Zju9OSzF3T2Er/7+ImLc4nU0tHJdHPPe3OqteoS6siTzZ/jHU0+6srSiOPICz\nvrdV8eYd5+VEHoONjDbXXGSrivyjb+LvVhQqQ+R71z983fnHco8RlyiypbmjZ3BUeKC5GlrN\neCI9NNDT2d7sdER+7kaF0lBgq268IvK9vkHuhqUo95LI+HqS+wPfxvebGYsq1kRuN1qg7489\nEQg55ZURtu9bEU880FRlUSsYU9Tv4ev3ZyVeHBmLRsXwlcMfRDx3f3tzI191XBXx3K2KQr6T\nuz+ywnFzcFWvyFjubgukbUQ+dyvzj9Of8kzsJL/fptJU9dWpfzKB906RSGz9hqO4nRx7s8RW\nxcGSRM0nu5a//f577733zttvv72/ym6vLC8vt5lfe+nFXbt2vbBz585X9fy9uVat/t8zO57Y\nHoCrcgQ4wFWutj/5zHM7dz6z229CgbWycv/+jz8/+Mm2a06PbFQ7euVN2/la0hfq7g6XvThP\nyxx48nF+X0889ezO10wmo5ZTifnfc089tYN/+vHHnz/ED85Q7f3ki+dffX/Pk0+8/J/PD+hG\nRgP0Hjrg58sDB3QDPH09Pa2f/Xffu2+8uuvZHdt3qgz5XIWoxlH53uuvvrT7heeeefIJf8wq\nba7JuOeV1954+929XB1LNdTX0VRjs+R++eT2Hc/ufvWt9z/4ZGxx4oEv9r/z74Ofv/L4S7te\nDN2HCascw9317+/Zvdlfd9rxb0drz0jUdl++8Xyp0TgU8dwnt/HfDqfu8d+vDB74Lf/Tcb/+\n1L+/AW1Je535hce3P/n40+9+8uVBTWeLo6LIqFYcePrJJ3ds3/7Urtf3Mvwd/Uh/V1vNvo8P\nfvzy48+//tKOFyyNg+H4vvz8kw8/2Pdpo/8GZj9345p12b5gzIN1uYyl2TsW34fPPnXAx1bz\n+XDe9g/f2bPraf7gP7PP2TnUZ8gbZFte3b77A7Eq1us7PohfFfM0mRWKFkrVuMmo2r3ZzPqa\nquhNWBlAcq6d138uj/Dnb6ejSMudnVqNkv8mLqlu6oxsh3Dvv5P/Opyz8vfvBUdW+/paqov1\nCoW+qLq518s3YPGrhlaWmHT8l6xSyf/LKPixtWqNXm/Qhlrjk0epVKqUKo1axe+LUWn0xvwi\na0V1fXOHu7N3oL+Hw83Ryl1iXU6nMzTadISrkdlL/UPiNHn8RAhdAyODfd3ddaq8Dn++tnd0\nmHt3l7u9pdnZ4Ki1V5WXWUssudzvrTGVVDU4H5jL+SDoVGL5nhplRKtJ/xP82JbTXg/f9g88\nfSL3zIkvcOfykL7Yv4OhQsZcoOZ+leDO2wJ2jnY7Ky1aBZNbUtPibxbpLWEs/L69HRU6halW\n5M9YwV9+zzNGPtVVpg4tk+e1M6Uj/Ohbrd6i94984v6Aw2y7UV3H2TpSqDIpisUHR1QnGFIV\n+D3Fhy5SQnb2d9aKLYck5DBO4sC+CAKNOaM9LXVl/J9DaSyuauwYGw3t/DAv0No12O4f16cp\nqHR2+U8j33Cfu7mhuqzIP15TZTAVFBUVWkz+BeO58z+P79ZiIhRRaY15ZnNhYUmJzVZZWV1d\nV9fY6GpudDU21NbW1FRWlNmsRRazKdegUY+9TckwjMA1vl7EoeOvtCou3pIal7u3t6ujpamh\nrspWmM9dfpURn6pkNBE/q9V6Q56pwFJiLSuvqm5s7ws1+VZfRsQ6lfp0FitTFzwafY/zGp3+\nZnQz8dDOk7hnT9rZawwMwe3QGXv8B6xDbETsiLuRH3SuzCstUhSGZx/qqclT6CujZ/bqu3c2\nV3/ZHbsTT7OFya0bYjv0mrJyi57PQmGKmiJyLHxNOl2Tr93MKESWkeWpUU5GKmryyBXp3U1f\nI2T+5U+Xxd06kikZ2BeLt7+9obIo198hUh5MfhvpbPD/6U22+o4+/6j0sdXbtHmFpfb65s7+\nyFPLM9DV0lBtKwxsoqnoHeUH0rlKVMoiZ7IjLvgLZk9Xk72Ev+ezllpLiiz5+XnGXL1Oowo0\no3GXLS3nkl4b0o5R6XLz8kx5RoNe478mag25piIT99WQb3O6e3oGBkZGE6bQfMT5kH13dOpB\nl9rqZVvUZr7m1fcU35Fwym5hI/TI66fyVfY7+fd6Kpiq8RPUhjocZaUxV4vBBgujtobziD7i\nanWHXS96aRis1TMqfrSftdrFT7/hiJnmx1OrUivLBlxKm1gotUqp06JQhkLSatPbt5zOqXHL\n+G+cgoF93DWls6XB4XBwdSRXC58o4O7u6enjqnxcTanRXpLHz9aRz1VG9OYSW1lpcD1RflR6\nRU1ja2f/uCNkvUPdET2P3vbyODUYIaPuhjKTUqEtCPTF+jOcAtjKy8tspcXcxSvflGfQqXhl\ndIZcfeAWnlEb8ktrXe29wfkmhvPy+prLDApNScP4WaPCTqV2VQW/nyGL2tm7jW+jO+MdcUlG\ndvMNpSc82d+dq5exGtdos1WtLGwcYr3WF/mGm+9ETfzoG+rt8K+Xl6/nfm1NfcQ1qMAc8x0x\n7M/M6NblC7+6HEz8tPWpgdIwiuo7j5jkgX2+4X4+ibW6wlpo0nO1IANXmzHzZ2N5eVVlZTl/\nHprz9IFrSm6+iTsh/c0RsXc0DPeUhr998k+Iwq9w3dErcVbDWLpFajAxsQ+01nC3YYyx1NEh\nWg0eHervdrc2NdTay61F+brADZm/9sbojKY8vZKr7VntLn+ubmi6oKEkZbJcwJ29PxlbiqF5\nbKBO2S18Ysg334v/2xdu4fuzj/utSWY6gLfD/OSGlQv4qsjxL3tDk6KNzUzjX8HV4Wrvjh70\nN6iOuQMIZjaMFAhmqqtnJvX+JxkoiNR38G/fn0OOXTv+G6UN7Avmf3PqWPL0fiH4YRX8XXpT\nW1uLk7t3sBaYDDpNoFrEKJVR9x5+1wwmUz735V/C16ZsNlt5RXlFJf+fvbahxR1Nd08sXaGX\n+FYAP84gLZ190VeuQA2mWeR883Q7K8wqhbqgsqkndMaODvbxdz/1tVXl1kKzUa8OKm7IKyiy\nltsdro6+oG6jfe2N1bYCPd9uouZ+UUZXaNcXhOucQy5OJm1JY0KZvLs4YeY95o94uDo0cXb3\nP/i2uW/sTfAlUqXuMj14Gn/2b5Ozumr5a5uCmY/kyF8bLEZ+UjSF1lhg9c9M0x9f0iZl9O8V\nymzwVcZknjoZ2ivdSUeuSJ/8eWU2Wbr++ZJkxsAlP7Dvl7mx6nT1+Qf6cTc8jXZrfrBCZi4u\nr4mcZIs79UYHB/p6ut3ujpaWZqeTq+TV+pvPbVbutp9PbcsN5bQF0trGJ7Sx1hAkzxicH6+w\ntKquifse9Z+Loy2lasbSEFGvH2qvs+YyCkNJtbPdn8hWaSsuyAtqo9QYTAXFpRX2ugZXq7tn\nYDhxgvlQd2t9pX+GJEZdH33qDbrK9AqtNZFMHbdkEXL6Iba3TGkIjFzq3cZnE37zsUMJ7n0c\nSjc7mnvo6bP4e6WtE8qt6dM8dVWoh3f59ds/KxT8vRJSbIraLpwi5FRGzubdNA08kt9qd8SN\nb9TF3TKG5Af2fbfKxasTPlpRk9eVO+RXyMTwjIZJuPdA615NWbHZ36CnMviNdtWX6hTG6u7h\n3mZ7Ua6Kk0Vv8N/x8O0TBpOlpKyqtsHVxmkzMtHYvQPuaq2qIlaaQZfNL1PcQQeqbxCSdcX+\ngsCY355tfKXuzNe9rFtnjHd/xy+SPmhR5ur7PuKTQI7YIvE+yfnRlouCvd7zLtry0UTuYUa0\nUePFI+Zs6NKax+rILpGpLaceuSJ9k2Sfu3l/ki0mUgb2BUu+AXdTTWnEdKrTblYSDz94sK6q\nNFBnYVSq0BXHzFXT6hqb27v6JIzuTQJvi0VhEqRrjsnEiypsyhv8++GEHPUU/66gRnsDE+aX\nxhk618adni1q84CnUD/g++hcv0rJNjCP5D91fWgq60VXPaWZ8FwjbUxkK2DknA1D+bpgdbMl\ndjX31CB/VfP3bv0ayfrWHXuTuLxKGNjXM9jpqpngBN+pY7ivs7mhqrp1sm3vr1Rp7SIj7TiZ\nAqOJFSqd0VxUWlHtcLZ0dPf3VGu0DJ8j9N2S7m18Jsq39o4dTpfKIhJup7J2pMQ/HshbzK8V\nc5DPg51/9/g9703chSg4CjjnnLv3ypxh2xZ3RlSvLbBuZev08IhOq53zrY2nJjWJftID+zZx\nAunMtpomt/j0TTMej9PIWOJUl0YHezvbXP4mQEt+rlYZqltuX0hI9nw+r+CTyM0HzWrBl2Cv\nurIjVOsLmMR+8h1epVWrV196VYhf8mzYFMEvQ6nFJ13zdB6FyTZH9fGHkjmZSh/nkXBp2JRA\nRaS+g1vXzE9ybqHkBvZ9s3HcdUFmPD02xuAY92T1tZgZc2N3R4vTUV3wG/5r7MyXO6KPrc/B\nWGMaMLQllUxlaCtfid8k9vOVJBlmX/j7f1O7a+lk4t82dKgLW5RJ36BPMrJF6vjojxdmkzmr\nHs2nFlPK57VLF4YdOqUt4ZEabdCryiOaICyrfrDfYVHy41kjr/TdBn1kq9xIrinqCV+pOnBD\ncnDtOeecExph+dXlkSw+mmfpzx7X0a3XVurif1cM6BWJxj7H4J3UZYjlivTtLJJ11j0HKMcI\nkZLE1yba8BBkgLuVqhE5Dz38RAsaqzN8l+WpjFiZzGPSMbaoqrfPpk48Ofjk4TWWjpVjRxi4\nlbnqJNu5PK1W1aRmP8gVacmGtyahER8iJU+/Xa2uFJsAr6tIYXLFvcMcbinX8dNphTxr1eQH\nr1xeM6OJPeV8ZSkzqWdsSbDYORu6VFW+GiaJup2nxapSWVtrlDRXX45hSmdaTRqIJAWPM08w\n+ZTXZWSKxjtvBpzW8Kwlw4Uq/52NN09RKHIVq1BN2nQH41Abaj6PmUSf84j7t11dnDiBiV/B\nRhVYDapSPXmn1dBSiJQB8HPERTQ8DNdoxK9SAvhJUxmVxT83XYOyeIQd0CpqRbesHG+8z2Th\nyw8uHhEtUrcqcH/Un2uMf2PhCVvE78kqNjsfFTqNv4dIGQE/a6k1cAnim/MaJPQBe4Jz0w31\n5WlrmLj5n1WpMqlfGWgEjFqNolcdGrkzUhhnNY1Rl5VfTS3iDtJbaJiEhSi4S3sJU3ocRMoQ\n+Hm0TU5Pm0Vhln5XPei0ahTGyhKlIv57a5QpGqvQoPJfcyIzG3o1ZWN1WZ9dpDNp1FWkjLaI\nx2My0V/bgF/DoAf3SJlEf6WaiWruloKvp86iVCTKXageGz3nGx0a6OlytzY7HbWRc+KqKybl\nD2eJHZrUq7FFPtOsKo2eeJqzSCOwiCe0pgY9fE1afg0DiJRZeNpk5RN4EktYq7SYTQbd2ABe\ntd5gKoicE9dVoMh30v/OH1JHX3L6NKUx8yzr88bufkYCFsVprxzUWqnmynTnq2p4NeWKpAkl\nMhr30QgrCESaprjsdfXO5lZ3V8/AkPgg9/4qjapczvAl8c+NWj25XyhDaLQfZxET3yKePnW8\nlWsmwJCNsQYMlj2M4oNg4fFj5Ic1BkRKY/jsdOP4uUvSsEasajSgKxaKwo/2G98ink5qaUWe\nGmV+qItBlkhVn39OHvjcz/4L5lGKjgcipTcDdq3SSrWNb0RrD2U2DOoLRW9zGhlGV9mZRL2t\nlaGztItLqwvfUcoS6dHIXMWrqAQXACKlOz63lcmleVnqYDoDmQ1DcTxi2Z6uJO9+nDSShUI3\nR0HkVe2aPiS/ftTPY/toXsshUgYwxPds0bsslel/yHfIDhkoNLvVKuWmaYRvjoLIvUe6wiAz\nIlEgUkZA9bLkMazkRBrONdNoFKySlywUeXMURH7zd2CKqAKqbYoQKVOgeFnqOp+sGjZS8Yhl\nbXKShVr0OkF3m1yRPLdz90a1pxJyMc1THyJlDvxlKf7ayFLY/bVXjHKn2Avhm3iyUJdJVSus\nXcoV6VHyB5a9POu22w97dIJxiQGRMgo+D7BIdG1kSXjLGMZOK1fOY45NFurrTUbSQSsjujqk\nXJG+9XOWbczayLI3rZC8n/hApAzD11HCGOpkSjBgMtYalDZKA6NikoU6C/k5DPOKK+uDy+CI\n4qlWmsU/X65IR+xi2VfI/1h259GS9xMfiJR5DNcZmBIZKQ/eWqVlxF9RjD8kWBJDunB+RFcR\nU9TjHXA77f61FpUGS7nD5RYMzxC7OQoiV6QFnEjr53NfNc/Nl7yf+ECkTMTnLmbKJnpV6jBo\ng0OxB+xq0bnIJDOWLNRlYawRSYZBobQBoWqc7uCCQN1mlT1uU4fsqt21bPMRP+MKt3xd8n7i\nA5EyFHdeVC9m0gzZGNvI2JwNnsZcxkphwG63ik8Was9nbKK5uqN9/Nq7/DzRytzCijorU5pg\nXhe5Ij1CLlpMlCz7+uw/Sd5PfCBSpuJzaQ2SJ430NahM3dFzNnQUM3lNsmt4bUxTm0lZNs7U\nPZ4+/3zzloQ3Z3JFGrzh8KOe4R5P/DbN1CqIlLmM2pUWaX/dTqO6ga9cRQ81H0p6OH18fJUK\npY3ODFi0xiMZKLXvB4BImcyAlbElf6s0UhnaOmbyE9bryos722wy+NrylPmyk4WCYGAfmHrc\nRrUjyXqZS5MXSsaJmrMhgH+22Yl9h/taclWVQ3KThcaQK5Jv79oVZwagEk8AiJTh+Jya3GSm\nduyNTLGOnLNhjGGHTlU+/hKgggBcnEb+C51NS6VuJ1ek7YTMOyoAjXCCQKSMh6uxWcY7/0cr\nmaLx74K8LWZF7LR+473FZQhqJCtZKBK5Ip20Js5y7bKASDOA/iKmPGFmeIvWkOSKTDHT+o2D\n16lT28P1QY85j8INvlyRcnLlxyAEIs0I2gyahrhXkn6LUkKX03AtV8Nr601CJk+9VlMXZQ6V\nmYVkX5EwHglMGF+DOlf8ouOpUVqkTSvmaylQ8V2nFlt1Y3tcozwOrdYRm54wqJM/s1DPj+SJ\n9Kfb5UYgBkSaKXC3SkUiN/ttoklt40614B1wuxzlRYF0ObPV3tDSE5WM4GkQ0Yjlk4UkLA8j\nSrvmFnki9a659oCtyo/MUCKBSDOHngKmMuYeZaCIqRQ53WNXo0iAqFEeh1rXIF6J61bVSok5\nFl8NU5YtczquMHIiiQEizSTa9FG3Sj6HskC0PS+2QzYZRnrbG2tsllwlZ5RKHz9tvJ1pkLzv\n8IcUatrl3iOt37AxxMQDEQCRZhReh8o0lmDgztXGGaowEZHG4IxKOOGdK+7iAePSqTUNIrMB\nTAeGyoM9RoE0b3FEMhsoUqec4Ip5df7VduWI5HJz/4eZWBiiQKQZR49ZafcE07zjIJrZQI94\nNcrEjBSp/ZcyOSKRLa1VTQAAGcFJREFUNbhHAtRw6XRGjZPqbFTS4Ns4pHbN9uiNAfvkiHTN\no9z/YSTvJz4QaSbiqamgPGO4VNr08e7P4uBUhlatpnaP1IeqHUh7vDVS6ncea3AtQZaiSG+f\nKHk/8YFIIEVIqN/1GXPDp6lskdqevWczx61LFkjeT3wgEhCBziIS45Fs/c6lKokwTq5ItV8J\nNjVk/13yfuIDkYAQCZkNskiqfuctU0Z14coV6boFzx0iLx/4y5IDkneTAIgEhMjqkJXE+PW7\n/jx9dDu9XJGW/YUdJAaWtRyrlbyf+EAkIGTqRIqXNTtGq7oopoFR9nikF7ldqLjC/asl7yc+\nEAkImdzMhhgS1e+8lUxNbH+XXJGOfZhlj9jDFd7FUHMwuUxyZkMscXLQWXYwXyuce06uSOuW\nMOxF53On/S0LJe8nPhAJTAPE63ftmgKRSR7kimScex77Kln6sxXkOsn7iQ9EAtMBkfqdr4ax\ni6Uxye5Hyn+e9d17OMn6CYX1bceASGB6EFu/G7ZoxMfG08lsGKylM+9rCIgEpgtR9btObX6c\n+cEwHgmkDVOT2RALPw9LsH7n8A89EkWuSOdcGOK7P3mM0jTKEAmIMVWZDQKC9buRQnX8hTRk\nT8d1FCFkFvf/nNmEnOyM+x5pQCQgZCo7ZGPg63fd+rwENzByReq/ctWBHrb/0I82jHbvmEVr\n3gaIBISkUCR+8VhFeaJpJOWKdMcPA3v3rnqAZTedJHlf4kAkIGRKMxsE9CeePlmuSAt3Bgu7\nlrPsizmS9yUORAJCpjizQRpyRZobGj3xrzksu5XW4D6IBNIMuSKdu8jsfyxbfgZrWriWUlQQ\nCaQZckX6aBY5Y+3VPzkri7zCfn+O9H2JA5FAmiG7Q1b5f3P5BvAL/8Oyr+bRigoigTSDRmaD\n2+4YxixCYNJJTWZDcmAWIZAupCyzIRkwixBIF1LZITsumEUIpAsZLRJmEQJTRWozG8YBswiB\ndCGjMxswixAALGYRAoAKmEUIAApgFiEAKIBZhEDakPmZDZhFCEw+mZ3ZMClAJCAkoztkJweI\nBIRAJMlAJCAkozMbJgeIBIRkdGbD5ACRQJoBkQCgAAWReqzUpioOAZFAmiF/zobzCPmcZa/8\nH7WQWIgE0g7ZKUKzF6zhRGpdNDufXlAQCYiRyZkNVyxrcPFXpJZl6+gFBZGACBmd2XDco6xf\nJPaRY6jFBJGAGBndIZv9VlCkPbTm/eaBSEBIRot00n1BkW48mVZILEQCYmR0ZsOmY8y8SO6/\nktvpBQWRgAgZndngWpp9LlmxYg5Z1kwvKIgE0g3Z/Ugttx1HCDn+tvira04AiATSDAqZDb7m\nKppXIx6IBNIM5NoBQAHZInl0+94NQC0miAREyeTMhvzlJIS0nbhrE7wIkYCQjM5sWHn05udf\nCpDEO4suP/ninR5/cUsi8SASEJLRHbLzP5DwRu0cMi+H/D83X4ZIQCIZLdIJZglvvCLnA9/Q\njpzv9LEQCUgmozMb7pJSbV16Pf/vodmXeyASkExGZzb0X/nLtxQaP+O/MecB/8Mb5G6IBDIL\n2QP7lkpotTvpJ4HHe8ljEAlkFHJFumDu1fdtDTD+G+/OenaEf/RtIL+7CyKBDEKuSHPflPDG\n9mXkUn/Bd3fiKxhEAmmG7BGyFinvbLv9d8HSf74KkYBEMjmz4eaH6cUSBiIBIRmd2dB56e0H\nbVV+6AUFkYAIGd0hS8gEc+0SApGAkIwWaf2GjSEk7cO+OtEq6BAJCMnozIaJYkGrHZBIxmY2\nuNzc/2Ek7WOwpCTBqxAJpBlyRCJrcI8EgB85Il3zKPd/mKTe66s+uH//oXqRV5zfPW+MZaRH\nclQApJCpvUdy37MwcPVa9pBgGfSBHdvG+BmuSCC9kCvSh1YJb2w6hZx+w9bHHvvb+sXkbHeC\nDVG1AyJkcmbD3G0S3rgxZ2+w5NmZtTnBhhAJCMnozIZLL/Mm/8ZFN4XL1yxNsCFEAkIyukO2\nef2P38lPNkUo55/h8oOzE2wIkYCQjBZJUvP3yVeHy+uWJ9gQIgEhGZ3ZcM2vb0o+RWhz1vah\nQKnvAbIlwYYQCQjJ2MwGyXSeSxasvuHOOzb8YB65JJEqEAmkGfJFsrbx/xQk92k7Vszia4E5\nK1/0JNoOIoE0Q65IIzcRhnt4ltyQ0Iwwg5Vmc9XwOBtBJJBmyBXpCXJFDfdQfg15ilpMEAmk\nHXJF+vbaYOHy05J+//bvjbcFRAIiZHJmw+FPBAuPJb+q+a3jtlZAJCAkozMbTrgrWLj9hKTf\nD5HARMjoDtmb5n3KP4y8mP3rpN8PkcBEyGiRmk4ky/5v7cXHkhMdSb8fIoGJkNGZDWzzb/lV\nzb9yS2Py7+9sGG8LiASEZHpmg89p76MUTQiIBNIMrGoOAAUgEgAUgEgAUAAigbQhkzMbJgeI\nBIRkdGbD5ACRgJCM7pCdHCASEJLRIvn2rl1xZgB6QUEkIEJGZzZsJ2TeUQHoBQWRgAgZndlw\n0ppqesGMAZFAmiFXpJxcerGEgUggzZB9RTLQiyUMRAJphlyR/nQ7vVjCQCSQZsgVqXfNtQew\nqjmYEjI5swEr9oGpIqMzGya6qnliIBIQktEdspMDRAJCMlYkOauaJwYiASEZm9mAVc3BVJKx\nmQ0TWdU8OSASSDNwjwQABSASABSASABQACKBtCGTMxsmB4gEhGR0ZsPkAJGAkIztkJ08IBIQ\nkukiNRUoilophRMEIgEhGZvZ4OfF5f60hjPepRYSC5GAGBmb2cDzPJlz6Ybbr7sgi7xOLyiI\nBNINuSJ9bU2X/7HmtG9SiogHIoE0Q65Is7XBws45VOIJAJFAmiFXpK+EJj/ZtYRKPMGdQSSQ\nXshejPmvwcLa31GJJwBEAiJkcmZD0wXXflTmsO27/NKqBg5KUUEkICSjMxtINJSigkhASEZ3\nyP70migoRQWRgJCMFmmMPszZACaXzM5sCPH2ibJjCQORgJCMzmxg2569ZzPHrUsWUIsJIoG0\nQ65ItV8JNjNk/51eUBAJpBtyRbpuwXOHyMsH/rLkAL2YIBJIO+SKtOwv7CAxsKzlWG3c7aUD\nkUCaIXuhsRe5Xai4wv2rqcUEkYAomZzZcOzDLHvEHq7wLtaQBZNLRmc2rFvCsBedz532tyyk\nFxREAiJkdIesce557Ktk6c9WkOvoBQWRgAgZLRKb/zzru/dwkvWTNmoxQSQgxgzIbBisHaAR\nzBgQCQjJ7MyGSQEigTRDrkjnXBjiuz95rJNWVBAJpBlyRTrpKELILO7/ObMJOdlJKSqIBNIM\nuSL1X7nqQA/bf+hHG0a7d8yitSAzRAJphlyR7vih1//oXfUAy246iVJUEAmIkMmZDQt3Bgu7\nlrPsizlUYoJIQIyMzmyYGxo98a85LLuV1uA+iASEZHSH7LmLzP7HsuVnsKaFaylFBZGAkIwW\n6aNZ5Iy1V//krCzyCvv9OdL3JQ5EAkIyO7NB+X9z+QbwC//Dsq/m0YoKIgEhGZ/Z4LY7hulE\nEwIigTQDKUIAUAAiAUABiAQABSASSBsyObNhcoBIQEhGZzZMDhAJCMnoDtnJASIBIRBJMhAJ\nCMnszIZJASIBIRmf2UAfiATSDIgEAAUgEgAUgEgAUAAigbQBmQ1SgUhACDIbJAORgBB0yEoG\nIgEhEEkyEAkIQWaDZCASEILMBslAJJBmQCQAKACRAKAARAKAAhAJpA3IbJAKRAJCkNkgGYgE\nhKBDVjIQCQiBSJKBSEAIMhsEdG8pS/g6RAJCkNkgoIF8nPB1iATSjCkVaWOI9eRHGxOtgA6R\nQJoxpSKRKBJsCJFAmjGlIv1+1ooDnTyl5L3OzgQbQiSQZkztPZJpRdZtXSzukcCEQGbDGKPb\nDl+8DyKBiYDMhkjsq8mV9RAJSAcdstHsOfaIrRAJSAYixdDyKwKRgGSQ2SDgs3tsCV+HSEAI\nMhskA5FAmgGRAKBAqkSyr16d4FWIBNKMVIlkQYoQyCRSJdJgSUmCVyESEAGZDVKBSEAIMhsi\n8FUf3L//UL3IKzULjxljHumT8RkgA3h/k4DF5EThk9OFm6dUJPc9CwNDKJY9NBD7mvfjvWM8\nTIYn/BkgI/jxGb9MK35xyRSK1HQKOf2GrY899rf1i8nZ7gQb6iDSTOfHf0l1BNIYmkqRNubs\nDZY8O7M2J9gQIs14IFICFt0ULl+zNMGGEGnGA5ESkPPPcPnB2Qk2hEgzHoiUgJOvDpfXLU+w\nIUSa8UCkBGzO2j4UKPU9QLYk2BAizXggUgI6zyULVt9w5x0bfjCPXJKoyxUizXggUiKGd6yY\nxXcj5ax80ZNoO4g044FI4zBYaTZXjacJRJrxQCQaQKQZD0Qan+3fG28LiDTjgUjjc+u4O4BI\nMx6IND4QCYwLRBofiATGBSKND0QC4yJfpKvJ7kBBecWi7GPX5cYUwxusI//yP7Zkk9EJf1oq\nROpsGG8LiDTjkS1S59yzvusvMDm//sL8wcp51qhixAbr5p3pf3xqbpqJND4QacYjW6Tnj/0f\nqeILV53N/9tz5gtRxYgN1v0fMfOP518MkUCmIVukC37rO/kBvvCTb4w9F1GM2GDdb87mB8eV\nk4cgEsg0YkR6fs2lifjxq7HvLyMG9m+n+LjSi+QXRm/gyYhixAbrrvvnQs6g+779b4gEMo1o\nkUYOI4mZF/v+P3+dZe1ExRe3LSBHXr67P6YY3mDddbXkY9Z3yiMQCWQcMVekreefl4jzt8W8\n3bP44dHR0e8FVvzu//CuM8hJpVHFiA3WXcd+95esmtRAJJBxyLxH+ixwoTpybLIqxTGXRRUj\nNuBEem5u160rWYgEMg6ZIl39PROHNucdlnUFRr5tPDGqGLEBJ1Jr9qvHPQ2RQOYhT6TOuf4W\nbvayH7PN2ffzJd/F50QWIzbgRWIv+/phLogEMg95Ij0/q8X/uGdWE/vnrFs+1u1bm/U+G1GM\n3IAX6U2ymoVIIPOQJ9KFPww8duZsZ9nXv78w+7g1X/A/jxUjN+BF6p33EkQCGQiSVmkAkWY8\nEIkGEGnGA5FoAJFmPBCJBhBpxgORaACRZjwQiQYQacYDkWgAkWY8EIkGEGnGM4UiHfewnJeD\nQCQwLZk0kZ7bEPvMOKa8Zk5mtxAJTEsmTaQbN8Q+k9wlZxwgEpiWyBRp+N4l87/HndpDfzwp\nZ9lfR1l24dP3LDlyrYv9f4QQy/FPXjana+ylkEj+Z0e3fn3u6c9zPzmvOPzEx+77RuBlwW5Y\n9SVHHfE9VfgDIRKYlsgU6c6vvJ+/4Yga9qZj37O/teD3LLtkyaujDSfexnad96s2z+Jv/Fk/\nOvZSSCT/s7+b93rVC7NfZtnLF39ZtPa0MwMvC3bTd+StttLb5rnHPhAigWlJjEgD9oRUD0a/\nu2fus9xV6Rdftmc/yf103/xhdsmlXOGm77DshRs4Hc5j2fBLIZH4Z7tnb+VKN5/GNh+2k9vN\n0QGRhLuxEQ3Ljir7xz4RIoFpSYxI3dbExKz/qCeB2VQPEQP37wfEyi75A1e457SgSHdGvjQm\nEvesivA+vEV6NaSQK1wVEEm4G8/Xlz1q9kV8IkQC0xJ5VbtPSYn/8QPCT3nyP86DJfdxhXu+\nGhTpvsiXxkTinv2YzJ4zZ04OqfovqeaeujUgkshuWv94Kln2RvgTIRKYlsgTKY8ETmuF/1Ky\nn9iEIoVfihRJTfaW8Qwd8Kv4y4BIIrvhKL2J5I99IkQC0xJ5InXPe4Rlvd9/vSP7ce6nPx01\nGiHSbwLKhF+KFKl7znNcqbWdrSavcPdIxwREEu6m5r/c48is18Y+ESKBaYncVrsj3si/ZZ6d\n3XTcfx2vz9vKhkX68bct7f4fxl6KFIm967j3apizr2DZc0/Vl637RrDVTrAb5azHyyseyKkY\n+0CIBKYlMkUavPuEeSuVLDt8z+LsUx7xRYj02XHzD/h/GHspSqTR+5flLL2jm2UrLp5zysu/\nPS/wsnA3b5w978iLPgl/IEQC05KUJ632d3L/rLoq2c0hEpiWpFykH35TU7mDfDL+hgEgEpiW\npFyk5vVfmXfWa+NvFwQigWlJykWSCEQC0xKIRAOINOOBSDSASDMeiEQDiDTjgUg0gEgzHohE\nA4g044FINIBIMx6IRAOINOOBSDSASDMeiEQDiDTjgUg0eJaAmc4lacb3TJJP86m4Iu15c9qy\ngzyV6hDi8zJ5KNUhJGDuH1IdQQIW/jVfDoXST/MZXrWrJo5UhxCfPpKX6hAScMTHqY4gAV99\neao/ESKlOoT4QKQJA5GmGIg0YSBSFBAp1SHEByJNGIg0xUCkCQORooBIqQ4hPhBpwkCkKQYi\nTRiIFAVESnUI8YFIEwYiTTEQacJApCggUqpDiA9EmjCZKJIpe3TSP2PCNBBXqkOIz9BhE0hV\nmTKO/SLVESTgG2+Mvw1dJl8ktnryP2LiILiJUutNdQQJqB+Z6k+cApEAyHwgEgAUgEgAUAAi\nAUABiAQABSASABSASABQACIBQAGIBAAFIBIAFIBIAFAAIgFAAYgEAAUgEgAUgEgAUAAiAUAB\n2iKN/OWw8wKlupsW5yz7Qw9fLLt+UfbxPzVypT3B5QkepvyxMoKLKHZuPjnnxI1NKYlt3OhS\neujc9yybvXydgS9GHCTx4rQLbkqOHGWRbOcuCJ4NNcdn/fKhH5OVIyxrXXDsA288vCj7EMs+\nSdZv4VHQ/VgZwUUUh88lv/jnTTmnuFMR3LjRpfLQdSwnV9x/Xfbc4qiDJF6cfsFNyZGjK1L3\n4edXzQmcDb8iL3H/biY7WfZawv8KReQHLLuVSF95ZnKDiyjuIP/iiu+Te6ZldKk8dHeQZ7l/\n/0MujzpI4sXpF9yUHDm6InXcM8IGz4YjF/u4fzsPX8myFxL/CPojl/OnRBXVD5QfXERxxYIh\n/sXTFvqmY3SpPHS/W83/BX2Hnxx1kMSL0y+4KTly9BsbAmdDH/m+/6ezZnvYDaSEK7UddhnL\nFds8DW3UP3PiwUUUB2et9hdvIKmadCRRdCk/dCw7lPM9NuIgiRenX3BTc+QmSyRv9jf9P60k\nDaztmLM1roLV83JZ9qfkvmMI+drb1D91osFFFCvJDf7iVnJwGkaX8kPHsk9zdaiIgyRenH7B\nTc2RmyyR2EuyuJs/tjyHlHH/fpMQskzP/fwDcuqjb9x7JNlF/WMnGly4aCZ3+F/cTvZPw+hS\nfuhY5eyLR9mIgyRenH7BTc2RmzSRFGT5B+XvnfpVUsPaTln6xMevnHkU9211aF8f91rpnGNT\nNPuqMLhw0Uzu9L/4GPkgNcEljC7lh+6dOed2sGzEQRIvTr/gpubITZpI7LPzCDniyetIJ7ty\nXiP3c/+SJWOz9v0sVZPxCoMLF6vIBv9rfyP/S01wCaMLbZOiQ+d7gPyY782KOEjixekXXGir\nyT1ykycS26NU97Dnnsj2Zv3Q//NviDW0za0kJR1JIsFFFIezf+B/aX3K5gNPFF2I1Bw6303k\nLg9fiDhI4sXpF1xos8k9cpMnkv9Xc2T9hm0lF/mfuJrk9z7/jr94capadwTBRRYvnNfPFb2L\nl6YmtsTRpfbQbSaPBEsRB0m8OO2Cm5ojN2ki/TmHu5B6f04MLHtKTgX3ROexRw55lxxRxhX/\nS86h/rETDS6i+CJ5kHvtBfL3FAWXMLqUHrr/kM2hYsRBEi9Ou+Cm5sjRFUm5ZcuWWYu4f9rZ\nonlHb/77+eRP3LP7Dzvuvlf/eQrfPf9h1vyN9/8s60gz1Y+VE1xE0XMJWff3X2V9uz8FwY0f\nXSoP3VfJXf4kmy3uyIMkXpx+wU3JkaMr0qPB9EC+K9mw5ti5577qf1r/069kH3Ppp/7iZUdn\nL/5NSvro4wQXUez948k5S+7oSEVwSUSXwkMXio3URh0k8eL0C24qjhyGUQBAAYgEAAUgEgAU\ngEgAUAAiAUABiAQABSASABSASABQACIBQAGIBAAFIBIAFIBIAFAAIgFAAYgEAAUgEgAUgEgA\nUAAiAUABiAQABSASABSASABQACIBQAGIBAAFIBIAFIBIAFAAIgFAAYgEAAUgEgAUgEgAUAAi\nAUABiAQABSASABSASABQACIBQAGIBAAFIBIAFIBIAFAAIgFAAYiULlx8WD3/0J69kmWbb1+W\nc/y6PP5n40+Pyzn5+lqudA1puXTuh6kMcSYDkdKF18g/+IfdZBfbevJRW9585KQ5SpbNn7v4\noRf/smBhO8v+mlx72SMlqQ5zpgKR0oX+o07nH1bP7WJvyzZxpfoF57Ps8+cyXPFZ8izL3kR+\n5E1phDMaiJQ2/JZoWbZ11nrWd/y5Lp41pNf/wsjgIXIPy24kb6c4wpkMREob8snNLPsC+ZJt\nJiFKWfaN7x/NlzbzIuWnOsQZDERKH845coD94VIvW0VWfB6gk72XnL9HaXg5IFJVqiOcwUCk\n9OE58m/XYfex3BVpReipwcOX8tW7AxAp1UCk9KHz8Kue8sty/NxO/udWlq0lP+NL90KkVAOR\n0ojr5q24mH+8jfyV+7d10Vp2IOscrmRZQm6FSKkFIqURDCEv848ty8iNrz2yLOdLll1Lbn33\n/mM+yz7pnT6IlEogUjqxbF6P/9F129Lso39i5Eqt137lqFUa9u9HLHJBpFQCkdKI+pzfpjoE\nEAeIlEb8Mqci1SGAOECkdKFq54/I1lQHAeIBkdKF/2R95RFfqoMA8YBIAFAAIgFAAYgE/n/7\ndCwAAAAAMMjfehI7yyEGIsFAJBiIBAORYCASDESCgUgwEAkGIsFAJBiIBAORYCASDESCgUgw\nEAkGIsFAJBiIBAORYCASDESCgUgwEAkGAQlbrJEqKJlWAAAAAElFTkSuQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7sNBajoFSvEY",
        "outputId": "4d79861e-8ff8-4a56-f775-eafd10ceba17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "# dot chart\n",
        "mse <- function(x){mean(x^2)}\n",
        "preloss.3 <- apply(store.3[gap.start:gap.end.pre,],2,mse)\n",
        "postloss.3 <- apply(store.3[(gap.end.pre+1):gap.end,],2,mse)\n",
        "\n",
        "names(preloss.3) = colnames(store.3)\n",
        "\n",
        "#pdf(\"2ratio_post_to_preperiod_rmse2a.pdf\")\n",
        "dotchart(sort(postloss.3/preloss.3),\n",
        "         xlab=\"Post-Period MSE / Pre-Period MSE\",\n",
        "         pch=19)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CCD5p8jX7I/B5jlbdsqE92pbAbIzSY2pNgN5rW+8smvXD9a0h+Lnu1rcu63l18Z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\nULfKAAAAAElFTkSuQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_05p4fyayqw"
      },
      "source": [
        "#### Placebo in time for 'fake' treatment in 1998 and 1992"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yATP4scHUzhN",
        "outputId": "ac7b44a1-6c0a-4428-bdd1-17a2cca24f5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        }
      },
      "source": [
        "## Placebo effect in time at 'fake' treatment = 1998  \n",
        "\n",
        "dataprep.out = dataprep(\n",
        "  foo = data,\n",
        "  predictors = c(\"pcgsdp\", \"pfemale\", \"plit\",\"pwlit\",\"prural\"),\n",
        "  predictors.op = \"median\",\n",
        "  time.predictors.prior = 1985:1998,\n",
        "  dependent = \"pcr_womtot\",\n",
        "  unit.variable = \"state\",\n",
        "  time.variable = \"year\",\n",
        "  #special.predictors = list(\n",
        "  #  list(\"pcgsdp\", seq(1985,k,2),\"mean\"),\n",
        "  #  list(\"pfemale\", seq(1985,k,2),\"mean\"),\n",
        "  #  list(\"plit\",  seq(1985,k,2),\"mean\"),\n",
        "  #  list(\"pwlit\",  seq(1985,k,2),\"mean\"),\n",
        "  #  list(\"prural\",  seq(1985,k,2),\"mean\")\n",
        "  #),\n",
        "  treatment.identifier = 2,\n",
        "  controls.identifier = c(1, 3:17),\n",
        "  time.optimize.ssr = c(1985:1998),\n",
        "  unit.names.variable = c(\"stateuni\"),\n",
        "  time.plot = c(1985:2007)\n",
        ")\n",
        "\n",
        "synth.out <- synth(\n",
        "  data.prep.obj = dataprep.out,\n",
        "  method = \"BFGS\"\n",
        ")\n",
        "\n",
        "#plot treatment vs synthetic control outcomes trend\n",
        "path.plot(synth.res = synth.out, dataprep.res = dataprep.out,\n",
        "          Ylab = \"Crime against women per 1000 women\", Xlab = 'year', tr.intake = 1998,\n",
        "          Legend = c(\"Assam\", \"Synthetic Assam\"), Legend.position = \"bottomright\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0002812801 \n",
            "\n",
            "solution.v:\n",
            " 0.010772 8.38e-07 0.3485767 0.1670458 0.4736047 \n",
            "\n",
            "solution.w:\n",
            " 0.003727256 0.4663653 0.001293254 0.001872675 0.5064547 0.003208589 0.001643212 0.0007814532 0.002067115 0.001102013 0.0008822391 0.003425231 0.002217716 0.001194164 0.00205559 0.001709502 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7/ryFZ34ALOGzIjSNbE19kQEpeXe2I5andoXp8aGI4rDPB1NoTE5eWeWIJ0flla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",
            "text/plain": [
              "Plot with title “NA”"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DrOUpzAZVRrx",
        "outputId": "3886d589-1e7a-4d10-d9ec-fa04f6a8bfe0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 898
        }
      },
      "source": [
        "#gap or difference in treatment and synthetic control\n",
        "gaps <- dataprep.out$Y1plot - (dataprep.out$Y0plot %*% synth.out$solution.w)\n",
        "\n",
        "#pre built tables from synth objects\n",
        "synth.tables <- synth.tab(dataprep.res = dataprep.out, synth.res = synth.out)\n",
        "\n",
        "#comparing pre-treatment predictor values for the treated unit, the synthetic control unit , and all the units in the sample\n",
        "synth.tables$tab.pred[1:5,]\n",
        "\n",
        "#View control unit weights\n",
        "synth.tables$tab.w\n",
        "\n",
        "gaps.plot(synth.res = synth.out,\n",
        "           dataprep.res = dataprep.out,\n",
        "           Ylab = \"Gap in crime against women (per 1000 women)\",\n",
        "           Xlab = \"year\",\n",
        "           Ylim = c(-.5,.5),\n",
        "           Main = NA,\n",
        "           tr.intake = 1998\n",
        "           )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        Treated Synthetic Sample Mean\n",
              "pcgsdp  1.295   1.186     1.372      \n",
              "pfemale 0.923   0.947     0.936      \n",
              "plit    0.430   0.438     0.469      \n",
              "pwlit   0.349   0.331     0.367      \n",
              "prural  0.888   0.887     0.750      "
            ],
            "text/latex": "A matrix: 5 × 3 of type dbl\n\\begin{tabular}{r|lll}\n  & Treated & Synthetic & Sample Mean\\\\\n\\hline\n\tpcgsdp & 1.295 & 1.186 & 1.372\\\\\n\tpfemale & 0.923 & 0.947 & 0.936\\\\\n\tplit & 0.430 & 0.438 & 0.469\\\\\n\tpwlit & 0.349 & 0.331 & 0.367\\\\\n\tprural & 0.888 & 0.887 & 0.750\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA matrix: 5 × 3 of type dbl\n\n| <!--/--> | Treated | Synthetic | Sample Mean |\n|---|---|---|---|\n| pcgsdp | 1.295 | 1.186 | 1.372 |\n| pfemale | 0.923 | 0.947 | 0.936 |\n| plit | 0.430 | 0.438 | 0.469 |\n| pwlit | 0.349 | 0.331 | 0.367 |\n| prural | 0.888 | 0.887 | 0.750 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A matrix: 5 × 3 of type dbl</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Treated</th><th scope=col>Synthetic</th><th scope=col>Sample Mean</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>pcgsdp</th><td>1.295</td><td>1.186</td><td>1.372</td></tr>\n",
              "\t<tr><th scope=row>pfemale</th><td>0.923</td><td>0.947</td><td>0.936</td></tr>\n",
              "\t<tr><th scope=row>plit</th><td>0.430</td><td>0.438</td><td>0.469</td></tr>\n",
              "\t<tr><th scope=row>pwlit</th><td>0.349</td><td>0.331</td><td>0.367</td></tr>\n",
              "\t<tr><th scope=row>prural</th><td>0.888</td><td>0.887</td><td>0.750</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   w.weights unit.names       unit.numbers\n",
              "1  0.004     ANDHRA PRADESH    1          \n",
              "3  0.466     BIHAR             3          \n",
              "4  0.001     GUJARAT           4          \n",
              "5  0.002     HARYANA           5          \n",
              "6  0.506     HIMACHAL PRADESH  6          \n",
              "7  0.003     JAMMU & KASHMIR   7          \n",
              "8  0.002     KARNATAKA         8          \n",
              "9  0.001     KERALA            9          \n",
              "10 0.002     MADHYA PRADESH   10          \n",
              "11 0.001     MAHARASHTRA      11          \n",
              "12 0.001     ORISSA           12          \n",
              "13 0.003     PUNJAB           13          \n",
              "14 0.002     RAJASTHAN        14          \n",
              "15 0.001     TAMIL NADU       15          \n",
              "16 0.002     UTTAR PRADESH    16          \n",
              "17 0.002     WEST BENGAL      17          "
            ],
            "text/latex": "A data.frame: 16 × 3\n\\begin{tabular}{r|lll}\n  & w.weights & unit.names & unit.numbers\\\\\n  & <dbl> & <fct> & <dbl>\\\\\n\\hline\n\t1 & 0.004 & ANDHRA PRADESH   &  1\\\\\n\t3 & 0.466 & BIHAR            &  3\\\\\n\t4 & 0.001 & GUJARAT          &  4\\\\\n\t5 & 0.002 & HARYANA          &  5\\\\\n\t6 & 0.506 & HIMACHAL PRADESH &  6\\\\\n\t7 & 0.003 & JAMMU \\& KASHMIR  &  7\\\\\n\t8 & 0.002 & KARNATAKA        &  8\\\\\n\t9 & 0.001 & KERALA           &  9\\\\\n\t10 & 0.002 & MADHYA PRADESH   & 10\\\\\n\t11 & 0.001 & MAHARASHTRA      & 11\\\\\n\t12 & 0.001 & ORISSA           & 12\\\\\n\t13 & 0.003 & PUNJAB           & 13\\\\\n\t14 & 0.002 & RAJASTHAN        & 14\\\\\n\t15 & 0.001 & TAMIL NADU       & 15\\\\\n\t16 & 0.002 & UTTAR PRADESH    & 16\\\\\n\t17 & 0.002 & WEST BENGAL      & 17\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 16 × 3\n\n| <!--/--> | w.weights &lt;dbl&gt; | unit.names &lt;fct&gt; | unit.numbers &lt;dbl&gt; |\n|---|---|---|---|\n| 1 | 0.004 | ANDHRA PRADESH   |  1 |\n| 3 | 0.466 | BIHAR            |  3 |\n| 4 | 0.001 | GUJARAT          |  4 |\n| 5 | 0.002 | HARYANA          |  5 |\n| 6 | 0.506 | HIMACHAL PRADESH |  6 |\n| 7 | 0.003 | JAMMU &amp; KASHMIR  |  7 |\n| 8 | 0.002 | KARNATAKA        |  8 |\n| 9 | 0.001 | KERALA           |  9 |\n| 10 | 0.002 | MADHYA PRADESH   | 10 |\n| 11 | 0.001 | MAHARASHTRA      | 11 |\n| 12 | 0.001 | ORISSA           | 12 |\n| 13 | 0.003 | PUNJAB           | 13 |\n| 14 | 0.002 | RAJASTHAN        | 14 |\n| 15 | 0.001 | TAMIL NADU       | 15 |\n| 16 | 0.002 | UTTAR PRADESH    | 16 |\n| 17 | 0.002 | WEST BENGAL      | 17 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 16 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>w.weights</th><th scope=col>unit.names</th><th scope=col>unit.numbers</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>0.004</td><td>ANDHRA PRADESH  </td><td> 1</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>0.466</td><td>BIHAR           </td><td> 3</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>0.001</td><td>GUJARAT         </td><td> 4</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>0.002</td><td>HARYANA         </td><td> 5</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>0.506</td><td>HIMACHAL PRADESH</td><td> 6</td></tr>\n",
              "\t<tr><th scope=row>7</th><td>0.003</td><td>JAMMU &amp; KASHMIR </td><td> 7</td></tr>\n",
              "\t<tr><th scope=row>8</th><td>0.002</td><td>KARNATAKA       </td><td> 8</td></tr>\n",
              "\t<tr><th scope=row>9</th><td>0.001</td><td>KERALA          </td><td> 9</td></tr>\n",
              "\t<tr><th scope=row>10</th><td>0.002</td><td>MADHYA PRADESH  </td><td>10</td></tr>\n",
              "\t<tr><th scope=row>11</th><td>0.001</td><td>MAHARASHTRA     </td><td>11</td></tr>\n",
              "\t<tr><th scope=row>12</th><td>0.001</td><td>ORISSA          </td><td>12</td></tr>\n",
              "\t<tr><th scope=row>13</th><td>0.003</td><td>PUNJAB          </td><td>13</td></tr>\n",
              "\t<tr><th scope=row>14</th><td>0.002</td><td>RAJASTHAN       </td><td>14</td></tr>\n",
              "\t<tr><th scope=row>15</th><td>0.001</td><td>TAMIL NADU      </td><td>15</td></tr>\n",
              "\t<tr><th scope=row>16</th><td>0.002</td><td>UTTAR PRADESH   </td><td>16</td></tr>\n",
              "\t<tr><th scope=row>17</th><td>0.002</td><td>WEST BENGAL     </td><td>17</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Kndcf9EVD0mG7QSOiJUxv5Vq2KcykM7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\ngJAAAYQECCAkQAAhAQIICRBASICA/wclq8p+lonR6gAAAABJRU5ErkJggg==",
            "text/plain": [
              "Plot with title “NA”"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryYwTlzhWA19",
        "outputId": "0e81e981-234b-441d-9f69-d73d2fa02f1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        }
      },
      "source": [
        "## Placebo effect in time at 'fake' treatment = 1992  \n",
        "\n",
        "dataprep.out = dataprep(\n",
        "  foo = data,\n",
        "  predictors = c(\"pcgsdp\", \"pfemale\", \"plit\",\"pwlit\",\"prural\"),\n",
        "  predictors.op = \"median\",\n",
        "  time.predictors.prior = 1985:1992,\n",
        "  dependent = \"pcr_womtot\",\n",
        "  unit.variable = \"state\",\n",
        "  time.variable = \"year\",\n",
        "  #special.predictors = list(\n",
        "  #  list(\"pcgsdp\", seq(1985,k,2),\"mean\"),\n",
        "  #  list(\"pfemale\", seq(1985,k,2),\"mean\"),\n",
        "  #  list(\"plit\",  seq(1985,k,2),\"mean\"),\n",
        "  #  list(\"pwlit\",  seq(1985,k,2),\"mean\"),\n",
        "  #  list(\"prural\",  seq(1985,k,2),\"mean\")\n",
        "  #),\n",
        "  treatment.identifier = 2,\n",
        "  controls.identifier = c(1, 3:17),\n",
        "  time.optimize.ssr = c(1985:1992),\n",
        "  unit.names.variable = c(\"stateuni\"),\n",
        "  time.plot = c(1985:2007)\n",
        ")\n",
        "\n",
        "synth.out <- synth(\n",
        "  data.prep.obj = dataprep.out,\n",
        "  method = \"BFGS\"\n",
        ")\n",
        "\n",
        "#plot treatment vs synthetic control outcomes trend\n",
        "path.plot(synth.res = synth.out, dataprep.res = dataprep.out,\n",
        "          Ylab = \"Crime against women per 1000 women\", Xlab = 'year', tr.intake = 1992,\n",
        "          Legend = c(\"Assam\", \"Synthetic Assam\"), Legend.position = \"bottomright\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0003258889 \n",
            "\n",
            "solution.v:\n",
            " 0.0003749968 0.685861 0.2021482 0.0009821526 0.1106336 \n",
            "\n",
            "solution.w:\n",
            " 0.0001345309 6.91729e-05 0.0001929298 4.60742e-05 0.4463343 0.001208715 0.0001402082 3.05958e-05 0.0003702303 0.0001505258 0.0002355566 0.0009355249 0.0005685963 7.86877e-05 0.5490402 0.0004640967 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EK8syGjsz1G1T5RFx8nKvgSJrOInja1KFGZPWrtEj1qF9fnvb/4KnKROUghPiB7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\ngg==",
            "text/plain": [
              "Plot with title “NA”"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tL2bSMePWFaQ",
        "outputId": "9e569b6c-4de7-44b7-8aef-4ec1dad91679",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 898
        }
      },
      "source": [
        "#gap or difference in treatment and synthetic control\n",
        "gaps <- dataprep.out$Y1plot - (dataprep.out$Y0plot %*% synth.out$solution.w)\n",
        "\n",
        "#pre built tables from synth objects\n",
        "synth.tables <- synth.tab(dataprep.res = dataprep.out, synth.res = synth.out)\n",
        "\n",
        "#comparing pre-treatment predictor values for the treated unit, the synthetic control unit , and all the units in the sample\n",
        "synth.tables$tab.pred[1:5,]\n",
        "\n",
        "#View control unit weights\n",
        "synth.tables$tab.w\n",
        "\n",
        "gaps.plot(synth.res = synth.out,\n",
        "           dataprep.res = dataprep.out,\n",
        "           Ylab = \"Gap in crime against women (per 1000 women)\",\n",
        "           Xlab = \"year\",\n",
        "           Ylim = c(-.5,.5),\n",
        "           Main = NA,\n",
        "           tr.intake = 1992\n",
        "           )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        Treated Synthetic Sample Mean\n",
              "pcgsdp  1.221   0.929     1.205      \n",
              "pfemale 0.920   0.929     0.935      \n",
              "plit    0.413   0.393     0.438      \n",
              "pwlit   0.330   0.276     0.333      \n",
              "prural  0.891   0.867     0.757      "
            ],
            "text/latex": "A matrix: 5 × 3 of type dbl\n\\begin{tabular}{r|lll}\n  & Treated & Synthetic & Sample Mean\\\\\n\\hline\n\tpcgsdp & 1.221 & 0.929 & 1.205\\\\\n\tpfemale & 0.920 & 0.929 & 0.935\\\\\n\tplit & 0.413 & 0.393 & 0.438\\\\\n\tpwlit & 0.330 & 0.276 & 0.333\\\\\n\tprural & 0.891 & 0.867 & 0.757\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA matrix: 5 × 3 of type dbl\n\n| <!--/--> | Treated | Synthetic | Sample Mean |\n|---|---|---|---|\n| pcgsdp | 1.221 | 0.929 | 1.205 |\n| pfemale | 0.920 | 0.929 | 0.935 |\n| plit | 0.413 | 0.393 | 0.438 |\n| pwlit | 0.330 | 0.276 | 0.333 |\n| prural | 0.891 | 0.867 | 0.757 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A matrix: 5 × 3 of type dbl</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Treated</th><th scope=col>Synthetic</th><th scope=col>Sample Mean</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>pcgsdp</th><td>1.221</td><td>0.929</td><td>1.205</td></tr>\n",
              "\t<tr><th scope=row>pfemale</th><td>0.920</td><td>0.929</td><td>0.935</td></tr>\n",
              "\t<tr><th scope=row>plit</th><td>0.413</td><td>0.393</td><td>0.438</td></tr>\n",
              "\t<tr><th scope=row>pwlit</th><td>0.330</td><td>0.276</td><td>0.333</td></tr>\n",
              "\t<tr><th scope=row>prural</th><td>0.891</td><td>0.867</td><td>0.757</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   w.weights unit.names       unit.numbers\n",
              "1  0.000     ANDHRA PRADESH    1          \n",
              "3  0.256     BIHAR             3          \n",
              "4  0.000     GUJARAT           4          \n",
              "5  0.001     HARYANA           5          \n",
              "6  0.413     HIMACHAL PRADESH  6          \n",
              "7  0.000     JAMMU & KASHMIR   7          \n",
              "8  0.000     KARNATAKA         8          \n",
              "9  0.000     KERALA            9          \n",
              "10 0.000     MADHYA PRADESH   10          \n",
              "11 0.000     MAHARASHTRA      11          \n",
              "12 0.000     ORISSA           12          \n",
              "13 0.000     PUNJAB           13          \n",
              "14 0.000     RAJASTHAN        14          \n",
              "15 0.000     TAMIL NADU       15          \n",
              "16 0.330     UTTAR PRADESH    16          \n",
              "17 0.000     WEST BENGAL      17          "
            ],
            "text/latex": "A data.frame: 16 × 3\n\\begin{tabular}{r|lll}\n  & w.weights & unit.names & unit.numbers\\\\\n  & <dbl> & <fct> & <dbl>\\\\\n\\hline\n\t1 & 0.000 & ANDHRA PRADESH   &  1\\\\\n\t3 & 0.256 & BIHAR            &  3\\\\\n\t4 & 0.000 & GUJARAT          &  4\\\\\n\t5 & 0.001 & HARYANA          &  5\\\\\n\t6 & 0.413 & HIMACHAL PRADESH &  6\\\\\n\t7 & 0.000 & JAMMU \\& KASHMIR  &  7\\\\\n\t8 & 0.000 & KARNATAKA        &  8\\\\\n\t9 & 0.000 & KERALA           &  9\\\\\n\t10 & 0.000 & MADHYA PRADESH   & 10\\\\\n\t11 & 0.000 & MAHARASHTRA      & 11\\\\\n\t12 & 0.000 & ORISSA           & 12\\\\\n\t13 & 0.000 & PUNJAB           & 13\\\\\n\t14 & 0.000 & RAJASTHAN        & 14\\\\\n\t15 & 0.000 & TAMIL NADU       & 15\\\\\n\t16 & 0.330 & UTTAR PRADESH    & 16\\\\\n\t17 & 0.000 & WEST BENGAL      & 17\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 16 × 3\n\n| <!--/--> | w.weights &lt;dbl&gt; | unit.names &lt;fct&gt; | unit.numbers &lt;dbl&gt; |\n|---|---|---|---|\n| 1 | 0.000 | ANDHRA PRADESH   |  1 |\n| 3 | 0.256 | BIHAR            |  3 |\n| 4 | 0.000 | GUJARAT          |  4 |\n| 5 | 0.001 | HARYANA          |  5 |\n| 6 | 0.413 | HIMACHAL PRADESH |  6 |\n| 7 | 0.000 | JAMMU &amp; KASHMIR  |  7 |\n| 8 | 0.000 | KARNATAKA        |  8 |\n| 9 | 0.000 | KERALA           |  9 |\n| 10 | 0.000 | MADHYA PRADESH   | 10 |\n| 11 | 0.000 | MAHARASHTRA      | 11 |\n| 12 | 0.000 | ORISSA           | 12 |\n| 13 | 0.000 | PUNJAB           | 13 |\n| 14 | 0.000 | RAJASTHAN        | 14 |\n| 15 | 0.000 | TAMIL NADU       | 15 |\n| 16 | 0.330 | UTTAR PRADESH    | 16 |\n| 17 | 0.000 | WEST BENGAL      | 17 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 16 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>w.weights</th><th scope=col>unit.names</th><th scope=col>unit.numbers</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>0.000</td><td>ANDHRA PRADESH  </td><td> 1</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>0.256</td><td>BIHAR           </td><td> 3</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>0.000</td><td>GUJARAT         </td><td> 4</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>0.001</td><td>HARYANA         </td><td> 5</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>0.413</td><td>HIMACHAL PRADESH</td><td> 6</td></tr>\n",
              "\t<tr><th scope=row>7</th><td>0.000</td><td>JAMMU &amp; KASHMIR </td><td> 7</td></tr>\n",
              "\t<tr><th scope=row>8</th><td>0.000</td><td>KARNATAKA       </td><td> 8</td></tr>\n",
              "\t<tr><th scope=row>9</th><td>0.000</td><td>KERALA          </td><td> 9</td></tr>\n",
              "\t<tr><th scope=row>10</th><td>0.000</td><td>MADHYA PRADESH  </td><td>10</td></tr>\n",
              "\t<tr><th scope=row>11</th><td>0.000</td><td>MAHARASHTRA     </td><td>11</td></tr>\n",
              "\t<tr><th scope=row>12</th><td>0.000</td><td>ORISSA          </td><td>12</td></tr>\n",
              "\t<tr><th scope=row>13</th><td>0.000</td><td>PUNJAB          </td><td>13</td></tr>\n",
              "\t<tr><th scope=row>14</th><td>0.000</td><td>RAJASTHAN       </td><td>14</td></tr>\n",
              "\t<tr><th scope=row>15</th><td>0.000</td><td>TAMIL NADU      </td><td>15</td></tr>\n",
              "\t<tr><th scope=row>16</th><td>0.330</td><td>UTTAR PRADESH   </td><td>16</td></tr>\n",
              "\t<tr><th scope=row>17</th><td>0.000</td><td>WEST BENGAL     </td><td>17</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9qUFfzbUWw+a6/dT39qUyyJjgb9He0C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\nABggJAAGCAmAAUICYPD/gKPTjWipxL8AAAAASUVORK5CYII=",
            "text/plain": [
              "Plot with title “NA”"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aY6OTHA9anjP"
      },
      "source": [
        "#### Placebo in time for all years (1988:2005)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVdrTfNZiw_L",
        "outputId": "f0dbda9f-5216-4c06-cc72-a71a125eba4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# placebo in time\n",
        "\n",
        "store <- matrix(NA, length(1985:2007), length(1988:2005))\n",
        "colnames(store) <- 1988:2005\n",
        "\n",
        "for (k in 1988:2005){\n",
        "  # print(k)\n",
        "  dataprep.out = dataprep(\n",
        "    foo = data,\n",
        "    predictors = c(\"pcgsdp\", \"pfemale\", \"plit\",\"pwlit\",\"prural\"),\n",
        "    predictors.op = \"median\",\n",
        "    time.predictors.prior = 1985:k,\n",
        "    dependent = \"pcr_womtot\",\n",
        "    unit.variable = \"state\",\n",
        "    time.variable = \"year\",\n",
        "    #special.predictors = list(\n",
        "    #  list(\"pcgsdp\", seq(1985,k,2),\"mean\"),\n",
        "    #  list(\"pfemale\", seq(1985,k,2),\"mean\"),\n",
        "    #  list(\"plit\",  seq(1985,k,2),\"mean\"),\n",
        "    #  list(\"pwlit\",  seq(1985,k,2),\"mean\"),\n",
        "    #  list(\"prural\",  seq(1985,k,2),\"mean\")\n",
        "    #),\n",
        "    treatment.identifier = 2,\n",
        "    controls.identifier = c(1, 3:17),\n",
        "    time.optimize.ssr = c(1985:k),\n",
        "    unit.names.variable = c(\"stateuni\"),\n",
        "    time.plot = c(1985:2007)\n",
        "  )\n",
        "\n",
        "  synth.out <- synth(\n",
        "    data.prep.obj = dataprep.out,\n",
        "    method = \"BFGS\"\n",
        "  )\n",
        "\n",
        "  # store gaps\n",
        "  print(k)\n",
        "  store[, k-1987] <- dataprep.out$Y1plot - (dataprep.out$Y0plot %*% synth.out$solution.w)\n",
        "}\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0002899322 \n",
            "\n",
            "solution.v:\n",
            " 0.008315458 0.7757674 0.00517139 0.02445318 0.1862926 \n",
            "\n",
            "solution.w:\n",
            " 8.9968e-06 9.04408e-05 9.2568e-06 2.46689e-05 0.4348233 0.0001047573 7.1101e-06 9.3e-07 2.69642e-05 5.3939e-06 1.81288e-05 6.70461e-05 4.68922e-05 1.7175e-06 0.5647357 2.86238e-05 \n",
            "\n",
            "[1] 1988\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.00041043 \n",
            "\n",
            "solution.v:\n",
            " 0.008291328 0.1016437 0.3140602 0.431337 0.1446678 \n",
            "\n",
            "solution.w:\n",
            " 0.0002341233 7.1999e-06 0.0002794289 0.02144436 0.5684861 0.002483961 0.0002290286 2.959e-07 0.0003719073 0.0002405535 0.0001854 0.004349875 0.0004715499 0.0001612319 0.4004269 0.0006280867 \n",
            "\n",
            "[1] 1989\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0003837452 \n",
            "\n",
            "solution.v:\n",
            " 3.8e-09 0.08904923 0.2935551 0.4680512 0.1493445 \n",
            "\n",
            "solution.w:\n",
            " 0.0002626256 1.08649e-05 0.0002999273 0.000744354 0.5794864 0.001736024 0.0002604309 3.618e-07 0.000426081 0.0002572149 0.0002295586 0.002434241 0.0004702397 0.0001821062 0.4125529 0.0006466752 \n",
            "\n",
            "[1] 1990\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0003738556 \n",
            "\n",
            "solution.v:\n",
            " 1.016e-07 0.06726694 0.2084276 0.607556 0.1167494 \n",
            "\n",
            "solution.w:\n",
            " 0.0001940938 2.9378e-06 0.0002111334 0.001470423 0.5918618 0.002069425 0.0001849497 2.035e-07 0.00030333 0.0001857113 0.0001972995 0.005083211 0.0003432384 0.0001318631 0.39726 0.0005004005 \n",
            "\n",
            "[1] 1991\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0003258889 \n",
            "\n",
            "solution.v:\n",
            " 0.0003749968 0.685861 0.2021482 0.0009821526 0.1106336 \n",
            "\n",
            "solution.w:\n",
            " 0.0001345309 6.91729e-05 0.0001929298 4.60742e-05 0.4463343 0.001208715 0.0001402082 3.05958e-05 0.0003702303 0.0001505258 0.0002355566 0.0009355249 0.0005685963 7.86877e-05 0.5490402 0.0004640967 \n",
            "\n",
            "[1] 1992\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0003950793 \n",
            "\n",
            "solution.v:\n",
            " 0.1759153 0.3665366 0.08198014 0.2937392 0.08182876 \n",
            "\n",
            "solution.w:\n",
            " 0.0003339026 2.1884e-05 0.0004271439 0.1350755 0.4853257 0.002622801 0.0003327708 7.64891e-05 0.0007397223 0.0003642919 0.0005145738 0.002877734 0.001135755 0.0002141811 0.3691134 0.0008241228 \n",
            "\n",
            "[1] 1993\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0003728728 \n",
            "\n",
            "solution.v:\n",
            " 0.1738212 0.03559882 0.01075507 0.7494674 0.03035748 \n",
            "\n",
            "solution.w:\n",
            " 0.0004857804 0.02036867 0.000505495 0.08348635 0.5485726 0.002525495 0.0004481411 4.67e-08 0.0008482752 0.0004517504 0.0007102582 0.00216078 0.001153544 0.0003166184 0.3370821 0.0008841442 \n",
            "\n",
            "[1] 1994\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0002999257 \n",
            "\n",
            "solution.v:\n",
            " 0.00316244 0 0.3126603 0.1395527 0.5446245 \n",
            "\n",
            "solution.w:\n",
            " 0.003561086 0.4469687 0.001274596 0.001647072 0.5259052 0.003031999 0.00168661 0.000963425 0.002235638 0.001086058 0.001364488 0.002933243 0.002087784 0.001213422 0.002242865 0.001797816 \n",
            "\n",
            "[1] 1995\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.000336694 \n",
            "\n",
            "solution.v:\n",
            " 0.02285461 0.3559173 0.3248594 9e-09 0.2963687 \n",
            "\n",
            "solution.w:\n",
            " 0.0002387196 0.4670514 0.0003298012 0.1596975 0.3653947 0.001738519 0.0002407292 6.05538e-05 0.0007697917 0.0001989369 0.0001186309 0.001164846 0.001152709 7.74254e-05 0.0008769557 0.0008888028 \n",
            "\n",
            "[1] 1996\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0003069527 \n",
            "\n",
            "solution.v:\n",
            " 0.0002402378 0.0001342664 0.302542 0.5512747 0.1458088 \n",
            "\n",
            "solution.w:\n",
            " 0.002917394 0.4674924 0.0008981595 0.0007116137 0.4951877 0.001739093 0.001284892 0.02324377 0.001146293 0.0008037302 0.0005341193 3.6859e-06 0.0007352927 0.0008911817 0.001042581 0.001368151 \n",
            "\n",
            "[1] 1997\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0002812801 \n",
            "\n",
            "solution.v:\n",
            " 0.010772 8.38e-07 0.3485767 0.1670458 0.4736047 \n",
            "\n",
            "solution.w:\n",
            " 0.003727256 0.4663653 0.001293254 0.001872675 0.5064547 0.003208589 0.001643212 0.0007814532 0.002067115 0.001102013 0.0008822391 0.003425231 0.002217716 0.001194164 0.00205559 0.001709502 \n",
            "\n",
            "[1] 1998\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0002743967 \n",
            "\n",
            "solution.v:\n",
            " 0.1292817 1.04686e-05 0.3633222 0.01100598 0.4963797 \n",
            "\n",
            "solution.w:\n",
            " 0.001888062 0.4641176 0.0008568575 0.03010158 0.4930762 0.00228353 0.00089461 0.000191796 0.001377683 0.0007349765 0.0001655297 0.001382749 4.5179e-06 0.0007240409 0.00135852 0.0008416732 \n",
            "\n",
            "[1] 1999\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.000280745 \n",
            "\n",
            "solution.v:\n",
            " 0.3333932 0.07193499 0.2998779 0.07097966 0.2238142 \n",
            "\n",
            "solution.w:\n",
            " 0.0003106536 0.4464229 0.000464168 0.09345819 0.4466943 0.002024241 0.0003725857 0.0001441128 0.0006644149 0.0003773807 0.0001568419 0.001757706 0.0005821207 0.0002397721 0.005462189 0.0008684482 \n",
            "\n",
            "[1] 2000\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0002522649 \n",
            "\n",
            "solution.v:\n",
            " 0.2841811 0.390198 0.3072904 0.01633752 0.001992897 \n",
            "\n",
            "solution.w:\n",
            " 0.006691906 0.2604135 0.01130387 0.0521979 0.3183894 0.04419417 0.007668815 3.47948e-05 0.009079267 0.01034078 7.09174e-05 0.0572272 0.01171948 0.005404259 0.1809671 0.02429663 \n",
            "\n",
            "[1] 2001\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0003042721 \n",
            "\n",
            "solution.v:\n",
            " 0.2300167 0.3700114 0.393585 0.006386841 0 \n",
            "\n",
            "solution.w:\n",
            " 0.05121218 0.2659888 0.03791813 0.05109935 0.02624605 0.07985753 0.04132484 0.05403987 0.03250165 0.03311749 0.03143242 0.08711503 0.03540303 0.02356482 0.06408271 0.0850961 \n",
            "\n",
            "[1] 2002\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.0007764939 \n",
            "\n",
            "solution.v:\n",
            " 0.001419114 0.5382115 0.02510224 0.3728741 0.06239307 \n",
            "\n",
            "solution.w:\n",
            " 1.1132e-05 0.3149149 2.24333e-05 0.2525168 0.4317323 0.0001018502 1.35182e-05 2.3663e-06 2.82547e-05 1.95779e-05 1.40228e-05 0.0001485664 3.13586e-05 7.8819e-06 0.000390189 4.47872e-05 \n",
            "\n",
            "[1] 2003\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.001120572 \n",
            "\n",
            "solution.v:\n",
            " 0.00266653 0.3546898 0.3480664 0.1969765 0.09760068 \n",
            "\n",
            "solution.w:\n",
            " 1.39574e-05 0.4003205 2.67686e-05 0.1917783 0.4061733 0.0001950806 1.72232e-05 4.8192e-06 3.24109e-05 2.21586e-05 3.1548e-06 0.0009970029 2.88173e-05 8.7826e-06 0.0003086974 6.89955e-05 \n",
            "\n",
            "[1] 2004\n",
            "\n",
            "X1, X0, Z1, Z0 all come directly from dataprep object.\n",
            "\n",
            "\n",
            "**************** \n",
            " searching for synthetic control unit  \n",
            " \n",
            "\n",
            "**************** \n",
            "**************** \n",
            "**************** \n",
            "\n",
            "MSPE (LOSS V): 0.001292329 \n",
            "\n",
            "solution.v:\n",
            " 0.1126964 0.1979929 0.005323103 0.6839873 2.981e-07 \n",
            "\n",
            "solution.w:\n",
            " 0.02035471 0.06228188 0.02965433 0.03795651 0.01986484 0.04828888 0.02556578 0.1371371 0.03739769 0.02801424 0.02561473 0.04820158 0.02952595 0.01818593 0.3690411 0.06291474 \n",
            "\n",
            "[1] 2005\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sEoOzu3Fi8wv",
        "outputId": "7440401d-5173-4196-eb37-8b822e0aaa38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "# now the figure\n",
        "rownames(store) <- 1985:2007\n",
        "\n",
        "# set bounds in gaps data:\n",
        "gap.start <- 1\n",
        "gap.end <- nrow(store)\n",
        "years <- 1985:2007\n",
        "# gap.end.pre <- which(rownames(store)==)\n",
        "\n",
        "\n",
        "# Plot\n",
        "plot(c(1,2),c(1,2),\n",
        "   ylim=c(-0.3,0.3),xlab=\"year\",\n",
        "   xlim=c(1985,2007),ylab=\"gap in crime against women per 1000 women\",\n",
        "   type=\"l\",lwd=2,col=\"black\",\n",
        "   xaxs=\"i\",yaxs=\"i\")\n",
        "\n",
        "# Add lines for control years \n",
        "for (i in 1:ncol(store)) { lines(years,store[gap.start:gap.end,i],col=\"gray\") }\n",
        "\n",
        "## Add ASSAM Line at 2002 as a treatment year\n",
        "lines(years,store[gap.start:gap.end,which(colnames(store)==2002)],lwd=2,col=\"black\")\n",
        "\n",
        "# Add grid\n",
        "abline(v=2002,lty=\"dotted\",lwd=2)\n",
        "abline(h=0,lty=\"dashed\",lwd=2)\n",
        "legend(\"bottomright\",legend=c(\"2002 as Treatment\",\"Control Years (1988:2005)\"),\n",
        "       lty=c(1,1),col=c(\"black\",\"gray\"),lwd=c(2,1),cex=.8)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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P/t9uAxltbCXB6fPmd4vRFMhxmboXiU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\n5pxG2WwAAAAASUVORK5CYII=",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPhq4SfQqjrM",
        "outputId": "5c2fe0fe-4cfc-4865-9ef9-65a5d6943641",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "# dot chart\n",
        "mse <- function(x){mean(x^2)}\n",
        "\n",
        "preloss.time = rep(NA,18)\n",
        "postloss.time = rep(NA,18)\n",
        "names(preloss.time) = as.character(1988:2005)\n",
        "names(postloss.time) = as.character(1988:2005)\n",
        "\n",
        "for (i in 1:ncol(store)){\n",
        "  treat.year = as.character(i+1987)\n",
        "  gap.end.pre <- which(rownames(store)==treat.year)\n",
        "  preloss.time[i] <- mse(store[gap.start:gap.end.pre,2])\n",
        "  postloss.time[i] <- mse(store[(gap.end.pre+1):gap.end,2])\n",
        "}\n",
        "\n",
        "\n",
        "#pdf(\"2ratio_post_to_preperiod_rmse2a.pdf\")\n",
        "dotchart(sort(postloss.time/preloss.time),\n",
        "         xlab= \"Post-Period MSE / Pre-Period MSE\",\n",
        "         pch=19)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PU9IYCQEAdRhTRJ3e2MM9XpWt+ubnGm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\nRU5ErkJggg==",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ugIRE64CsEcz"
      },
      "source": [
        "(postloss.time/preloss.time)[12]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VjGNp0-rrsaj"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}