{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "1d0d0503", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading required package: daltoolbox\n", "\n", "Registered S3 method overwritten by 'quantmod':\n", " method from\n", " as.zoo.data.frame zoo \n", "\n", "\n", "Attaching package: ‘daltoolbox’\n", "\n", "\n", "The following object is masked from ‘package:base’:\n", "\n", " transform\n", "\n", "\n", "Loading required package: harbinger\n", "\n" ] } ], "source": [ "# Harbinger Package\n", "# version 1.0.777\n", "\n", "source(\"https://raw.githubusercontent.com/cefet-rj-dal/harbinger/master/jupyter.R\")\n", "\n", "#loading Harbinger\n", "load_library(\"daltoolbox\") \n", "load_library(\"harbinger\") " ] }, { "cell_type": "code", "execution_count": 2, "id": "c0d41422", "metadata": {}, "outputs": [], "source": [ "#loading the example database\n", "data(examples_changepoints)" ] }, { "cell_type": "code", "execution_count": 3, "id": "9a12bab2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 2
serieevent
<dbl><lgl>
10.00FALSE
20.25FALSE
30.50FALSE
40.75FALSE
51.00FALSE
61.25FALSE
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & serie & event\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & 0.00 & FALSE\\\\\n", "\t2 & 0.25 & FALSE\\\\\n", "\t3 & 0.50 & FALSE\\\\\n", "\t4 & 0.75 & FALSE\\\\\n", "\t5 & 1.00 & FALSE\\\\\n", "\t6 & 1.25 & FALSE\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | serie <dbl> | event <lgl> |\n", "|---|---|---|\n", "| 1 | 0.00 | FALSE |\n", "| 2 | 0.25 | FALSE |\n", "| 3 | 0.50 | FALSE |\n", "| 4 | 0.75 | FALSE |\n", "| 5 | 1.00 | FALSE |\n", "| 6 | 1.25 | FALSE |\n", "\n" ], "text/plain": [ " serie event\n", "1 0.00 FALSE\n", "2 0.25 FALSE\n", "3 0.50 FALSE\n", "4 0.75 FALSE\n", "5 1.00 FALSE\n", "6 1.25 FALSE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Using the simple time series \n", "dataset <- examples_changepoints$simple\n", "head(dataset)" ] }, { "cell_type": "code", "execution_count": 4, "id": "b744a3ad", "metadata": {}, "outputs": [ { "data": { "image/png": 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v8oqvndBkUistjcj4uQvW/2sTn/ufup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\nKHWGU+hcpKyMm9l80gRApj978YBKX0T1LdKa6xMYq00YACjtSo5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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "#ploting the time series\n", "plot_ts(x = 1:length(dataset$serie), y = dataset$serie)" ] }, { "cell_type": "code", "execution_count": 5, "id": "e0c8a397", "metadata": {}, "outputs": [], "source": [ "# establishing change point method \n", " model <- hcp_chow()" ] }, { "cell_type": "code", "execution_count": 6, "id": "9d0079cf", "metadata": {}, "outputs": [], "source": [ "# fitting the model\n", " model <- fit(model, dataset$serie)" ] }, { "cell_type": "code", "execution_count": 7, "id": "863506fd", "metadata": {}, "outputs": [], "source": [ "# making detections\n", " detection <- detect(model, dataset$serie)" ] }, { "cell_type": "code", "execution_count": 8, "id": "1241ed10", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " idx event type\n", "1 50 TRUE changepoint\n" ] } ], "source": [ "# filtering detected events\n", " print(detection |> dplyr::filter(event==TRUE))" ] }, { "cell_type": "code", "execution_count": 9, "id": "10456892", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " event \n", "detection TRUE FALSE\n", "TRUE 1 0 \n", "FALSE 0 100 \n" ] } ], "source": [ "# evaluating the detections\n", " evaluation <- evaluate(model, detection$event, dataset$event)\n", " print(evaluation$confMatrix)" ] }, { "cell_type": "code", "execution_count": 10, "id": "075f7d2f", "metadata": {}, "outputs": [ { "data": { "image/png": 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tLW1jYyMlJ0C8qFo6NjcHBwYWEhV2QBS8ewA/AvVq9effny5dGjRzdo0EB0C8qL\n8e/v+vXrz549K7oFQOkx7AD8k7y8vNjYWOMVHdEtKEfGK7I6nS4iIkJ0C4DSY9gB+CeLFi26\nffu28TVYoltQvgYPHtyqVatPPvnkxIkTolsAlBLDDsDfysrKSkxMNL5rUnQLyp1CoYiJiTEY\nDCEhIaJbAJQSww7Ao3344YddunTJzMwMCAioVKmS6ByTOn78+E8//fTTTz9dunRJdItJ9ejR\no0OHDnv27Bk4cCDX7QBLxLAD8Ajvvffem2++efz4cUmS2rZtKzrH1AoLCwsKCgoKCrRaregW\nU6tXr54kSdu2bevQoQOffgJYHIYdgEf48ccfS85JSUkCS2BiJWMuLy/v5MmTYmMAPCmGHYBH\naNq0qfFgb2//8ssvC22BSXXq1Ml4UKlUzz77rNgYAE+KYQfgEY4cOSJJUv/+/Q8dOsS/3a2K\nv7//hx9++Mwzz+h0uv3794vOAfBkGHYA/urXX3/dunVr8+bNt27daoUvsLNySqVy0KBBn3/+\nuZ2dnVqtLioqEl0E4Akw7AD8VWBgoF6vj4+PVyp5irBSdevWHT169NWrV9euXSu6BcAT4Fkb\nwJ8cPnz4q6++evHFF3v27Cm6BSKFh4e7urpGRUXl5OSIbgHwuBh2AP4kNDRUkqSYmBjRIRDM\nw8Nj0qRJd+/eXbJkiegWAI+LYQfgD1988cV3333Xs2dP3gkLSZL8/f0rVao0b968e/fuiW4B\n8FgYdgB+p9fr1Wq1QqGIjo4W3QKzULFiRX9//wcPHsyZM0d0C4DHwrAD8LstW7YkJSUZ7wQv\nugXmYsqUKTVr1ly6dCl3oQAsAsMOgCRJklarjYiIsLW1jYyMFN0CM+Lo6BgSElJYWMjLLgGL\nwLADIN24cWPWrFmXL18ePXp0gwYNROfAvIwaNapBgwbvvvvupk2b8vPzRecA+CcMO8Dafffd\ndw0bNlyyZIlCoRgzZozoHLPQuHFjX19fX1/fGjVqiG4Rz9bWtm/fvjqdbsSIES1btszKyhJd\nBOBvMewAa7dp06aCggJJkgwGw7Fjx0TnmIUqVap4eHh4eHi4ubmJbjELFy5cMB4uXry4d+9e\nsTEA/gHDDrB2VatWLTnXrl1bYAnM1sN/MPhDApgzG9EBAAQrLi6WJKlmzZoTJ07s1q2b6ByY\no8jIyOzs7B07dmRnZ/MyO8CcccUOsGq3bt1atWpV9erVz507FxQUJDoHZsrd3Y0XAQcAACAA\nSURBVH3jxo27d++WJCkwMNBgMIguAvBoDDvAqkVFReXn54eGhjo7O4tugbkz3kH46NGjX3zx\nhegWAI/GsAOsV0pKyvr16+vWrTt69GjRLbAM8fHxSqUyJCREr9eLbgHwCAw7wHqFhIRoNJqY\nmBg7OzvRLbAMzZs3HzRoUHJy8ubNm0W3AHgEhh1gpU6fPv3xxx83b9588ODBoltgSYz/JaBW\nq4uKikS3APgrhh1gpQIDA/V6fVxcnFLJ8wCegLe396hRo1JTU9etWye6BcBf8YQOWKPDhw/v\n3r27Xbt2PXv2FN0CyxMWFubs7BwVFZWTkyO6BcCfMOwAaxQaGipJUkJCgkKhEN0Cy1O9evXJ\nkyenp6cvXbpUdAuAP2HYAVZn165d3333XY8ePV5++WXRLbBUAQEBlSpVmjt37r1790S3APgD\nww6wIlevXm3btu1rr70mSVJUVJToHFiwihUrjhkz5sGDB56eniNHjtTpdKKLAEgSww6wKnFx\ncceOHTN+AtnNmzdF55iv69evp6WlpaWlcTnqH2RkZEiSVFxcvHHjxl27donOASBJ3CsWsCoF\nBQUl58LCQoElZi41NVWr1UqS5OnpWblyZdE5Zurhq3T8cQLMBFfsACtSu3Zt46FTp069e/cW\nGwNLN2vWLC8vL0mSFApF48aNRecAkCSGHWA9CgoKNm7c6ODgkJSU9O2339rb24sugmXz9fVN\nS0tbsWKFwWBISEgQnQNAkhh2gPVYtGjRjRs3pkyZ0qJFC9EtkAmVSjVu3LiWLVt+/PHHv/zy\ni+gcAAw7wDpkZWXNnz+/QoUK/v7+olsgK0qlMjo62mAwGD8cEYBYDDvAKiQkJNy/f9/f35+3\nAqDMvfrqqx06dPjqq68OHDggugWwdgw7QP5u3bq1dOlSDw+PyZMni26BPMXExEiSFBgYaDAY\nRLcAVo1hB8hfVFRUfn6+Wq12dXUV3QJ5at++fY8ePY4ePcoH2gFiMewAmUtJSVm/fn3dunVH\njx4tugVylpCQoFQqg4ODjZ+ADUAIhh0gcyEhIRqNJjo6ms83Qblq3rz5G2+8kZyc/OGHH4pu\nAawXww6Qs9OnT3/88cfNmjV78803RbdA/mJjY+3s7EJDQzUajegWwEox7AB5KigoCAsL69q1\nq16vj4uLUyr5hx3lztvbe8SIEampqX5+fps3bxadA1gjnusBeYqKioqJibl165YkSd7e3qJz\nYC2qVasmSdLJkyeHDh36/fffi84BrA7DDpCnM2fOlJzPnj0rsMQSubi4uLq6urq6Ojg4iG6x\nMNeuXSs5JycnCywBrJON6AAA5aJBgwbGg4eHR4cOHcTGWJyWLVuKTrBU/fr127hxoyRJCoXC\nz89PdA5gdbhiB8iQXq8/cOCAQqGIiIj49ddfPTw8RBfBWvTq1evkyZMDBgwwGAwff/yx6BzA\n6jDsABn68MMPk5KSBg0aFB4ebnzNE2AyLVu2fO+992rUqLFkyZLr16+LzgGsC8MOkButVhse\nHm5jYxMeHi66BVbK0dExODi4oKAgNjZWdAtgXRh2gNysXbv28uXLo0eP9vHxEd0C6zVmzJj6\n9eu/++67ly5dEt0CWBGGHSArBQUFcXFxDg4OISEholtg1WxtbSMjI7VarVqtFt0CWBGGHSAr\nixYtunHjxtSpU2vWrCm6BdZu8ODBLVu2/Oijj06ePCm6BbAWDDtAPrKysubPn1+hQgV/f3/R\nLYCkVCqjo6MNBkNoaKjoFsBaMOwA+ZgzZ879+/f9/f0rVaokugWQJEl69dVXO3TosHv37oMH\nD4puAawCww6QiVu3bi1ZssTDw2Py5MmiW4A/xMTESJLERTvANBh2gBwcPnx46NCh+fn5arXa\n1dVVdA7wh/bt2/fo0eOHH36YNm3alStXROcAMsewAyzeypUrX3rppf3796tUqv79+4vOAf7q\ntddekyRp8eLFzzzzzLlz50TnAHLGsAMs3vbt240HnU53/PhxsTHA/3fq1CnjIS8vb/fu3WJj\nAHlj2AEWz9PT03hwcHBo3Lix2Bjg/2vWrFnJuUmTJgJLANmzER0A4GllZGRIktS+ffugoKAG\nDRqIzpGDw4cPa7VaSZI8PT0ZIk9v7Nixubm5q1atunLlSkpKiugcQM64YgdYtsOHD+/Zs6dt\n27aHDh3q0aOH6BzgEVQq1ezZsw8fPuzk5BQZGZmTkyO6CJAthh1g2cLCwiRJSkhIUCgUoluA\nf1K9evVJkyalp6cvXbpUdAsgWww7wIJ9+eWXBw8e7N69e8eOHUW3AP8uKCioUqVKc+fOvX//\nvugWQJ4YdoCl0uv1YWFhCoXC+AGwgPmrWLHizJkzHzx4MHfuXNEtgDwx7ABLZby3+htvvPHs\ns8+KbgEe1/Tp02vUqLF48eLr16+LbgFkSPCwy83NXbp06VtvvTV06NAFCxY8ePBAbA9gKbRa\nbXh4uEqlCg8PF90CPAFHR8egoKDCwsLY2FjRLYAMCR52y5YtO3PmzIwZMwICAq5cuZKYmCi2\nB7AU69atu3Tp0ujRo/ngOlicsWPH1q9ff926defPnxfdAsiNyGGn0+l+/vnnPn36tGzZsnnz\n5v369UtKSsrPzxeYBFiEgoKCuLg4BwcHbqwOS2RraxsREVFcXBwZGSm6BZAbwVfsVCqVjc3v\nH5Jsb2/P5zUA/0qj0cTFxV2/fn3KlCk1a9YUnQOUxptvvtmyZcuPPvro22+/NRgMonMA+RA5\n7FQqVbt27T7//PMrV65cvXp1+/btrVu3dnJyEpgEmLmDBw96enrGxMTY2trOmjVLdA5QSkql\ncsqUKQaDoUuXLs8++yyffgKUFcG3FBszZsw777wzbdo06X+vqH34V2NjY/ft22c8u7m5OTs7\nC0gEzElMTExmZqYkSVqt9uLFi1WrVhVdBJTS5cuXjYekpKSNGzfOmDFDbA8gDyKHXX5+vr+/\n/0svvTR48GCFQvHpp58GBAQsXLiwQoUKxt/g7u5eo0YN49nBwYGX3wEPc3BwEJ0AlN7Df4D5\nwwyUFZHD7sSJE9nZ2ePGjTO+tG7EiBGHDh06evRoly5djL9h4sSJEydONJ4zMzMnTZokrBUw\nD1WqVJEkyc7Obvr06Xx8HSzapEmTDh48ePDgQZ1O5+npKToHkAnBP4rV6XRardbOzq7kzPsn\ngL+Tmpq6Y8eOunXrnjt3zt7eXnSOnDVr1sz4in7jsxPKQ8WKFb/99tvTp0+3bNkyIiKiT58+\nSiWfmQ88LZH/FLVu3drNzW3u3Lnnzp27cOFCYmKiUqls27atwCTAnIWGhmo0mqioKFZdeatY\nsaK7u7u7uzsv7S1vzZs3f+ONN06fPv3hhx+KbgHkQCH2fea3b9/etGlTcnKyXq9v2rTpiBEj\nSl5U9xfGH8Vu3rzZxIWAmTBe2GjatOmpU6e4sAE5SUlJady4sZeX1/nz57lECvwzrVY7YMCA\nzz///O9+g+AfxXp6egYEBIhtACxCcHCwXq+PjY1l1UFmvL2933777VWrVq1bt67kddUASod/\nQwAW4Icffti1a1fbtm179eolugUoe2q12snJKSYmJi8vT3QLYNkYdoAFMN46LCEhgXcXQZaq\nV68+adKkW7duLVmyRHQLYNkYdoC5271798GDB7t169axY0fRLUB5CQoKqlSp0ty5c7kLBfA0\nGHaAWTMYDKGhoQqFIjY2VnQLUI4qVqw4c+bMrKysefPmiW4BLBjDDjBrH3300cmTJ19//XU+\njhiyN3XqVE9Pz8WLF1+/fl10C2CpGHaAmbp27Vr//v1HjRqlVCojIiJE5wDlztnZOTg4uKCg\noHXr1v7+/sXFxaKLAMvDsAPM1JQpUz799NOCggJJkpycnETnAKZgY2MjSVJ6evq8efM2bNgg\nOgewPAw7wExdvXrVeNDr9bdu3RIbA5jG7du3S87Xrl0TWAJYKIYdYKa8vb2NBz8/P15gBysx\nePDgihUrGs+tWrUSGwNYIoYdYI4ePHhw8OBBV1fXL7744tChQ7a2tqKLAFNo3LjxpUuXYmJi\nJEl69913RecAlodhB5gj46d5+fv7v/rqq6w600tNTb18+fLly5fT09NFt1idypUrh4SEvPji\ni19++eXBgwdF5wAWhmEHmJ309PSlS5dWrVp16tSpolus1PXr19PS0tLS0u7duye6xUolJCRI\n/7vnCoDHx7ADzE5UVFROTk5YWJirq6voFkCM9u3bd+/e/Ycffvjyyy9FtwCWhGEHmJfU1NS1\na9fWqVNn7NixolsAkRISEpRKZVBQkF6vF90CWAyGHWBewsLCNBpNVFSUvb296BZApGeeeWbg\nwIGnT5/+6KOPRLcAFoNhB5iR5OTkLVu2+Pr6DhkyRHQLIF50dLStrW1oaKhGoxHdAlgGhh1g\nRoKDg/V6fWxsrEqlEt0CiNewYcORI0empKTw0SfAY2LYAebi6NGju3btatu2be/evUW3AOYi\nPDzcyckpOjo6Pz9fdAtgARh2gHgajWbdunWDBw82GAzx8fEKhUJ0EWAuvLy83nnnnVu3br32\n2mt79+4VnQOYO4YdIN60adPGjBlz5coVBwcHPz8/0TmAeenYsaMkSd9+++0rr7yya9cu0TmA\nWWPYAeLt37/feCgsLDx//rzYGMDc/PLLLyXnffv2CSwBzB/DDhCvevXqxkO1atUaNmwoNgYw\nNy+88ELJuWnTpgJLAPPHsAMEKy4uvnHjhlKpHDVq1KFDh1xcXEQXAealY8eOu3fvNv5ANikp\nSXQOYNYYdoBg69atu3jx4qhRo9atW9eoUSPROYA56t69+549e+rXr7927drLly+LzgHMF8MO\nEKmwsDA2NtbBwSEsLEx0C/5QpUoVDw8PDw8PbtdrPmxtbcPDw7VabXh4uOgWwHwx7ACRFi9e\nfP369UmTJtWqVUt0C/7QuHFjX19fX1/fmjVrim7BH4YMGdKiRYsPP/yQH8gCf4dhBwiTlZU1\nd+5cV1fX2bNni24BLIBSqYyKitLr9VzhBv4Oww4QZu7cuffv3589e7aHh4foFsAy9O7d+4UX\nXti1a9d3330nugUwRww7QIz09PRly5ZVrVp12rRpolsAS5KQkCBJUmhoqOgQwBwx7AAxIiMj\nc3JyQkNDeXk+8EReeumlbt26HT58ePfu3aJbALPDsAMESE1NXbduXZ06dcaNGye6BbA8c+bM\nUSqVgYGBer1edAtgXhh2gKmlpaVNmjRJo9FERkba29uLzgEszzPPPDNw4MDTp0/HxcU9ePBA\ndA5gRhh2gEktX768Tp06X375paur65tvvik6B7BUU6ZMUSgUYWFh3t7ev/76q+gcwFww7ACT\nWrhwofGQk5OTlpYmNgawXCdOnDAYDJIkZWZmrl27VnQOYC4YdoBJOTo6Gg92dnbu7u5iYwDL\n9fCHBFWqVElgCWBWGHaASTk5OUmSVK9evffee49/GwGlNnDgwGnTphn/IbKxsRGdA5gLhh1g\nOl999dXRo0e7du16+fLlN954Q3QOYMGUSuXChQsvXbrk7u6+YMGC+/fviy4CzALDDjARg8EQ\nGhqqUChiY2NFtwAy4e7uPmPGjKysrMTERNEtgFlg2AEm8vHHH//yyy8DBw5s3bq16Bb8i+L/\n0el0olvwL6ZPn+7p6blw4cIbN26IbgHEY9gBplBcXBwZGalSqSIiIkS34N/99NNP33///fff\nf3/hwgXRLfgXzs7OwcHBBQUF8fHxolsA8Rh2gCm8++67586de/vtt5s0aSK6BZCb8ePH16tX\nb82aNZcvXxbdAgjGsAPKXWFhYUxMjIODQ1hYmOgWQIZsbW3Dw8O1Wi1XxAGGHVDulixZcv36\n9UmTJtWqVUt0CyBPQ4cObdGixZYtW5KSkkS3ACIx7IDy9eDBg7lz57q6us6ePVt0CyBbSqUy\nMjJSr9er1WrRLYBIDDugfM2dO/fevXv+/v4Pf1A+gDL32muvvfDCC1988cV3330nugUQhmEH\nlJfvv/++cePGCQkJrq6uU6dOFZ0DyF9cXJwkSV26dOnevXtGRoboHEAAhh1QXsaNG3f+/Hm9\nXp+fn2+8WzmAcmUcc1qt9uuvv54zZ47oHEAAhh1QXjIzM40HnU5XUFAgNgawBtnZ2SXnBw8e\nCCwBRGHYAeWlTp06xsOkSZOqVasmNgawBv3792/ZsqXx3LBhQ7ExgBAMO6BcJCcnHzt2rEGD\nBikpKUuXLhWdA1gFNze348ePf/vtt7a2titXrtRoNKKLAFNj2AHlIjg4WK/Xz5s3r27duqJb\nACuiUqk6deo0YsSIlJSUDRs2iM4BTI1hB5S9o0eP7tq167nnnnvttddEtwDWKCIiwsnJKTIy\nMj8/X3QLYFIMO6DsBQYGGgyGhIQEhUIhugWwRl5eXhMnTrx169ayZctEtwAmxbADytjXX399\n4MCBV1555b///a/oFpRSmzZt/Pz8/Pz86tevL7oFpRQcHOzu7h4fH3///n3RLYDpMOyAsmQw\nGEJCQhQKRVRUlOgWlJ6Dg4Ojo6Ojo6OdnZ3oFpSSu7v7jBkzsrKyEhMTRbcApsOwA8rS1q1b\nf/nllwEDBrRr1050C2Dtpk+f7unpuXDhwhs3bohuAUyEYQeUGZ1OFxERoVKpIiMjRbcAkJyd\nnYODgwsKCuLj40W3ACbCsAPKzLvvvnvu3LmRI0c2adJEdAsASZKkcePG1atXb82aNZcvXxbd\nApgCww4oA7dv3542bdrMmTPt7e3VarXoHAC/s7OzCw8P12q1Xbt2XbRoUXFxsegioHzZiA4A\n5OCNN944dOiQJElubm41atQQnQPgD82aNZMk6fLly9OnT9doNP7+/qKLgHLEFTugDJw4ccJ4\nyM7OzsjIEBsD4GGnT58uOZf8owrIFcMOKAN16tQxHtq2bevh4SE2BsDDOnTo4OLiYjw3aNBA\nbAxQ3hh2wNO6e/futWvXXF1dlyxZsm/fPtE5AP6kbt26J0+enDJliiRJxpdMADLGsAOeVnR0\ndE5OTnR09OTJk0suDAAwHw0aNFi8eHHXrl0PHz781Vdfic4ByhHDDngqV69eXbNmTZ06dcaP\nHy+6BcA/mTNnjlKpDAwM1Ov1oluA8sKwA56KWq0uKiqKiIiwt7cX3QLgn7Ro0WLAgAG//vrr\n1q1bRbcA5YVhB5TemTNnNm/e7OPjM3ToUNEtAP5dTEyMjY2NWq3WarWiW4BywbADSi8kJESn\n08XHx9vY8JGQsnLp0qXz58+fP3/+1q1boltQlho2bDhy5MiLFy+uX79edAtQLhh2QCkdPXp0\n586dzz33XJ8+fUS3oIzdvn375s2bN2/ezMrKEt2CMhYREeHk5BQZGZmfny+6BSh7DDuglIKC\nggwGQ3x8vEKhEN0C4HF5eXlNnDjx1q1by5YtE90ClD2GHVAae/bs2b9/f5cuXTp16iS6BcCT\nCQ4Odnd3j4+Pz8zMFN0ClDGGHfBkDAbDvn37Jk+erFAooqOjRecAeGLu7u7Tp0/PysoaO3bs\nb7/9JjoHKEsMO+DJjBo1qnPnzhcvXvTy8mrXrp3oHAClMWLECJVKtW3bNl9f308++UR0DlBm\nGHbAE9Dr9Zs3bzaeb968mZubK7YHQOn8/PPPOp3OeH7//ffFxgBliGEHPAGlUlm5cmXj2cvL\ny9nZWWwPgNKpX79+ydnDw0NgCVC2GHbAEygsLDQYDCqVqmvXrl988QXvhwUsVKtWrTZt2tS8\neXNJkvLy8kTnAGWGYQc8gWXLlt2+fXvq1Klff/11q1atROcAKL233norKSmpRYsWW7duPXXq\nlOgcoGww7IDH9eDBg4SEBBcXl4CAANEtAMqAUqmMiIjQ6/VqtVp0C1A2GHbA45o/f/69e/dm\nz57NK3IA2ejTp8/zzz+/c+fOH3/8UXQLUAYYdsBjuXv37uLFi6tUqTJt2jTRLQDKUkJCgiRJ\ngYGBokOAMsCwAx5LdHR0Tk5OaGiom5ub6BYAZek///nPK6+88v3333/99deiW4CnxbAD/t3V\nq1fXrFlTu3bt8ePHi26BKXh6enp5eXl5eVWsWFF0C0whLi5OoVAEBATo9XrRLcBTYdgB/06t\nVhcVFUVERNjb24tugSk0aNDAx8fHx8enevXqoltgCq1btx4wYMCvv/7KXShg6Rh2wL84d+7c\nli1bfHx8hg0bJroFQHmJiYmxsbEJCwvTarWiW4DSY9gB/6S4uDggIKC4uDguLs7GxkZ0DoDy\n0qhRoxEjRly8eHHNmjWiW4DSY9gBf2vNmjUuLi47d+6sW7du3759RecAKF/h4eEqlWrSpEl1\n6tRJSkoSnQOUBsMOeLTi4uLp06cXFRVJknTnzh1eUg3I3sWLF3U6nSRJaWlpkZGRonOA0mDY\nAX/L+BQvSZJKpRJbAsAEHr77M6+0g4Vi2AGPplKpvLy8JElydHRcsmQJ2w6Qvf/85z/Dhw83\nzjveEA0LxbADHu2TTz5JSUnp169fTk7OyJEjRecAKHdKpXLjxo13796tVq3ahx9+ePv2bdFF\nwBNj2AGPoNPpIiIiVCpVdHQ01+oAq1K5cuWgoKC8vLzY2FjRLcATY9gBj7B+/fqzZ88OHz68\nadOmolsAmNqECRPq1au3evXqy5cvi24BngzDDvirwsLC6OhoBweH8PBw0S0ABLCzszN+UjHv\njYXFYdgBf7V8+fJr165NmDChdu3aolsAiDFs2DBfX9/NmzefOnVKdAvwBBh2wJ/k5OTMnTvX\nxcUlMDBQdAsAYYwvsdXr9Vy5h2Vh2AF/Mm/evPT09FmzZnl4eIhugTC5ubk5OTk5OTmFhYWi\nWyBM3759n3/++c8///zHH38U3QI8LoYd8Ie7d+8uWrSoSpUq06dPF90CkZKSko4fP378+PGU\nlBTRLRApISFBkiSu38OCMOyAP8TExOTk5ISEhLi5uYluASDef/7zny5dunz//fd79uwR3QI8\nFoYdIEmSdOzYsVdeeWXZsmXVqlUbN26c6BwA5iI+Pl6hUAwcOHDs2LH37t0TnQP8CxvRAYB4\ner2+V69ed+7ckSTJxsbG0dFRdBEAc1GxYkWDwZCTk7N27VqtVrthwwbRRcA/4YodIOXm5hpX\nnSRJ9+/fNxgMYnsAmI+rV6+WnC9evCiwBHgcDDtAcnNzK7nhd8ktwAFAkqR27dr5+PgYz/Xq\n1RMbA/wrhh0gHT169Pbt240aNTpw4MCKFStE5wAwI87OzidOnFi/fr29vf2+ffvy8/NFFwH/\nhGEHSEFBQQaDYfny5S+//DKX6wD8hbOz88iRI995552bN2/y334wcww7WLs9e/bs37+/Q4cO\nnTt3Ft0CwHwFBQW5ubnFxcVlZmaKbgH+FsMOVs1gMKjVaul/H0MKAH+nSpUqM2fOzMzMXLBg\ngegW4G8x7GDVPvnkk6NHj/bv39/Pz090CwBzN2PGjGrVqi1cuPD27duiW4BHY9jBeul0uoiI\nCJVKFRUVJboFgAVwcXEJCgrKy8uLi4sT3QI8GsMO1mv9+vVnz54dPnx406ZNRbcAsAwTJkyo\nV6/e6tWrr1y5IroFeASGHaxUYWFhdHS0nZ1daGio6BYAFsPOzi4sLEyj0URGRopuAR6BYQcr\ntWzZsmvXrr3zzjve3t6iW2B2/Pz8XnrppZdeeqlRo0aiW2B2hg0b5uvr+8EHH5w6dUp0C/BX\nDDtYnezs7EWLFkVHR7u4uAQGBorOgTmy+R+VSiW6BWZHpVJFR0fr9fphw4Z9+umn3IQQZoVh\nB+tiMBhefvnl6dOnZ2dne3t7e3h4iC4CYHk6d+5sZ2d3+vTp/v37h4WFic4B/sCwg3W5c+fO\nyZMnjeeH7+0NAI/v9OnTGo3GeN69e7fYGOBhDDtYl6pVq7q6uhrPbdu2FRsDwEI1atTIzc3N\neK5evbrYGOBhDDtYl+vXrxcVFbm6uvr7+2/ZskV0DgCLVKVKlX379vXr10+hUFy7dk2v14su\nAn7HsIN1CQ8P12g0ixcvnjNnTtWqVUXnALBUbdq02b59e//+/U+fPr1t2zbROcDvGHawIufP\nn9+8ebOPj8+wYcNEtwCQg9jYWBsbm9DQ0OLiYtEtgCQx7GBVAgMDi4uL4+LibGxsRLcAkING\njRoNHz784sWLGzZsEN0CSBLDDtbj2LFjn3/+eZs2bfr27Su6BYB8REZGOjo6RkRE5Ofni24B\nGHawGoGBgQaDISEhQaFQiG4BIB81atSYMGHCzZs3V6xYIboFYNjBOnzzzTf79+/v0KFDp06d\nRLcAkJugoCA3N7e4uLjMzEzRLbB2DDvIn8FgUKvVkiQlJCSIbgEgQ1WqVJkxY0ZmZubChQtF\nt8DaMewgf9u2bfv555/79evn5+cnugWAPM2cObNatWoLFiy4c+eO6BZYNYYdZO7KlSuhoaHG\nm3aLboHFSE5OTkpKSkpKSktLE90Cy+Di4hIYGJiXlxcYGMgPZCEQww5yNnbs2Pr161+4cMHP\nz69p06aic2AxsrKyMjMzMzMz8/LyRLfAYkyYMMHZ2Xnjxo3Vq1ffunWr6BxYKYYdZOvGjRtr\n1641nlNTU4W2AJC/y5cvG/9LoKioKC4uTnQOrBTDDrLl5OSkVP7+J7xy5cpiYwDInqura8nZ\n1tZWYAmsGcMOsmVra+vi4qJQKHx9fVevXi06B4DM1apVa+nSpVWqVJH+PPIAU2LYQbbmzZuX\nnZ2tVquTk5N5PywAE5g0adLdu3f9/PwOHDhw5MgR0TmwRgw7yFNGRsaiRYuMHy4lugWAdTF+\nZGZgYKDoEFgjhh3kKSYmJjs7Ozg42M3NTXQLAOvSoUOHzp07Hzp06JtvvhHdAqvDsIMMXb16\nddWqVTVq1Bg/frzoFgDWKD4+XqFQ+Pv76/V60S2wLgw7yFBERERRUVFUVJSjo6PoFgDWqE2b\nNv369Tt16tT27dtFt8C6MOwgN+fPn//ggw8aNWr01ltviW4BYL3i4uJsGzJ+KgAAIABJREFU\nbGxCQ0OLi4tFt8CKMOwgN0FBQcXFxcanVNEtAKyX8T8vL1y4sHHjRtEtsCIMO8jKsWPHPvvs\nM+MPQUS3ALB2xheEhIeH5+fni26BtWDYQVaCgoIMBoPxZcuiWwBYO+NbuG7evLly5UrRLbAW\nDDvIxAcffFC3bt19+/a1adOmc+fOonNg2WrWrFm7du3atWtzMzo8peDgYEdHx4CAgBdffPH0\n6dOicyB/vAgJcpCenj5y5EjjK5Q1Go3oHFi8unXrik6ATCgUCo1Go9Ppfvzxx7Fjx3I7CpQ3\nrthBDrKyskred1ZYWCg2BgBKZGVl6XQ64/n27dtiY2ANGHaQg3r16hlvua1SqWbOnCk6BwB+\nV69evf79+xvP3t7eYmNgDRh2kINNmzbl5OT06dMnNTV17NixonMA4HcKhWLbtm2//PJLrVq1\nfvjhhytXrogugswx7GDxCgsLo6Ki7OzsFixYULNmTdE5APBXrVq1ioyM1Gg0UVFRolsgcww7\nWLwVK1akpaVNnDiRH3MAMFtvvfVW06ZNP/jggzNnzohugZwx7GDZcnNz58yZ4+LiEhgYKLoF\nAP6WSqWKjo7W6XShoaGiWyBnDDtYtvnz56enp8+YMaNatWqiWwDgn/Tr18/Pz++zzz7jQ09Q\nfhh2sGAZGRkLFy6sUqUK74QFYBESEhIkSeInDCg/DDtYsNjY2Ozs7KCgIDc3N9EtAPDvOnTo\n0KlTp0OHDn3zzTeiWyBPDDtYqrS0tJUrV9aoUWPChAmiWwDgcSUkJCgUiuDgYIPBILoFMsSw\ng6WKiIgoKiqKjIx0dHQU3QIAj6tNmzZ9+/Y9ceLEtm3bRLdAhhh2sDxnz559++23N23a1LBh\nw+HDh4vOAYAnExcXp1Kpxo8fb3w9iegcyIqN6ADgyTx48OA///lPRkaGJEm1a9e2seHPMMpe\nVlaW8cdkdnZ2zs7OonMgN/Xr13dwcLh//35oaOixY8c+++wz0UWQD/6lCAtz8eJF46qTJOnW\nrVtiYyBXycnJWq1WkiRPT88mTZqIzoHcXL9+PS8vz3j+4YcfxMZAZvhRLCxM48aN7e3tjefO\nnTuLjQGAUqhVq5aPj0/JWWwMZIZhBwvz008/FRUV1a1bd+PGjfPnzxedAwBPTKVSHTp0SK1W\nOzg4XL16lZfZoQwx7GBJDAZDWFiYJElbtmwZPny4ra2t6CIAKA0PD4/IyMiAgID79+8nJiaK\nzoF8MOxgST799NOffvqpb9++zz//vOgWAHhas2bN8vDwWLBgwZ07d0S3QCYYdrAYOp1OrVYb\nb6QtugUAyoCLi0tgYGBubm58fLzoFsgEww4WY9OmTb/99tuwYcN8fX1FtwBA2ZgwYULt2rVX\nrlx55coV0S2QA4YdLINGo4mJibGzszO+xg4A5MHBwSE8PFyj0fCzCJQJhh0sw/Lly1NSUiZM\nmFCvXj3RLQBQloYPH960adP333//zJkzoltg8Rh2sAC5ubkJCQkuLi5BQUGiWwCgjKlUqqio\nKJ1Ox08k8PQYdrAA8+fPT09PnzFjRrVq1US3AEDZ69evn5+f344dO44cOSK6BZaNYQezptFo\nPvvss8TExMqVK8+cOVN0DgCUC4VCYXyN3cSJE5OSkkTnwIIx7GC+ioqKXnjhhb59++bm5nbq\n1MnNzU10EQCUl86dO3t4eCQlJbVq1YqXnaDUGHYwXydOnDhx4oTxnJKSIjYGVsXmf1QqlegW\nWIt79+6lp6cbzytXrhQbA8tlIzoA+FteXl4l57p164oLgdXx8/MTnQCr4+rqWrFixaysLEmS\nKlSoIDoHloordjBfGo1GpVI5Ojq+/vrrixYtEp0DAOXIzs7u008/bdeunUKhUCgUxcXFootg\nkRh2MF/BwcE6nW7Tpk0ff/zxw1fvAECWOnbs+NNPP40YMeLq1aubNm0SnfN/7d15XFSF/v/x\nMwMjICKLaaK4pbgkippLrqn3e03FrO8v1yzvRTGt3LIUkH0RR000zdRyyXJPzRaXhy3mEnmV\n1FwKTNQURVwABdkGZn5/zP1yresGwXxmzryefx2Ix+31/ULHt4eZc2CTGHawUsnJydu2bXvq\nqaeGDBki3QIAlhMdHe3k5BQVFVVQUCDdAtvDsIOVCg0NNZlMCQkJGo1GugUALKdhw4YTJky4\nfPkyb6FABTDsYI327dv3zTff9OrVq1+/ftItAGBp4eHhNWvWnD179u3bt6VbYGMYdrBGISEh\niqLo9XrpEAAQ8Nhjj02dOvXGjRuJiYnSLbAxDDtYna1btx46dOiFF17o2rWrdAsAyJg+fXqd\nOnXmz5+fmZkp3QJbwrCDdSktLY2MjHRwcIiPj5duAQAxNWrUCA4OzsvL43cXKBeGHazLmjVr\nfvnll5dffrl169bSLQAg6fXXX2/YsOH777/Po3fw6Bh2sCLFxcXx8fHVqlWLjIyUbgEAYc7O\nzpGRkcXFxXFxcdItsBkMO1iLadOmubq6nj9/fujQoU888YR0DgDI++c//1mvXr3Vq1c/9thj\n27Ztk86BDWDYwSocPnx4wYIF5kfo3LhxQzoHAKyC0Wi8fv26oig3b94cP368dA5sAMMOVqGo\nqEg6AQCsTmlpqclkMh8XFhaWHQP3w7CDVWjRooVOp1MUpVatWhEREdI5sHfHjx9PTk5OTk7m\nReuQ5ezsPGvWLAcHB0VR6tWrx5N48FAMO1gFvV5vMBhiY2MzMjK6d+8unQN7l5eXl5ubm5ub\nW1hYKN0Cezdjxozs7OxnnnnmzJkz33zzjXQOrB3DDvIuX768bNmyevXqvfXWW+brdgCAMm5u\nbvPmzdNoNOaHaEvnwKox7CAvMjKyoKAgJiamevXq0i0AYI06der0/PPPJycn895YPBjDDsLO\nnDnz8ccfN2/e/J///Kd0CwBYL71e7+joOHPmTPMNBIB7YthBmPkkFR8f7+joKN0CANarRYsW\no0aNMv9lWLoF1othB0nmXyv4+/u/+OKL0i0AYO1iYmKcnJzML1+RboGVYthBkvmFwHPnztVq\n+VEEgIdo1KjR+PHjzW84k26BleJPU4jZt2/fN99806tXr379+km3AIBtCA8Pd3NzS0hIuH37\ntnQLrBHDDmJCQkIURdHr9dIhAGAzateuPXXq1Bs3bixYsEC6BdaIYQcBX3zxRbdu3Q4dOvTc\nc8917dpVOgcAbMn06dNr1ao1a9askSNHnjlzRjoH1oX3IcLSUlJSnn/+efOxp6enbAwA2Bw3\nNzcHBweDwbBx48ajR4+mpqZKF8GKcMUOlnb3OSgzM1OwBABskcFguHnzpvn47NmzRUVFsj2w\nKgw7WFrnzp3ND7RWFGXo0KGyMQBgc3Q6XdnvPXx8fJycnGR7YFUYdrC0TZs2lZaW/v3vfz90\n6NDYsWOlc4B7aNasWYsWLVq0aFG3bl3pFuAeNm3atGXLFh8fn8uXL//yyy/SObAivMYOFpWX\nl6fX611dXT/55JPHH39cOge4N/YcrJyjo+OLL75YWlo6fPjwiIiIrVu3ShfBWnDFDhY1f/78\nzMzMadOmseoA4C8aOnRo586dt23bdujQIekWWAuGHSznxo0biYmJnp6eb775pnQLANg8jUYT\nGxur/N9tQQGFYQdLmj179u3bt2fOnMldTgCgUjz77LN9+/Y1P8hHugVWgWEHC7l8+fLSpUvr\n1av3+uuvS7cAgHro9XqNRmN+9LZ0C+RZy7D7/fffX3311by8POkQVJWoqKiCgoLo6Ojq1atL\ntwCAenTq1On5559PTk7etm2bdAvkWcWwMxgM8+fPv3r1Kn/bUKszZ86sWbPG19c3MDBQugUA\n1Eav1zs6Os6cObOkpES6BcKsYth9/PHH/CyqW1hYWElJSXx8vKMjd9gBgErWokWLUaNGnTlz\n5uOPP5ZugTD5Yffzzz8fPHgwKChIOgRV4sqVK1FRUVu3bvX39x8yZIh0DgCoU0xMjJOT0/Tp\n0z/55JPi4mLpHIgRvnySm5u7cOHCSZMm1axZ87//aVpaWtnj8AoKCiybhkqQkZHRtm1b8zfx\n2Wef1Wrl/yIBAKrUqFGjRo0anTlzZvTo0Z988smePXukiyBDeNgtWbLk6aef7tChw9mzZ//7\nn65evXr37t3mYw8PD25pa3P27dtXNs1TU1NlYwBAxUwm04ULF8zHX3/99e3bt+95xQSqJzns\nvvvuu4sXL06bNu1+X9CrV6+7xxx31rY5rVq1Kjtu27atYAkAqJtGo/Hz8zt69KiiKB4eHm5u\nbtJFkCE57FJTU9PT0+9+3dWoUaP+9re/TZkyxfxhv379+vXrZz7Ozs5m2Nmcc+fOKYpSv379\ncePGBQcHS+cAgJpt3bo1NjZ2w4YNxcXF169fr1OnjnQRBEgOu+HDhwcEBJiPf//993nz5un1\nen7fqhqlpaURERFarXbHjh3+/v7SOUA5XL161Wg0Kori4uLCg1JgKxo3brxq1arWrVu//fbb\ner0+MTFRuggCJF/M7uXl1fD/eHt7K4rSoEGDWrVqCSahEn3yySenT59++eWXWXWwOWfPnk1N\nTU1NTb169ap0C1A+b7zxRoMGDZYsWXL+/HnpFgjgXYqoEsXFxXFxcTqdLioqSroFAOyIs7Nz\nZGRkcXFxfHy8dAsEWMuwa9as2RdffMGLPVVj6dKl586dmzBhwhNPPCHdAgD2JTAwsFWrVmvW\nrPnll1+kW2Bp1jLsoCZ5eXmzZ892dXWdOXOmdAsA2B0HB4eYmJjS0tLIyEjpFlgaww6VLzEx\nMTMz880336xbt650CwDYoyFDhnTp0mXr1q3cUMLeMOxQyW7cuDF//nxPT88H3KEQAFClNBpN\nbGysoighISHSLbAohh0q2ezZs2/fvh0aGspNIgBAUL9+/fr27btv375vv/1WugWWw7BDZTp+\n/PjSpUvr1av3xhtvSLcAgL3T6/UajWbGjBnZ2dnSLbAQhh0qR0FBQa9evdq3b19QUDBu3Ljq\n1atLFwGAvevUqVO7du2OHj1aq1Yt3khhJxh2qBzbt28/cOCA+bjsQdQAAFmXLl1SFMVkMsXH\nx3Pdzh4w7FA5nJ2dy45dXV0FSwAAZWrUqGE+0Gq1Op1ONgYWwLBD5fDx8VEURavVdu3aNTQ0\nVDoHAKAoirJ06VJvb2+NRuPu7s6wswcMO1SOsLAwRVF27NiRlJRkHnkAAHH9+/e/cuXKpEmT\nsrKyli5dKp2DKsewQyXYv3//119/3bNnz/79+0u3AJXA2dnZxcXFxcWFKxxQh/DwcDc3t1mz\nZt2+fVu6BVWLYYdKYL4Bpl6vlw4BKkfHjh2ffvrpp59+ulmzZtItQCWoXbv21KlTb9y4sWDB\nAukWVC2GHf6q7du3//jjj4MHD+7WrZt0CwDg3qZPn16nTp133nnn2rVr0i2oQgw7/CWlpaXh\n4eFardb87BoAgHVyc3ObPn16Xl7enDlzpFtQhRh2+EvWrl17+vTpUaNG+fv7S7cAAB5k4sSJ\nDRo0eP/99y9evCjdgqrCsEPFFRcXx8bG6nS6qKgo6RYAwEM4OztHREQUFhbyOxYVY9ih4pYt\nW3bu3Lnx48c3bdpUugUA8HBjxoxp1arVRx999Ouvv0q3oEow7FBBeXl5CQkJrq6u5jvYAQCs\nn4ODQ3R0dGlpKY+OVSuGHcrNZDLFxsa2bNkyMzPzjTfeqFu3rnQRAOBRDR06tE2bNlu2bPH3\n99+1a5d0DiqZo3QAbM+OHTvKXlTn4OAgGwMAKBeNRlNcXKwoyokTJ4YMGZKZmVn2PFmoAFfs\nUG7p6ellxzdu3BAsAQBUQF5envkgPz8/KytLNgaVi2GHcnv66ac1Go2iKNWrVw8MDJTOAQCU\nz2uvvWY+8PDwaNCggWwMKhfDDuW2ZMkSk8n01ltvpaWlde3aVToHAFA+YWFhJ0+e7NmzZ05O\nzvbt26VzUJkYdiifM2fOfPTRR76+vrNnz+ZtEwBgo/z8/JYuXerg4BAaGlpSUiKdg0rDsEP5\nhIeHl5SUxMXF6XQ66RYAQMW1bt161KhRqampa9eulW5BpWHYoRx++umnLVu2tG3bdujQodIt\nQBU6dOjQgQMHDhw4cObMGekWoArFxsY6OTlFR0cXFRVJt6ByMOxQDjNnzjSZTHPmzNFq+cmB\nmpX8n9LSUukWoAo1atTo1Vdf/f3335ctWybdgsrBH894VPv379+zZ0/Pnj379+8v3QIAqBwR\nERFubm7x8fG5ubnSLagEDDs8qpCQEEVR9Hq9dAgAoNLUrl17ypQpN27cWLBggXQLKgHDDo9k\n+/btP/744+DBg7t16ybdAgCoTG+//XatWrXmzZt37do16Rb8VQw7PFxpaWl4eLhWq42NjZVu\nAQBUMnd39+Dg4Ly8vLlz50q34K9i2OEhfvjhhyFDhpw+fXrUqFH+/v7SOQCAyjdp0iQfH59F\nixYFBwdfvnxZOgcVx7DDgxw+fLhHjx7m+5I//fTT0jkAgCrh7OzcsGFDg8Ewd+7c7t27FxYW\nShehghh2eJD9+/eXHZ8+fVqwBABQpcou1P3+++9paWmyMagwhh0epH379mXHPXv2FCwBAFSp\nspO8i4vLE088IRuDCmPY4UGSkpIURenSpcvmzZtHjBghnQMAqCrLli2bN29e3bp1CwsLT5w4\nIZ2DCmLY4b6ys7MXLFjg4eGxa9cuniEGAOrm6ur69ttvr1692mQyRUZGSuegghh2uK9Zs2Zl\nZ2eHhoZ6enpKtwAALKF///59+vTZs2fPd999J92CimDY4d6uXLmydOlSb2/viRMnSrcAACxH\nr9drNJqQkBCTySTdgnJzlA6AlYqKisrPz09MTKxevbp0C2BpLVu2NBqNiqI4OztLtwCW1rlz\n5+eee+6LL774/PPPX3jhBekclA9X7HAPZ86c+eijj3x9fceMGSPdAgh47LHH6tSpU6dOnZo1\na0q3AAJmzZql1WpDQkJKSkqkW1A+DDvcQ3h4eElJSWxsrE6nk24BAFian5/fqFGjUlNT161b\nJ92C8mHY4c9+/vnnrVu3tm3bdtiwYdItAAAZsbGxTk5OUVFRRUVF0i0oB4Yd/mzGjBlGo1Gv\n12u1/HgAgJ1q3LjxuHHjfv/99+XLl0u3oBz4kxv/cefOneXLl+/Zs6dHjx4DBgyQzgEASIqM\njHRzc4uNjT148CDvkLUVDDv824ULF3x9fSdMmKAoyuuvvy6dAwAQVrt27WeeeebmzZs9e/Yc\nOHBgaWmpdBEejmGHf9uwYUNGRob5mIfJAAAURblw4YL5YPfu3adOnRJtwSNh2OHfateuXXZc\nt25dwRIAgJXw9vYuO37ssccES/CIGHb4NwcHB0VRatasGRQUZP6FLADAzr333nt9+/bV6XQ6\nnc58125YOYYdFEVRiouL4+PjdTrd0aNHP/zwQycnJ+kiAIC85s2bf/vtt4sXLzYYDLGxsdI5\neDiGHRRFUZYvX37u3LlXX321adOm0i0AAOsyduzYli1brl69+tdff5VuwUMw7KDcuXMnISHB\nxcUlNDRUugUAYHUcHR2joqJKS0ujoqKkW/AQDDsoCxYsuHr16ptvvlm/fn3pFgCANRo+fHiH\nDh22bNnyr3/9S7oFD8Kws3fZ2dmJiYkeHh5vvfWWdAtgLdLT0y9evHjx4sWbN29KtwBWQaPR\nxMfHm0ymyMhI6RY8CMPO3iUkJGRnZ4eGhnp5eUm3ANbiwoULaWlpaWlp165dk24BrMWAAQP6\n9OmzZ8+eb7/9VroF98Wws2tXrlx5//33vb29J06cKN0CALB2er1eo9GEhobyhDGrxbCza9HR\n0fn5+VFRUdWrV5duAQBYu86dOw8aNOjIkSPbt2+XbsG9Mezs12+//fbRRx81adIkMDBQugUA\nYBsSEhK0Wm1oaGhJSYl0C+6BYWeP7ty58+KLL7Zp08ZgMERHR1erVk26CABgG/z8/AYOHJia\nmlq7du25c+dK5+DPGHb2aPny5du2bSsqKlIUJTs7WzoHAGBLbt++rShKTk5OcHBwSkqKdA7+\ngGFnj8z/TZrl5eUJlgAAbM7dv4TNzc0VLMF/Y9jZozZt2pgPmjZtygvsAADlEhoaan7LnZOT\nU/PmzaVz8AcMO3uUmJioKMr69etTUlLq1asnnQMAsCWDBg3KyMiYOHFiUVHRokWLpHPwBww7\nu/P5558nJSUNGjRo5MiRjo6O0jkAANtTs2bN+Pj4WrVqzZs3j/t4WxWGnX0xGo1RUVFarTYu\nLk66BQBgw9zd3WfMmJGbm8t7Y60Kw86+rF279ueffx45cmS7du2kWwAAtm3y5Mk+Pj5Lliy5\ndOmSdAv+jWFnRwwGQ0xMjE6ni4mJkW4BANg8Z2fn8PDwwsLC2NhY6Rb8G8POjixbtuzcuXPj\nxo1r2rSpdAtg1Tw8PDw9PT09PV1dXaVbAKsWFBTUsmXL1atX//rrr9ItUBSGnf24c+dOQkKC\ni4vLzJkzpVsAa+fn59euXbt27do1bNhQugWwag4ODlFRUaWlpVFRUdItUBSGnf1YsGDB1atX\np06dWr9+fekWAIB6DB8+vEOHDlu2bPnXv/4l3QKGnX3Izs5OTEz08PB4++23pVsAAKqi0Wji\n4+NNJlNkZKR0Cxh2amcymZYvX96rV6/s7OyQkBAvLy/pIgCA2gwYMKBXr1579uwZOHDgwYMH\npXPsGvenVblPPvlkwoQJ5uNWrVrJxgAA1Mrb21tRlF27du3bty8lJaVBgwbSRXaKK3Yqd/To\n0bLj06dPC5YAAFTs8uXL5oP8/HzeISuIYadyZTcidnJy6t+/v2wMAECtBg0aZD5wdHRs3769\nbIw9Y9ip3O7duxVFGT16dHJyMv+lAQCqSHBw8I4dOzp16lRSUrJr1y7pHPvFsFOzEydOfPrp\np23atFm9erWfn590DgBAzQYOHLh582YnJ6fIyMiioiLpHDvFsFOz4OBgo9Go1+u1Wr7RAIAq\n17hx46CgoN9///2DDz6QbrFT/HmvWgcOHNi9e3ePHj0GDhwo3QIAsBeRkZFubm5xcXG5ubnS\nLfaIYadaISEhiqLEx8dLhwAA7EidOnUmTZp0/fr1d999V7rFHjHs1OmLL75ISkoKCAh45pln\npFsAAPZlxowZXl5e77zzzs2bN6Vb7A7DToWMRmNkZKRWq42Li5NuAQDYHXd39xkzZty6dUuv\n10u32B2GnQqtW7fu559/HjFiBPc3ASrm4MGDe/fu3bt3L/dZBSpmypQpPj4+77333qVLl6Rb\n7AvDTm0MBkNMTIxOp4uJiZFuAQDYKWdn57CwsMLCQn53ZGEMO1VJS0t7/fXX09LSgoKCmjVr\nJp0DALBfQUFBLVq0WLly5ZIlS27duiWdYy8Ydurx/ffft27desWKFRqN5pVXXpHOAQDYNUdH\nx759+xqNxokTJ7Zr1y4nJ0e6yC4w7NRj06ZN5jt9m0ymI0eOSOcAAOxdWlqa+eDChQv79++X\njbETDDv1qF+/ftlx8+bNBUsAAFD++IcRLxCyDIadepivcvv6+i5atKh///7SOQAAexcfHz9x\n4kQvLy9FUTIzM6Vz7ALDTiWuXLmydOlSb2/vY8eOTZo0SToHAADF3d198eLFO3fu1Gg0ISEh\nJpNJukj9GHYqERMTk5+fHx4e7urqKt0CAMB/dOnSJSAg4PDhw19++aV0i/ox7NTgt99+W716\ndePGjYOCgqRbAAD4s4SEBK1WGxYWVlpaKt2icgw7NYiIiDAYDPHx8dWqVZNuAQDgz9q0aTNy\n5MhTp06tX79eukXlGHY278SJE59++qn5vxnpFgAA7s189SEyMtJ8Zy5UEYadzQsODjYajbNn\nz9Zq+W4CAKyU+fVCFy5c+PDDD6Vb1IwpYNsOHDiwe/fu7t27BwQESLcAAPAg5nf4xcbG5ubm\nSreoFsPOtoWEhCiKEh8fLx0CqEq7du06duzYsWPHJk2aSLcA6uHt7T158uTr168vWrRIukW1\nGHY2bMuWLUlJSQEBAb1795ZuAVSlRo0abm5ubm5uzs7O0i2AqsyYMcPLy2vevHmXL1+WblEn\nhp1NOnfuXKtWrYYOHaooSkREhHQOAACPxMPDY/To0bdu3fLx8Rk2bFhJSYl0kdow7GzS/Pnz\nU1JSzMdlj1gGAMD6ZWRkmA8+/fTT3bt3y8aoD8POJhmNxrJjjUYjWAIAQLnodLqyY/4Iq3QM\nO5vk7e1tPhg8ePCLL74oGwMAwKMLCwvz9fVVFEWj0TRu3Fg6R20Ydrbnzp07S5cudXFxOXfu\n3Oeff87TJgAANqRly5ZnzpxZs2aNyWSKi4uTzlEbhp3tWbhw4dWrV6dMmcKNGAAANuqVV15p\n37795s2bjx49Kt2iKgw7G5OTkzN//nwPD4/p06dLtwAAUEEajSYuLs5kMoWFhUm3qArDzsbM\nnj07Ozs7ODjYy8tLugUAgIoz34d19+7de/fulW5RD4adLcnIyHjvvfe8vb0nTZok3QIAwF9l\nfo1dSEiIyWSSblEJhp0tiYmJyc/Pj4iIcHV1lW4BAOCv6tGjR0BAwOHDh7/88kvpFpVg2NmM\n3377bdWqVU2aNBk7dqx0CwAAlWP27NlarTYsLOzuW7Siwhh2NiMyMtJgMMTHx3N/EwCAarRp\n02bEiBGnTp1av369dIsaMOxsw4kTJzZv3mz+6ZduAdTvwoULaWlpaWlp165dk24B1M98zSIi\nIqKoqEi6xeYx7KxdXl7elClT+vbtazQazderpYsA9UtPT7948eLFixdv3rwp3QKoX5MmTf7x\nj39cuHDB399/+fLl0jm2jZVg7WbNmrVo0SLzny7c4gQAoEo1atRQFCU1NXXChAkHDx6UzrFh\nDDtrl5aWVnZ87tw5wRIAAKpIZmZm2fHdf/ChvBh21q5Fixbmg3r16vXr1082BgCAqjBq1Cjz\nWwM1Gk2XLl2kc2wYw86qGY3Gr776SqPRJCYmnj59unbt2tJFAAD6/cGiAAAa6klEQVRUvoED\nB546derll182mUyrVq2SzrFhDDurtn79+uPHj48cOfLNN9/08PCQzgEAoKr4+vp+8MEH9evX\nX7x48aVLl6RzbBXDznoZDIbo6GidThcTEyPdAgBAlXNxcZk5c2ZhYWF8fLx0i61i2FmvDz74\nIC0tLSgoqFmzZtItAABYwrhx45o1a7Zy5cqUlBTpFpvEsLNSBQUFs2fPNv/dRboFAAALMf+e\nqrS0NDo6WrrFJjHsrNSCBQsuX748ZcoUHx8f6RYAACxnxIgR7du337x589GjR6VbbA/Dzhrl\n5OTMnz/fw8Nj+vTp0i0AAFiUVquNi4szmUxhYWHSLbaHYWeN9Hp9VlbWjBkzeNQEAMAOBQQE\n9O7de/fu3Xv37pVusTEMO6uTkZGxePFib2/vyZMnS7cAACAjLi5OUZSQkBCTySTdYksYdtZl\n586dQ4YMyc/PDw8Pd3V1lc4B7NRjjz1Wp06dOnXquLm5SbcAdqpHjx4BAQGHDx8OCgo6ffq0\ndI7NYNhZkffeey8gICApKUmr1QYEBEjnAParZcuWrVu3bt26Ne9eAgT16dNHUZRVq1Z16tTp\n119/lc6xDQw7K7Jr1y7zgdFoPHz4sGwMAACyyi7UFRQUfPfdd7IxtoJhZ0UaNWpkPnB2dm7X\nrp1sDAAAsjp27Fh27O/vL1hiQxh2VuT8+fOKovTv33/Xrl2+vr7SOQAASJowYcLSpUuffPJJ\nRVGOHz8unWMbGHbW4uDBg7t37+7evfuuXbt69+4tnQMAgDCtVjthwoRvvvnG1dU1NjY2NzdX\nusgGMOysRXh4uKIoPPYYAIC7eXt7T5o06fr164sWLZJusQEMO6vw1Vdf7du3b+DAgVyrAwDg\nT4KDg728vObNm3fz5k3pFmvHsJNnNBojIiI0Gg2X6wAA+G/mZ2zeunVr7ty50i3WjmEnb8OG\nDcePHzc/81i6BQAAazRlyhQfH59Fixalp6dLt1g1hp0wg8EQFRXl6OgYFRUl3QIAgJVycXGZ\nOXNmYWEhv916MIadsA8//DAtLS0oKKhFixbSLQAAWK+goKBmzZqtWLEiJSVFusV6MewkFRQU\nJCQkODs7h4WFSbcAAGDVdDpdTExMaWlpTEyMdIv1YtiJuXXrVnh4+OXLl82vG5DOAQDA2pl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A6pw6daqkpERRFC8vr4YNG0rnALA6zz//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\nAKgEww4AAEAlGHYAAAAqwbADAABQif8PxzbVlSnrgdAAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# ploting the results\n", " grf <- har_plot(model, dataset$serie, detection, dataset$event)\n", " plot(grf)" ] }, { "cell_type": "code", "execution_count": null, "id": "07c53f34", "metadata": {}, "outputs": [], "source": [] } ], "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": "4.4.0" } }, "nbformat": 4, "nbformat_minor": 5 }