{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DA Algorithm\n",
    "### 中田 竜明"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DA"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "include(\"da.jl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "[ソースコード:da.jl](https://github.com/nswa17/DA_alg.jl/blob/master/da.jl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m, n = 100, 100\n",
    "m_prefs, f_prefs = DA.generate_random_preference_data(m, n)\n",
    "DA.check_data(m_prefs, f_prefs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "まずm, nそれぞれに100を設定し, ランダムな選好表を作成します. ついでにデータのチェックも行っています."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_matched, f_matched = DA.call_match(m_prefs, f_prefs)\n",
    "DA.check_results(m_matched, f_matched)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DAアルゴリズムを走らせて結果を得ました. 結果も問題ないようです."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DA.stable_matching(m_matched, f_matched, m_prefs, f_prefs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "きちんと安定マッチングになっています. もちろん次のマッチングは安定ではないので,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "false"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DA.stable_matching([1 2], [1 2], [2 1; 1 2; 0 0], [2 1; 1 2; 0 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1x100 Array{Int64,2}:\n",
       " 90  21  31  20  38  73  96  22  27  61  …  80  44  62  7  9  69  33  66  41"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpose(m_matched)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1x100 Array{Int64,2}:\n",
       " 63  67  0  23  90  21  6  86  5  32  …  13  0  24  83  94  48  17  79  34"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpose(f_matched)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "シンプル版でも同様に安定マッチングが得られます."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m, n = 100, 100\n",
    "m_prefs, f_prefs = DA.generate_random_preference_data(m, n)\n",
    "DA.check_data(m_prefs, f_prefs)\n",
    "\n",
    "m_matched, f_matched = DA.call_simple_match(m_prefs, f_prefs)\n",
    "DA.check_results(m_matched, f_matched)\n",
    "DA.stable_matching(m_matched, f_matched, m_prefs, f_prefs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "テストを行います."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Summary: | Pass  Total\n",
      "Testing da.jl |    4      4\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: replacing module DA\n"
     ]
    }
   ],
   "source": [
    "deferred_acceptance = DA.call_match\n",
    "include(\"test_deferred_acceptance.jl\")\n",
    "println()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通りました. 再帰版, シンプル版では,,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Summary: | Pass  Total\n",
      "Testing da.jl |    4      4\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: replacing module DA\n"
     ]
    }
   ],
   "source": [
    "deferred_acceptance = (x, y) -> DA.call_match(x, y, true)\n",
    "include(\"test_deferred_acceptance.jl\")\n",
    "println()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Summary: | Pass  Total\n",
      "Testing da.jl |    4      4\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: redefining constant file_name\n",
      "WARNING: redefining constant function_name\n",
      "WARNING: replacing module DA\n"
     ]
    }
   ],
   "source": [
    "deferred_accpetance = DA.call_simple_match\n",
    "include(\"test_deferred_acceptance.jl\")\n",
    "println()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通ったようです.\n",
    "\n",
    "次にスピードを計測します. m,nと繰り返し回数, 再帰かどうかを引数に取る関数をとりあえず作りました. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "speedtest1 (generic function with 1 method)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function speedtest1(m, n, l, rec)\n",
    "    m_prefs, f_prefs = DA.generate_random_preference_data(m, n)\n",
    "    for i in 1:l\n",
    "        DA.call_match(m_prefs, f_prefs, rec)\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以下の結果を得ました.再帰かそうでないかではコードの一部しか変わっていないのですが, かなり速度差が出ているようです.\n",
    "\n",
    "当然のことながらメモリ割り当ても再帰のほうが多く, それぞれ１０倍近いようです.\n",
    "```\n",
    "@time speedtest1(10, 10, 1000, true) # => 0.311742 seconds (1.41 M allocations: 89.571 MB, 9.54% gc time)\n",
    "@time speedtest1(10, 10, 1000, false) # =>  0.051650 seconds (228.50 k allocations: 19.520 MB, 7.10% gc time)\n",
    "@time speedtest1(100, 100, 100, true) # =>  5.819466 seconds (20.06 M allocations: 1.587 GB, 4.37% gc time)\n",
    "@time speedtest1(100, 100, 100, false) # =>  0.166037 seconds (216.80 k allocations: 74.657 MB, 8.77% gc time)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "次にProfileでどこが遅いかを見てみます. まずは再帰を使わない時から.\n",
    "```\n",
    "@profile test1(100, 100, 100, false)\n",
    "Profile.print()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "```\n",
    "244 task.jl; anonymous; line: 447\n",
    " 244 .../IJulia/src/IJulia.jl; eventloop; line: 143\n",
    "  244 ...rc/execute_request.jl; execute_request_0x535c5df2; line: 183\n",
    "   244 loading.jl; include_string; line: 282\n",
    "    244 profile.jl; anonymous; line: 16\n",
    "     167 In[167]; test1; line: 3\n",
    "      2  ...zemi/DA_alg.jl/da.jl; generate_random_preference_data; line: 37\n",
    "      92 ...zemi/DA_alg.jl/da.jl; generate_random_preference_data; line: 39\n",
    "       7  ...lib/julia/sys.dylib; vcat; (unknown line)\n",
    "       10 array.jl; copy; line: 100\n",
    "       3  multidimensional.jl; _unsafe_batchsetindex!; line: 328\n",
    "       1  multidimensional.jl; _unsafe_batchsetindex!; line: 329\n",
    "       1  random.jl; call; line: 475\n",
    "       66 random.jl; shuffle!; line: 1330\n",
    "        4  random.jl; call; line: 471\n",
    "        14 random.jl; call; line: 475\n",
    "         14 random.jl; maxmultiplemix; line: 445\n",
    "        10 random.jl; rand; line: 514\n",
    "        1  random.jl; rand; line: 515\n",
    "        12 random.jl; rand; line: 516\n",
    "         7 random.jl; gen_rand; line: 88\n",
    "          7 dSFMT.jl; dsfmt_fill_array_close1_open2!; line: 67\n",
    "        1  random.jl; rand; line: 518\n",
    "        20 random.jl; rand; line: 526\n",
    "       1  random.jl; shuffle!; line: 1333\n",
    "      72 ...zemi/DA_alg.jl/da.jl; generate_random_preference_data; line: 42\n",
    "       6  ...lib/julia/sys.dylib; vcat; (unknown line)\n",
    "       3  cartesian.jl; _unsafe_batchsetindex!; line: 34\n",
    "       4  multidimensional.jl; _unsafe_batchsetindex!; line: 328\n",
    "       1  random.jl; call; line: 475\n",
    "       55 random.jl; shuffle!; line: 1330\n",
    "        3  random.jl; call; line: 471\n",
    "        16 random.jl; call; line: 475\n",
    "         16 random.jl; maxmultiplemix; line: 445\n",
    "        6  random.jl; rand; line: 514\n",
    "        6  random.jl; rand; line: 516\n",
    "         2 random.jl; gen_rand; line: 88\n",
    "          2 dSFMT.jl; dsfmt_fill_array_close1_open2!; line: 67\n",
    "        1  random.jl; rand; line: 518\n",
    "        1  random.jl; rand; line: 523\n",
    "        17 random.jl; rand; line: 526\n",
    "       3  random.jl; shuffle!; line: 1331\n",
    "     77  In[167]; test1; line: 4\n",
    "      6  ...zemi/DA_alg.jl/da.jl; call_match; line: 51\n",
    "       1 array.jl; findnext; line: 717\n",
    "       2 multidimensional.jl; _unsafe_getindex; line: 193\n",
    "       3 multidimensional.jl; _unsafe_getindex; line: 195\n",
    "      70 ...zemi/DA_alg.jl/da.jl; call_match; line: 57\n",
    "       70 ...emi/DA_alg.jl/da.jl; da_match; line: 143\n",
    "        70 ...emi/DA_alg.jl/da.jl; decide_to_accept!; line: 111\n",
    "         46 ...emi/DA_alg.jl/da.jl; get_best_male_pointers!; line: 100\n",
    "          1  array.jl; findin; line: 889\n",
    "          28 array.jl; findin; line: 890\n",
    "           25 ...lib/julia/sys.dylib; zeros; (unknown line)\n",
    "            3 ...lib/julia/sys.dylib; call; (unknown line)\n",
    "          17 array.jl; findin; line: 892\n",
    "           14 ...lib/julia/sys.dylib; ht_keyindex; (unknown line)\n",
    "            3 ...lib/julia/sys.dylib; hash_64_64; (unknown line)\n",
    "         24 ...emi/DA_alg.jl/da.jl; get_best_male_pointers!; line: 102\n",
    "          11 abstractarray.jl; map; line: 1305\n",
    "           11 ...mi/DA_alg.jl/da.jl; anonymous; line: 102\n",
    "            6 abstractarray.jl; getindex; line: 488\n",
    "             5 multidimensional.jl; _unsafe_getindex; line: 193\n",
    "          4  abstractarray.jl; map; line: 1306\n",
    "           3 ...lib/julia/sys.dylib; call; (unknown line)\n",
    "          1  abstractarray.jl; map; line: 1307\n",
    "          6  abstractarray.jl; map; line: 1308\n",
    "           1 abstractarray.jl; map_to!; line: 1286\n",
    "            1 ...mi/DA_alg.jl/da.jl; anonymous; line: 102\n",
    "      1  ...zemi/DA_alg.jl/da.jl; call_match; line: 58\n",
    "       1 ...zemi/DA_alg.jl/da.jl; convert_pointer_to_list; line: 71\n",
    "        1 array.jl; findnext; line: 717\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "再帰を使うと...\n",
    "```\n",
    "@profile test1(100, 100, 100, true)\n",
    "Profile.print()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "```\n",
    "4    ...a/lib/julia/sys.dylib; ht_keyindex; (unknown line)\n",
    "4    abstractarray.jl; map; line: 1306\n",
    "3    array.jl; findin; line: 890\n",
    "1    array.jl; findnext; line: 715\n",
    "1    multidimensional.jl; checksize; line: 270\n",
    "1    random.jl; maxmultiplemix; line: 445\n",
    "4887 task.jl; anonymous; line: 447\n",
    " 4887 .../IJulia/src/IJulia.jl; eventloop; line: 143\n",
    "  4887 ...rc/execute_request.jl; execute_request_0x535c5df2; line: 183\n",
    "   4887 loading.jl; include_string; line: 282\n",
    "    4887 profile.jl; anonymous; line: 16\n",
    "     237  In[167]; test1; line: 3\n",
    "      2   ...zemi/DA_alg.jl/da.jl; generate_random_preference_data; line: 37\n",
    "      126 ...zemi/DA_alg.jl/da.jl; generate_random_preference_data; line: 39\n",
    "       7  ...lib/julia/sys.dylib; vcat; (unknown line)\n",
    "       10 array.jl; copy; line: 100\n",
    "       3  multidimensional.jl; _unsafe_batchsetindex!; line: 328\n",
    "       1  multidimensional.jl; _unsafe_batchsetindex!; line: 329\n",
    "       1  random.jl; call; line: 475\n",
    "       92 random.jl; shuffle!; line: 1330\n",
    "        7  random.jl; call; line: 471\n",
    "        22 random.jl; call; line: 475\n",
    "         21 random.jl; maxmultiplemix; line: 445\n",
    "        14 random.jl; rand; line: 514\n",
    "        1  random.jl; rand; line: 515\n",
    "        15 random.jl; rand; line: 516\n",
    "         9 random.jl; gen_rand; line: 88\n",
    "          9 dSFMT.jl; dsfmt_fill_array_close1_open2!; line: 67\n",
    "        1  random.jl; rand; line: 518\n",
    "        25 random.jl; rand; line: 526\n",
    "       5  random.jl; shuffle!; line: 1331\n",
    "       1  random.jl; shuffle!; line: 1333\n",
    "      108 ...zemi/DA_alg.jl/da.jl; generate_random_preference_data; line: 42\n",
    "       7  ...lib/julia/sys.dylib; vcat; (unknown line)\n",
    "       3  array.jl; copy; line: 100\n",
    "       3  cartesian.jl; _unsafe_batchsetindex!; line: 34\n",
    "       5  multidimensional.jl; _unsafe_batchsetindex!; line: 328\n",
    "       1  multidimensional.jl; _unsafe_batchsetindex!; line: 329\n",
    "       1  random.jl; call; line: 475\n",
    "       81 random.jl; shuffle!; line: 1330\n",
    "        3  random.jl; call; line: 471\n",
    "        22 random.jl; call; line: 475\n",
    "         22 random.jl; maxmultiplemix; line: 445\n",
    "        12 random.jl; rand; line: 514\n",
    "        9  random.jl; rand; line: 516\n",
    "         4 random.jl; gen_rand; line: 88\n",
    "          4 dSFMT.jl; dsfmt_fill_array_close1_open2!; line: 67\n",
    "        1  random.jl; rand; line: 518\n",
    "        1  random.jl; rand; line: 523\n",
    "        25 random.jl; rand; line: 526\n",
    "       7  random.jl; shuffle!; line: 1331\n",
    "     4650 In[167]; test1; line: 4\n",
    "      7    ...emi/DA_alg.jl/da.jl; call_match; line: 51\n",
    "       1 array.jl; findnext; line: 717\n",
    "       3 multidimensional.jl; _unsafe_getindex; line: 193\n",
    "       3 multidimensional.jl; _unsafe_getindex; line: 195\n",
    "      1    ...emi/DA_alg.jl/da.jl; call_match; line: 53\n",
    "       1 ...a/lib/julia/sys.dylib; call; (unknown line)\n",
    "        1 .../lib/julia/sys.dylib; call; (unknown line)\n",
    "      4641 ...emi/DA_alg.jl/da.jl; call_match; line: 57\n",
    "       70   ...emi/DA_alg.jl/da.jl; da_match; line: 143\n",
    "        70 ...emi/DA_alg.jl/da.jl; decide_to_accept!; line: 111\n",
    "         46 ...emi/DA_alg.jl/da.jl; get_best_male_pointers!; line: 100\n",
    "          1  array.jl; findin; line: 889\n",
    "          28 array.jl; findin; line: 890\n",
    "           25 ...lib/julia/sys.dylib; zeros; (unknown line)\n",
    "            3 ...lib/julia/sys.dylib; call; (unknown line)\n",
    "          17 array.jl; findin; line: 892\n",
    "           14 ...lib/julia/sys.dylib; ht_keyindex; (unknown line)\n",
    "            3 ...lib/julia/sys.dylib; hash_64_64; (unknown line)\n",
    "         24 ...emi/DA_alg.jl/da.jl; get_best_male_pointers!; line: 102\n",
    "          11 abstractarray.jl; map; line: 1305\n",
    "           11 ...mi/DA_alg.jl/da.jl; anonymous; line: 102\n",
    "            6 abstractarray.jl; getindex; line: 488\n",
    "             5 multidimensional.jl; _unsafe_getindex; line: 193\n",
    "          4  abstractarray.jl; map; line: 1306\n",
    "           3 ...lib/julia/sys.dylib; call; (unknown line)\n",
    "          1  abstractarray.jl; map; line: 1307\n",
    "          6  abstractarray.jl; map; line: 1308\n",
    "           1 abstractarray.jl; map_to!; line: 1286\n",
    "            1 ...mi/DA_alg.jl/da.jl; anonymous; line: 102\n",
    "       38   ...emi/DA_alg.jl/da.jl; recursive_da_match; line: 128\n",
    "        38 ...emi/DA_alg.jl/da.jl; decide_to_accept!; line: 111\n",
    "         25 ...emi/DA_alg.jl/da.jl; get_best_male_pointers!; line: 100\n",
    "          18 array.jl; findin; line: 890\n",
    "           17 ...lib/julia/sys.dylib; zeros; (unknown line)\n",
    "            1 ...lib/julia/sys.dylib; call; (unknown line)\n",
    "          7  array.jl; findin; line: 892\n",
    "           5 ...lib/julia/sys.dylib; ht_keyindex; (unknown line)\n",
    "            1 ...lib/julia/sys.dylib; hash_64_64; (unknown line)\n",
    "         13 ...emi/DA_alg.jl/da.jl; get_best_male_pointers!; line: 102\n",
    "          9 abstractarray.jl; map; line: 1305\n",
    "           9 ...mi/DA_alg.jl/da.jl; anonymous; line: 102\n",
    "            6 abstractarray.jl; getindex; line: 488\n",
    "             4 multidimensional.jl; _unsafe_getindex; line: 193\n",
    "             1 multidimensional.jl; _unsafe_getindex; line: 194\n",
    "              1 multidimensional.jl; checksize; line: 270\n",
    "             1 multidimensional.jl; _unsafe_getindex; line: 195\n",
    "            1 array.jl; findfirst; line: 722\n",
    "             1 array.jl; findnext; line: 716\n",
    "          1 abstractarray.jl; map; line: 1306\n",
    "           1 ...lib/julia/sys.dylib; call; (unknown line)\n",
    "          2 abstractarray.jl; map; line: 1307\n",
    "          1 abstractarray.jl; map; line: 1308\n",
    "       4533 ...emi/DA_alg.jl/da.jl; recursive_da_match; line: 133\n",
    "(以下長いので略)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "再帰を使うところで激遅になっている事がわかります. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "この後速度を早くするためにアルゴリズムを調整しました.　map関数を書き換えたり...\n",
    "\n",
    "結果がこちらです."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.008510 seconds (104.07 k allocations: 8.050 MB, 15.10% gc time)\n",
      "  0.004992 seconds (75.07 k allocations: 5.960 MB)\n",
      "  0.061316 seconds (235.61 k allocations: 97.252 MB, 7.98% gc time)\n",
      "  0.047922 seconds (188.11 k allocations: 75.882 MB, 10.25% gc time)\n"
     ]
    }
   ],
   "source": [
    "@time speedtest1(10, 10, 1000, true)\n",
    "@time speedtest1(10, 10, 1000, false)\n",
    "@time speedtest1(100, 100, 100, true)\n",
    "@time speedtest1(100, 100, 100, false)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参考までに,先ほどの結果は\n",
    "```\n",
    "@time speedtest1(10, 10, 1000, true) # => 0.311742 seconds (1.41 M allocations: 89.571 MB, 9.54% gc time)\n",
    "@time speedtest1(10, 10, 1000, false) # =>  0.051650 seconds (228.50 k allocations: 19.520 MB, 7.10% gc time)\n",
    "@time speedtest1(100, 100, 100, true) # =>  5.819466 seconds (20.06 M allocations: 1.587 GB, 4.37% gc time)\n",
    "@time speedtest1(100, 100, 100, false) # =>  0.166037 seconds (216.80 k allocations: 74.657 MB, 8.77% gc time)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再帰に関して速度が30~100倍ほど早くなってしまいました. 普通のアルゴリズムでも4~10倍の速度が出ています.\n",
    "\n",
    "さらに再帰なし版について測ってみます."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.004421 seconds (71.06 k allocations: 5.624 MB)\n",
      "  0.037412 seconds (248.12 k allocations: 27.522 MB, 7.17% gc time)\n",
      "  0.099779 seconds (521.18 k allocations: 81.949 MB, 22.67% gc time)\n",
      "  0.133260 seconds (634.24 k allocations: 131.947 MB, 24.82% gc time)\n",
      "  0.191103 seconds (855.30 k allocations: 216.210 MB, 27.00% gc time)\n",
      "  0.310273 seconds (1.25 M allocations: 333.764 MB, 26.73% gc time)\n",
      "  0.457669 seconds (1.62 M allocations: 497.376 MB, 27.68% gc time)\n",
      "  0.692682 seconds (2.14 M allocations: 701.836 MB, 25.76% gc time)\n",
      "  0.832984 seconds (2.29 M allocations: 840.361 MB, 26.24% gc time)\n",
      "  0.878811 seconds (3.46 M allocations: 1.400 GB, 9.25% gc time)\n",
      "  0.993192 seconds (2.74 M allocations: 1.256 GB, 30.86% gc time)\n",
      "  1.367815 seconds (3.09 M allocations: 1.532 GB, 32.09% gc time)\n",
      "  1.301243 seconds (2.55 M allocations: 1.312 GB, 30.13% gc time)\n",
      "  2.237044 seconds (4.48 M allocations: 2.468 GB, 29.92% gc time)\n",
      "  2.205013 seconds (4.70 M allocations: 2.874 GB, 31.66% gc time)\n",
      "  2.086873 seconds (4.37 M allocations: 2.701 GB, 32.63% gc time)\n",
      "  2.101123 seconds (4.19 M allocations: 2.788 GB, 31.85% gc time)\n",
      "  2.567895 seconds (4.72 M allocations: 3.490 GB, 34.02% gc time)\n",
      "  2.969407 seconds (5.13 M allocations: 3.732 GB, 31.52% gc time)\n",
      "  3.714042 seconds (6.52 M allocations: 5.371 GB, 35.73% gc time)\n",
      "  3.787283 seconds (6.32 M allocations: 5.131 GB, 33.18% gc time)\n",
      "  4.514897 seconds (6.49 M allocations: 5.807 GB, 31.63% gc time)\n",
      "  5.164590 seconds (7.76 M allocations: 7.152 GB, 34.44% gc time)\n",
      "  5.747971 seconds (8.46 M allocations: 7.853 GB, 34.77% gc time)\n",
      "  6.480615 seconds (8.99 M allocations: 8.347 GB, 24.20% gc time)\n",
      "  6.782499 seconds (9.74 M allocations: 9.585 GB, 26.25% gc time)\n",
      "  6.782342 seconds (8.88 M allocations: 8.910 GB, 23.63% gc time)\n",
      "  8.031126 seconds (10.02 M allocations: 10.516 GB, 23.85% gc time)\n",
      " 10.519969 seconds (11.87 M allocations: 12.479 GB, 20.23% gc time)\n",
      " 11.867743 seconds (14.76 M allocations: 16.530 GB, 22.95% gc time)\n",
      " 10.758971 seconds (12.08 M allocations: 13.944 GB, 23.81% gc time)\n",
      " 15.402221 seconds (15.50 M allocations: 18.253 GB, 21.33% gc time)\n",
      " 15.754614 seconds (15.44 M allocations: 18.548 GB, 20.83% gc time)\n",
      " 14.622754 seconds (14.73 M allocations: 18.252 GB, 21.03% gc time)\n",
      " 14.426842 seconds (16.11 M allocations: 20.856 GB, 23.57% gc time)\n",
      " 13.807909 seconds (14.75 M allocations: 19.492 GB, 21.82% gc time)\n",
      " 17.929274 seconds (20.84 M allocations: 28.949 GB, 24.73% gc time)\n",
      " 18.154148 seconds (18.20 M allocations: 25.866 GB, 24.07% gc time)\n",
      " 20.904895 seconds (18.72 M allocations: 26.485 GB, 20.84% gc time)\n",
      " 19.857537 seconds (19.01 M allocations: 28.425 GB, 23.69% gc time)\n"
     ]
    }
   ],
   "source": [
    "for i in 1:40\n",
    "    @time speedtest1(10*i, 10*i, 1000, false)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "get_seconds (generic function with 1 method)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k1 = \"0.004421 seconds (71.06 k allocations: 5.624 MB)\n",
    "  0.037412 seconds (248.12 k allocations: 27.522 MB, 7.17% gc time)\n",
    "  0.099779 seconds (521.18 k allocations: 81.949 MB, 22.67% gc time)\n",
    "  0.133260 seconds (634.24 k allocations: 131.947 MB, 24.82% gc time)\n",
    "  0.191103 seconds (855.30 k allocations: 216.210 MB, 27.00% gc time)\n",
    "  0.310273 seconds (1.25 M allocations: 333.764 MB, 26.73% gc time)\n",
    "  0.457669 seconds (1.62 M allocations: 497.376 MB, 27.68% gc time)\n",
    "  0.692682 seconds (2.14 M allocations: 701.836 MB, 25.76% gc time)\n",
    "  0.832984 seconds (2.29 M allocations: 840.361 MB, 26.24% gc time)\n",
    "  0.878811 seconds (3.46 M allocations: 1.400 GB, 9.25% gc time)\n",
    "  0.993192 seconds (2.74 M allocations: 1.256 GB, 30.86% gc time)\n",
    "  1.367815 seconds (3.09 M allocations: 1.532 GB, 32.09% gc time)\n",
    "  1.301243 seconds (2.55 M allocations: 1.312 GB, 30.13% gc time)\n",
    "  2.237044 seconds (4.48 M allocations: 2.468 GB, 29.92% gc time)\n",
    "  2.205013 seconds (4.70 M allocations: 2.874 GB, 31.66% gc time)\n",
    "  2.086873 seconds (4.37 M allocations: 2.701 GB, 32.63% gc time)\n",
    "  2.101123 seconds (4.19 M allocations: 2.788 GB, 31.85% gc time)\n",
    "  2.567895 seconds (4.72 M allocations: 3.490 GB, 34.02% gc time)\n",
    "  2.969407 seconds (5.13 M allocations: 3.732 GB, 31.52% gc time)\n",
    "  3.714042 seconds (6.52 M allocations: 5.371 GB, 35.73% gc time)\n",
    "  3.787283 seconds (6.32 M allocations: 5.131 GB, 33.18% gc time)\n",
    "  4.514897 seconds (6.49 M allocations: 5.807 GB, 31.63% gc time)\n",
    "  5.164590 seconds (7.76 M allocations: 7.152 GB, 34.44% gc time)\n",
    "  5.747971 seconds (8.46 M allocations: 7.853 GB, 34.77% gc time)\n",
    "  6.480615 seconds (8.99 M allocations: 8.347 GB, 24.20% gc time)\n",
    "  6.782499 seconds (9.74 M allocations: 9.585 GB, 26.25% gc time)\n",
    "  6.782342 seconds (8.88 M allocations: 8.910 GB, 23.63% gc time)\n",
    "  8.031126 seconds (10.02 M allocations: 10.516 GB, 23.85% gc time)\n",
    " 10.519969 seconds (11.87 M allocations: 12.479 GB, 20.23% gc time)\n",
    " 11.867743 seconds (14.76 M allocations: 16.530 GB, 22.95% gc time)\n",
    " 10.758971 seconds (12.08 M allocations: 13.944 GB, 23.81% gc time)\n",
    " 15.402221 seconds (15.50 M allocations: 18.253 GB, 21.33% gc time)\n",
    " 15.754614 seconds (15.44 M allocations: 18.548 GB, 20.83% gc time)\n",
    " 14.622754 seconds (14.73 M allocations: 18.252 GB, 21.03% gc time)\n",
    " 14.426842 seconds (16.11 M allocations: 20.856 GB, 23.57% gc time)\n",
    " 13.807909 seconds (14.75 M allocations: 19.492 GB, 21.82% gc time)\n",
    " 17.929274 seconds (20.84 M allocations: 28.949 GB, 24.73% gc time)\n",
    " 18.154148 seconds (18.20 M allocations: 25.866 GB, 24.07% gc time)\n",
    " 20.904895 seconds (18.72 M allocations: 26.485 GB, 20.84% gc time)\n",
    " 19.857537 seconds (19.01 M allocations: 28.425 GB, 23.69% gc time)\"\n",
    "\n",
    "function get_seconds(s::AbstractString)\n",
    "    return matchall(r\"\\d+\\.\\d{6}\", s)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "得たデータをプロットしてみます."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x322bbf710>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1-element Array{Any,1}:\n",
       " PyObject <matplotlib.lines.Line2D object at 0x3229b4d68>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using PyPlot\n",
    "\n",
    "plot(get_seconds(k1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "たまたまの可能性がありますがDAアルゴリズムの計算量$n^2$を表してるように見えなくもない(?). \n",
    "再帰なし版だと..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.000931 seconds (14.01 k allocations: 750.563 KB)\n",
      "  0.000957 seconds (18.02 k allocations: 985.500 KB)\n",
      "  0.001312 seconds (24.02 k allocations: 1.299 MB)\n",
      "  0.001952 seconds (35.03 k allocations: 2.001 MB)\n",
      "  0.002749 seconds (46.03 k allocations: 2.719 MB)\n",
      "  0.003422 seconds (56.04 k allocations: 3.636 MB)\n",
      "  0.004385 seconds (63.05 k allocations: 4.048 MB)\n",
      "  0.007769 seconds (63.05 k allocations: 4.630 MB, 49.62% gc time)\n",
      "  0.004949 seconds (71.06 k allocations: 5.210 MB)\n",
      "  0.004288 seconds (65.06 k allocations: 5.258 MB)\n",
      "  0.006425 seconds (85.07 k allocations: 6.617 MB)\n",
      "  0.007728 seconds (74.08 k allocations: 6.481 MB, 31.38% gc time)\n",
      "  0.007383 seconds (105.08 k allocations: 9.336 MB)\n",
      "  0.015663 seconds (160.09 k allocations: 14.709 MB, 20.79% gc time)\n",
      "  0.022754 seconds (169.09 k allocations: 15.610 MB, 29.25% gc time)\n",
      "  0.019703 seconds (198.10 k allocations: 19.336 MB)\n",
      "  0.019738 seconds (134.10 k allocations: 13.691 MB, 28.81% gc time)\n",
      "  0.022365 seconds (181.11 k allocations: 18.851 MB, 24.45% gc time)\n",
      "  0.033865 seconds (245.12 k allocations: 25.673 MB, 16.76% gc time)\n",
      "  0.030471 seconds (244.12 k allocations: 27.248 MB, 17.19% gc time)\n",
      "  0.046028 seconds (262.13 k allocations: 29.965 MB, 25.92% gc time)\n",
      "  0.024981 seconds (201.13 k allocations: 23.926 MB, 21.84% gc time)\n",
      "  0.035885 seconds (296.14 k allocations: 35.082 MB, 16.36% gc time)\n",
      "  0.062064 seconds (410.15 k allocations: 63.610 MB, 24.60% gc time)\n",
      "  0.051262 seconds (316.15 k allocations: 48.902 MB, 29.87% gc time)\n",
      "  0.024780 seconds (172.16 k allocations: 27.786 MB, 25.54% gc time)\n",
      "  0.039818 seconds (293.17 k allocations: 46.725 MB, 25.64% gc time)\n",
      "  0.074092 seconds (453.17 k allocations: 67.174 MB, 20.40% gc time)\n",
      "  0.097192 seconds (515.18 k allocations: 78.498 MB, 22.08% gc time)\n",
      "  0.056652 seconds (370.18 k allocations: 59.198 MB, 18.52% gc time)\n",
      "  0.066648 seconds (368.19 k allocations: 57.338 MB, 26.43% gc time)\n",
      "  0.072351 seconds (404.19 k allocations: 74.438 MB, 23.31% gc time)\n",
      "  0.072605 seconds (383.20 k allocations: 71.191 MB, 28.22% gc time)\n",
      "  0.103042 seconds (535.21 k allocations: 96.584 MB, 23.62% gc time)\n",
      "  0.198075 seconds (765.21 k allocations: 139.647 MB, 21.58% gc time)\n",
      "  0.087888 seconds (421.22 k allocations: 74.342 MB, 25.63% gc time)\n",
      "  0.128518 seconds (531.23 k allocations: 99.827 MB, 21.67% gc time)\n",
      "  0.176163 seconds (554.23 k allocations: 102.744 MB, 20.51% gc time)\n",
      "  0.189753 seconds (697.24 k allocations: 122.507 MB, 19.74% gc time)\n",
      "  0.149114 seconds (729.24 k allocations: 146.580 MB, 22.64% gc time)\n"
     ]
    }
   ],
   "source": [
    "for i in 1:40\n",
    "    @time speedtest1(1*i, 1*i, 1000, false)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x3210b81d0>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k2 = \" 0.000931 seconds (14.01 k allocations: 750.563 KB)\n",
    "  0.000957 seconds (18.02 k allocations: 985.500 KB)\n",
    "  0.001312 seconds (24.02 k allocations: 1.299 MB)\n",
    "  0.001952 seconds (35.03 k allocations: 2.001 MB)\n",
    "  0.002749 seconds (46.03 k allocations: 2.719 MB)\n",
    "  0.003422 seconds (56.04 k allocations: 3.636 MB)\n",
    "  0.004385 seconds (63.05 k allocations: 4.048 MB)\n",
    "  0.007769 seconds (63.05 k allocations: 4.630 MB, 49.62% gc time)\n",
    "  0.004949 seconds (71.06 k allocations: 5.210 MB)\n",
    "  0.004288 seconds (65.06 k allocations: 5.258 MB)\n",
    "  0.006425 seconds (85.07 k allocations: 6.617 MB)\n",
    "  0.007728 seconds (74.08 k allocations: 6.481 MB, 31.38% gc time)\n",
    "  0.007383 seconds (105.08 k allocations: 9.336 MB)\n",
    "  0.015663 seconds (160.09 k allocations: 14.709 MB, 20.79% gc time)\n",
    "  0.022754 seconds (169.09 k allocations: 15.610 MB, 29.25% gc time)\n",
    "  0.019703 seconds (198.10 k allocations: 19.336 MB)\n",
    "  0.019738 seconds (134.10 k allocations: 13.691 MB, 28.81% gc time)\n",
    "  0.022365 seconds (181.11 k allocations: 18.851 MB, 24.45% gc time)\n",
    "  0.033865 seconds (245.12 k allocations: 25.673 MB, 16.76% gc time)\n",
    "  0.030471 seconds (244.12 k allocations: 27.248 MB, 17.19% gc time)\n",
    "  0.046028 seconds (262.13 k allocations: 29.965 MB, 25.92% gc time)\n",
    "  0.024981 seconds (201.13 k allocations: 23.926 MB, 21.84% gc time)\n",
    "  0.035885 seconds (296.14 k allocations: 35.082 MB, 16.36% gc time)\n",
    "  0.062064 seconds (410.15 k allocations: 63.610 MB, 24.60% gc time)\n",
    "  0.051262 seconds (316.15 k allocations: 48.902 MB, 29.87% gc time)\n",
    "  0.024780 seconds (172.16 k allocations: 27.786 MB, 25.54% gc time)\n",
    "  0.039818 seconds (293.17 k allocations: 46.725 MB, 25.64% gc time)\n",
    "  0.074092 seconds (453.17 k allocations: 67.174 MB, 20.40% gc time)\n",
    "  0.097192 seconds (515.18 k allocations: 78.498 MB, 22.08% gc time)\n",
    "  0.056652 seconds (370.18 k allocations: 59.198 MB, 18.52% gc time)\n",
    "  0.066648 seconds (368.19 k allocations: 57.338 MB, 26.43% gc time)\n",
    "  0.072351 seconds (404.19 k allocations: 74.438 MB, 23.31% gc time)\n",
    "  0.072605 seconds (383.20 k allocations: 71.191 MB, 28.22% gc time)\n",
    "  0.103042 seconds (535.21 k allocations: 96.584 MB, 23.62% gc time)\n",
    "  0.198075 seconds (765.21 k allocations: 139.647 MB, 21.58% gc time)\n",
    "  0.087888 seconds (421.22 k allocations: 74.342 MB, 25.63% gc time)\n",
    "  0.128518 seconds (531.23 k allocations: 99.827 MB, 21.67% gc time)\n",
    "  0.176163 seconds (554.23 k allocations: 102.744 MB, 20.51% gc time)\n",
    "  0.189753 seconds (697.24 k allocations: 122.507 MB, 19.74% gc time)\n",
    "  0.149114 seconds (729.24 k allocations: 146.580 MB, 22.64% gc time)\"\n",
    "\n",
    "plot(get_seconds(k2))\n",
    "legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "simple版でも測度を計測します."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "speedtest2 (generic function with 1 method)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function speedtest2(m, n, l)\n",
    "    m_prefs, f_prefs = DA.generate_random_preference_data(m, n)\n",
    "    for i in 1:l\n",
    "        DA.call_simple_match(m_prefs, f_prefs)\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.014245 seconds (135.07 k allocations: 11.316 MB, 38.51% gc time)\n",
      "  0.240136 seconds (1.80 M allocations: 479.303 MB, 13.51% gc time)\n",
      "  1.246197 seconds (4.97 M allocations: 2.110 GB, 26.12% gc time)\n",
      "  3.106522 seconds (7.13 M allocations: 4.570 GB, 38.96% gc time)\n",
      "  8.143171 seconds (14.36 M allocations: 12.194 GB, 39.38% gc time)\n",
      " 10.232993 seconds (16.44 M allocations: 16.173 GB, 32.66% gc time)\n",
      " 16.158505 seconds (24.54 M allocations: 28.712 GB, 30.88% gc time)\n"
     ]
    }
   ],
   "source": [
    "@time speedtest2(10, 10, 1000)\n",
    "@time speedtest2(50, 50, 1000)\n",
    "@time speedtest2(100, 100, 1000)\n",
    "@time speedtest2(150, 150, 1000)\n",
    "@time speedtest2(200, 200, 1000)\n",
    "@time speedtest2(250, 250, 1000)\n",
    "@time speedtest2(300, 300, 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 再帰なし普通版だと以下のようになるので,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.012600 seconds (92.07 k allocations: 7.043 MB, 30.42% gc time)\n",
      "  0.119666 seconds (787.31 k allocations: 201.074 MB, 9.79% gc time)\n",
      "  0.679825 seconds (2.23 M allocations: 927.489 MB, 21.45% gc time)\n",
      "  1.786538 seconds (3.14 M allocations: 1.890 GB, 30.68% gc time)\n",
      "  3.544882 seconds (5.64 M allocations: 4.535 GB, 32.97% gc time)\n",
      "  6.438534 seconds (8.78 M allocations: 8.235 GB, 26.43% gc time)\n",
      "  8.091873 seconds (9.68 M allocations: 10.706 GB, 22.88% gc time)\n"
     ]
    }
   ],
   "source": [
    "@time speedtest1(10, 10, 1000, false)\n",
    "@time speedtest1(50, 50, 1000, false)\n",
    "@time speedtest1(100, 100, 1000, false)\n",
    "@time speedtest1(150, 150, 1000, false)\n",
    "@time speedtest1(200, 200, 1000, false)\n",
    "@time speedtest1(250, 250, 1000, false)\n",
    "@time speedtest1(300, 300, 1000, false)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " シンプル版は少しだけ遅いようです."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x3207d2ef0>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x3202729e8>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(get_seconds(\"0.014245 seconds (135.07 k allocations: 11.316 MB, 38.51% gc time)\n",
    "  0.240136 seconds (1.80 M allocations: 479.303 MB, 13.51% gc time)\n",
    "  1.246197 seconds (4.97 M allocations: 2.110 GB, 26.12% gc time)\n",
    "  3.106522 seconds (7.13 M allocations: 4.570 GB, 38.96% gc time)\n",
    "  8.143171 seconds (14.36 M allocations: 12.194 GB, 39.38% gc time)\n",
    " 10.232993 seconds (16.44 M allocations: 16.173 GB, 32.66% gc time)\n",
    "16.158505 seconds (24.54 M allocations: 28.712 GB, 30.88% gc time)\"), label = \"simple\")\n",
    "plot(get_seconds(\"  0.012600 seconds (92.07 k allocations: 7.043 MB, 30.42% gc time)\n",
    "  0.119666 seconds (787.31 k allocations: 201.074 MB, 9.79% gc time)\n",
    "  0.679825 seconds (2.23 M allocations: 927.489 MB, 21.45% gc time)\n",
    "  1.786538 seconds (3.14 M allocations: 1.890 GB, 30.68% gc time)\n",
    "  3.544882 seconds (5.64 M allocations: 4.535 GB, 32.97% gc time)\n",
    "  6.438534 seconds (8.78 M allocations: 8.235 GB, 26.43% gc time)\n",
    "8.091873 seconds (9.68 M allocations: 10.706 GB, 22.88% gc time)\"), label = \"normal\")\n",
    "legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "通常版だとシンプル版よりループを回す回数が少なく抑えられているからでしょうか."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "この後コードを変更してfindfirstをなくしたり, 配列の型を小さくしたりデータ構造を少しだけ変えました.(見づらくなりました)\n",
    "\n",
    "スピード計測時にrandomな選好表を作る関数の分も上乗せされていたのを修正します."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "まずはプログラムの変更でのスピード上昇分を測ります.(再帰なし通常版)\n",
    "\n",
    "※上のデータと比べるためにとりあえず選好表を作る関数は含んだままにしておきます."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.013687 seconds (92.06 k allocations: 7.226 MB)\n",
      "  0.058758 seconds (197.13 k allocations: 20.595 MB, 20.35% gc time)\n",
      "  0.056876 seconds (314.18 k allocations: 40.490 MB, 17.28% gc time)\n",
      "  0.098616 seconds (502.24 k allocations: 72.133 MB, 16.36% gc time)\n",
      "  0.147537 seconds (660.30 k allocations: 105.889 MB, 11.82% gc time)\n",
      "  0.150112 seconds (758.37 k allocations: 132.577 MB, 16.39% gc time)\n",
      "  0.215729 seconds (974.42 k allocations: 179.978 MB, 16.46% gc time)\n",
      "  0.340091 seconds (1.50 M allocations: 251.397 MB, 11.37% gc time)\n",
      "  0.359890 seconds (1.89 M allocations: 318.328 MB, 13.88% gc time)\n",
      "  0.382034 seconds (1.74 M allocations: 349.349 MB, 15.41% gc time)\n",
      "  0.402355 seconds (1.69 M allocations: 399.832 MB, 16.97% gc time)\n",
      "  0.471069 seconds (2.01 M allocations: 476.572 MB, 16.98% gc time)\n",
      "  0.844814 seconds (2.66 M allocations: 582.390 MB, 12.74% gc time)\n",
      "  0.638066 seconds (1.99 M allocations: 592.895 MB, 18.47% gc time)\n",
      "  0.939131 seconds (3.14 M allocations: 755.757 MB, 15.80% gc time)\n",
      "  2.077835 seconds (4.02 M allocations: 869.611 MB, 10.27% gc time)\n",
      "  1.380199 seconds (3.12 M allocations: 890.686 MB, 16.30% gc time)\n",
      "  1.455479 seconds (2.95 M allocations: 979.229 MB, 19.10% gc time)\n",
      "  1.750106 seconds (4.07 M allocations: 1.095 GB, 13.67% gc time)\n",
      "  1.771202 seconds (4.91 M allocations: 1.280 GB, 15.24% gc time)\n",
      "  1.573289 seconds (4.71 M allocations: 1.326 GB, 17.05% gc time)\n",
      "  1.611797 seconds (4.71 M allocations: 1.481 GB, 19.21% gc time)\n",
      "  2.166091 seconds (5.48 M allocations: 1.607 GB, 16.41% gc time)\n",
      "  2.323979 seconds (5.11 M allocations: 1.649 GB, 16.06% gc time)\n",
      "  1.946263 seconds (5.22 M allocations: 1.759 GB, 14.44% gc time)\n",
      "  2.750414 seconds (5.83 M allocations: 1.916 GB, 11.25% gc time)\n",
      "  1.841744 seconds (4.29 M allocations: 1.921 GB, 15.72% gc time)\n",
      "  2.726255 seconds (6.36 M allocations: 2.198 GB, 11.83% gc time)\n",
      "  3.779822 seconds (9.24 M allocations: 2.539 GB, 9.01% gc time)\n",
      "  3.140624 seconds (7.61 M allocations: 2.549 GB, 11.84% gc time)\n",
      "  3.736348 seconds (6.74 M allocations: 2.619 GB, 10.90% gc time)\n",
      "  3.082158 seconds (6.34 M allocations: 2.729 GB, 13.26% gc time)\n",
      "  3.630857 seconds (7.52 M allocations: 2.962 GB, 12.96% gc time)\n",
      "  4.041891 seconds (7.68 M allocations: 3.123 GB, 11.17% gc time)\n",
      "  4.781519 seconds (8.48 M allocations: 3.336 GB, 10.73% gc time)\n",
      "  4.502419 seconds (8.14 M allocations: 3.468 GB, 12.22% gc time)\n",
      "  5.470003 seconds (11.34 M allocations: 3.870 GB, 10.31% gc time)\n",
      "  6.241392 seconds (10.09 M allocations: 3.943 GB, 10.08% gc time)\n",
      "  7.008831 seconds (11.93 M allocations: 4.251 GB, 9.33% gc time)\n",
      "  6.944698 seconds (11.19 M allocations: 4.372 GB, 12.22% gc time)\n"
     ]
    }
   ],
   "source": [
    "for i in 1:40\n",
    "    @time speedtest1(10*i, 10*i, 1000, false)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "using PyPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31d76a9e8>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x315ab2198>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revs = get_seconds(\"0.013687 seconds (92.06 k allocations: 7.226 MB)\n",
    "  0.058758 seconds (197.13 k allocations: 20.595 MB, 20.35% gc time)\n",
    "  0.056876 seconds (314.18 k allocations: 40.490 MB, 17.28% gc time)\n",
    "  0.098616 seconds (502.24 k allocations: 72.133 MB, 16.36% gc time)\n",
    "  0.147537 seconds (660.30 k allocations: 105.889 MB, 11.82% gc time)\n",
    "  0.150112 seconds (758.37 k allocations: 132.577 MB, 16.39% gc time)\n",
    "  0.215729 seconds (974.42 k allocations: 179.978 MB, 16.46% gc time)\n",
    "  0.340091 seconds (1.50 M allocations: 251.397 MB, 11.37% gc time)\n",
    "  0.359890 seconds (1.89 M allocations: 318.328 MB, 13.88% gc time)\n",
    "  0.382034 seconds (1.74 M allocations: 349.349 MB, 15.41% gc time)\n",
    "  0.402355 seconds (1.69 M allocations: 399.832 MB, 16.97% gc time)\n",
    "  0.471069 seconds (2.01 M allocations: 476.572 MB, 16.98% gc time)\n",
    "  0.844814 seconds (2.66 M allocations: 582.390 MB, 12.74% gc time)\n",
    "  0.638066 seconds (1.99 M allocations: 592.895 MB, 18.47% gc time)\n",
    "  0.939131 seconds (3.14 M allocations: 755.757 MB, 15.80% gc time)\n",
    "  2.077835 seconds (4.02 M allocations: 869.611 MB, 10.27% gc time)\n",
    "  1.380199 seconds (3.12 M allocations: 890.686 MB, 16.30% gc time)\n",
    "  1.455479 seconds (2.95 M allocations: 979.229 MB, 19.10% gc time)\n",
    "  1.750106 seconds (4.07 M allocations: 1.095 GB, 13.67% gc time)\n",
    "  1.771202 seconds (4.91 M allocations: 1.280 GB, 15.24% gc time)\n",
    "  1.573289 seconds (4.71 M allocations: 1.326 GB, 17.05% gc time)\n",
    "  1.611797 seconds (4.71 M allocations: 1.481 GB, 19.21% gc time)\n",
    "  2.166091 seconds (5.48 M allocations: 1.607 GB, 16.41% gc time)\n",
    "  2.323979 seconds (5.11 M allocations: 1.649 GB, 16.06% gc time)\n",
    "  1.946263 seconds (5.22 M allocations: 1.759 GB, 14.44% gc time)\n",
    "  2.750414 seconds (5.83 M allocations: 1.916 GB, 11.25% gc time)\n",
    "  1.841744 seconds (4.29 M allocations: 1.921 GB, 15.72% gc time)\n",
    "  2.726255 seconds (6.36 M allocations: 2.198 GB, 11.83% gc time)\n",
    "  3.779822 seconds (9.24 M allocations: 2.539 GB, 9.01% gc time)\n",
    "  3.140624 seconds (7.61 M allocations: 2.549 GB, 11.84% gc time)\n",
    "  3.736348 seconds (6.74 M allocations: 2.619 GB, 10.90% gc time)\n",
    "  3.082158 seconds (6.34 M allocations: 2.729 GB, 13.26% gc time)\n",
    "  3.630857 seconds (7.52 M allocations: 2.962 GB, 12.96% gc time)\n",
    "  4.041891 seconds (7.68 M allocations: 3.123 GB, 11.17% gc time)\n",
    "  4.781519 seconds (8.48 M allocations: 3.336 GB, 10.73% gc time)\n",
    "  4.502419 seconds (8.14 M allocations: 3.468 GB, 12.22% gc time)\n",
    "  5.470003 seconds (11.34 M allocations: 3.870 GB, 10.31% gc time)\n",
    "  6.241392 seconds (10.09 M allocations: 3.943 GB, 10.08% gc time)\n",
    "  7.008831 seconds (11.93 M allocations: 4.251 GB, 9.33% gc time)\n",
    "  6.944698 seconds (11.19 M allocations: 4.372 GB, 12.22% gc time)\")\n",
    "k1 = \"0.004421 seconds (71.06 k allocations: 5.624 MB)\n",
    "  0.037412 seconds (248.12 k allocations: 27.522 MB, 7.17% gc time)\n",
    "  0.099779 seconds (521.18 k allocations: 81.949 MB, 22.67% gc time)\n",
    "  0.133260 seconds (634.24 k allocations: 131.947 MB, 24.82% gc time)\n",
    "  0.191103 seconds (855.30 k allocations: 216.210 MB, 27.00% gc time)\n",
    "  0.310273 seconds (1.25 M allocations: 333.764 MB, 26.73% gc time)\n",
    "  0.457669 seconds (1.62 M allocations: 497.376 MB, 27.68% gc time)\n",
    "  0.692682 seconds (2.14 M allocations: 701.836 MB, 25.76% gc time)\n",
    "  0.832984 seconds (2.29 M allocations: 840.361 MB, 26.24% gc time)\n",
    "  0.878811 seconds (3.46 M allocations: 1.400 GB, 9.25% gc time)\n",
    "  0.993192 seconds (2.74 M allocations: 1.256 GB, 30.86% gc time)\n",
    "  1.367815 seconds (3.09 M allocations: 1.532 GB, 32.09% gc time)\n",
    "  1.301243 seconds (2.55 M allocations: 1.312 GB, 30.13% gc time)\n",
    "  2.237044 seconds (4.48 M allocations: 2.468 GB, 29.92% gc time)\n",
    "  2.205013 seconds (4.70 M allocations: 2.874 GB, 31.66% gc time)\n",
    "  2.086873 seconds (4.37 M allocations: 2.701 GB, 32.63% gc time)\n",
    "  2.101123 seconds (4.19 M allocations: 2.788 GB, 31.85% gc time)\n",
    "  2.567895 seconds (4.72 M allocations: 3.490 GB, 34.02% gc time)\n",
    "  2.969407 seconds (5.13 M allocations: 3.732 GB, 31.52% gc time)\n",
    "  3.714042 seconds (6.52 M allocations: 5.371 GB, 35.73% gc time)\n",
    "  3.787283 seconds (6.32 M allocations: 5.131 GB, 33.18% gc time)\n",
    "  4.514897 seconds (6.49 M allocations: 5.807 GB, 31.63% gc time)\n",
    "  5.164590 seconds (7.76 M allocations: 7.152 GB, 34.44% gc time)\n",
    "  5.747971 seconds (8.46 M allocations: 7.853 GB, 34.77% gc time)\n",
    "  6.480615 seconds (8.99 M allocations: 8.347 GB, 24.20% gc time)\n",
    "  6.782499 seconds (9.74 M allocations: 9.585 GB, 26.25% gc time)\n",
    "  6.782342 seconds (8.88 M allocations: 8.910 GB, 23.63% gc time)\n",
    "  8.031126 seconds (10.02 M allocations: 10.516 GB, 23.85% gc time)\n",
    " 10.519969 seconds (11.87 M allocations: 12.479 GB, 20.23% gc time)\n",
    " 11.867743 seconds (14.76 M allocations: 16.530 GB, 22.95% gc time)\n",
    " 10.758971 seconds (12.08 M allocations: 13.944 GB, 23.81% gc time)\n",
    " 15.402221 seconds (15.50 M allocations: 18.253 GB, 21.33% gc time)\n",
    " 15.754614 seconds (15.44 M allocations: 18.548 GB, 20.83% gc time)\n",
    " 14.622754 seconds (14.73 M allocations: 18.252 GB, 21.03% gc time)\n",
    " 14.426842 seconds (16.11 M allocations: 20.856 GB, 23.57% gc time)\n",
    " 13.807909 seconds (14.75 M allocations: 19.492 GB, 21.82% gc time)\n",
    " 17.929274 seconds (20.84 M allocations: 28.949 GB, 24.73% gc time)\n",
    " 18.154148 seconds (18.20 M allocations: 25.866 GB, 24.07% gc time)\n",
    " 20.904895 seconds (18.72 M allocations: 26.485 GB, 20.84% gc time)\n",
    " 19.857537 seconds (19.01 M allocations: 28.425 GB, 23.69% gc time)\"\n",
    "\n",
    "plot(get_seconds(k1), label=\"before\")\n",
    "plot(revs, label=\"after\")\n",
    "legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "速度が半分くらいになっています.\n",
    "次にランダムな選好表を先に生成しておいて,シンプル版, 再帰版, 再帰無し版のスピードを計測します."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "get_seconds2 (generic function with 1 method)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_seconds2(s) = map(i -> parse(Float64, i), get_seconds(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "prefss = [DA.generate_random_preference_data(i*10, i*10) for i in 1:30]\n",
    "prefss2 = [DA.generate_random_preference_data(i*10, i*10) for i in 1:30]\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.000061 seconds (46 allocations: 3.547 KB)\n",
      "  0.000065 seconds (76 allocations: 9.469 KB)\n",
      "  0.000156 seconds (106 allocations: 19.031 KB)\n",
      "  0.000173 seconds (136 allocations: 32.641 KB)\n",
      "  0.000317 seconds (167 allocations: 49.281 KB)\n",
      "  0.000371 seconds (197 allocations: 65.453 KB)\n",
      "  0.000525 seconds (227 allocations: 88.344 KB)\n",
      "  0.000531 seconds (257 allocations: 112.109 KB)\n",
      "  0.000866 seconds (287 allocations: 140.078 KB)\n",
      "  0.000944 seconds (317 allocations: 171.203 KB)\n",
      "  0.000643 seconds (347 allocations: 208.891 KB)\n",
      "  0.000910 seconds (377 allocations: 246.578 KB)\n",
      "  0.001028 seconds (407 allocations: 287.406 KB)\n",
      "  0.001367 seconds (437 allocations: 331.313 KB)\n",
      "  0.001895 seconds (467 allocations: 383.094 KB)\n",
      "  0.002124 seconds (497 allocations: 421.016 KB)\n",
      "  0.002138 seconds (527 allocations: 481.844 KB)\n",
      "  0.003025 seconds (557 allocations: 549.594 KB)\n",
      "  0.002201 seconds (587 allocations: 594.828 KB)\n",
      "  0.001959 seconds (617 allocations: 676.328 KB)\n",
      "  0.003388 seconds (647 allocations: 726.406 KB)\n",
      "  0.002952 seconds (677 allocations: 826.391 KB)\n",
      "  0.004581 seconds (707 allocations: 881.781 KB)\n",
      "  0.003709 seconds (737 allocations: 938.734 KB)\n",
      "  0.004778 seconds (767 allocations: 1012.922 KB)\n",
      "  0.005388 seconds (797 allocations: 1.069 MB)\n",
      "  0.009387 seconds (827 allocations: 1.151 MB)\n",
      "  0.009082 seconds (857 allocations: 1.237 MB)\n",
      "  0.010223 seconds (887 allocations: 1.325 MB)\n",
      "  0.009350 seconds (917 allocations: 1.416 MB)\n"
     ]
    }
   ],
   "source": [
    "for prefs in prefss\n",
    "    @time DA.call_simple_match(prefs[1], prefs[2])\n",
    "end\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.000062 seconds (46 allocations: 3.547 KB)\n",
      "  0.000072 seconds (76 allocations: 9.469 KB)\n",
      "  0.000095 seconds (106 allocations: 19.031 KB)\n",
      "  0.000174 seconds (136 allocations: 32.641 KB)\n",
      "  0.000215 seconds (167 allocations: 49.281 KB)\n",
      "  0.000343 seconds (197 allocations: 65.453 KB)\n",
      "  0.000502 seconds (227 allocations: 88.344 KB)\n",
      "  0.000612 seconds (257 allocations: 112.109 KB)\n",
      "  0.000670 seconds (287 allocations: 140.078 KB)\n",
      "  0.000685 seconds (317 allocations: 171.203 KB)\n",
      "  0.000786 seconds (347 allocations: 208.891 KB)\n",
      "  0.001125 seconds (377 allocations: 246.578 KB)\n",
      "  0.001506 seconds (407 allocations: 287.406 KB)\n",
      "  0.001837 seconds (437 allocations: 331.313 KB)\n",
      "  0.001571 seconds (467 allocations: 383.094 KB)\n",
      "  0.002576 seconds (497 allocations: 421.016 KB)\n",
      "  0.002097 seconds (527 allocations: 481.844 KB)\n",
      "  0.002457 seconds (557 allocations: 549.594 KB)\n",
      "  0.003104 seconds (587 allocations: 594.828 KB)\n",
      "  0.002999 seconds (617 allocations: 676.328 KB)\n",
      "  0.003713 seconds (647 allocations: 726.406 KB)\n",
      "  0.003957 seconds (677 allocations: 826.391 KB)\n",
      "  0.004147 seconds (707 allocations: 881.781 KB)\n",
      "  0.004865 seconds (737 allocations: 938.734 KB)\n",
      "  0.006430 seconds (767 allocations: 1012.922 KB)\n",
      "  0.071584 seconds (797 allocations: 1.069 MB, 91.29% gc time)\n",
      "  0.007282 seconds (827 allocations: 1.151 MB)\n",
      "  0.006026 seconds (857 allocations: 1.237 MB)\n",
      "  0.009872 seconds (887 allocations: 1.325 MB)\n",
      "  0.009277 seconds (917 allocations: 1.416 MB)\n"
     ]
    }
   ],
   "source": [
    "for prefs in prefss2\n",
    "    @time DA.call_simple_match(prefs[1], prefs[2])\n",
    "end\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s1 =  \" 0.000061 seconds (46 allocations: 3.547 KB)\n",
    "  0.000065 seconds (76 allocations: 9.469 KB)\n",
    "  0.000156 seconds (106 allocations: 19.031 KB)\n",
    "  0.000173 seconds (136 allocations: 32.641 KB)\n",
    "  0.000317 seconds (167 allocations: 49.281 KB)\n",
    "  0.000371 seconds (197 allocations: 65.453 KB)\n",
    "  0.000525 seconds (227 allocations: 88.344 KB)\n",
    "  0.000531 seconds (257 allocations: 112.109 KB)\n",
    "  0.000866 seconds (287 allocations: 140.078 KB)\n",
    "  0.000944 seconds (317 allocations: 171.203 KB)\n",
    "  0.000643 seconds (347 allocations: 208.891 KB)\n",
    "  0.000910 seconds (377 allocations: 246.578 KB)\n",
    "  0.001028 seconds (407 allocations: 287.406 KB)\n",
    "  0.001367 seconds (437 allocations: 331.313 KB)\n",
    "  0.001895 seconds (467 allocations: 383.094 KB)\n",
    "  0.002124 seconds (497 allocations: 421.016 KB)\n",
    "  0.002138 seconds (527 allocations: 481.844 KB)\n",
    "  0.003025 seconds (557 allocations: 549.594 KB)\n",
    "  0.002201 seconds (587 allocations: 594.828 KB)\n",
    "  0.001959 seconds (617 allocations: 676.328 KB)\n",
    "  0.003388 seconds (647 allocations: 726.406 KB)\n",
    "  0.002952 seconds (677 allocations: 826.391 KB)\n",
    "  0.004581 seconds (707 allocations: 881.781 KB)\n",
    "  0.003709 seconds (737 allocations: 938.734 KB)\n",
    "  0.004778 seconds (767 allocations: 1012.922 KB)\n",
    "  0.005388 seconds (797 allocations: 1.069 MB)\n",
    "  0.009387 seconds (827 allocations: 1.151 MB)\n",
    "  0.009082 seconds (857 allocations: 1.237 MB)\n",
    "  0.010223 seconds (887 allocations: 1.325 MB)\n",
    "  0.009350 seconds (917 allocations: 1.416 MB)\"\n",
    "s2 =  \"0.000062 seconds (46 allocations: 3.547 KB)\n",
    "  0.000072 seconds (76 allocations: 9.469 KB)\n",
    "  0.000095 seconds (106 allocations: 19.031 KB)\n",
    "  0.000174 seconds (136 allocations: 32.641 KB)\n",
    "  0.000215 seconds (167 allocations: 49.281 KB)\n",
    "  0.000343 seconds (197 allocations: 65.453 KB)\n",
    "  0.000502 seconds (227 allocations: 88.344 KB)\n",
    "  0.000612 seconds (257 allocations: 112.109 KB)\n",
    "  0.000670 seconds (287 allocations: 140.078 KB)\n",
    "  0.000685 seconds (317 allocations: 171.203 KB)\n",
    "  0.000786 seconds (347 allocations: 208.891 KB)\n",
    "  0.001125 seconds (377 allocations: 246.578 KB)\n",
    "  0.001506 seconds (407 allocations: 287.406 KB)\n",
    "  0.001837 seconds (437 allocations: 331.313 KB)\n",
    "  0.001571 seconds (467 allocations: 383.094 KB)\n",
    "  0.002576 seconds (497 allocations: 421.016 KB)\n",
    "  0.002097 seconds (527 allocations: 481.844 KB)\n",
    "  0.002457 seconds (557 allocations: 549.594 KB)\n",
    "  0.003104 seconds (587 allocations: 594.828 KB)\n",
    "  0.002999 seconds (617 allocations: 676.328 KB)\n",
    "  0.003713 seconds (647 allocations: 726.406 KB)\n",
    "  0.003957 seconds (677 allocations: 826.391 KB)\n",
    "  0.004147 seconds (707 allocations: 881.781 KB)\n",
    "  0.004865 seconds (737 allocations: 938.734 KB)\n",
    "  0.006430 seconds (767 allocations: 1012.922 KB)\n",
    "  0.071584 seconds (797 allocations: 1.069 MB, 91.29% gc time)\n",
    "  0.007282 seconds (827 allocations: 1.151 MB)\n",
    "  0.006026 seconds (857 allocations: 1.237 MB)\n",
    "  0.009872 seconds (887 allocations: 1.325 MB)\n",
    "  0.009277 seconds (917 allocations: 1.416 MB)\"\n",
    "s1 = get_seconds2(s1)\n",
    "s2 = get_seconds2(s2)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.000334 seconds (92 allocations: 7.391 KB)\n",
      "  0.000098 seconds (209 allocations: 22.000 KB)\n",
      "  0.000192 seconds (423 allocations: 49.922 KB)\n",
      "  0.000217 seconds (504 allocations: 73.922 KB)\n",
      "  0.000347 seconds (819 allocations: 120.703 KB)\n",
      "  0.000372 seconds (948 allocations: 150.406 KB)\n",
      "  0.000408 seconds (1.25 k allocations: 205.672 KB)\n",
      "  0.000417 seconds (1.25 k allocations: 237.719 KB)\n",
      "  0.000669 seconds (1.85 k allocations: 322.969 KB)\n",
      "  0.000754 seconds (1.95 k allocations: 373.938 KB)\n",
      "  0.000564 seconds (1.51 k allocations: 394.578 KB)\n",
      "  0.000814 seconds (1.90 k allocations: 478.047 KB)\n",
      "  0.001072 seconds (2.08 k allocations: 550.656 KB)\n",
      "  0.001338 seconds (2.44 k allocations: 641.516 KB)\n",
      "  0.001461 seconds (3.32 k allocations: 786.797 KB)\n",
      "  0.001478 seconds (3.48 k allocations: 846.953 KB)\n",
      "  0.001751 seconds (3.57 k allocations: 945.766 KB)\n",
      "  0.001740 seconds (4.03 k allocations: 1.061 MB)\n",
      "  0.001684 seconds (3.47 k allocations: 1.074 MB)\n",
      "  0.002708 seconds (3.18 k allocations: 1.177 MB)\n",
      "  0.003021 seconds (5.34 k allocations: 1.405 MB)\n",
      "  0.002511 seconds (4.49 k allocations: 1.498 MB)\n",
      "  0.003007 seconds (4.89 k allocations: 1.598 MB)\n",
      "  0.057599 seconds (5.12 k allocations: 1.687 MB, 95.02% gc time)\n",
      "  0.004027 seconds (7.50 k allocations: 1.972 MB)\n",
      "  0.003803 seconds (6.30 k allocations: 1.998 MB)\n",
      "  0.005217 seconds (6.79 k allocations: 2.155 MB)\n",
      "  0.005191 seconds (6.56 k allocations: 2.262 MB)\n",
      "  0.007261 seconds (7.17 k allocations: 2.438 MB)\n",
      "  0.009698 seconds (8.76 k allocations: 2.693 MB)\n"
     ]
    }
   ],
   "source": [
    "for prefs in prefss\n",
    "    @time DA.call_match(prefs[1], prefs[2])\n",
    "end\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.000152 seconds (100 allocations: 8.016 KB)\n",
      "  0.000079 seconds (226 allocations: 23.328 KB)\n",
      "  0.000065 seconds (313 allocations: 41.328 KB)\n",
      "  0.000139 seconds (492 allocations: 72.984 KB)\n",
      "  0.000149 seconds (609 allocations: 104.297 KB)\n",
      "  0.000210 seconds (865 allocations: 143.922 KB)\n",
      "  0.000821 seconds (961 allocations: 183.016 KB)\n",
      "  0.000368 seconds (1.29 k allocations: 240.141 KB)\n",
      "  0.000292 seconds (1.31 k allocations: 280.547 KB)\n",
      "  0.000356 seconds (1.40 k allocations: 331.359 KB)\n",
      "  0.000439 seconds (1.51 k allocations: 394.969 KB)\n",
      "  0.000598 seconds (2.11 k allocations: 494.688 KB)\n",
      "  0.000552 seconds (2.12 k allocations: 553.703 KB)\n",
      "  0.000609 seconds (2.58 k allocations: 652.219 KB)\n",
      "  0.000451 seconds (2.06 k allocations: 688.438 KB)\n",
      "  0.000724 seconds (3.22 k allocations: 826.641 KB)\n",
      "  0.000622 seconds (2.40 k allocations: 854.438 KB)\n",
      "  0.000857 seconds (2.99 k allocations: 1004.734 KB)\n",
      "  0.000925 seconds (3.83 k allocations: 1.101 MB)\n",
      "  0.000984 seconds (3.41 k allocations: 1.195 MB)\n",
      "  0.001077 seconds (3.79 k allocations: 1.286 MB)\n",
      "  0.002550 seconds (3.75 k allocations: 1.441 MB)\n",
      "  0.001409 seconds (4.24 k allocations: 1.549 MB)\n",
      "  0.006566 seconds (4.96 k allocations: 1.675 MB, 70.20% gc time)\n",
      "  0.002900 seconds (7.43 k allocations: 1.967 MB)\n",
      "  0.003138 seconds (5.65 k allocations: 1.948 MB)\n",
      "  0.002964 seconds (7.36 k allocations: 2.198 MB)\n",
      "  0.003244 seconds (5.51 k allocations: 2.183 MB)\n",
      "  0.008754 seconds (7.48 k allocations: 2.462 MB)\n",
      "  0.009590 seconds (8.63 k allocations: 2.683 MB)\n"
     ]
    }
   ],
   "source": [
    "for prefs in prefss2\n",
    "    @time DA.call_match(prefs[1], prefs[2])\n",
    "end\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n1 =  \"  0.000334 seconds (92 allocations: 7.391 KB)\n",
    "  0.000098 seconds (209 allocations: 22.000 KB)\n",
    "  0.000192 seconds (423 allocations: 49.922 KB)\n",
    "  0.000217 seconds (504 allocations: 73.922 KB)\n",
    "  0.000347 seconds (819 allocations: 120.703 KB)\n",
    "  0.000372 seconds (948 allocations: 150.406 KB)\n",
    "  0.000408 seconds (1.25 k allocations: 205.672 KB)\n",
    "  0.000417 seconds (1.25 k allocations: 237.719 KB)\n",
    "  0.000669 seconds (1.85 k allocations: 322.969 KB)\n",
    "  0.000754 seconds (1.95 k allocations: 373.938 KB)\n",
    "  0.000564 seconds (1.51 k allocations: 394.578 KB)\n",
    "  0.000814 seconds (1.90 k allocations: 478.047 KB)\n",
    "  0.001072 seconds (2.08 k allocations: 550.656 KB)\n",
    "  0.001338 seconds (2.44 k allocations: 641.516 KB)\n",
    "  0.001461 seconds (3.32 k allocations: 786.797 KB)\n",
    "  0.001478 seconds (3.48 k allocations: 846.953 KB)\n",
    "  0.001751 seconds (3.57 k allocations: 945.766 KB)\n",
    "  0.001740 seconds (4.03 k allocations: 1.061 MB)\n",
    "  0.001684 seconds (3.47 k allocations: 1.074 MB)\n",
    "  0.002708 seconds (3.18 k allocations: 1.177 MB)\n",
    "  0.003021 seconds (5.34 k allocations: 1.405 MB)\n",
    "  0.002511 seconds (4.49 k allocations: 1.498 MB)\n",
    "  0.003007 seconds (4.89 k allocations: 1.598 MB)\n",
    "  0.057599 seconds (5.12 k allocations: 1.687 MB, 95.02% gc time)\n",
    "  0.004027 seconds (7.50 k allocations: 1.972 MB)\n",
    "  0.003803 seconds (6.30 k allocations: 1.998 MB)\n",
    "  0.005217 seconds (6.79 k allocations: 2.155 MB)\n",
    "  0.005191 seconds (6.56 k allocations: 2.262 MB)\n",
    "  0.007261 seconds (7.17 k allocations: 2.438 MB)\n",
    "  0.009698 seconds (8.76 k allocations: 2.693 MB)\"\n",
    "n2 =  \" 0.000152 seconds (100 allocations: 8.016 KB)\n",
    "  0.000079 seconds (226 allocations: 23.328 KB)\n",
    "  0.000065 seconds (313 allocations: 41.328 KB)\n",
    "  0.000139 seconds (492 allocations: 72.984 KB)\n",
    "  0.000149 seconds (609 allocations: 104.297 KB)\n",
    "  0.000210 seconds (865 allocations: 143.922 KB)\n",
    "  0.000821 seconds (961 allocations: 183.016 KB)\n",
    "  0.000368 seconds (1.29 k allocations: 240.141 KB)\n",
    "  0.000292 seconds (1.31 k allocations: 280.547 KB)\n",
    "  0.000356 seconds (1.40 k allocations: 331.359 KB)\n",
    "  0.000439 seconds (1.51 k allocations: 394.969 KB)\n",
    "  0.000598 seconds (2.11 k allocations: 494.688 KB)\n",
    "  0.000552 seconds (2.12 k allocations: 553.703 KB)\n",
    "  0.000609 seconds (2.58 k allocations: 652.219 KB)\n",
    "  0.000451 seconds (2.06 k allocations: 688.438 KB)\n",
    "  0.000724 seconds (3.22 k allocations: 826.641 KB)\n",
    "  0.000622 seconds (2.40 k allocations: 854.438 KB)\n",
    "  0.000857 seconds (2.99 k allocations: 1004.734 KB)\n",
    "  0.000925 seconds (3.83 k allocations: 1.101 MB)\n",
    "  0.000984 seconds (3.41 k allocations: 1.195 MB)\n",
    "  0.001077 seconds (3.79 k allocations: 1.286 MB)\n",
    "  0.002550 seconds (3.75 k allocations: 1.441 MB)\n",
    "  0.001409 seconds (4.24 k allocations: 1.549 MB)\n",
    "  0.006566 seconds (4.96 k allocations: 1.675 MB, 70.20% gc time)\n",
    "  0.002900 seconds (7.43 k allocations: 1.967 MB)\n",
    "  0.003138 seconds (5.65 k allocations: 1.948 MB)\n",
    "  0.002964 seconds (7.36 k allocations: 2.198 MB)\n",
    "  0.003244 seconds (5.51 k allocations: 2.183 MB)\n",
    "  0.008754 seconds (7.48 k allocations: 2.462 MB)\n",
    "  0.009590 seconds (8.63 k allocations: 2.683 MB)\"\n",
    "n1 = get_seconds2(n1)\n",
    "n2 = get_seconds2(n2)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.000068 seconds (92 allocations: 7.391 KB)\n",
      "  0.000035 seconds (209 allocations: 22.000 KB)\n",
      "  0.000063 seconds (423 allocations: 49.922 KB)\n",
      "  0.000079 seconds (504 allocations: 73.922 KB)\n",
      "  0.000136 seconds (819 allocations: 120.703 KB)\n",
      "  0.000178 seconds (948 allocations: 150.406 KB)\n",
      "  0.000204 seconds (1.25 k allocations: 205.672 KB)\n",
      "  0.000205 seconds (1.25 k allocations: 237.719 KB)\n",
      "  0.000344 seconds (1.85 k allocations: 322.969 KB)\n",
      "  0.000415 seconds (1.95 k allocations: 373.938 KB)\n",
      "  0.000327 seconds (1.51 k allocations: 394.578 KB)\n",
      "  0.000563 seconds (1.90 k allocations: 478.047 KB)\n",
      "  0.000571 seconds (2.08 k allocations: 550.656 KB)\n",
      "  0.000832 seconds (2.44 k allocations: 641.516 KB)\n",
      "  0.001060 seconds (3.32 k allocations: 786.797 KB)\n",
      "  0.001185 seconds (3.48 k allocations: 846.953 KB)\n",
      "  0.004882 seconds (3.57 k allocations: 945.766 KB, 80.88% gc time)\n",
      "  0.001088 seconds (4.03 k allocations: 1.061 MB)\n",
      "  0.000897 seconds (3.47 k allocations: 1.074 MB)\n",
      "  0.000860 seconds (3.18 k allocations: 1.177 MB)\n",
      "  0.001890 seconds (5.34 k allocations: 1.405 MB)\n",
      "  0.001359 seconds (4.49 k allocations: 1.498 MB)\n",
      "  0.001623 seconds (4.89 k allocations: 1.598 MB)\n",
      "  0.002285 seconds (5.12 k allocations: 1.687 MB)\n",
      "  0.002709 seconds (7.50 k allocations: 1.972 MB)\n",
      "  0.002560 seconds (6.30 k allocations: 1.998 MB)\n",
      "  0.004497 seconds (6.79 k allocations: 2.155 MB)\n",
      "  0.003503 seconds (6.56 k allocations: 2.262 MB)\n",
      "  0.004586 seconds (7.17 k allocations: 2.438 MB)\n",
      "  0.006513 seconds (8.76 k allocations: 2.693 MB, 41.48% gc time)\n"
     ]
    }
   ],
   "source": [
    "for prefs in prefss\n",
    "    @time DA.call_match(prefs[1], prefs[2], true)\n",
    "end\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.000062 seconds (100 allocations: 8.016 KB)\n",
      "  0.000053 seconds (226 allocations: 23.328 KB)\n",
      "  0.000066 seconds (313 allocations: 41.328 KB)\n",
      "  0.000106 seconds (492 allocations: 72.984 KB)\n",
      "  0.000142 seconds (609 allocations: 104.297 KB)\n",
      "  0.000201 seconds (865 allocations: 143.922 KB)\n",
      "  0.000239 seconds (961 allocations: 183.016 KB)\n",
      "  0.000507 seconds (1.29 k allocations: 240.141 KB)\n",
      "  0.000349 seconds (1.31 k allocations: 280.547 KB)\n",
      "  0.000422 seconds (1.40 k allocations: 331.359 KB)\n",
      "  0.000513 seconds (1.51 k allocations: 394.969 KB)\n",
      "  0.000635 seconds (2.11 k allocations: 494.688 KB)\n",
      "  0.000778 seconds (2.12 k allocations: 553.703 KB)\n",
      "  0.000919 seconds (2.58 k allocations: 652.219 KB)\n",
      "  0.000831 seconds (2.06 k allocations: 688.438 KB)\n",
      "  0.001183 seconds (3.22 k allocations: 826.641 KB)\n",
      "  0.001054 seconds (2.40 k allocations: 854.438 KB)\n",
      "  0.001314 seconds (2.99 k allocations: 1004.734 KB)\n",
      "  0.001537 seconds (3.83 k allocations: 1.101 MB)\n",
      "  0.001691 seconds (3.41 k allocations: 1.195 MB)\n",
      "  0.001940 seconds (3.79 k allocations: 1.286 MB)\n",
      "  0.001943 seconds (3.75 k allocations: 1.441 MB)\n",
      "  0.002052 seconds (4.24 k allocations: 1.549 MB)\n",
      "  0.002619 seconds (4.96 k allocations: 1.675 MB)\n",
      "  0.003852 seconds (7.43 k allocations: 1.967 MB)\n",
      "  0.003359 seconds (5.65 k allocations: 1.948 MB)\n",
      "  0.007033 seconds (7.36 k allocations: 2.198 MB, 37.46% gc time)\n",
      "  0.003122 seconds (5.51 k allocations: 2.183 MB)\n",
      "  0.005775 seconds (7.48 k allocations: 2.462 MB)\n",
      "  0.013919 seconds (8.63 k allocations: 2.683 MB)\n"
     ]
    }
   ],
   "source": [
    "for prefs in prefss2\n",
    "    @time DA.call_match(prefs[1], prefs[2], true)\n",
    "end\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30-element Array{Float64,1}:\n",
       " 6.2e-5  \n",
       " 5.3e-5  \n",
       " 6.6e-5  \n",
       " 0.000106\n",
       " 0.000142\n",
       " 0.000201\n",
       " 0.000239\n",
       " 0.000507\n",
       " 0.000349\n",
       " 0.000422\n",
       " 0.000513\n",
       " 0.000635\n",
       " 0.000778\n",
       " ⋮       \n",
       " 0.001537\n",
       " 0.001691\n",
       " 0.00194 \n",
       " 0.001943\n",
       " 0.002052\n",
       " 0.002619\n",
       " 0.003852\n",
       " 0.003359\n",
       " 0.007033\n",
       " 0.003122\n",
       " 0.005775\n",
       " 0.013919"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1 = \"0.000068 seconds (92 allocations: 7.391 KB)\n",
    "  0.000035 seconds (209 allocations: 22.000 KB)\n",
    "  0.000063 seconds (423 allocations: 49.922 KB)\n",
    "  0.000079 seconds (504 allocations: 73.922 KB)\n",
    "  0.000136 seconds (819 allocations: 120.703 KB)\n",
    "  0.000178 seconds (948 allocations: 150.406 KB)\n",
    "  0.000204 seconds (1.25 k allocations: 205.672 KB)\n",
    "  0.000205 seconds (1.25 k allocations: 237.719 KB)\n",
    "  0.000344 seconds (1.85 k allocations: 322.969 KB)\n",
    "  0.000415 seconds (1.95 k allocations: 373.938 KB)\n",
    "  0.000327 seconds (1.51 k allocations: 394.578 KB)\n",
    "  0.000563 seconds (1.90 k allocations: 478.047 KB)\n",
    "  0.000571 seconds (2.08 k allocations: 550.656 KB)\n",
    "  0.000832 seconds (2.44 k allocations: 641.516 KB)\n",
    "  0.001060 seconds (3.32 k allocations: 786.797 KB)\n",
    "  0.001185 seconds (3.48 k allocations: 846.953 KB)\n",
    "  0.004882 seconds (3.57 k allocations: 945.766 KB, 80.88% gc time)\n",
    "  0.001088 seconds (4.03 k allocations: 1.061 MB)\n",
    "  0.000897 seconds (3.47 k allocations: 1.074 MB)\n",
    "  0.000860 seconds (3.18 k allocations: 1.177 MB)\n",
    "  0.001890 seconds (5.34 k allocations: 1.405 MB)\n",
    "  0.001359 seconds (4.49 k allocations: 1.498 MB)\n",
    "  0.001623 seconds (4.89 k allocations: 1.598 MB)\n",
    "  0.002285 seconds (5.12 k allocations: 1.687 MB)\n",
    "  0.002709 seconds (7.50 k allocations: 1.972 MB)\n",
    "  0.002560 seconds (6.30 k allocations: 1.998 MB)\n",
    "  0.004497 seconds (6.79 k allocations: 2.155 MB)\n",
    "  0.003503 seconds (6.56 k allocations: 2.262 MB)\n",
    "  0.004586 seconds (7.17 k allocations: 2.438 MB)\n",
    "  0.006513 seconds (8.76 k allocations: 2.693 MB, 41.48% gc time)\"\n",
    "r2 = \"0.000062 seconds (100 allocations: 8.016 KB)\n",
    "  0.000053 seconds (226 allocations: 23.328 KB)\n",
    "  0.000066 seconds (313 allocations: 41.328 KB)\n",
    "  0.000106 seconds (492 allocations: 72.984 KB)\n",
    "  0.000142 seconds (609 allocations: 104.297 KB)\n",
    "  0.000201 seconds (865 allocations: 143.922 KB)\n",
    "  0.000239 seconds (961 allocations: 183.016 KB)\n",
    "  0.000507 seconds (1.29 k allocations: 240.141 KB)\n",
    "  0.000349 seconds (1.31 k allocations: 280.547 KB)\n",
    "  0.000422 seconds (1.40 k allocations: 331.359 KB)\n",
    "  0.000513 seconds (1.51 k allocations: 394.969 KB)\n",
    "  0.000635 seconds (2.11 k allocations: 494.688 KB)\n",
    "  0.000778 seconds (2.12 k allocations: 553.703 KB)\n",
    "  0.000919 seconds (2.58 k allocations: 652.219 KB)\n",
    "  0.000831 seconds (2.06 k allocations: 688.438 KB)\n",
    "  0.001183 seconds (3.22 k allocations: 826.641 KB)\n",
    "  0.001054 seconds (2.40 k allocations: 854.438 KB)\n",
    "  0.001314 seconds (2.99 k allocations: 1004.734 KB)\n",
    "  0.001537 seconds (3.83 k allocations: 1.101 MB)\n",
    "  0.001691 seconds (3.41 k allocations: 1.195 MB)\n",
    "  0.001940 seconds (3.79 k allocations: 1.286 MB)\n",
    "  0.001943 seconds (3.75 k allocations: 1.441 MB)\n",
    "  0.002052 seconds (4.24 k allocations: 1.549 MB)\n",
    "  0.002619 seconds (4.96 k allocations: 1.675 MB)\n",
    "  0.003852 seconds (7.43 k allocations: 1.967 MB)\n",
    "  0.003359 seconds (5.65 k allocations: 1.948 MB)\n",
    "  0.007033 seconds (7.36 k allocations: 2.198 MB, 37.46% gc time)\n",
    "  0.003122 seconds (5.51 k allocations: 2.183 MB)\n",
    "  0.005775 seconds (7.48 k allocations: 2.462 MB)\n",
    "  0.013919 seconds (8.63 k allocations: 2.683 MB)\"\n",
    "\n",
    "r1 = get_seconds2(r1)\n",
    "r2 = get_seconds2(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x315ab5320>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x3218567b8>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(s1, label =\"simple(1)\")\n",
    "plot(s2, label = \"simple(2)\")\n",
    "plot(n1, label = \"normal(1)\")\n",
    "plot(n2, label = \"normal(2)\")\n",
    "plot(r1, label = \"recursive(1)\")\n",
    "plot(r2, label = \"recursive(2)\")\n",
    "legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "外れ値が邪魔なので除外すると,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x321687128>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x31d0dfa20>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2[26] = 0\n",
    "n1[24] = 0\n",
    "xs = 10:10:300\n",
    "\n",
    "plot(xs, s1, label =\"simple(1)\")\n",
    "plot(xs, s2, label = \"simple(2)\")\n",
    "plot(xs, n1, label = \"normal(1)\")\n",
    "plot(xs, n2, label = \"normal(2)\")\n",
    "plot(xs, r1, label = \"recursive(1)\")\n",
    "plot(xs, r2, label = \"recursive(2)\")\n",
    "legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "どれも似たようなものとなりました."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "m, nが300以降の時に差が出そうなのでそこをplotしてみます."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prefss = [DA.generate_random_preference_data(i*10, i*10) for i in 30:100]\n",
    "prefss2 = [DA.generate_random_preference_data(i*10, i*10) for i in 30:100]\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.009129 seconds (917 allocations: 1.416 MB)\n",
      "  0.009274 seconds (947 allocations: 1.511 MB)\n",
      "  0.007484 seconds (977 allocations: 1.608 MB)\n",
      "  0.010769 seconds (1.01 k allocations: 1.709 MB)\n",
      "  0.012465 seconds (1.04 k allocations: 1.813 MB)\n",
      "  0.014293 seconds (1.07 k allocations: 1.919 MB)\n",
      "  0.011384 seconds (1.10 k allocations: 2.029 MB)\n",
      "  0.014248 seconds (1.13 k allocations: 2.142 MB)\n",
      "  0.011885 seconds (1.16 k allocations: 2.258 MB)\n",
      "  0.029350 seconds (1.19 k allocations: 2.377 MB)\n",
      "  0.020161 seconds (1.22 k allocations: 2.499 MB)\n",
      "  0.017643 seconds (1.25 k allocations: 2.624 MB)\n",
      "  0.029638 seconds (1.28 k allocations: 2.752 MB)\n",
      "  0.045103 seconds (1.31 k allocations: 2.883 MB)\n",
      "  0.018109 seconds (1.34 k allocations: 3.017 MB)\n",
      "  0.024426 seconds (1.37 k allocations: 3.154 MB)\n",
      "  0.020370 seconds (1.40 k allocations: 3.294 MB)\n",
      "  0.021987 seconds (1.43 k allocations: 3.438 MB)\n",
      "  0.032624 seconds (1.46 k allocations: 3.584 MB)\n",
      "  0.029399 seconds (1.49 k allocations: 3.734 MB)\n",
      "  0.027961 seconds (1.52 k allocations: 3.886 MB)\n",
      "  0.029888 seconds (1.55 k allocations: 4.041 MB)\n",
      "  0.028471 seconds (7.86 k allocations: 4.296 MB)\n",
      "  0.542545 seconds (8.24 k allocations: 4.463 MB, 93.79% gc time)\n",
      "  0.026858 seconds (10.20 k allocations: 4.657 MB)\n",
      "  0.028771 seconds (12.07 k allocations: 4.853 MB)\n",
      "  0.024150 seconds (12.18 k allocations: 5.025 MB)\n",
      "  0.051690 seconds (18.61 k allocations: 5.296 MB, 27.21% gc time)\n",
      "  0.033867 seconds (14.94 k allocations: 5.417 MB)\n",
      "  0.036329 seconds (20.13 k allocations: 5.675 MB)\n",
      "  0.027228 seconds (16.95 k allocations: 5.810 MB)\n",
      "  0.078439 seconds (22.36 k allocations: 6.078 MB, 54.62% gc time)\n",
      "  0.029622 seconds (23.37 k allocations: 6.282 MB)\n",
      "  0.032879 seconds (21.20 k allocations: 6.440 MB)\n",
      "  0.040030 seconds (26.75 k allocations: 6.720 MB)\n",
      "  0.033104 seconds (27.55 k allocations: 6.930 MB, 9.65% gc time)\n",
      "  0.045393 seconds (30.37 k allocations: 7.174 MB)\n",
      "  0.043350 seconds (29.34 k allocations: 7.362 MB)\n",
      "  0.082619 seconds (32.67 k allocations: 7.620 MB, 4.64% gc time)\n",
      "  0.072746 seconds (30.70 k allocations: 7.800 MB)\n",
      "  0.058841 seconds (29.28 k allocations: 7.991 MB)\n",
      "  0.076485 seconds (41.90 k allocations: 8.399 MB, 5.68% gc time)\n",
      "  0.073314 seconds (41.51 k allocations: 8.613 MB)\n",
      "  0.068518 seconds (36.63 k allocations: 8.761 MB, 4.81% gc time)\n",
      "  0.053133 seconds (41.00 k allocations: 9.052 MB)\n",
      "  0.046012 seconds (39.34 k allocations: 9.256 MB)\n",
      "  0.044011 seconds (39.96 k allocations: 9.496 MB, 6.92% gc time)\n",
      "  0.050118 seconds (52.26 k allocations: 9.918 MB)\n",
      "  0.061661 seconds (49.89 k allocations: 10.120 MB, 4.68% gc time)\n",
      "  0.048913 seconds (51.11 k allocations: 10.379 MB)\n",
      "  0.087765 seconds (55.99 k allocations: 10.697 MB, 6.74% gc time)\n",
      "  0.058032 seconds (54.27 k allocations: 10.917 MB)\n",
      "  0.086060 seconds (72.74 k allocations: 11.449 MB, 8.90% gc time)\n",
      "  0.072596 seconds (69.57 k allocations: 11.653 MB)\n",
      "  0.078794 seconds (66.23 k allocations: 11.858 MB, 4.44% gc time)\n",
      "  0.086769 seconds (64.69 k allocations: 12.093 MB)\n",
      "  0.070877 seconds (69.40 k allocations: 12.427 MB, 8.11% gc time)\n",
      "  0.060147 seconds (75.33 k allocations: 12.782 MB)\n",
      "  0.069880 seconds (67.88 k allocations: 12.937 MB, 4.64% gc time)\n",
      "  0.053227 seconds (69.98 k allocations: 13.240 MB)\n",
      "  0.065903 seconds (56.41 k allocations: 13.307 MB, 4.78% gc time)\n",
      "  0.070496 seconds (83.80 k allocations: 14.002 MB)\n",
      "  0.086989 seconds (95.89 k allocations: 14.467 MB, 3.80% gc time)\n",
      "  0.077386 seconds (81.78 k allocations: 14.535 MB, 3.38% gc time)\n",
      "  0.070979 seconds (68.77 k allocations: 14.622 MB)\n",
      "  0.084061 seconds (94.25 k allocations: 15.300 MB, 3.60% gc time)\n",
      "  0.109442 seconds (87.19 k allocations: 15.485 MB, 3.00% gc time)\n",
      "  0.082028 seconds (83.36 k allocations: 15.722 MB)\n",
      "  0.075921 seconds (79.42 k allocations: 15.960 MB, 4.07% gc time)\n",
      "  0.110161 seconds (84.74 k allocations: 16.343 MB, 3.77% gc time)\n",
      "  0.170235 seconds (78.11 k allocations: 16.547 MB)\n",
      "  0.014590 seconds (917 allocations: 1.416 MB)\n",
      "  0.023831 seconds (947 allocations: 1.511 MB, 29.94% gc time)\n",
      "  0.011429 seconds (977 allocations: 1.608 MB)\n",
      "  0.021358 seconds (1.01 k allocations: 1.709 MB)\n",
      "  0.013277 seconds (1.04 k allocations: 1.813 MB)\n",
      "  0.014333 seconds (1.07 k allocations: 1.919 MB)\n",
      "  0.020227 seconds (1.10 k allocations: 2.029 MB)\n",
      "  0.014717 seconds (1.13 k allocations: 2.142 MB)\n",
      "  0.011713 seconds (1.16 k allocations: 2.258 MB)\n",
      "  0.010292 seconds (1.19 k allocations: 2.377 MB)\n",
      "  0.010109 seconds (1.22 k allocations: 2.499 MB)\n",
      "  0.012681 seconds (1.25 k allocations: 2.624 MB)\n",
      "  0.012653 seconds (1.28 k allocations: 2.752 MB, 21.27% gc time)\n",
      "  0.011812 seconds (1.31 k allocations: 2.883 MB)\n",
      "  0.014040 seconds (1.34 k allocations: 3.017 MB)\n",
      "  0.025403 seconds (1.37 k allocations: 3.154 MB)\n",
      "  0.020901 seconds (1.40 k allocations: 3.294 MB)\n",
      "  0.015928 seconds (1.43 k allocations: 3.438 MB)\n",
      "  0.019094 seconds (1.46 k allocations: 3.584 MB)\n",
      "  0.021860 seconds (1.49 k allocations: 3.734 MB, 18.25% gc time)\n",
      "  0.033024 seconds (1.52 k allocations: 3.886 MB)\n",
      "  0.029245 seconds (1.55 k allocations: 4.041 MB)\n",
      "  0.030801 seconds (8.09 k allocations: 4.300 MB)\n",
      "  0.022904 seconds (9.74 k allocations: 4.486 MB)\n",
      "  0.026552 seconds (9.10 k allocations: 4.640 MB, 11.16% gc time)\n",
      "  0.022630 seconds (11.09 k allocations: 4.838 MB)\n",
      "  0.024616 seconds (13.17 k allocations: 5.040 MB)\n",
      "  0.020642 seconds (13.27 k allocations: 5.215 MB)\n",
      "  0.028246 seconds (17.25 k allocations: 5.452 MB, 9.50% gc time)\n",
      "  0.026836 seconds (18.67 k allocations: 5.653 MB)\n",
      "  0.029671 seconds (17.57 k allocations: 5.819 MB)\n",
      "  0.026636 seconds (19.08 k allocations: 6.028 MB)\n",
      "  0.028267 seconds (20.76 k allocations: 6.242 MB, 11.13% gc time)\n",
      "  0.032968 seconds (21.48 k allocations: 6.445 MB)\n",
      "  0.034690 seconds (26.30 k allocations: 6.713 MB)\n",
      "  0.035694 seconds (30.06 k allocations: 6.968 MB, 9.05% gc time)\n",
      "  0.051677 seconds (28.50 k allocations: 7.145 MB)\n",
      "  0.047230 seconds (32.26 k allocations: 7.406 MB)\n",
      "  0.052814 seconds (33.03 k allocations: 7.625 MB, 6.41% gc time)\n",
      "  0.035839 seconds (31.30 k allocations: 7.809 MB)\n",
      "  0.035595 seconds (34.71 k allocations: 8.074 MB)\n",
      "  0.055290 seconds (37.16 k allocations: 8.327 MB, 5.14% gc time)\n",
      "  0.052409 seconds (42.60 k allocations: 8.630 MB)\n",
      "  0.055728 seconds (41.22 k allocations: 8.831 MB)\n",
      "  0.041856 seconds (40.14 k allocations: 9.039 MB, 8.56% gc time)\n",
      "  0.047205 seconds (49.50 k allocations: 9.410 MB)\n",
      "  0.047857 seconds (44.02 k allocations: 9.558 MB, 6.16% gc time)\n",
      "  0.063556 seconds (57.30 k allocations: 9.995 MB)\n",
      "  0.087841 seconds (59.66 k allocations: 10.269 MB, 3.98% gc time)\n",
      "  0.080251 seconds (50.36 k allocations: 10.368 MB)\n",
      "  0.067195 seconds (66.76 k allocations: 10.861 MB)\n",
      "  0.058766 seconds (55.03 k allocations: 10.929 MB, 5.41% gc time)\n",
      "  0.068546 seconds (61.50 k allocations: 11.277 MB)\n",
      "  0.051247 seconds (49.58 k allocations: 11.348 MB, 5.61% gc time)\n",
      "  0.070344 seconds (65.41 k allocations: 11.845 MB)\n",
      "  0.054621 seconds (61.34 k allocations: 12.042 MB, 5.07% gc time)\n",
      "  0.055674 seconds (57.85 k allocations: 12.251 MB)\n",
      "  0.067895 seconds (80.43 k allocations: 12.860 MB, 4.75% gc time)\n",
      "  0.069375 seconds (72.48 k allocations: 13.007 MB)\n",
      "  0.051531 seconds (64.14 k allocations: 13.151 MB, 6.92% gc time)\n",
      "  0.078942 seconds (76.93 k allocations: 13.620 MB)\n",
      "  0.077981 seconds (76.92 k allocations: 13.897 MB, 5.51% gc time)\n",
      "  0.093429 seconds (84.87 k allocations: 14.299 MB, 3.04% gc time)\n",
      "  0.082465 seconds (82.84 k allocations: 14.551 MB)\n",
      "  0.102572 seconds (102.51 k allocations: 15.137 MB, 4.24% gc time)\n",
      "  0.070548 seconds (81.48 k allocations: 15.106 MB, 3.69% gc time)\n",
      "  0.072033 seconds (90.17 k allocations: 15.530 MB)\n",
      "  0.100468 seconds (106.83 k allocations: 16.080 MB, 5.06% gc time)\n",
      "  0.079815 seconds (91.27 k allocations: 16.141 MB, 3.80% gc time)\n",
      "  0.062726 seconds (86.01 k allocations: 16.362 MB)\n",
      "  0.094010 seconds (115.80 k allocations: 17.122 MB, 4.99% gc time)\n",
      "  0.004642 seconds (8.73 k allocations: 2.691 MB)\n",
      "  0.004758 seconds (7.33 k allocations: 2.723 MB)\n",
      "  0.007265 seconds (6.97 k allocations: 2.839 MB)\n",
      "  0.009612 seconds (7.86 k allocations: 3.054 MB)\n",
      "  0.012493 seconds (8.06 k allocations: 3.221 MB, 45.50% gc time)\n",
      "  0.006049 seconds (7.25 k allocations: 3.316 MB)\n",
      "  0.006061 seconds (8.59 k allocations: 3.580 MB)\n",
      "  0.007366 seconds (11.01 k allocations: 3.930 MB)\n",
      "  0.008260 seconds (10.33 k allocations: 4.049 MB)\n",
      "  0.010297 seconds (12.00 k allocations: 4.351 MB)\n",
      "  0.013648 seconds (9.44 k allocations: 4.336 MB, 41.53% gc time)\n",
      "  0.009034 seconds (12.14 k allocations: 4.726 MB)\n",
      "  0.011327 seconds (15.94 k allocations: 5.205 MB)\n",
      "  0.010038 seconds (11.21 k allocations: 5.038 MB)\n",
      "  0.016368 seconds (12.79 k allocations: 5.356 MB, 28.86% gc time)\n",
      "  0.010953 seconds (15.00 k allocations: 5.727 MB)\n",
      "  0.010378 seconds (12.78 k allocations: 5.765 MB)\n",
      "  0.008863 seconds (13.51 k allocations: 6.032 MB)\n",
      "  0.014849 seconds (14.30 k allocations: 6.309 MB, 22.36% gc time)\n",
      "  0.010181 seconds (13.22 k allocations: 6.448 MB)\n",
      "  0.010599 seconds (14.46 k allocations: 6.767 MB)\n",
      "  0.016141 seconds (16.54 k allocations: 7.156 MB, 22.86% gc time)\n",
      "  0.016953 seconds (18.61 k allocations: 7.447 MB)\n",
      "  0.012894 seconds (17.53 k allocations: 7.589 MB)\n",
      "  0.016898 seconds (19.93 k allocations: 7.971 MB, 17.94% gc time)\n",
      "  0.015011 seconds (23.25 k allocations: 8.435 MB)\n",
      "  0.014746 seconds (19.36 k allocations: 8.376 MB)\n",
      "  0.022936 seconds (26.43 k allocations: 9.087 MB, 13.17% gc time)\n",
      "  0.015819 seconds (21.67 k allocations: 9.004 MB)\n",
      "  0.024263 seconds (26.09 k allocations: 9.517 MB, 14.74% gc time)\n",
      "  0.017480 seconds (22.77 k allocations: 9.567 MB)\n",
      "  0.025643 seconds (28.22 k allocations: 10.174 MB, 14.60% gc time)\n",
      "  0.028060 seconds (28.04 k allocations: 10.408 MB)\n",
      "  0.030852 seconds (24.89 k allocations: 10.478 MB, 10.60% gc time)\n",
      "  0.033995 seconds (30.81 k allocations: 11.126 MB)\n",
      "  0.024125 seconds (30.12 k allocations: 11.318 MB, 13.68% gc time)\n",
      "  0.021458 seconds (32.74 k allocations: 11.766 MB)\n",
      "  0.027426 seconds (30.94 k allocations: 11.948 MB, 11.26% gc time)\n",
      "  0.023079 seconds (33.29 k allocations: 12.358 MB)\n",
      "  0.024997 seconds (31.99 k allocations: 12.604 MB, 11.94% gc time)\n",
      "  0.018521 seconds (29.85 k allocations: 12.794 MB)\n",
      "  0.025482 seconds (40.91 k allocations: 13.701 MB, 11.42% gc time)\n",
      "  0.025320 seconds (39.21 k allocations: 13.885 MB, 14.05% gc time)\n",
      "  0.021363 seconds (34.74 k allocations: 13.982 MB)\n",
      "  0.029802 seconds (38.03 k allocations: 14.471 MB, 17.67% gc time)\n",
      "  0.026147 seconds (36.89 k allocations: 14.765 MB, 11.81% gc time)\n",
      "  0.020503 seconds (37.81 k allocations: 15.140 MB)\n",
      "  0.033436 seconds (47.64 k allocations: 16.000 MB, 10.41% gc time)\n",
      "  0.026870 seconds (43.88 k allocations: 16.108 MB, 13.56% gc time)\n",
      "  0.029028 seconds (44.32 k allocations: 16.475 MB)\n",
      "  0.034226 seconds (48.31 k allocations: 17.033 MB, 11.57% gc time)\n",
      "  0.031410 seconds (47.57 k allocations: 17.383 MB, 8.47% gc time)\n",
      "  0.035468 seconds (61.78 k allocations: 18.431 MB, 8.49% gc time)\n",
      "  0.034408 seconds (58.88 k allocations: 18.652 MB, 7.54% gc time)\n",
      "  0.025174 seconds (54.55 k allocations: 18.760 MB)\n",
      "  0.046667 seconds (53.35 k allocations: 19.155 MB, 8.88% gc time)\n",
      "  0.038927 seconds (57.34 k allocations: 19.666 MB, 8.46% gc time)\n",
      "  0.056845 seconds (61.55 k allocations: 20.272 MB, 10.87% gc time)\n",
      "  0.037021 seconds (55.11 k allocations: 20.332 MB, 13.94% gc time)\n",
      "  0.036554 seconds (55.60 k allocations: 20.730 MB, 9.89% gc time)\n",
      "  0.033103 seconds (46.36 k allocations: 20.726 MB, 8.95% gc time)\n",
      "  0.038685 seconds (65.90 k allocations: 22.040 MB, 8.15% gc time)\n",
      "  0.048327 seconds (75.17 k allocations: 22.897 MB, 6.84% gc time)\n",
      "  0.040950 seconds (65.16 k allocations: 22.855 MB, 6.85% gc time)\n",
      "  0.027835 seconds (54.20 k allocations: 22.702 MB, 10.09% gc time)\n",
      "  0.038785 seconds (74.12 k allocations: 24.082 MB, 7.90% gc time)\n",
      "  0.040684 seconds (68.20 k allocations: 24.240 MB, 6.55% gc time)\n",
      "  0.033529 seconds (63.57 k allocations: 24.417 MB, 9.74% gc time)\n",
      "  0.035949 seconds (61.57 k allocations: 24.769 MB, 8.60% gc time)\n",
      "  0.040421 seconds (64.29 k allocations: 25.345 MB, 8.94% gc time)\n",
      "  0.032776 seconds (60.47 k allocations: 25.622 MB, 10.01% gc time)\n",
      "  0.003604 seconds (6.50 k allocations: 2.521 MB)\n",
      "  0.008793 seconds (7.66 k allocations: 2.748 MB, 57.39% gc time)\n",
      "  0.003432 seconds (8.49 k allocations: 2.954 MB)\n",
      "  0.004118 seconds (7.95 k allocations: 3.061 MB)\n",
      "  0.004849 seconds (9.08 k allocations: 3.299 MB)\n",
      "  0.003378 seconds (7.63 k allocations: 3.345 MB)\n",
      "  0.004961 seconds (10.69 k allocations: 3.740 MB)\n",
      "  0.005679 seconds (11.75 k allocations: 3.987 MB)\n",
      "  0.012225 seconds (9.75 k allocations: 4.005 MB, 60.42% gc time)\n",
      "  0.005300 seconds (10.45 k allocations: 4.234 MB)\n",
      "  0.004952 seconds (8.71 k allocations: 4.281 MB)\n",
      "  0.005007 seconds (9.68 k allocations: 4.539 MB)\n",
      "  0.004728 seconds (9.61 k allocations: 4.722 MB)\n",
      "  0.010661 seconds (10.96 k allocations: 5.019 MB, 48.84% gc time)\n",
      "  0.005491 seconds (11.53 k allocations: 5.260 MB)\n",
      "  0.007962 seconds (13.95 k allocations: 5.647 MB)\n",
      "  0.006266 seconds (11.33 k allocations: 5.654 MB)\n",
      "  0.012811 seconds (14.79 k allocations: 6.130 MB, 38.33% gc time)\n",
      "  0.010861 seconds (14.91 k allocations: 6.356 MB)\n",
      "  0.006877 seconds (12.93 k allocations: 6.425 MB)\n",
      "  0.014493 seconds (19.30 k allocations: 7.137 MB, 27.02% gc time)\n",
      "  0.010596 seconds (20.20 k allocations: 7.435 MB)\n",
      "  0.011462 seconds (20.92 k allocations: 7.631 MB)\n",
      "  0.014140 seconds (19.02 k allocations: 7.693 MB, 17.79% gc time)\n",
      "  0.008853 seconds (17.61 k allocations: 7.809 MB)\n",
      "  0.010372 seconds (19.28 k allocations: 8.138 MB)\n",
      "  0.017731 seconds (20.72 k allocations: 8.467 MB, 20.54% gc time)\n",
      "  0.010050 seconds (19.59 k allocations: 8.631 MB)\n",
      "  0.016669 seconds (25.54 k allocations: 9.267 MB, 20.82% gc time)\n",
      "  0.013116 seconds (25.47 k allocations: 9.490 MB)\n",
      "  0.012985 seconds (23.80 k allocations: 9.647 MB)\n",
      "  0.015012 seconds (24.07 k allocations: 9.914 MB, 19.98% gc time)\n",
      "  0.012967 seconds (24.67 k allocations: 10.201 MB)\n",
      "  0.016788 seconds (24.89 k allocations: 10.482 MB, 19.33% gc time)\n",
      "  0.016997 seconds (30.18 k allocations: 11.065 MB)\n",
      "  0.020691 seconds (33.91 k allocations: 11.561 MB, 15.81% gc time)\n",
      "  0.015363 seconds (30.74 k allocations: 11.656 MB)\n",
      "  0.021387 seconds (33.41 k allocations: 12.076 MB, 12.53% gc time)\n",
      "  0.016026 seconds (33.24 k allocations: 12.350 MB)\n",
      "  0.017328 seconds (31.56 k allocations: 12.559 MB, 19.19% gc time)\n",
      "  0.020016 seconds (33.25 k allocations: 12.944 MB, 12.80% gc time)\n",
      "  0.016312 seconds (36.22 k allocations: 13.427 MB)\n",
      "  0.022431 seconds (39.62 k allocations: 13.904 MB, 13.45% gc time)\n",
      "  0.017013 seconds (38.80 k allocations: 14.182 MB)\n",
      "  0.020921 seconds (37.21 k allocations: 14.421 MB, 16.99% gc time)\n",
      "  0.028011 seconds (44.57 k allocations: 15.137 MB, 11.13% gc time)\n",
      "  0.017841 seconds (40.78 k allocations: 15.276 MB)\n",
      "  0.029219 seconds (51.52 k allocations: 16.178 MB, 12.90% gc time)\n",
      "  0.039776 seconds (52.21 k allocations: 16.613 MB, 7.49% gc time)\n",
      "  0.028273 seconds (45.05 k allocations: 16.536 MB, 11.95% gc time)\n",
      "  0.025869 seconds (58.67 k allocations: 17.569 MB)\n",
      "  0.033463 seconds (47.11 k allocations: 17.340 MB, 11.60% gc time)\n",
      "  0.027999 seconds (52.82 k allocations: 17.979 MB, 12.02% gc time)\n",
      "  0.028646 seconds (41.63 k allocations: 17.760 MB, 11.85% gc time)\n",
      "  0.035324 seconds (54.66 k allocations: 18.822 MB, 9.16% gc time)\n",
      "  0.026098 seconds (51.75 k allocations: 19.044 MB)\n",
      "  0.026520 seconds (48.72 k allocations: 19.259 MB, 16.13% gc time)\n",
      "  0.039626 seconds (67.92 k allocations: 20.630 MB, 8.04% gc time)\n",
      "  0.029693 seconds (58.46 k allocations: 20.483 MB, 11.08% gc time)\n",
      "  0.027381 seconds (51.92 k allocations: 20.550 MB, 23.93% gc time)\n",
      "  0.036596 seconds (62.62 k allocations: 21.525 MB, 8.31% gc time)\n",
      "  0.041954 seconds (60.28 k allocations: 21.767 MB, 7.90% gc time)\n",
      "  0.045189 seconds (66.65 k allocations: 22.521 MB, 7.07% gc time)\n",
      "  0.039452 seconds (65.38 k allocations: 22.861 MB, 7.82% gc time)\n",
      "  0.059630 seconds (79.61 k allocations: 23.988 MB, 4.85% gc time)\n",
      "  0.034271 seconds (65.16 k allocations: 23.693 MB, 9.49% gc time)\n",
      "  0.043374 seconds (70.09 k allocations: 24.345 MB, 9.93% gc time)\n",
      "  0.061856 seconds (82.25 k allocations: 25.340 MB, 5.59% gc time)\n",
      "  0.060462 seconds (69.33 k allocations: 25.146 MB, 8.67% gc time)\n",
      "  0.043116 seconds (66.48 k allocations: 25.437 MB, 8.58% gc time)\n",
      "  0.049483 seconds (88.13 k allocations: 26.925 MB, 5.84% gc time)\n",
      "  0.006589 seconds (8.73 k allocations: 2.691 MB, 54.02% gc time)\n",
      "  0.003249 seconds (7.33 k allocations: 2.723 MB)\n",
      "  0.003632 seconds (6.97 k allocations: 2.839 MB)\n",
      "  0.003801 seconds (7.86 k allocations: 3.054 MB)\n",
      "  0.004274 seconds (8.06 k allocations: 3.221 MB)\n",
      "  0.003632 seconds (7.25 k allocations: 3.316 MB)\n",
      "  0.005034 seconds (8.59 k allocations: 3.580 MB)\n",
      "  0.012580 seconds (11.01 k allocations: 3.930 MB, 48.76% gc time)\n",
      "  0.004917 seconds (10.33 k allocations: 4.049 MB)\n",
      "  0.005836 seconds (12.00 k allocations: 4.351 MB)\n",
      "  0.004431 seconds (9.44 k allocations: 4.336 MB)\n",
      "  0.007131 seconds (12.14 k allocations: 4.726 MB)\n",
      "  0.015123 seconds (15.94 k allocations: 5.205 MB, 37.50% gc time)\n",
      "  0.005408 seconds (11.21 k allocations: 5.038 MB)\n",
      "  0.006367 seconds (12.79 k allocations: 5.356 MB)\n",
      "  0.007304 seconds (15.00 k allocations: 5.727 MB)\n",
      "  0.011275 seconds (12.78 k allocations: 5.765 MB, 43.18% gc time)\n",
      "  0.006424 seconds (13.51 k allocations: 6.032 MB)\n",
      "  0.009857 seconds (14.30 k allocations: 6.309 MB)\n",
      "  0.011529 seconds (13.22 k allocations: 6.448 MB)\n",
      "  0.011786 seconds (14.46 k allocations: 6.767 MB, 31.26% gc time)\n",
      "  0.008753 seconds (16.54 k allocations: 7.156 MB)\n",
      "  0.010711 seconds (18.61 k allocations: 7.447 MB)\n",
      "  0.013086 seconds (17.53 k allocations: 7.589 MB, 24.43% gc time)\n",
      "  0.011017 seconds (19.93 k allocations: 7.971 MB)\n",
      "  0.013524 seconds (23.25 k allocations: 8.435 MB)\n",
      "  0.015167 seconds (19.36 k allocations: 8.376 MB, 23.92% gc time)\n",
      "  0.017475 seconds (26.43 k allocations: 9.087 MB)\n",
      "  0.016884 seconds (21.67 k allocations: 9.004 MB, 19.56% gc time)\n",
      "  0.013372 seconds (26.09 k allocations: 9.517 MB)\n",
      "  0.010962 seconds (22.77 k allocations: 9.567 MB)\n",
      "  0.017027 seconds (28.22 k allocations: 10.174 MB, 18.19% gc time)\n",
      "  0.012840 seconds (28.04 k allocations: 10.408 MB)\n",
      "  0.015339 seconds (24.89 k allocations: 10.478 MB, 23.54% gc time)\n",
      "  0.020042 seconds (30.81 k allocations: 11.126 MB)\n",
      "  0.017576 seconds (30.12 k allocations: 11.318 MB, 24.26% gc time)\n",
      "  0.015900 seconds (32.74 k allocations: 11.766 MB)\n",
      "  0.017840 seconds (30.94 k allocations: 11.948 MB, 19.50% gc time)\n",
      "  0.016023 seconds (33.29 k allocations: 12.358 MB)\n",
      "  0.018511 seconds (31.99 k allocations: 12.604 MB, 18.14% gc time)\n",
      "  0.017776 seconds (29.85 k allocations: 12.794 MB)\n",
      "  0.025330 seconds (40.91 k allocations: 13.701 MB, 14.17% gc time)\n",
      "  0.020060 seconds (39.21 k allocations: 13.885 MB, 16.47% gc time)\n",
      "  0.017396 seconds (34.74 k allocations: 13.982 MB)\n",
      "  0.025626 seconds (38.03 k allocations: 14.471 MB, 15.42% gc time)\n",
      "  0.021876 seconds (36.89 k allocations: 14.765 MB, 12.85% gc time)\n",
      "  0.016570 seconds (37.81 k allocations: 15.140 MB)\n",
      "  0.031655 seconds (47.64 k allocations: 16.000 MB, 11.07% gc time)\n",
      "  0.023329 seconds (43.88 k allocations: 16.108 MB, 14.14% gc time)\n",
      "  0.025606 seconds (44.32 k allocations: 16.475 MB)\n",
      "  0.028833 seconds (48.31 k allocations: 17.033 MB, 15.21% gc time)\n",
      "  0.027924 seconds (47.57 k allocations: 17.383 MB, 10.69% gc time)\n",
      "  0.035790 seconds (61.78 k allocations: 18.431 MB, 9.41% gc time)\n",
      "  0.031238 seconds (58.88 k allocations: 18.652 MB, 10.23% gc time)\n",
      "  0.027286 seconds (54.55 k allocations: 18.760 MB)\n",
      "  0.045904 seconds (53.35 k allocations: 19.155 MB, 8.32% gc time)\n",
      "  0.038778 seconds (57.34 k allocations: 19.666 MB, 18.79% gc time)\n",
      "  0.036941 seconds (61.55 k allocations: 20.272 MB, 7.71% gc time)\n",
      "  0.030104 seconds (55.11 k allocations: 20.332 MB, 8.84% gc time)\n",
      "  0.036669 seconds (55.60 k allocations: 20.730 MB, 9.47% gc time)\n",
      "  0.032629 seconds (46.36 k allocations: 20.726 MB, 10.25% gc time)\n",
      "  0.037777 seconds (65.90 k allocations: 22.040 MB, 6.94% gc time)\n",
      "  0.045457 seconds (75.17 k allocations: 22.897 MB, 6.53% gc time)\n",
      "  0.035020 seconds (65.16 k allocations: 22.855 MB, 7.64% gc time)\n",
      "  0.027268 seconds (54.20 k allocations: 22.702 MB, 11.67% gc time)\n",
      "  0.046413 seconds (74.12 k allocations: 24.082 MB, 7.79% gc time)\n",
      "  0.052153 seconds (68.20 k allocations: 24.240 MB, 7.23% gc time)\n",
      "  0.040505 seconds (63.57 k allocations: 24.417 MB, 7.06% gc time)\n",
      "  0.036070 seconds (61.57 k allocations: 24.769 MB, 10.03% gc time)\n",
      "  0.039951 seconds (64.29 k allocations: 25.345 MB, 8.23% gc time)\n",
      "  0.041532 seconds (60.47 k allocations: 25.622 MB, 8.02% gc time)\n",
      "  0.002599 seconds (6.50 k allocations: 2.521 MB)\n",
      "  0.006836 seconds (7.66 k allocations: 2.748 MB, 58.74% gc time)\n",
      "  0.003305 seconds (8.49 k allocations: 2.954 MB)\n",
      "  0.003128 seconds (7.95 k allocations: 3.061 MB)\n",
      "  0.004070 seconds (9.08 k allocations: 3.299 MB)\n",
      "  0.003673 seconds (7.63 k allocations: 3.345 MB)\n",
      "  0.006342 seconds (10.69 k allocations: 3.740 MB)\n",
      "  0.006638 seconds (11.75 k allocations: 3.987 MB)\n",
      "  0.009969 seconds (9.75 k allocations: 4.005 MB, 57.81% gc time)\n",
      "  0.004508 seconds (10.45 k allocations: 4.234 MB)\n",
      "  0.004797 seconds (8.71 k allocations: 4.281 MB)\n",
      "  0.006353 seconds (9.68 k allocations: 4.539 MB)\n",
      "  0.004810 seconds (9.61 k allocations: 4.722 MB)\n",
      "  0.013081 seconds (10.96 k allocations: 5.019 MB, 44.13% gc time)\n",
      "  0.007698 seconds (11.53 k allocations: 5.260 MB)\n",
      "  0.008938 seconds (13.95 k allocations: 5.647 MB)\n",
      "  0.005823 seconds (11.33 k allocations: 5.654 MB)\n",
      "  0.012086 seconds (14.79 k allocations: 6.130 MB, 37.19% gc time)\n",
      "  0.008034 seconds (14.91 k allocations: 6.356 MB)\n",
      "  0.007149 seconds (12.93 k allocations: 6.425 MB)\n",
      "  0.013705 seconds (19.30 k allocations: 7.137 MB, 23.53% gc time)\n",
      "  0.009575 seconds (20.20 k allocations: 7.435 MB)\n",
      "  0.011568 seconds (20.92 k allocations: 7.631 MB)\n",
      "  0.018461 seconds (19.02 k allocations: 7.693 MB, 14.32% gc time)\n",
      "  0.010301 seconds (17.61 k allocations: 7.809 MB)\n",
      "  0.008896 seconds (19.28 k allocations: 8.138 MB)\n",
      "  0.013671 seconds (20.72 k allocations: 8.467 MB, 19.51% gc time)\n",
      "  0.010877 seconds (19.59 k allocations: 8.631 MB)\n",
      "  0.016074 seconds (25.54 k allocations: 9.267 MB, 20.26% gc time)\n",
      "  0.012419 seconds (25.47 k allocations: 9.490 MB)\n",
      "  0.012113 seconds (23.80 k allocations: 9.647 MB)\n",
      "  0.019602 seconds (24.07 k allocations: 9.914 MB, 16.97% gc time)\n",
      "  0.014886 seconds (24.67 k allocations: 10.201 MB)\n",
      "  0.016880 seconds (24.89 k allocations: 10.482 MB, 15.43% gc time)\n",
      "  0.014733 seconds (30.18 k allocations: 11.065 MB)\n",
      "  0.017765 seconds (33.91 k allocations: 11.561 MB, 17.40% gc time)\n",
      "  0.016559 seconds (30.74 k allocations: 11.656 MB)\n",
      "  0.023122 seconds (33.41 k allocations: 12.076 MB, 12.54% gc time)\n",
      "  0.017788 seconds (33.24 k allocations: 12.350 MB)\n",
      "  0.017359 seconds (31.56 k allocations: 12.559 MB, 19.49% gc time)\n",
      "  0.019088 seconds (33.25 k allocations: 12.944 MB, 16.00% gc time)\n",
      "  0.016608 seconds (36.22 k allocations: 13.427 MB)\n",
      "  0.027365 seconds (39.62 k allocations: 13.904 MB, 13.31% gc time)\n",
      "  0.018486 seconds (38.80 k allocations: 14.182 MB)\n",
      "  0.021597 seconds (37.21 k allocations: 14.421 MB, 17.35% gc time)\n",
      "  0.024346 seconds (44.57 k allocations: 15.137 MB, 12.61% gc time)\n",
      "  0.017586 seconds (40.78 k allocations: 15.276 MB)\n",
      "  0.030376 seconds (51.52 k allocations: 16.178 MB, 12.68% gc time)\n",
      "  0.044137 seconds (52.21 k allocations: 16.613 MB, 8.64% gc time)\n",
      "  0.024680 seconds (45.05 k allocations: 16.536 MB, 12.73% gc time)\n",
      "  0.027421 seconds (58.67 k allocations: 17.569 MB)\n",
      "  0.032013 seconds (47.11 k allocations: 17.340 MB, 12.49% gc time)\n",
      "  0.026549 seconds (52.82 k allocations: 17.979 MB, 11.33% gc time)\n",
      "  0.027171 seconds (41.63 k allocations: 17.760 MB, 10.83% gc time)\n",
      "  0.039746 seconds (54.66 k allocations: 18.822 MB, 8.30% gc time)\n",
      "  0.029633 seconds (51.75 k allocations: 19.044 MB)\n",
      "  0.025696 seconds (48.72 k allocations: 19.259 MB, 15.94% gc time)\n",
      "  0.033821 seconds (67.92 k allocations: 20.630 MB, 9.36% gc time)\n",
      "  0.032545 seconds (58.46 k allocations: 20.483 MB, 10.17% gc time)\n",
      "  0.024917 seconds (51.92 k allocations: 20.550 MB, 15.42% gc time)\n",
      "  0.036521 seconds (62.62 k allocations: 21.525 MB, 8.44% gc time)\n",
      "  0.034935 seconds (60.28 k allocations: 21.767 MB, 9.05% gc time)\n",
      "  0.046860 seconds (66.65 k allocations: 22.521 MB, 5.96% gc time)\n",
      "  0.040968 seconds (65.38 k allocations: 22.861 MB, 7.63% gc time)\n",
      "  0.055117 seconds (79.61 k allocations: 23.988 MB, 5.76% gc time)\n",
      "  0.037854 seconds (65.16 k allocations: 23.693 MB, 9.01% gc time)\n",
      "  0.043963 seconds (70.09 k allocations: 24.345 MB, 6.29% gc time)\n",
      "  0.047014 seconds (82.25 k allocations: 25.340 MB, 7.90% gc time)\n",
      "  0.064176 seconds (69.33 k allocations: 25.146 MB, 7.52% gc time)\n",
      "  0.032589 seconds (66.48 k allocations: 25.437 MB, 10.90% gc time)\n",
      "  0.051156 seconds (88.13 k allocations: 26.925 MB, 7.90% gc time)\n"
     ]
    }
   ],
   "source": [
    "xs = 300:10:1000\n",
    "\n",
    "labels = [\"simple1\", \"simple2\", \"normal1\", \"normal2\", \"recursive1\", \"recursive2\"]\n",
    "fs = [DA.call_simple_match, DA.call_match, DA.call_match, (m, f) -> DA.call_match(m, f, true)]\n",
    "for i in 1:3\n",
    "    for (m, f) in prefss\n",
    "        @time fs[i](m, f)\n",
    "    end\n",
    "    for (m, f) in prefss2\n",
    "        @time fs[i](m, f)\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "426-element Array{Float64,1}:\n",
       " 0.009129\n",
       " 0.009274\n",
       " 0.007484\n",
       " 0.010769\n",
       " 0.012465\n",
       " 0.014293\n",
       " 0.011384\n",
       " 0.014248\n",
       " 0.011885\n",
       " 0.02935 \n",
       " 0.020161\n",
       " 0.017643\n",
       " 0.029638\n",
       " ⋮       \n",
       " 0.024917\n",
       " 0.036521\n",
       " 0.034935\n",
       " 0.04686 \n",
       " 0.040968\n",
       " 0.055117\n",
       " 0.037854\n",
       " 0.043963\n",
       " 0.047014\n",
       " 0.064176\n",
       " 0.032589\n",
       " 0.051156"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allss = get_seconds2(\"0.009129 seconds (917 allocations: 1.416 MB)\n",
    "  0.009274 seconds (947 allocations: 1.511 MB)\n",
    "  0.007484 seconds (977 allocations: 1.608 MB)\n",
    "  0.010769 seconds (1.01 k allocations: 1.709 MB)\n",
    "  0.012465 seconds (1.04 k allocations: 1.813 MB)\n",
    "  0.014293 seconds (1.07 k allocations: 1.919 MB)\n",
    "  0.011384 seconds (1.10 k allocations: 2.029 MB)\n",
    "  0.014248 seconds (1.13 k allocations: 2.142 MB)\n",
    "  0.011885 seconds (1.16 k allocations: 2.258 MB)\n",
    "  0.029350 seconds (1.19 k allocations: 2.377 MB)\n",
    "  0.020161 seconds (1.22 k allocations: 2.499 MB)\n",
    "  0.017643 seconds (1.25 k allocations: 2.624 MB)\n",
    "  0.029638 seconds (1.28 k allocations: 2.752 MB)\n",
    "  0.045103 seconds (1.31 k allocations: 2.883 MB)\n",
    "  0.018109 seconds (1.34 k allocations: 3.017 MB)\n",
    "  0.024426 seconds (1.37 k allocations: 3.154 MB)\n",
    "  0.020370 seconds (1.40 k allocations: 3.294 MB)\n",
    "  0.021987 seconds (1.43 k allocations: 3.438 MB)\n",
    "  0.032624 seconds (1.46 k allocations: 3.584 MB)\n",
    "  0.029399 seconds (1.49 k allocations: 3.734 MB)\n",
    "  0.027961 seconds (1.52 k allocations: 3.886 MB)\n",
    "  0.029888 seconds (1.55 k allocations: 4.041 MB)\n",
    "  0.028471 seconds (7.86 k allocations: 4.296 MB)\n",
    "  0.542545 seconds (8.24 k allocations: 4.463 MB, 93.79% gc time)\n",
    "  0.026858 seconds (10.20 k allocations: 4.657 MB)\n",
    "  0.028771 seconds (12.07 k allocations: 4.853 MB)\n",
    "  0.024150 seconds (12.18 k allocations: 5.025 MB)\n",
    "  0.051690 seconds (18.61 k allocations: 5.296 MB, 27.21% gc time)\n",
    "  0.033867 seconds (14.94 k allocations: 5.417 MB)\n",
    "  0.036329 seconds (20.13 k allocations: 5.675 MB)\n",
    "  0.027228 seconds (16.95 k allocations: 5.810 MB)\n",
    "  0.078439 seconds (22.36 k allocations: 6.078 MB, 54.62% gc time)\n",
    "  0.029622 seconds (23.37 k allocations: 6.282 MB)\n",
    "  0.032879 seconds (21.20 k allocations: 6.440 MB)\n",
    "  0.040030 seconds (26.75 k allocations: 6.720 MB)\n",
    "  0.033104 seconds (27.55 k allocations: 6.930 MB, 9.65% gc time)\n",
    "  0.045393 seconds (30.37 k allocations: 7.174 MB)\n",
    "  0.043350 seconds (29.34 k allocations: 7.362 MB)\n",
    "  0.082619 seconds (32.67 k allocations: 7.620 MB, 4.64% gc time)\n",
    "  0.072746 seconds (30.70 k allocations: 7.800 MB)\n",
    "  0.058841 seconds (29.28 k allocations: 7.991 MB)\n",
    "  0.076485 seconds (41.90 k allocations: 8.399 MB, 5.68% gc time)\n",
    "  0.073314 seconds (41.51 k allocations: 8.613 MB)\n",
    "  0.068518 seconds (36.63 k allocations: 8.761 MB, 4.81% gc time)\n",
    "  0.053133 seconds (41.00 k allocations: 9.052 MB)\n",
    "  0.046012 seconds (39.34 k allocations: 9.256 MB)\n",
    "  0.044011 seconds (39.96 k allocations: 9.496 MB, 6.92% gc time)\n",
    "  0.050118 seconds (52.26 k allocations: 9.918 MB)\n",
    "  0.061661 seconds (49.89 k allocations: 10.120 MB, 4.68% gc time)\n",
    "  0.048913 seconds (51.11 k allocations: 10.379 MB)\n",
    "  0.087765 seconds (55.99 k allocations: 10.697 MB, 6.74% gc time)\n",
    "  0.058032 seconds (54.27 k allocations: 10.917 MB)\n",
    "  0.086060 seconds (72.74 k allocations: 11.449 MB, 8.90% gc time)\n",
    "  0.072596 seconds (69.57 k allocations: 11.653 MB)\n",
    "  0.078794 seconds (66.23 k allocations: 11.858 MB, 4.44% gc time)\n",
    "  0.086769 seconds (64.69 k allocations: 12.093 MB)\n",
    "  0.070877 seconds (69.40 k allocations: 12.427 MB, 8.11% gc time)\n",
    "  0.060147 seconds (75.33 k allocations: 12.782 MB)\n",
    "  0.069880 seconds (67.88 k allocations: 12.937 MB, 4.64% gc time)\n",
    "  0.053227 seconds (69.98 k allocations: 13.240 MB)\n",
    "  0.065903 seconds (56.41 k allocations: 13.307 MB, 4.78% gc time)\n",
    "  0.070496 seconds (83.80 k allocations: 14.002 MB)\n",
    "  0.086989 seconds (95.89 k allocations: 14.467 MB, 3.80% gc time)\n",
    "  0.077386 seconds (81.78 k allocations: 14.535 MB, 3.38% gc time)\n",
    "  0.070979 seconds (68.77 k allocations: 14.622 MB)\n",
    "  0.084061 seconds (94.25 k allocations: 15.300 MB, 3.60% gc time)\n",
    "  0.109442 seconds (87.19 k allocations: 15.485 MB, 3.00% gc time)\n",
    "  0.082028 seconds (83.36 k allocations: 15.722 MB)\n",
    "  0.075921 seconds (79.42 k allocations: 15.960 MB, 4.07% gc time)\n",
    "  0.110161 seconds (84.74 k allocations: 16.343 MB, 3.77% gc time)\n",
    "  0.170235 seconds (78.11 k allocations: 16.547 MB)\n",
    "  0.014590 seconds (917 allocations: 1.416 MB)\n",
    "  0.023831 seconds (947 allocations: 1.511 MB, 29.94% gc time)\n",
    "  0.011429 seconds (977 allocations: 1.608 MB)\n",
    "  0.021358 seconds (1.01 k allocations: 1.709 MB)\n",
    "  0.013277 seconds (1.04 k allocations: 1.813 MB)\n",
    "  0.014333 seconds (1.07 k allocations: 1.919 MB)\n",
    "  0.020227 seconds (1.10 k allocations: 2.029 MB)\n",
    "  0.014717 seconds (1.13 k allocations: 2.142 MB)\n",
    "  0.011713 seconds (1.16 k allocations: 2.258 MB)\n",
    "  0.010292 seconds (1.19 k allocations: 2.377 MB)\n",
    "  0.010109 seconds (1.22 k allocations: 2.499 MB)\n",
    "  0.012681 seconds (1.25 k allocations: 2.624 MB)\n",
    "  0.012653 seconds (1.28 k allocations: 2.752 MB, 21.27% gc time)\n",
    "  0.011812 seconds (1.31 k allocations: 2.883 MB)\n",
    "  0.014040 seconds (1.34 k allocations: 3.017 MB)\n",
    "  0.025403 seconds (1.37 k allocations: 3.154 MB)\n",
    "  0.020901 seconds (1.40 k allocations: 3.294 MB)\n",
    "  0.015928 seconds (1.43 k allocations: 3.438 MB)\n",
    "  0.019094 seconds (1.46 k allocations: 3.584 MB)\n",
    "  0.021860 seconds (1.49 k allocations: 3.734 MB, 18.25% gc time)\n",
    "  0.033024 seconds (1.52 k allocations: 3.886 MB)\n",
    "  0.029245 seconds (1.55 k allocations: 4.041 MB)\n",
    "  0.030801 seconds (8.09 k allocations: 4.300 MB)\n",
    "  0.022904 seconds (9.74 k allocations: 4.486 MB)\n",
    "  0.026552 seconds (9.10 k allocations: 4.640 MB, 11.16% gc time)\n",
    "  0.022630 seconds (11.09 k allocations: 4.838 MB)\n",
    "  0.024616 seconds (13.17 k allocations: 5.040 MB)\n",
    "  0.020642 seconds (13.27 k allocations: 5.215 MB)\n",
    "  0.028246 seconds (17.25 k allocations: 5.452 MB, 9.50% gc time)\n",
    "  0.026836 seconds (18.67 k allocations: 5.653 MB)\n",
    "  0.029671 seconds (17.57 k allocations: 5.819 MB)\n",
    "  0.026636 seconds (19.08 k allocations: 6.028 MB)\n",
    "  0.028267 seconds (20.76 k allocations: 6.242 MB, 11.13% gc time)\n",
    "  0.032968 seconds (21.48 k allocations: 6.445 MB)\n",
    "  0.034690 seconds (26.30 k allocations: 6.713 MB)\n",
    "  0.035694 seconds (30.06 k allocations: 6.968 MB, 9.05% gc time)\n",
    "  0.051677 seconds (28.50 k allocations: 7.145 MB)\n",
    "  0.047230 seconds (32.26 k allocations: 7.406 MB)\n",
    "  0.052814 seconds (33.03 k allocations: 7.625 MB, 6.41% gc time)\n",
    "  0.035839 seconds (31.30 k allocations: 7.809 MB)\n",
    "  0.035595 seconds (34.71 k allocations: 8.074 MB)\n",
    "  0.055290 seconds (37.16 k allocations: 8.327 MB, 5.14% gc time)\n",
    "  0.052409 seconds (42.60 k allocations: 8.630 MB)\n",
    "  0.055728 seconds (41.22 k allocations: 8.831 MB)\n",
    "  0.041856 seconds (40.14 k allocations: 9.039 MB, 8.56% gc time)\n",
    "  0.047205 seconds (49.50 k allocations: 9.410 MB)\n",
    "  0.047857 seconds (44.02 k allocations: 9.558 MB, 6.16% gc time)\n",
    "  0.063556 seconds (57.30 k allocations: 9.995 MB)\n",
    "  0.087841 seconds (59.66 k allocations: 10.269 MB, 3.98% gc time)\n",
    "  0.080251 seconds (50.36 k allocations: 10.368 MB)\n",
    "  0.067195 seconds (66.76 k allocations: 10.861 MB)\n",
    "  0.058766 seconds (55.03 k allocations: 10.929 MB, 5.41% gc time)\n",
    "  0.068546 seconds (61.50 k allocations: 11.277 MB)\n",
    "  0.051247 seconds (49.58 k allocations: 11.348 MB, 5.61% gc time)\n",
    "  0.070344 seconds (65.41 k allocations: 11.845 MB)\n",
    "  0.054621 seconds (61.34 k allocations: 12.042 MB, 5.07% gc time)\n",
    "  0.055674 seconds (57.85 k allocations: 12.251 MB)\n",
    "  0.067895 seconds (80.43 k allocations: 12.860 MB, 4.75% gc time)\n",
    "  0.069375 seconds (72.48 k allocations: 13.007 MB)\n",
    "  0.051531 seconds (64.14 k allocations: 13.151 MB, 6.92% gc time)\n",
    "  0.078942 seconds (76.93 k allocations: 13.620 MB)\n",
    "  0.077981 seconds (76.92 k allocations: 13.897 MB, 5.51% gc time)\n",
    "  0.093429 seconds (84.87 k allocations: 14.299 MB, 3.04% gc time)\n",
    "  0.082465 seconds (82.84 k allocations: 14.551 MB)\n",
    "  0.102572 seconds (102.51 k allocations: 15.137 MB, 4.24% gc time)\n",
    "  0.070548 seconds (81.48 k allocations: 15.106 MB, 3.69% gc time)\n",
    "  0.072033 seconds (90.17 k allocations: 15.530 MB)\n",
    "  0.100468 seconds (106.83 k allocations: 16.080 MB, 5.06% gc time)\n",
    "  0.079815 seconds (91.27 k allocations: 16.141 MB, 3.80% gc time)\n",
    "  0.062726 seconds (86.01 k allocations: 16.362 MB)\n",
    "  0.094010 seconds (115.80 k allocations: 17.122 MB, 4.99% gc time)\n",
    "  0.004642 seconds (8.73 k allocations: 2.691 MB)\n",
    "  0.004758 seconds (7.33 k allocations: 2.723 MB)\n",
    "  0.007265 seconds (6.97 k allocations: 2.839 MB)\n",
    "  0.009612 seconds (7.86 k allocations: 3.054 MB)\n",
    "  0.012493 seconds (8.06 k allocations: 3.221 MB, 45.50% gc time)\n",
    "  0.006049 seconds (7.25 k allocations: 3.316 MB)\n",
    "  0.006061 seconds (8.59 k allocations: 3.580 MB)\n",
    "  0.007366 seconds (11.01 k allocations: 3.930 MB)\n",
    "  0.008260 seconds (10.33 k allocations: 4.049 MB)\n",
    "  0.010297 seconds (12.00 k allocations: 4.351 MB)\n",
    "  0.013648 seconds (9.44 k allocations: 4.336 MB, 41.53% gc time)\n",
    "  0.009034 seconds (12.14 k allocations: 4.726 MB)\n",
    "  0.011327 seconds (15.94 k allocations: 5.205 MB)\n",
    "  0.010038 seconds (11.21 k allocations: 5.038 MB)\n",
    "  0.016368 seconds (12.79 k allocations: 5.356 MB, 28.86% gc time)\n",
    "  0.010953 seconds (15.00 k allocations: 5.727 MB)\n",
    "  0.010378 seconds (12.78 k allocations: 5.765 MB)\n",
    "  0.008863 seconds (13.51 k allocations: 6.032 MB)\n",
    "  0.014849 seconds (14.30 k allocations: 6.309 MB, 22.36% gc time)\n",
    "  0.010181 seconds (13.22 k allocations: 6.448 MB)\n",
    "  0.010599 seconds (14.46 k allocations: 6.767 MB)\n",
    "  0.016141 seconds (16.54 k allocations: 7.156 MB, 22.86% gc time)\n",
    "  0.016953 seconds (18.61 k allocations: 7.447 MB)\n",
    "  0.012894 seconds (17.53 k allocations: 7.589 MB)\n",
    "  0.016898 seconds (19.93 k allocations: 7.971 MB, 17.94% gc time)\n",
    "  0.015011 seconds (23.25 k allocations: 8.435 MB)\n",
    "  0.014746 seconds (19.36 k allocations: 8.376 MB)\n",
    "  0.022936 seconds (26.43 k allocations: 9.087 MB, 13.17% gc time)\n",
    "  0.015819 seconds (21.67 k allocations: 9.004 MB)\n",
    "  0.024263 seconds (26.09 k allocations: 9.517 MB, 14.74% gc time)\n",
    "  0.017480 seconds (22.77 k allocations: 9.567 MB)\n",
    "  0.025643 seconds (28.22 k allocations: 10.174 MB, 14.60% gc time)\n",
    "  0.028060 seconds (28.04 k allocations: 10.408 MB)\n",
    "  0.030852 seconds (24.89 k allocations: 10.478 MB, 10.60% gc time)\n",
    "  0.033995 seconds (30.81 k allocations: 11.126 MB)\n",
    "  0.024125 seconds (30.12 k allocations: 11.318 MB, 13.68% gc time)\n",
    "  0.021458 seconds (32.74 k allocations: 11.766 MB)\n",
    "  0.027426 seconds (30.94 k allocations: 11.948 MB, 11.26% gc time)\n",
    "  0.023079 seconds (33.29 k allocations: 12.358 MB)\n",
    "  0.024997 seconds (31.99 k allocations: 12.604 MB, 11.94% gc time)\n",
    "  0.018521 seconds (29.85 k allocations: 12.794 MB)\n",
    "  0.025482 seconds (40.91 k allocations: 13.701 MB, 11.42% gc time)\n",
    "  0.025320 seconds (39.21 k allocations: 13.885 MB, 14.05% gc time)\n",
    "  0.021363 seconds (34.74 k allocations: 13.982 MB)\n",
    "  0.029802 seconds (38.03 k allocations: 14.471 MB, 17.67% gc time)\n",
    "  0.026147 seconds (36.89 k allocations: 14.765 MB, 11.81% gc time)\n",
    "  0.020503 seconds (37.81 k allocations: 15.140 MB)\n",
    "  0.033436 seconds (47.64 k allocations: 16.000 MB, 10.41% gc time)\n",
    "  0.026870 seconds (43.88 k allocations: 16.108 MB, 13.56% gc time)\n",
    "  0.029028 seconds (44.32 k allocations: 16.475 MB)\n",
    "  0.034226 seconds (48.31 k allocations: 17.033 MB, 11.57% gc time)\n",
    "  0.031410 seconds (47.57 k allocations: 17.383 MB, 8.47% gc time)\n",
    "  0.035468 seconds (61.78 k allocations: 18.431 MB, 8.49% gc time)\n",
    "  0.034408 seconds (58.88 k allocations: 18.652 MB, 7.54% gc time)\n",
    "  0.025174 seconds (54.55 k allocations: 18.760 MB)\n",
    "  0.046667 seconds (53.35 k allocations: 19.155 MB, 8.88% gc time)\n",
    "  0.038927 seconds (57.34 k allocations: 19.666 MB, 8.46% gc time)\n",
    "  0.056845 seconds (61.55 k allocations: 20.272 MB, 10.87% gc time)\n",
    "  0.037021 seconds (55.11 k allocations: 20.332 MB, 13.94% gc time)\n",
    "  0.036554 seconds (55.60 k allocations: 20.730 MB, 9.89% gc time)\n",
    "  0.033103 seconds (46.36 k allocations: 20.726 MB, 8.95% gc time)\n",
    "  0.038685 seconds (65.90 k allocations: 22.040 MB, 8.15% gc time)\n",
    "  0.048327 seconds (75.17 k allocations: 22.897 MB, 6.84% gc time)\n",
    "  0.040950 seconds (65.16 k allocations: 22.855 MB, 6.85% gc time)\n",
    "  0.027835 seconds (54.20 k allocations: 22.702 MB, 10.09% gc time)\n",
    "  0.038785 seconds (74.12 k allocations: 24.082 MB, 7.90% gc time)\n",
    "  0.040684 seconds (68.20 k allocations: 24.240 MB, 6.55% gc time)\n",
    "  0.033529 seconds (63.57 k allocations: 24.417 MB, 9.74% gc time)\n",
    "  0.035949 seconds (61.57 k allocations: 24.769 MB, 8.60% gc time)\n",
    "  0.040421 seconds (64.29 k allocations: 25.345 MB, 8.94% gc time)\n",
    "  0.032776 seconds (60.47 k allocations: 25.622 MB, 10.01% gc time)\n",
    "  0.003604 seconds (6.50 k allocations: 2.521 MB)\n",
    "  0.008793 seconds (7.66 k allocations: 2.748 MB, 57.39% gc time)\n",
    "  0.003432 seconds (8.49 k allocations: 2.954 MB)\n",
    "  0.004118 seconds (7.95 k allocations: 3.061 MB)\n",
    "  0.004849 seconds (9.08 k allocations: 3.299 MB)\n",
    "  0.003378 seconds (7.63 k allocations: 3.345 MB)\n",
    "  0.004961 seconds (10.69 k allocations: 3.740 MB)\n",
    "  0.005679 seconds (11.75 k allocations: 3.987 MB)\n",
    "  0.012225 seconds (9.75 k allocations: 4.005 MB, 60.42% gc time)\n",
    "  0.005300 seconds (10.45 k allocations: 4.234 MB)\n",
    "  0.004952 seconds (8.71 k allocations: 4.281 MB)\n",
    "  0.005007 seconds (9.68 k allocations: 4.539 MB)\n",
    "  0.004728 seconds (9.61 k allocations: 4.722 MB)\n",
    "  0.010661 seconds (10.96 k allocations: 5.019 MB, 48.84% gc time)\n",
    "  0.005491 seconds (11.53 k allocations: 5.260 MB)\n",
    "  0.007962 seconds (13.95 k allocations: 5.647 MB)\n",
    "  0.006266 seconds (11.33 k allocations: 5.654 MB)\n",
    "  0.012811 seconds (14.79 k allocations: 6.130 MB, 38.33% gc time)\n",
    "  0.010861 seconds (14.91 k allocations: 6.356 MB)\n",
    "  0.006877 seconds (12.93 k allocations: 6.425 MB)\n",
    "  0.014493 seconds (19.30 k allocations: 7.137 MB, 27.02% gc time)\n",
    "  0.010596 seconds (20.20 k allocations: 7.435 MB)\n",
    "  0.011462 seconds (20.92 k allocations: 7.631 MB)\n",
    "  0.014140 seconds (19.02 k allocations: 7.693 MB, 17.79% gc time)\n",
    "  0.008853 seconds (17.61 k allocations: 7.809 MB)\n",
    "  0.010372 seconds (19.28 k allocations: 8.138 MB)\n",
    "  0.017731 seconds (20.72 k allocations: 8.467 MB, 20.54% gc time)\n",
    "  0.010050 seconds (19.59 k allocations: 8.631 MB)\n",
    "  0.016669 seconds (25.54 k allocations: 9.267 MB, 20.82% gc time)\n",
    "  0.013116 seconds (25.47 k allocations: 9.490 MB)\n",
    "  0.012985 seconds (23.80 k allocations: 9.647 MB)\n",
    "  0.015012 seconds (24.07 k allocations: 9.914 MB, 19.98% gc time)\n",
    "  0.012967 seconds (24.67 k allocations: 10.201 MB)\n",
    "  0.016788 seconds (24.89 k allocations: 10.482 MB, 19.33% gc time)\n",
    "  0.016997 seconds (30.18 k allocations: 11.065 MB)\n",
    "  0.020691 seconds (33.91 k allocations: 11.561 MB, 15.81% gc time)\n",
    "  0.015363 seconds (30.74 k allocations: 11.656 MB)\n",
    "  0.021387 seconds (33.41 k allocations: 12.076 MB, 12.53% gc time)\n",
    "  0.016026 seconds (33.24 k allocations: 12.350 MB)\n",
    "  0.017328 seconds (31.56 k allocations: 12.559 MB, 19.19% gc time)\n",
    "  0.020016 seconds (33.25 k allocations: 12.944 MB, 12.80% gc time)\n",
    "  0.016312 seconds (36.22 k allocations: 13.427 MB)\n",
    "  0.022431 seconds (39.62 k allocations: 13.904 MB, 13.45% gc time)\n",
    "  0.017013 seconds (38.80 k allocations: 14.182 MB)\n",
    "  0.020921 seconds (37.21 k allocations: 14.421 MB, 16.99% gc time)\n",
    "  0.028011 seconds (44.57 k allocations: 15.137 MB, 11.13% gc time)\n",
    "  0.017841 seconds (40.78 k allocations: 15.276 MB)\n",
    "  0.029219 seconds (51.52 k allocations: 16.178 MB, 12.90% gc time)\n",
    "  0.039776 seconds (52.21 k allocations: 16.613 MB, 7.49% gc time)\n",
    "  0.028273 seconds (45.05 k allocations: 16.536 MB, 11.95% gc time)\n",
    "  0.025869 seconds (58.67 k allocations: 17.569 MB)\n",
    "  0.033463 seconds (47.11 k allocations: 17.340 MB, 11.60% gc time)\n",
    "  0.027999 seconds (52.82 k allocations: 17.979 MB, 12.02% gc time)\n",
    "  0.028646 seconds (41.63 k allocations: 17.760 MB, 11.85% gc time)\n",
    "  0.035324 seconds (54.66 k allocations: 18.822 MB, 9.16% gc time)\n",
    "  0.026098 seconds (51.75 k allocations: 19.044 MB)\n",
    "  0.026520 seconds (48.72 k allocations: 19.259 MB, 16.13% gc time)\n",
    "  0.039626 seconds (67.92 k allocations: 20.630 MB, 8.04% gc time)\n",
    "  0.029693 seconds (58.46 k allocations: 20.483 MB, 11.08% gc time)\n",
    "  0.027381 seconds (51.92 k allocations: 20.550 MB, 23.93% gc time)\n",
    "  0.036596 seconds (62.62 k allocations: 21.525 MB, 8.31% gc time)\n",
    "  0.041954 seconds (60.28 k allocations: 21.767 MB, 7.90% gc time)\n",
    "  0.045189 seconds (66.65 k allocations: 22.521 MB, 7.07% gc time)\n",
    "  0.039452 seconds (65.38 k allocations: 22.861 MB, 7.82% gc time)\n",
    "  0.059630 seconds (79.61 k allocations: 23.988 MB, 4.85% gc time)\n",
    "  0.034271 seconds (65.16 k allocations: 23.693 MB, 9.49% gc time)\n",
    "  0.043374 seconds (70.09 k allocations: 24.345 MB, 9.93% gc time)\n",
    "  0.061856 seconds (82.25 k allocations: 25.340 MB, 5.59% gc time)\n",
    "  0.060462 seconds (69.33 k allocations: 25.146 MB, 8.67% gc time)\n",
    "  0.043116 seconds (66.48 k allocations: 25.437 MB, 8.58% gc time)\n",
    "  0.049483 seconds (88.13 k allocations: 26.925 MB, 5.84% gc time)\n",
    "  0.006589 seconds (8.73 k allocations: 2.691 MB, 54.02% gc time)\n",
    "  0.003249 seconds (7.33 k allocations: 2.723 MB)\n",
    "  0.003632 seconds (6.97 k allocations: 2.839 MB)\n",
    "  0.003801 seconds (7.86 k allocations: 3.054 MB)\n",
    "  0.004274 seconds (8.06 k allocations: 3.221 MB)\n",
    "  0.003632 seconds (7.25 k allocations: 3.316 MB)\n",
    "  0.005034 seconds (8.59 k allocations: 3.580 MB)\n",
    "  0.012580 seconds (11.01 k allocations: 3.930 MB, 48.76% gc time)\n",
    "  0.004917 seconds (10.33 k allocations: 4.049 MB)\n",
    "  0.005836 seconds (12.00 k allocations: 4.351 MB)\n",
    "  0.004431 seconds (9.44 k allocations: 4.336 MB)\n",
    "  0.007131 seconds (12.14 k allocations: 4.726 MB)\n",
    "  0.015123 seconds (15.94 k allocations: 5.205 MB, 37.50% gc time)\n",
    "  0.005408 seconds (11.21 k allocations: 5.038 MB)\n",
    "  0.006367 seconds (12.79 k allocations: 5.356 MB)\n",
    "  0.007304 seconds (15.00 k allocations: 5.727 MB)\n",
    "  0.011275 seconds (12.78 k allocations: 5.765 MB, 43.18% gc time)\n",
    "  0.006424 seconds (13.51 k allocations: 6.032 MB)\n",
    "  0.009857 seconds (14.30 k allocations: 6.309 MB)\n",
    "  0.011529 seconds (13.22 k allocations: 6.448 MB)\n",
    "  0.011786 seconds (14.46 k allocations: 6.767 MB, 31.26% gc time)\n",
    "  0.008753 seconds (16.54 k allocations: 7.156 MB)\n",
    "  0.010711 seconds (18.61 k allocations: 7.447 MB)\n",
    "  0.013086 seconds (17.53 k allocations: 7.589 MB, 24.43% gc time)\n",
    "  0.011017 seconds (19.93 k allocations: 7.971 MB)\n",
    "  0.013524 seconds (23.25 k allocations: 8.435 MB)\n",
    "  0.015167 seconds (19.36 k allocations: 8.376 MB, 23.92% gc time)\n",
    "  0.017475 seconds (26.43 k allocations: 9.087 MB)\n",
    "  0.016884 seconds (21.67 k allocations: 9.004 MB, 19.56% gc time)\n",
    "  0.013372 seconds (26.09 k allocations: 9.517 MB)\n",
    "  0.010962 seconds (22.77 k allocations: 9.567 MB)\n",
    "  0.017027 seconds (28.22 k allocations: 10.174 MB, 18.19% gc time)\n",
    "  0.012840 seconds (28.04 k allocations: 10.408 MB)\n",
    "  0.015339 seconds (24.89 k allocations: 10.478 MB, 23.54% gc time)\n",
    "  0.020042 seconds (30.81 k allocations: 11.126 MB)\n",
    "  0.017576 seconds (30.12 k allocations: 11.318 MB, 24.26% gc time)\n",
    "  0.015900 seconds (32.74 k allocations: 11.766 MB)\n",
    "  0.017840 seconds (30.94 k allocations: 11.948 MB, 19.50% gc time)\n",
    "  0.016023 seconds (33.29 k allocations: 12.358 MB)\n",
    "  0.018511 seconds (31.99 k allocations: 12.604 MB, 18.14% gc time)\n",
    "  0.017776 seconds (29.85 k allocations: 12.794 MB)\n",
    "  0.025330 seconds (40.91 k allocations: 13.701 MB, 14.17% gc time)\n",
    "  0.020060 seconds (39.21 k allocations: 13.885 MB, 16.47% gc time)\n",
    "  0.017396 seconds (34.74 k allocations: 13.982 MB)\n",
    "  0.025626 seconds (38.03 k allocations: 14.471 MB, 15.42% gc time)\n",
    "  0.021876 seconds (36.89 k allocations: 14.765 MB, 12.85% gc time)\n",
    "  0.016570 seconds (37.81 k allocations: 15.140 MB)\n",
    "  0.031655 seconds (47.64 k allocations: 16.000 MB, 11.07% gc time)\n",
    "  0.023329 seconds (43.88 k allocations: 16.108 MB, 14.14% gc time)\n",
    "  0.025606 seconds (44.32 k allocations: 16.475 MB)\n",
    "  0.028833 seconds (48.31 k allocations: 17.033 MB, 15.21% gc time)\n",
    "  0.027924 seconds (47.57 k allocations: 17.383 MB, 10.69% gc time)\n",
    "  0.035790 seconds (61.78 k allocations: 18.431 MB, 9.41% gc time)\n",
    "  0.031238 seconds (58.88 k allocations: 18.652 MB, 10.23% gc time)\n",
    "  0.027286 seconds (54.55 k allocations: 18.760 MB)\n",
    "  0.045904 seconds (53.35 k allocations: 19.155 MB, 8.32% gc time)\n",
    "  0.038778 seconds (57.34 k allocations: 19.666 MB, 18.79% gc time)\n",
    "  0.036941 seconds (61.55 k allocations: 20.272 MB, 7.71% gc time)\n",
    "  0.030104 seconds (55.11 k allocations: 20.332 MB, 8.84% gc time)\n",
    "  0.036669 seconds (55.60 k allocations: 20.730 MB, 9.47% gc time)\n",
    "  0.032629 seconds (46.36 k allocations: 20.726 MB, 10.25% gc time)\n",
    "  0.037777 seconds (65.90 k allocations: 22.040 MB, 6.94% gc time)\n",
    "  0.045457 seconds (75.17 k allocations: 22.897 MB, 6.53% gc time)\n",
    "  0.035020 seconds (65.16 k allocations: 22.855 MB, 7.64% gc time)\n",
    "  0.027268 seconds (54.20 k allocations: 22.702 MB, 11.67% gc time)\n",
    "  0.046413 seconds (74.12 k allocations: 24.082 MB, 7.79% gc time)\n",
    "  0.052153 seconds (68.20 k allocations: 24.240 MB, 7.23% gc time)\n",
    "  0.040505 seconds (63.57 k allocations: 24.417 MB, 7.06% gc time)\n",
    "  0.036070 seconds (61.57 k allocations: 24.769 MB, 10.03% gc time)\n",
    "  0.039951 seconds (64.29 k allocations: 25.345 MB, 8.23% gc time)\n",
    "  0.041532 seconds (60.47 k allocations: 25.622 MB, 8.02% gc time)\n",
    "  0.002599 seconds (6.50 k allocations: 2.521 MB)\n",
    "  0.006836 seconds (7.66 k allocations: 2.748 MB, 58.74% gc time)\n",
    "  0.003305 seconds (8.49 k allocations: 2.954 MB)\n",
    "  0.003128 seconds (7.95 k allocations: 3.061 MB)\n",
    "  0.004070 seconds (9.08 k allocations: 3.299 MB)\n",
    "  0.003673 seconds (7.63 k allocations: 3.345 MB)\n",
    "  0.006342 seconds (10.69 k allocations: 3.740 MB)\n",
    "  0.006638 seconds (11.75 k allocations: 3.987 MB)\n",
    "  0.009969 seconds (9.75 k allocations: 4.005 MB, 57.81% gc time)\n",
    "  0.004508 seconds (10.45 k allocations: 4.234 MB)\n",
    "  0.004797 seconds (8.71 k allocations: 4.281 MB)\n",
    "  0.006353 seconds (9.68 k allocations: 4.539 MB)\n",
    "  0.004810 seconds (9.61 k allocations: 4.722 MB)\n",
    "  0.013081 seconds (10.96 k allocations: 5.019 MB, 44.13% gc time)\n",
    "  0.007698 seconds (11.53 k allocations: 5.260 MB)\n",
    "  0.008938 seconds (13.95 k allocations: 5.647 MB)\n",
    "  0.005823 seconds (11.33 k allocations: 5.654 MB)\n",
    "  0.012086 seconds (14.79 k allocations: 6.130 MB, 37.19% gc time)\n",
    "  0.008034 seconds (14.91 k allocations: 6.356 MB)\n",
    "  0.007149 seconds (12.93 k allocations: 6.425 MB)\n",
    "  0.013705 seconds (19.30 k allocations: 7.137 MB, 23.53% gc time)\n",
    "  0.009575 seconds (20.20 k allocations: 7.435 MB)\n",
    "  0.011568 seconds (20.92 k allocations: 7.631 MB)\n",
    "  0.018461 seconds (19.02 k allocations: 7.693 MB, 14.32% gc time)\n",
    "  0.010301 seconds (17.61 k allocations: 7.809 MB)\n",
    "  0.008896 seconds (19.28 k allocations: 8.138 MB)\n",
    "  0.013671 seconds (20.72 k allocations: 8.467 MB, 19.51% gc time)\n",
    "  0.010877 seconds (19.59 k allocations: 8.631 MB)\n",
    "  0.016074 seconds (25.54 k allocations: 9.267 MB, 20.26% gc time)\n",
    "  0.012419 seconds (25.47 k allocations: 9.490 MB)\n",
    "  0.012113 seconds (23.80 k allocations: 9.647 MB)\n",
    "  0.019602 seconds (24.07 k allocations: 9.914 MB, 16.97% gc time)\n",
    "  0.014886 seconds (24.67 k allocations: 10.201 MB)\n",
    "  0.016880 seconds (24.89 k allocations: 10.482 MB, 15.43% gc time)\n",
    "  0.014733 seconds (30.18 k allocations: 11.065 MB)\n",
    "  0.017765 seconds (33.91 k allocations: 11.561 MB, 17.40% gc time)\n",
    "  0.016559 seconds (30.74 k allocations: 11.656 MB)\n",
    "  0.023122 seconds (33.41 k allocations: 12.076 MB, 12.54% gc time)\n",
    "  0.017788 seconds (33.24 k allocations: 12.350 MB)\n",
    "  0.017359 seconds (31.56 k allocations: 12.559 MB, 19.49% gc time)\n",
    "  0.019088 seconds (33.25 k allocations: 12.944 MB, 16.00% gc time)\n",
    "  0.016608 seconds (36.22 k allocations: 13.427 MB)\n",
    "  0.027365 seconds (39.62 k allocations: 13.904 MB, 13.31% gc time)\n",
    "  0.018486 seconds (38.80 k allocations: 14.182 MB)\n",
    "  0.021597 seconds (37.21 k allocations: 14.421 MB, 17.35% gc time)\n",
    "  0.024346 seconds (44.57 k allocations: 15.137 MB, 12.61% gc time)\n",
    "  0.017586 seconds (40.78 k allocations: 15.276 MB)\n",
    "  0.030376 seconds (51.52 k allocations: 16.178 MB, 12.68% gc time)\n",
    "  0.044137 seconds (52.21 k allocations: 16.613 MB, 8.64% gc time)\n",
    "  0.024680 seconds (45.05 k allocations: 16.536 MB, 12.73% gc time)\n",
    "  0.027421 seconds (58.67 k allocations: 17.569 MB)\n",
    "  0.032013 seconds (47.11 k allocations: 17.340 MB, 12.49% gc time)\n",
    "  0.026549 seconds (52.82 k allocations: 17.979 MB, 11.33% gc time)\n",
    "  0.027171 seconds (41.63 k allocations: 17.760 MB, 10.83% gc time)\n",
    "  0.039746 seconds (54.66 k allocations: 18.822 MB, 8.30% gc time)\n",
    "  0.029633 seconds (51.75 k allocations: 19.044 MB)\n",
    "  0.025696 seconds (48.72 k allocations: 19.259 MB, 15.94% gc time)\n",
    "  0.033821 seconds (67.92 k allocations: 20.630 MB, 9.36% gc time)\n",
    "  0.032545 seconds (58.46 k allocations: 20.483 MB, 10.17% gc time)\n",
    "  0.024917 seconds (51.92 k allocations: 20.550 MB, 15.42% gc time)\n",
    "  0.036521 seconds (62.62 k allocations: 21.525 MB, 8.44% gc time)\n",
    "  0.034935 seconds (60.28 k allocations: 21.767 MB, 9.05% gc time)\n",
    "  0.046860 seconds (66.65 k allocations: 22.521 MB, 5.96% gc time)\n",
    "  0.040968 seconds (65.38 k allocations: 22.861 MB, 7.63% gc time)\n",
    "  0.055117 seconds (79.61 k allocations: 23.988 MB, 5.76% gc time)\n",
    "  0.037854 seconds (65.16 k allocations: 23.693 MB, 9.01% gc time)\n",
    "  0.043963 seconds (70.09 k allocations: 24.345 MB, 6.29% gc time)\n",
    "  0.047014 seconds (82.25 k allocations: 25.340 MB, 7.90% gc time)\n",
    "  0.064176 seconds (69.33 k allocations: 25.146 MB, 7.52% gc time)\n",
    "  0.032589 seconds (66.48 k allocations: 25.437 MB, 10.90% gc time)\n",
    "  0.051156 seconds (88.13 k allocations: 26.925 MB, 7.90% gc time)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x3215cefd0>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x3207dc978>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in 1:6\n",
    "    plot(xs, allss[(i-1)*70+1: (i-1)*70+71], label =labels[i])\n",
    "end\n",
    "legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "simple版が少し遅いようです. 再帰版と再帰なし通常版が同じになったのは, ほぼすべての変数を使い回すようにしたことでスタックに詰める必要のある変数が減ったからでしょうか."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m_prefs, f_prefs = DA.generate_random_preference_data(5000, 5000)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1.513338 seconds (1.23 M allocations: 616.941 MB, 8.54% gc time)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([3948,1561,48,4823,1299,672,2213,187,2373,3606  …  2948,4821,2959,4898,3901,4191,1078,492,945,3125],[3248,268,4940,2461,4249,3323,31,2771,1372,2052  …  814,197,1209,192,1872,2859,2689,3962,2223,4024])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_matched, f_matched = @time DA.call_match(m_prefs, f_prefs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DA.stable_matching(m_matched, f_matched, m_prefs, f_prefs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DA.check_results(m_matched, f_matched)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m_prefs, f_prefs = DA.generate_random_preference_data(10000, 10000)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 11.013483 seconds (3.12 M allocations: 2.343 GB, 5.02% gc time)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([6456,2,442,3300,9679,3334,4573,669,488,3163  …  2306,2597,4002,1180,5376,721,303,6752,1903,8266],[7047,2,9722,2898,6964,3727,1807,5150,4333,615  …  1527,5911,7902,5506,1233,2316,1569,5235,5949,2546])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_matched, f_matched = @time DA.call_match(m_prefs, f_prefs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DA.stable_matching(m_matched, f_matched, m_prefs, f_prefs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m_prefs, f_prefs = DA.generate_random_preference_data(1000, 1000)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.036207 seconds (72.02 k allocations: 26.139 MB, 11.47% gc time)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([143,631,393,839,702,299,317,816,0,3  …  108,447,964,156,81,682,834,811,27,490],[346,22,10,318,812,411,198,840,797,231  …  466,116,478,321,135,360,260,500,738,530])"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@time DA.call_match(m_prefs, f_prefs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m_prefs, f_prefs = DA.generate_random_preference_data(4, 4)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.000061 seconds (50 allocations: 2.844 KB)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([0,2,4,0],[0,2,0,3])"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@time DA.call_match(m_prefs, f_prefs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "チューニングしてる時にenumerateが遅いのではないかと思ったので実際そうなのか調べてみます."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "noenu (generic function with 1 method)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function enu()\n",
    "    for (i, j) in enumerate(randn(10000000))\n",
    "        i + j\n",
    "    end\n",
    "end\n",
    "\n",
    "function noenu()\n",
    "    c :: Int = 1\n",
    "    for j in randn(10000000)\n",
    "        c + j\n",
    "        c += 1\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.167198 seconds (5 allocations: 76.294 MB, 3.80% gc time)\n",
      "  0.137401 seconds (3 allocations: 76.294 MB, 6.48% gc time)\n",
      "  0.094964 seconds (3 allocations: 76.294 MB, 8.64% gc time)\n",
      "  0.085530 seconds (3 allocations: 76.294 MB, 8.29% gc time)\n",
      "  0.093725 seconds (3 allocations: 76.294 MB, 9.21% gc time)\n",
      "  0.093181 seconds (3 allocations: 76.294 MB, 8.48% gc time)\n",
      "  0.099892 seconds (3 allocations: 76.294 MB, 7.97% gc time)\n",
      "  0.111176 seconds (3 allocations: 76.294 MB, 6.37% gc time)\n",
      "  0.094983 seconds (3 allocations: 76.294 MB, 7.49% gc time)\n",
      "  0.086224 seconds (3 allocations: 76.294 MB, 8.46% gc time)\n",
      "  0.096930 seconds (3 allocations: 76.294 MB, 8.14% gc time)\n",
      "  0.084723 seconds (3 allocations: 76.294 MB, 8.33% gc time)\n",
      "  0.096486 seconds (3 allocations: 76.294 MB, 9.24% gc time)\n",
      "  0.092817 seconds (3 allocations: 76.294 MB, 8.66% gc time)\n",
      "  0.100877 seconds (3 allocations: 76.294 MB, 7.73% gc time)\n",
      "  0.101852 seconds (3 allocations: 76.294 MB, 7.67% gc time)\n",
      "  0.100883 seconds (3 allocations: 76.294 MB, 8.22% gc time)\n",
      "  0.106081 seconds (3 allocations: 76.294 MB, 7.48% gc time)\n",
      "  0.095241 seconds (3 allocations: 76.294 MB, 8.88% gc time)\n",
      "  0.085609 seconds (3 allocations: 76.294 MB, 8.70% gc time)\n",
      "  0.094668 seconds (3 allocations: 76.294 MB, 9.43% gc time)\n",
      "  0.096187 seconds (3 allocations: 76.294 MB, 8.15% gc time)\n",
      "  0.095109 seconds (3 allocations: 76.294 MB, 8.12% gc time)\n",
      "  0.092209 seconds (3 allocations: 76.294 MB, 9.45% gc time)\n",
      "  0.103816 seconds (3 allocations: 76.294 MB, 8.28% gc time)\n",
      "  0.095587 seconds (3 allocations: 76.294 MB, 7.82% gc time)\n",
      "  0.083891 seconds (3 allocations: 76.294 MB, 8.64% gc time)\n",
      "  0.098340 seconds (3 allocations: 76.294 MB, 8.01% gc time)\n",
      "  0.105465 seconds (3 allocations: 76.294 MB, 8.63% gc time)\n",
      "  0.097537 seconds (3 allocations: 76.294 MB, 8.74% gc time)\n",
      "  0.091972 seconds (3 allocations: 76.294 MB, 8.27% gc time)\n",
      "  0.102637 seconds (3 allocations: 76.294 MB, 8.07% gc time)\n",
      "  0.100160 seconds (3 allocations: 76.294 MB, 8.84% gc time)\n",
      "  0.093555 seconds (3 allocations: 76.294 MB, 7.99% gc time)\n",
      "  0.093715 seconds (3 allocations: 76.294 MB, 7.79% gc time)\n",
      "  0.098847 seconds (3 allocations: 76.294 MB, 8.85% gc time)\n",
      "  0.095440 seconds (3 allocations: 76.294 MB, 8.68% gc time)\n",
      "  0.094865 seconds (3 allocations: 76.294 MB, 8.66% gc time)\n",
      "  0.127038 seconds (3 allocations: 76.294 MB, 6.91% gc time)\n",
      "  0.121834 seconds (3 allocations: 76.294 MB, 6.29% gc time)\n",
      "  0.101561 seconds (3 allocations: 76.294 MB, 7.80% gc time)\n",
      "  0.093185 seconds (3 allocations: 76.294 MB, 7.66% gc time)\n",
      "  0.087617 seconds (3 allocations: 76.294 MB, 8.28% gc time)\n",
      "  0.091587 seconds (3 allocations: 76.294 MB, 7.74% gc time)\n",
      "  0.089649 seconds (3 allocations: 76.294 MB, 8.26% gc time)\n",
      "  0.107887 seconds (3 allocations: 76.294 MB, 7.70% gc time)\n",
      "  0.098736 seconds (3 allocations: 76.294 MB, 8.03% gc time)\n",
      "  0.091489 seconds (3 allocations: 76.294 MB, 8.07% gc time)\n",
      "  0.106101 seconds (3 allocations: 76.294 MB, 8.08% gc time)\n",
      "  0.099142 seconds (3 allocations: 76.294 MB, 7.99% gc time)\n",
      "  0.098876 seconds (3 allocations: 76.294 MB, 7.57% gc time)\n",
      "  0.096953 seconds (3 allocations: 76.294 MB, 8.94% gc time)\n",
      "  0.094164 seconds (3 allocations: 76.294 MB, 8.98% gc time)\n",
      "  0.095573 seconds (3 allocations: 76.294 MB, 9.20% gc time)\n",
      "  0.094691 seconds (3 allocations: 76.294 MB, 8.43% gc time)\n",
      "  0.106655 seconds (3 allocations: 76.294 MB, 8.39% gc time)\n",
      "  0.106039 seconds (3 allocations: 76.294 MB, 8.24% gc time)\n",
      "  0.109750 seconds (3 allocations: 76.294 MB, 8.70% gc time)\n",
      "  0.101317 seconds (3 allocations: 76.294 MB, 7.82% gc time)\n",
      "  0.101220 seconds (3 allocations: 76.294 MB, 8.68% gc time)\n",
      "  0.101049 seconds (3 allocations: 76.294 MB, 7.70% gc time)\n",
      "  0.099545 seconds (3 allocations: 76.294 MB, 8.23% gc time)\n",
      "  0.094794 seconds (3 allocations: 76.294 MB, 8.16% gc time)\n",
      "  0.089064 seconds (3 allocations: 76.294 MB, 8.99% gc time)\n",
      "  0.105141 seconds (3 allocations: 76.294 MB, 7.25% gc time)\n",
      "  0.104650 seconds (3 allocations: 76.294 MB, 8.36% gc time)\n",
      "  0.100310 seconds (3 allocations: 76.294 MB, 7.88% gc time)\n",
      "  0.106429 seconds (3 allocations: 76.294 MB, 7.67% gc time)\n",
      "  0.100348 seconds (3 allocations: 76.294 MB, 8.95% gc time)\n",
      "  0.105576 seconds (3 allocations: 76.294 MB, 8.02% gc time)\n",
      "  0.105155 seconds (3 allocations: 76.294 MB, 8.08% gc time)\n",
      "  0.126339 seconds (3 allocations: 76.294 MB, 7.04% gc time)\n",
      "  0.132549 seconds (3 allocations: 76.294 MB, 7.82% gc time)\n",
      "  0.116282 seconds (3 allocations: 76.294 MB, 7.24% gc time)\n",
      "  0.098279 seconds (3 allocations: 76.294 MB, 8.62% gc time)\n",
      "  0.094495 seconds (3 allocations: 76.294 MB, 8.02% gc time)\n",
      "  0.095569 seconds (3 allocations: 76.294 MB, 8.92% gc time)\n",
      "  0.113337 seconds (3 allocations: 76.294 MB, 7.99% gc time)\n",
      "  0.097303 seconds (3 allocations: 76.294 MB, 7.78% gc time)\n",
      "  0.084931 seconds (3 allocations: 76.294 MB, 8.82% gc time)\n",
      "  0.098834 seconds (3 allocations: 76.294 MB, 7.99% gc time)\n",
      "  0.091671 seconds (3 allocations: 76.294 MB, 8.73% gc time)\n",
      "  0.098296 seconds (3 allocations: 76.294 MB, 9.07% gc time)\n",
      "  0.092456 seconds (3 allocations: 76.294 MB, 8.59% gc time)\n",
      "  0.097847 seconds (3 allocations: 76.294 MB, 7.67% gc time)\n",
      "  0.093101 seconds (3 allocations: 76.294 MB, 8.04% gc time)\n",
      "  0.105560 seconds (3 allocations: 76.294 MB, 8.57% gc time)\n",
      "  0.117957 seconds (3 allocations: 76.294 MB, 6.57% gc time)\n",
      "  0.095713 seconds (3 allocations: 76.294 MB, 8.30% gc time)\n",
      "  0.092020 seconds (3 allocations: 76.294 MB, 7.81% gc time)\n",
      "  0.097419 seconds (3 allocations: 76.294 MB, 7.72% gc time)\n",
      "  0.087838 seconds (3 allocations: 76.294 MB, 8.64% gc time)\n",
      "  0.099408 seconds (3 allocations: 76.294 MB, 7.12% gc time)\n",
      "  0.092004 seconds (3 allocations: 76.294 MB, 7.77% gc time)\n",
      "  0.083295 seconds (3 allocations: 76.294 MB, 8.63% gc time)\n",
      "  0.101462 seconds (3 allocations: 76.294 MB, 7.12% gc time)\n",
      "  0.100868 seconds (3 allocations: 76.294 MB, 8.45% gc time)\n",
      "  0.110701 seconds (3 allocations: 76.294 MB, 6.44% gc time)\n",
      "  0.109719 seconds (3 allocations: 76.294 MB, 7.37% gc time)\n",
      "  0.106502 seconds (3 allocations: 76.294 MB, 7.72% gc time)\n",
      "  0.000010 seconds (66 allocations: 35.594 KB)\n",
      "  0.100181 seconds (2 allocations: 76.294 MB)\n",
      "  0.093043 seconds (2 allocations: 76.294 MB)\n",
      "  0.093525 seconds (2 allocations: 76.294 MB)\n",
      "  0.086604 seconds (2 allocations: 76.294 MB)\n",
      "  0.076211 seconds (2 allocations: 76.294 MB)\n",
      "  0.088520 seconds (2 allocations: 76.294 MB)\n",
      "  0.082384 seconds (2 allocations: 76.294 MB)\n",
      "  0.088881 seconds (2 allocations: 76.294 MB)\n",
      "  0.076502 seconds (2 allocations: 76.294 MB)\n",
      "  0.089691 seconds (2 allocations: 76.294 MB)\n",
      "  0.078698 seconds (2 allocations: 76.294 MB)\n",
      "  0.095124 seconds (2 allocations: 76.294 MB)\n",
      "  0.089579 seconds (2 allocations: 76.294 MB)\n",
      "  0.076285 seconds (2 allocations: 76.294 MB)\n",
      "  0.089481 seconds (2 allocations: 76.294 MB)\n",
      "  0.076313 seconds (2 allocations: 76.294 MB)\n",
      "  0.098841 seconds (2 allocations: 76.294 MB)\n",
      "  0.104256 seconds (2 allocations: 76.294 MB)\n",
      "  0.092785 seconds (2 allocations: 76.294 MB)\n",
      "  0.091698 seconds (2 allocations: 76.294 MB)\n",
      "  0.093382 seconds (2 allocations: 76.294 MB)\n",
      "  0.092864 seconds (2 allocations: 76.294 MB)\n",
      "  0.092272 seconds (2 allocations: 76.294 MB)\n",
      "  0.096945 seconds (2 allocations: 76.294 MB)\n",
      "  0.089382 seconds (2 allocations: 76.294 MB)\n",
      "  0.085519 seconds (2 allocations: 76.294 MB)\n",
      "  0.086801 seconds (2 allocations: 76.294 MB)\n",
      "  0.085951 seconds (2 allocations: 76.294 MB)\n",
      "  0.085906 seconds (2 allocations: 76.294 MB)\n",
      "  0.080510 seconds (2 allocations: 76.294 MB)\n",
      "  0.092428 seconds (2 allocations: 76.294 MB)\n",
      "  0.086367 seconds (2 allocations: 76.294 MB)\n",
      "  0.077330 seconds (2 allocations: 76.294 MB)\n",
      "  0.088862 seconds (2 allocations: 76.294 MB)\n",
      "  0.080822 seconds (2 allocations: 76.294 MB)\n",
      "  0.084744 seconds (2 allocations: 76.294 MB)\n",
      "  0.078448 seconds (2 allocations: 76.294 MB)\n",
      "  0.087923 seconds (2 allocations: 76.294 MB)\n",
      "  0.085362 seconds (2 allocations: 76.294 MB)\n",
      "  0.086068 seconds (2 allocations: 76.294 MB)\n",
      "  0.083691 seconds (2 allocations: 76.294 MB)\n",
      "  0.101103 seconds (2 allocations: 76.294 MB)\n",
      "  0.103427 seconds (2 allocations: 76.294 MB)\n",
      "  0.107509 seconds (2 allocations: 76.294 MB)\n",
      "  0.086434 seconds (2 allocations: 76.294 MB)\n",
      "  0.082921 seconds (2 allocations: 76.294 MB)\n",
      "  0.090709 seconds (2 allocations: 76.294 MB)\n",
      "  0.088764 seconds (2 allocations: 76.294 MB)\n",
      "  0.085019 seconds (2 allocations: 76.294 MB)\n",
      "  0.092333 seconds (2 allocations: 76.294 MB)\n",
      "  0.093482 seconds (2 allocations: 76.294 MB)\n",
      "  0.094873 seconds (2 allocations: 76.294 MB)\n",
      "  0.094542 seconds (2 allocations: 76.294 MB)\n",
      "  0.097255 seconds (2 allocations: 76.294 MB)\n",
      "  0.085828 seconds (2 allocations: 76.294 MB)\n",
      "  0.091740 seconds (2 allocations: 76.294 MB)\n",
      "  0.092887 seconds (2 allocations: 76.294 MB)\n",
      "  0.087477 seconds (2 allocations: 76.294 MB)\n",
      "  0.084804 seconds (2 allocations: 76.294 MB)\n",
      "  0.091296 seconds (2 allocations: 76.294 MB)\n",
      "  0.092048 seconds (2 allocations: 76.294 MB)\n",
      "  0.120658 seconds (2 allocations: 76.294 MB)\n",
      "  0.109678 seconds (2 allocations: 76.294 MB)\n",
      "  0.098632 seconds (2 allocations: 76.294 MB)\n",
      "  0.098096 seconds (2 allocations: 76.294 MB)\n",
      "  0.086975 seconds (2 allocations: 76.294 MB)\n",
      "  0.085568 seconds (2 allocations: 76.294 MB)\n",
      "  0.101370 seconds (2 allocations: 76.294 MB)\n",
      "  0.092510 seconds (2 allocations: 76.294 MB)\n",
      "  0.097316 seconds (2 allocations: 76.294 MB)\n",
      "  0.107693 seconds (2 allocations: 76.294 MB)\n",
      "  0.097088 seconds (2 allocations: 76.294 MB)\n",
      "  0.089996 seconds (2 allocations: 76.294 MB)\n",
      "  0.087665 seconds (2 allocations: 76.294 MB)\n",
      "  0.081469 seconds (2 allocations: 76.294 MB)\n",
      "  0.085978 seconds (2 allocations: 76.294 MB)\n",
      "  0.080920 seconds (2 allocations: 76.294 MB)\n",
      "  0.094150 seconds (2 allocations: 76.294 MB)\n",
      "  0.088175 seconds (2 allocations: 76.294 MB)\n",
      "  0.076120 seconds (2 allocations: 76.294 MB)\n",
      "  0.083732 seconds (2 allocations: 76.294 MB)\n",
      "  0.079208 seconds (2 allocations: 76.294 MB)\n",
      "  0.089776 seconds (2 allocations: 76.294 MB)\n",
      "  0.082563 seconds (2 allocations: 76.294 MB)\n",
      "  0.085032 seconds (2 allocations: 76.294 MB)\n",
      "  0.081346 seconds (2 allocations: 76.294 MB)\n",
      "  0.089990 seconds (2 allocations: 76.294 MB)\n",
      "  0.085733 seconds (2 allocations: 76.294 MB)\n",
      "  0.096164 seconds (2 allocations: 76.294 MB)\n",
      "  0.084092 seconds (2 allocations: 76.294 MB)\n",
      "  0.085948 seconds (2 allocations: 76.294 MB)\n",
      "  0.087956 seconds (2 allocations: 76.294 MB)\n",
      "  0.080338 seconds (2 allocations: 76.294 MB)\n",
      "  0.086184 seconds (2 allocations: 76.294 MB)\n",
      "  0.082534 seconds (2 allocations: 76.294 MB)\n",
      "  0.093430 seconds (2 allocations: 76.294 MB)\n",
      "  0.089243 seconds (2 allocations: 76.294 MB)\n",
      "  0.084519 seconds (2 allocations: 76.294 MB)\n",
      "  0.089227 seconds (2 allocations: 76.294 MB)\n",
      "  0.091484 seconds (2 allocations: 76.294 MB)\n"
     ]
    }
   ],
   "source": [
    "for i in 1:100;@time enu();end\n",
    "@time 2\n",
    "for i in 1:100; @time noenu();end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "enumerateを使うバージョンは少し遅そうです."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31e3084a8>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x31e9d5828>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using PyPlot\n",
    "enumeratess = map(x->parse(Float64, x), get_seconds(\"0.167198 seconds (5 allocations: 76.294 MB, 3.80% gc time)\n",
    "  0.137401 seconds (3 allocations: 76.294 MB, 6.48% gc time)\n",
    "  0.094964 seconds (3 allocations: 76.294 MB, 8.64% gc time)\n",
    "  0.085530 seconds (3 allocations: 76.294 MB, 8.29% gc time)\n",
    "  0.093725 seconds (3 allocations: 76.294 MB, 9.21% gc time)\n",
    "  0.093181 seconds (3 allocations: 76.294 MB, 8.48% gc time)\n",
    "  0.099892 seconds (3 allocations: 76.294 MB, 7.97% gc time)\n",
    "  0.111176 seconds (3 allocations: 76.294 MB, 6.37% gc time)\n",
    "  0.094983 seconds (3 allocations: 76.294 MB, 7.49% gc time)\n",
    "  0.086224 seconds (3 allocations: 76.294 MB, 8.46% gc time)\n",
    "  0.096930 seconds (3 allocations: 76.294 MB, 8.14% gc time)\n",
    "  0.084723 seconds (3 allocations: 76.294 MB, 8.33% gc time)\n",
    "  0.096486 seconds (3 allocations: 76.294 MB, 9.24% gc time)\n",
    "  0.092817 seconds (3 allocations: 76.294 MB, 8.66% gc time)\n",
    "  0.100877 seconds (3 allocations: 76.294 MB, 7.73% gc time)\n",
    "  0.101852 seconds (3 allocations: 76.294 MB, 7.67% gc time)\n",
    "  0.100883 seconds (3 allocations: 76.294 MB, 8.22% gc time)\n",
    "  0.106081 seconds (3 allocations: 76.294 MB, 7.48% gc time)\n",
    "  0.095241 seconds (3 allocations: 76.294 MB, 8.88% gc time)\n",
    "  0.085609 seconds (3 allocations: 76.294 MB, 8.70% gc time)\n",
    "  0.094668 seconds (3 allocations: 76.294 MB, 9.43% gc time)\n",
    "  0.096187 seconds (3 allocations: 76.294 MB, 8.15% gc time)\n",
    "  0.095109 seconds (3 allocations: 76.294 MB, 8.12% gc time)\n",
    "  0.092209 seconds (3 allocations: 76.294 MB, 9.45% gc time)\n",
    "  0.103816 seconds (3 allocations: 76.294 MB, 8.28% gc time)\n",
    "  0.095587 seconds (3 allocations: 76.294 MB, 7.82% gc time)\n",
    "  0.083891 seconds (3 allocations: 76.294 MB, 8.64% gc time)\n",
    "  0.098340 seconds (3 allocations: 76.294 MB, 8.01% gc time)\n",
    "  0.105465 seconds (3 allocations: 76.294 MB, 8.63% gc time)\n",
    "  0.097537 seconds (3 allocations: 76.294 MB, 8.74% gc time)\n",
    "  0.091972 seconds (3 allocations: 76.294 MB, 8.27% gc time)\n",
    "  0.102637 seconds (3 allocations: 76.294 MB, 8.07% gc time)\n",
    "  0.100160 seconds (3 allocations: 76.294 MB, 8.84% gc time)\n",
    "  0.093555 seconds (3 allocations: 76.294 MB, 7.99% gc time)\n",
    "  0.093715 seconds (3 allocations: 76.294 MB, 7.79% gc time)\n",
    "  0.098847 seconds (3 allocations: 76.294 MB, 8.85% gc time)\n",
    "  0.095440 seconds (3 allocations: 76.294 MB, 8.68% gc time)\n",
    "  0.094865 seconds (3 allocations: 76.294 MB, 8.66% gc time)\n",
    "  0.127038 seconds (3 allocations: 76.294 MB, 6.91% gc time)\n",
    "  0.121834 seconds (3 allocations: 76.294 MB, 6.29% gc time)\n",
    "  0.101561 seconds (3 allocations: 76.294 MB, 7.80% gc time)\n",
    "  0.093185 seconds (3 allocations: 76.294 MB, 7.66% gc time)\n",
    "  0.087617 seconds (3 allocations: 76.294 MB, 8.28% gc time)\n",
    "  0.091587 seconds (3 allocations: 76.294 MB, 7.74% gc time)\n",
    "  0.089649 seconds (3 allocations: 76.294 MB, 8.26% gc time)\n",
    "  0.107887 seconds (3 allocations: 76.294 MB, 7.70% gc time)\n",
    "  0.098736 seconds (3 allocations: 76.294 MB, 8.03% gc time)\n",
    "  0.091489 seconds (3 allocations: 76.294 MB, 8.07% gc time)\n",
    "  0.106101 seconds (3 allocations: 76.294 MB, 8.08% gc time)\n",
    "  0.099142 seconds (3 allocations: 76.294 MB, 7.99% gc time)\n",
    "  0.098876 seconds (3 allocations: 76.294 MB, 7.57% gc time)\n",
    "  0.096953 seconds (3 allocations: 76.294 MB, 8.94% gc time)\n",
    "  0.094164 seconds (3 allocations: 76.294 MB, 8.98% gc time)\n",
    "  0.095573 seconds (3 allocations: 76.294 MB, 9.20% gc time)\n",
    "  0.094691 seconds (3 allocations: 76.294 MB, 8.43% gc time)\n",
    "  0.106655 seconds (3 allocations: 76.294 MB, 8.39% gc time)\n",
    "  0.106039 seconds (3 allocations: 76.294 MB, 8.24% gc time)\n",
    "  0.109750 seconds (3 allocations: 76.294 MB, 8.70% gc time)\n",
    "  0.101317 seconds (3 allocations: 76.294 MB, 7.82% gc time)\n",
    "  0.101220 seconds (3 allocations: 76.294 MB, 8.68% gc time)\n",
    "  0.101049 seconds (3 allocations: 76.294 MB, 7.70% gc time)\n",
    "  0.099545 seconds (3 allocations: 76.294 MB, 8.23% gc time)\n",
    "  0.094794 seconds (3 allocations: 76.294 MB, 8.16% gc time)\n",
    "  0.089064 seconds (3 allocations: 76.294 MB, 8.99% gc time)\n",
    "  0.105141 seconds (3 allocations: 76.294 MB, 7.25% gc time)\n",
    "  0.104650 seconds (3 allocations: 76.294 MB, 8.36% gc time)\n",
    "  0.100310 seconds (3 allocations: 76.294 MB, 7.88% gc time)\n",
    "  0.106429 seconds (3 allocations: 76.294 MB, 7.67% gc time)\n",
    "  0.100348 seconds (3 allocations: 76.294 MB, 8.95% gc time)\n",
    "  0.105576 seconds (3 allocations: 76.294 MB, 8.02% gc time)\n",
    "  0.105155 seconds (3 allocations: 76.294 MB, 8.08% gc time)\n",
    "  0.126339 seconds (3 allocations: 76.294 MB, 7.04% gc time)\n",
    "  0.132549 seconds (3 allocations: 76.294 MB, 7.82% gc time)\n",
    "  0.116282 seconds (3 allocations: 76.294 MB, 7.24% gc time)\n",
    "  0.098279 seconds (3 allocations: 76.294 MB, 8.62% gc time)\n",
    "  0.094495 seconds (3 allocations: 76.294 MB, 8.02% gc time)\n",
    "  0.095569 seconds (3 allocations: 76.294 MB, 8.92% gc time)\n",
    "  0.113337 seconds (3 allocations: 76.294 MB, 7.99% gc time)\n",
    "  0.097303 seconds (3 allocations: 76.294 MB, 7.78% gc time)\n",
    "  0.084931 seconds (3 allocations: 76.294 MB, 8.82% gc time)\n",
    "  0.098834 seconds (3 allocations: 76.294 MB, 7.99% gc time)\n",
    "  0.091671 seconds (3 allocations: 76.294 MB, 8.73% gc time)\n",
    "  0.098296 seconds (3 allocations: 76.294 MB, 9.07% gc time)\n",
    "  0.092456 seconds (3 allocations: 76.294 MB, 8.59% gc time)\n",
    "  0.097847 seconds (3 allocations: 76.294 MB, 7.67% gc time)\n",
    "  0.093101 seconds (3 allocations: 76.294 MB, 8.04% gc time)\n",
    "  0.105560 seconds (3 allocations: 76.294 MB, 8.57% gc time)\n",
    "  0.117957 seconds (3 allocations: 76.294 MB, 6.57% gc time)\n",
    "  0.095713 seconds (3 allocations: 76.294 MB, 8.30% gc time)\n",
    "  0.092020 seconds (3 allocations: 76.294 MB, 7.81% gc time)\n",
    "  0.097419 seconds (3 allocations: 76.294 MB, 7.72% gc time)\n",
    "  0.087838 seconds (3 allocations: 76.294 MB, 8.64% gc time)\n",
    "  0.099408 seconds (3 allocations: 76.294 MB, 7.12% gc time)\n",
    "  0.092004 seconds (3 allocations: 76.294 MB, 7.77% gc time)\n",
    "  0.083295 seconds (3 allocations: 76.294 MB, 8.63% gc time)\n",
    "  0.101462 seconds (3 allocations: 76.294 MB, 7.12% gc time)\n",
    "  0.100868 seconds (3 allocations: 76.294 MB, 8.45% gc time)\n",
    "  0.110701 seconds (3 allocations: 76.294 MB, 6.44% gc time)\n",
    "  0.109719 seconds (3 allocations: 76.294 MB, 7.37% gc time)\n",
    "  0.106502 seconds (3 allocations: 76.294 MB, 7.72% gc time)\"))\n",
    "nonenumeratess = map(x -> parse(Float64, x), get_seconds(\"0.100181 seconds (2 allocations: 76.294 MB)\n",
    "  0.093043 seconds (2 allocations: 76.294 MB)\n",
    "  0.093525 seconds (2 allocations: 76.294 MB)\n",
    "  0.086604 seconds (2 allocations: 76.294 MB)\n",
    "  0.076211 seconds (2 allocations: 76.294 MB)\n",
    "  0.088520 seconds (2 allocations: 76.294 MB)\n",
    "  0.082384 seconds (2 allocations: 76.294 MB)\n",
    "  0.088881 seconds (2 allocations: 76.294 MB)\n",
    "  0.076502 seconds (2 allocations: 76.294 MB)\n",
    "  0.089691 seconds (2 allocations: 76.294 MB)\n",
    "  0.078698 seconds (2 allocations: 76.294 MB)\n",
    "  0.095124 seconds (2 allocations: 76.294 MB)\n",
    "  0.089579 seconds (2 allocations: 76.294 MB)\n",
    "  0.076285 seconds (2 allocations: 76.294 MB)\n",
    "  0.089481 seconds (2 allocations: 76.294 MB)\n",
    "  0.076313 seconds (2 allocations: 76.294 MB)\n",
    "  0.098841 seconds (2 allocations: 76.294 MB)\n",
    "  0.104256 seconds (2 allocations: 76.294 MB)\n",
    "  0.092785 seconds (2 allocations: 76.294 MB)\n",
    "  0.091698 seconds (2 allocations: 76.294 MB)\n",
    "  0.093382 seconds (2 allocations: 76.294 MB)\n",
    "  0.092864 seconds (2 allocations: 76.294 MB)\n",
    "  0.092272 seconds (2 allocations: 76.294 MB)\n",
    "  0.096945 seconds (2 allocations: 76.294 MB)\n",
    "  0.089382 seconds (2 allocations: 76.294 MB)\n",
    "  0.085519 seconds (2 allocations: 76.294 MB)\n",
    "  0.086801 seconds (2 allocations: 76.294 MB)\n",
    "  0.085951 seconds (2 allocations: 76.294 MB)\n",
    "  0.085906 seconds (2 allocations: 76.294 MB)\n",
    "  0.080510 seconds (2 allocations: 76.294 MB)\n",
    "  0.092428 seconds (2 allocations: 76.294 MB)\n",
    "  0.086367 seconds (2 allocations: 76.294 MB)\n",
    "  0.077330 seconds (2 allocations: 76.294 MB)\n",
    "  0.088862 seconds (2 allocations: 76.294 MB)\n",
    "  0.080822 seconds (2 allocations: 76.294 MB)\n",
    "  0.084744 seconds (2 allocations: 76.294 MB)\n",
    "  0.078448 seconds (2 allocations: 76.294 MB)\n",
    "  0.087923 seconds (2 allocations: 76.294 MB)\n",
    "  0.085362 seconds (2 allocations: 76.294 MB)\n",
    "  0.086068 seconds (2 allocations: 76.294 MB)\n",
    "  0.083691 seconds (2 allocations: 76.294 MB)\n",
    "  0.101103 seconds (2 allocations: 76.294 MB)\n",
    "  0.103427 seconds (2 allocations: 76.294 MB)\n",
    "  0.107509 seconds (2 allocations: 76.294 MB)\n",
    "  0.086434 seconds (2 allocations: 76.294 MB)\n",
    "  0.082921 seconds (2 allocations: 76.294 MB)\n",
    "  0.090709 seconds (2 allocations: 76.294 MB)\n",
    "  0.088764 seconds (2 allocations: 76.294 MB)\n",
    "  0.085019 seconds (2 allocations: 76.294 MB)\n",
    "  0.092333 seconds (2 allocations: 76.294 MB)\n",
    "  0.093482 seconds (2 allocations: 76.294 MB)\n",
    "  0.094873 seconds (2 allocations: 76.294 MB)\n",
    "  0.094542 seconds (2 allocations: 76.294 MB)\n",
    "  0.097255 seconds (2 allocations: 76.294 MB)\n",
    "  0.085828 seconds (2 allocations: 76.294 MB)\n",
    "  0.091740 seconds (2 allocations: 76.294 MB)\n",
    "  0.092887 seconds (2 allocations: 76.294 MB)\n",
    "  0.087477 seconds (2 allocations: 76.294 MB)\n",
    "  0.084804 seconds (2 allocations: 76.294 MB)\n",
    "  0.091296 seconds (2 allocations: 76.294 MB)\n",
    "  0.092048 seconds (2 allocations: 76.294 MB)\n",
    "  0.120658 seconds (2 allocations: 76.294 MB)\n",
    "  0.109678 seconds (2 allocations: 76.294 MB)\n",
    "  0.098632 seconds (2 allocations: 76.294 MB)\n",
    "  0.098096 seconds (2 allocations: 76.294 MB)\n",
    "  0.086975 seconds (2 allocations: 76.294 MB)\n",
    "  0.085568 seconds (2 allocations: 76.294 MB)\n",
    "  0.101370 seconds (2 allocations: 76.294 MB)\n",
    "  0.092510 seconds (2 allocations: 76.294 MB)\n",
    "  0.097316 seconds (2 allocations: 76.294 MB)\n",
    "  0.107693 seconds (2 allocations: 76.294 MB)\n",
    "  0.097088 seconds (2 allocations: 76.294 MB)\n",
    "  0.089996 seconds (2 allocations: 76.294 MB)\n",
    "  0.087665 seconds (2 allocations: 76.294 MB)\n",
    "  0.081469 seconds (2 allocations: 76.294 MB)\n",
    "  0.085978 seconds (2 allocations: 76.294 MB)\n",
    "  0.080920 seconds (2 allocations: 76.294 MB)\n",
    "  0.094150 seconds (2 allocations: 76.294 MB)\n",
    "  0.088175 seconds (2 allocations: 76.294 MB)\n",
    "  0.076120 seconds (2 allocations: 76.294 MB)\n",
    "  0.083732 seconds (2 allocations: 76.294 MB)\n",
    "  0.079208 seconds (2 allocations: 76.294 MB)\n",
    "  0.089776 seconds (2 allocations: 76.294 MB)\n",
    "  0.082563 seconds (2 allocations: 76.294 MB)\n",
    "  0.085032 seconds (2 allocations: 76.294 MB)\n",
    "  0.081346 seconds (2 allocations: 76.294 MB)\n",
    "  0.089990 seconds (2 allocations: 76.294 MB)\n",
    "  0.085733 seconds (2 allocations: 76.294 MB)\n",
    "  0.096164 seconds (2 allocations: 76.294 MB)\n",
    "  0.084092 seconds (2 allocations: 76.294 MB)\n",
    "  0.085948 seconds (2 allocations: 76.294 MB)\n",
    "  0.087956 seconds (2 allocations: 76.294 MB)\n",
    "  0.080338 seconds (2 allocations: 76.294 MB)\n",
    "  0.086184 seconds (2 allocations: 76.294 MB)\n",
    "  0.082534 seconds (2 allocations: 76.294 MB)\n",
    "  0.093430 seconds (2 allocations: 76.294 MB)\n",
    "  0.089243 seconds (2 allocations: 76.294 MB)\n",
    "  0.084519 seconds (2 allocations: 76.294 MB)\n",
    "  0.089227 seconds (2 allocations: 76.294 MB)\n",
    "  0.091484 seconds (2 allocations: 76.294 MB)\"))\n",
    "plot(enumeratess, label=\"enumerate()\")\n",
    "plot(nonenumeratess, label =\"no enumerate()\")\n",
    "legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10024653000000003\n",
      "0.08943185999999997\n"
     ]
    }
   ],
   "source": [
    "println(mean(enumeratess)) # enumerate\n",
    "println(mean(nonenumeratess)) #non enumerate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "差がありそうですね. そこで検定をしてみます. (分散が等しいと仮定して帰無仮説: 両方の平均が等しい に対し両側t検定)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Precompiling module HypothesisTests...\n",
      "INFO: Recompiling stale cache file /Users/neon/.julia/lib/v0.4/Polynomials.ji for module Polynomials.\n",
      "INFO: Recompiling stale cache file /Users/neon/.julia/lib/v0.4/Calculus.ji for module Calculus.\n"
     ]
    }
   ],
   "source": [
    "using HypothesisTests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Two sample t-test (equal variance)\n",
       "----------------------------------\n",
       "Population details:\n",
       "    parameter of interest:   Mean difference\n",
       "    value under h_0:         0\n",
       "    point estimate:          0.010814670000000054\n",
       "    95% confidence interval: (0.008009160104718117,0.01362017989528199)\n",
       "\n",
       "Test summary:\n",
       "    outcome with 95% confidence: reject h_0\n",
       "    two-sided p-value:           1.1453797369173158e-12 (extremely significant)\n",
       "\n",
       "Details:\n",
       "    number of observations:   [100,100]\n",
       "    t-statistic:              7.601726264768331\n",
       "    degrees of freedom:       198\n",
       "    empirical standard error: 0.0014226597516570324\n"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EqualVarianceTTest(enumeratess, nonenumeratess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "どうやらenumerateの速度がカウンターを使う場合と同じというのは棄却されたようです."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の行と列どちらが最適化されてるかについても調べてみます."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "accessbycol (generic function with 1 method)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function accessbyrow(M, nrows, ncols)\n",
    "    for c in 1:nrows\n",
    "        for e in M[c, :]\n",
    "            e += 1\n",
    "        end\n",
    "    end\n",
    "end\n",
    "\n",
    "function accessbycol(M, nrows, ncols)\n",
    "    for c in 1:ncols\n",
    "        for e in M[:, c]\n",
    "            e += 1\n",
    "        end\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.011085 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.002932 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.013357 seconds (3.98 k allocations: 7.766 MB, 18.84% gc time)\n",
      "  0.003136 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.013264 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.009050 seconds (3.98 k allocations: 7.751 MB, 36.69% gc time)\n",
      "  0.021915 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.003747 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.018888 seconds (3.98 k allocations: 7.766 MB, 16.53% gc time)\n",
      "  0.002876 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.017344 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.006816 seconds (3.98 k allocations: 7.751 MB, 49.66% gc time)\n",
      "  0.021415 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.005029 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.022290 seconds (3.98 k allocations: 7.766 MB, 21.16% gc time)\n",
      "  0.003039 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.015354 seconds (3.98 k allocations: 7.766 MB, 19.79% gc time)\n",
      "  0.003054 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.010739 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.005081 seconds (3.98 k allocations: 7.751 MB, 46.68% gc time)\n",
      "  0.011655 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.003272 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.026492 seconds (3.98 k allocations: 7.766 MB, 25.97% gc time)\n",
      "  0.003186 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.012102 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.004561 seconds (3.98 k allocations: 7.751 MB, 49.06% gc time)\n",
      "  0.011768 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.003375 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.018634 seconds (3.98 k allocations: 7.766 MB, 16.62% gc time)\n",
      "  0.004808 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.013937 seconds (3.98 k allocations: 7.766 MB, 16.49% gc time)\n",
      "  0.003166 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.012127 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.005617 seconds (3.98 k allocations: 7.751 MB, 52.71% gc time)\n",
      "  0.015503 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.002987 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.015352 seconds (3.98 k allocations: 7.766 MB, 19.67% gc time)\n",
      "  0.002370 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.010550 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.004034 seconds (3.98 k allocations: 7.751 MB, 46.81% gc time)\n",
      "  0.011186 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.004250 seconds (3.98 k allocations: 7.751 MB, 44.13% gc time)\n",
      "  0.011084 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.007388 seconds (3.98 k allocations: 7.751 MB, 39.72% gc time)\n",
      "  0.016335 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.002385 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.012555 seconds (3.98 k allocations: 7.766 MB, 11.49% gc time)\n",
      "  0.002182 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.011406 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.004352 seconds (3.98 k allocations: 7.751 MB, 48.66% gc time)\n",
      "  0.011143 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.002799 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.014171 seconds (3.98 k allocations: 7.766 MB, 16.97% gc time)\n",
      "  0.002394 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.012421 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.003992 seconds (3.98 k allocations: 7.751 MB, 37.39% gc time)\n",
      "  0.015291 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.008095 seconds (3.98 k allocations: 7.751 MB, 50.98% gc time)\n",
      "  0.010390 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.003190 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.013779 seconds (3.98 k allocations: 7.766 MB, 21.01% gc time)\n",
      "  0.002708 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.010881 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.005634 seconds (3.98 k allocations: 7.751 MB, 50.61% gc time)\n",
      "  0.010563 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.002560 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.013010 seconds (3.98 k allocations: 7.766 MB, 17.67% gc time)\n",
      "  0.003136 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.027117 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.005320 seconds (3.98 k allocations: 7.751 MB, 54.46% gc time)\n",
      "  0.011453 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.003000 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.012691 seconds (3.98 k allocations: 7.766 MB, 17.85% gc time)\n",
      "  0.002749 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.012887 seconds (3.98 k allocations: 7.766 MB, 16.36% gc time)\n",
      "  0.002448 seconds (3.98 k allocations: 7.751 MB)\n",
      "  0.011816 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.005378 seconds (3.98 k allocations: 7.751 MB, 41.76% gc time)\n",
      "  0.010930 seconds (3.98 k allocations: 7.766 MB)\n",
      "  0.002924 seconds (3.98 k allocations: 7.751 MB)\n"
     ]
    }
   ],
   "source": [
    "nrows, ncols = 1000, 1000\n",
    "M = randn(nrows, ncols)\n",
    "for i in 1:20\n",
    "    @time accessbyrow(M, nrows, ncols)\n",
    "    @time accessbycol(M, nrows, ncols)\n",
    "end\n",
    "M = randn(nrows, ncols)\n",
    "for i in 1:20\n",
    "    @time accessbyrow(M, nrows, ncols)\n",
    "    @time accessbycol(M, nrows, ncols)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31f9f8fd0>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x31fbd0a20>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss = map(x -> parse(Float64, x), get_seconds(\"0.011085 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.002932 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.013357 seconds (3.98 k allocations: 7.766 MB, 18.84% gc time)\n",
    "  0.003136 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.013264 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.009050 seconds (3.98 k allocations: 7.751 MB, 36.69% gc time)\n",
    "  0.021915 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.003747 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.018888 seconds (3.98 k allocations: 7.766 MB, 16.53% gc time)\n",
    "  0.002876 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.017344 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.006816 seconds (3.98 k allocations: 7.751 MB, 49.66% gc time)\n",
    "  0.021415 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.005029 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.022290 seconds (3.98 k allocations: 7.766 MB, 21.16% gc time)\n",
    "  0.003039 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.015354 seconds (3.98 k allocations: 7.766 MB, 19.79% gc time)\n",
    "  0.003054 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.010739 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.005081 seconds (3.98 k allocations: 7.751 MB, 46.68% gc time)\n",
    "  0.011655 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.003272 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.026492 seconds (3.98 k allocations: 7.766 MB, 25.97% gc time)\n",
    "  0.003186 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.012102 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.004561 seconds (3.98 k allocations: 7.751 MB, 49.06% gc time)\n",
    "  0.011768 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.003375 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.018634 seconds (3.98 k allocations: 7.766 MB, 16.62% gc time)\n",
    "  0.004808 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.013937 seconds (3.98 k allocations: 7.766 MB, 16.49% gc time)\n",
    "  0.003166 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.012127 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.005617 seconds (3.98 k allocations: 7.751 MB, 52.71% gc time)\n",
    "  0.015503 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.002987 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.015352 seconds (3.98 k allocations: 7.766 MB, 19.67% gc time)\n",
    "  0.002370 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.010550 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.004034 seconds (3.98 k allocations: 7.751 MB, 46.81% gc time)\n",
    "  0.011186 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.004250 seconds (3.98 k allocations: 7.751 MB, 44.13% gc time)\n",
    "  0.011084 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.007388 seconds (3.98 k allocations: 7.751 MB, 39.72% gc time)\n",
    "  0.016335 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.002385 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.012555 seconds (3.98 k allocations: 7.766 MB, 11.49% gc time)\n",
    "  0.002182 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.011406 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.004352 seconds (3.98 k allocations: 7.751 MB, 48.66% gc time)\n",
    "  0.011143 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.002799 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.014171 seconds (3.98 k allocations: 7.766 MB, 16.97% gc time)\n",
    "  0.002394 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.012421 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.003992 seconds (3.98 k allocations: 7.751 MB, 37.39% gc time)\n",
    "  0.015291 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.008095 seconds (3.98 k allocations: 7.751 MB, 50.98% gc time)\n",
    "  0.010390 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.003190 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.013779 seconds (3.98 k allocations: 7.766 MB, 21.01% gc time)\n",
    "  0.002708 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.010881 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.005634 seconds (3.98 k allocations: 7.751 MB, 50.61% gc time)\n",
    "  0.010563 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.002560 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.013010 seconds (3.98 k allocations: 7.766 MB, 17.67% gc time)\n",
    "  0.003136 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.027117 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.005320 seconds (3.98 k allocations: 7.751 MB, 54.46% gc time)\n",
    "  0.011453 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.003000 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.012691 seconds (3.98 k allocations: 7.766 MB, 17.85% gc time)\n",
    "  0.002749 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.012887 seconds (3.98 k allocations: 7.766 MB, 16.36% gc time)\n",
    "  0.002448 seconds (3.98 k allocations: 7.751 MB)\n",
    "  0.011816 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.005378 seconds (3.98 k allocations: 7.751 MB, 41.76% gc time)\n",
    "  0.010930 seconds (3.98 k allocations: 7.766 MB)\n",
    "  0.002924 seconds (3.98 k allocations: 7.751 MB)\"))\n",
    "\n",
    "plot(ss[1:2:80], label=\"access by row\")\n",
    "plot(ss[2:2:80], label=\"access by column\")\n",
    "legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "列ごとにアクセスするほうが三倍くらい早いようです."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最後にもう一つ, 以下の２つの関数を比べます."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ncln (generic function with 2 methods)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function cln(V, l)#要素ごとにアクセスして演算\n",
    "    for e in V[:]\n",
    "        e += 1\n",
    "    end\n",
    "end\n",
    "\n",
    "function cln2(V, l)#要素全てにまとめて演算\n",
    "    V .+= 1\n",
    "end\n",
    "\n",
    "function ncln(V, l)#要素ごとに配列でアクセスして演算\n",
    "    for i in 1:l\n",
    "        V[i] += 1\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.000302 seconds (2 allocations: 390.688 KB)\n",
      "  0.000241 seconds (2 allocations: 390.688 KB)\n",
      "  0.000073 seconds\n",
      "  0.000226 seconds (2 allocations: 390.688 KB)\n",
      "  0.000187 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000262 seconds (2 allocations: 390.688 KB)\n",
      "  0.000181 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000244 seconds (2 allocations: 390.688 KB)\n",
      "  0.000227 seconds (2 allocations: 390.688 KB)\n",
      "  0.000130 seconds\n",
      "  0.000078 seconds (2 allocations: 390.688 KB)\n",
      "  0.000115 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000129 seconds (2 allocations: 390.688 KB)\n",
      "  0.000107 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000123 seconds (2 allocations: 390.688 KB)\n",
      "  0.000108 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000172 seconds (2 allocations: 390.688 KB)\n",
      "  0.000109 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000068 seconds (2 allocations: 390.688 KB)\n",
      "  0.000050 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000068 seconds (2 allocations: 390.688 KB)\n",
      "  0.000050 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000068 seconds (2 allocations: 390.688 KB)\n",
      "  0.000052 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000069 seconds (2 allocations: 390.688 KB)\n",
      "  0.000052 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000294 seconds (2 allocations: 390.688 KB)\n",
      "  0.000206 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000286 seconds (2 allocations: 390.688 KB)\n",
      "  0.000210 seconds (2 allocations: 390.688 KB)\n",
      "  0.000108 seconds\n",
      "  0.000271 seconds (2 allocations: 390.688 KB)\n",
      "  0.000210 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000242 seconds (2 allocations: 390.688 KB)\n",
      "  0.000195 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000265 seconds (2 allocations: 390.688 KB)\n",
      "  0.000194 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000265 seconds (2 allocations: 390.688 KB)\n",
      "  0.000174 seconds (2 allocations: 390.688 KB)\n",
      "  0.000070 seconds\n",
      "  0.000259 seconds (2 allocations: 390.688 KB)\n",
      "  0.000200 seconds (2 allocations: 390.688 KB)\n",
      "  0.000096 seconds\n",
      "  0.000232 seconds (2 allocations: 390.688 KB)\n",
      "  0.000221 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000297 seconds (2 allocations: 390.688 KB)\n",
      "  0.000261 seconds (2 allocations: 390.688 KB)\n",
      "  0.000077 seconds\n",
      "  0.000969 seconds (2 allocations: 390.688 KB)\n",
      "  0.000352 seconds (2 allocations: 390.688 KB)\n",
      "  0.000078 seconds\n",
      "  0.000412 seconds (2 allocations: 390.688 KB)\n",
      "  0.000342 seconds (2 allocations: 390.688 KB)\n",
      "  0.000078 seconds\n",
      "  0.000309 seconds (2 allocations: 390.688 KB)\n",
      "  0.000345 seconds (2 allocations: 390.688 KB)\n",
      "  0.000078 seconds\n",
      "  0.000424 seconds (2 allocations: 390.688 KB)\n",
      "  0.000303 seconds (2 allocations: 390.688 KB)\n",
      "  0.000114 seconds\n",
      "  0.000390 seconds (2 allocations: 390.688 KB)\n",
      "  0.000336 seconds (2 allocations: 390.688 KB)\n",
      "  0.000120 seconds\n",
      "  0.000386 seconds (2 allocations: 390.688 KB)\n",
      "  0.000361 seconds (2 allocations: 390.688 KB)\n",
      "  0.000080 seconds\n",
      "  0.000335 seconds (2 allocations: 390.688 KB)\n",
      "  0.000248 seconds (2 allocations: 390.688 KB)\n",
      "  0.000077 seconds\n",
      "  0.000301 seconds (2 allocations: 390.688 KB)\n",
      "  0.000258 seconds (2 allocations: 390.688 KB)\n",
      "  0.000077 seconds\n",
      "  0.000588 seconds (2 allocations: 390.688 KB)\n",
      "  0.000394 seconds (2 allocations: 390.688 KB)\n",
      "  0.000118 seconds\n",
      "  0.000389 seconds (2 allocations: 390.688 KB)\n",
      "  0.000089 seconds (2 allocations: 390.688 KB)\n",
      "  0.000118 seconds\n",
      "  0.000164 seconds (2 allocations: 390.688 KB)\n",
      "  0.000192 seconds (2 allocations: 390.688 KB)\n",
      "  0.000139 seconds\n",
      "  0.000119 seconds (2 allocations: 390.688 KB)\n",
      "  0.000209 seconds (2 allocations: 390.688 KB)\n",
      "  0.000119 seconds\n",
      "  0.000190 seconds (2 allocations: 390.688 KB)\n",
      "  0.000120 seconds (2 allocations: 390.688 KB)\n",
      "  0.000073 seconds\n",
      "  0.000126 seconds (2 allocations: 390.688 KB)\n",
      "  0.000107 seconds (2 allocations: 390.688 KB)\n",
      "  0.000070 seconds\n",
      "  0.000163 seconds (2 allocations: 390.688 KB)\n",
      "  0.000116 seconds (2 allocations: 390.688 KB)\n",
      "  0.000099 seconds\n",
      "  0.000072 seconds (2 allocations: 390.688 KB)\n",
      "  0.000054 seconds (2 allocations: 390.688 KB)\n",
      "  0.000071 seconds\n",
      "  0.000355 seconds (2 allocations: 390.688 KB)\n",
      "  0.000344 seconds (2 allocations: 390.688 KB)\n",
      "  0.000118 seconds\n",
      "  0.000436 seconds (2 allocations: 390.688 KB)\n",
      "  0.000371 seconds (2 allocations: 390.688 KB)\n",
      "  0.000118 seconds\n",
      "  0.000401 seconds (2 allocations: 390.688 KB)\n",
      "  0.000355 seconds (2 allocations: 390.688 KB)\n",
      "  0.000128 seconds\n"
     ]
    }
   ],
   "source": [
    "l = 50000\n",
    "V = randn(l)\n",
    "for i in 1:20\n",
    "    @time cln(V, l)\n",
    "    @time cln2(V, l)\n",
    "    @time ncln(V, l)\n",
    "end\n",
    "V = randn(l)\n",
    "for i in 1:20\n",
    "    @time cln(V, l)\n",
    "    @time cln2(V, l)\n",
    "    @time ncln(V, l)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31e2c2f98>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x31e335128>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss = map(x -> parse(Float64, x), get_seconds(\" 0.000302 seconds (2 allocations: 390.688 KB)\n",
    "  0.000241 seconds (2 allocations: 390.688 KB)\n",
    "  0.000073 seconds\n",
    "  0.000226 seconds (2 allocations: 390.688 KB)\n",
    "  0.000187 seconds (2 allocations: 390.688 KB)\n",
    "  0.000071 seconds\n",
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    "  0.000071 seconds\n",
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    "  0.000130 seconds\n",
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    "  0.000115 seconds (2 allocations: 390.688 KB)\n",
    "  0.000071 seconds\n",
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    "  0.000107 seconds (2 allocations: 390.688 KB)\n",
    "  0.000071 seconds\n",
    "  0.000123 seconds (2 allocations: 390.688 KB)\n",
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    "  0.000071 seconds\n",
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    "  0.000071 seconds\n",
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    "  0.000071 seconds\n",
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    "  0.000052 seconds (2 allocations: 390.688 KB)\n",
    "  0.000071 seconds\n",
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    "  0.000071 seconds\n",
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    "  0.000071 seconds\n",
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    "  0.000210 seconds (2 allocations: 390.688 KB)\n",
    "  0.000108 seconds\n",
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    "  0.000210 seconds (2 allocations: 390.688 KB)\n",
    "  0.000071 seconds\n",
    "  0.000242 seconds (2 allocations: 390.688 KB)\n",
    "  0.000195 seconds (2 allocations: 390.688 KB)\n",
    "  0.000071 seconds\n",
    "  0.000265 seconds (2 allocations: 390.688 KB)\n",
    "  0.000194 seconds (2 allocations: 390.688 KB)\n",
    "  0.000071 seconds\n",
    "  0.000265 seconds (2 allocations: 390.688 KB)\n",
    "  0.000174 seconds (2 allocations: 390.688 KB)\n",
    "  0.000070 seconds\n",
    "  0.000259 seconds (2 allocations: 390.688 KB)\n",
    "  0.000200 seconds (2 allocations: 390.688 KB)\n",
    "  0.000096 seconds\n",
    "  0.000232 seconds (2 allocations: 390.688 KB)\n",
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    "  0.000071 seconds\n",
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    "  0.000077 seconds\n",
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    "  0.000078 seconds\n",
    "  0.000412 seconds (2 allocations: 390.688 KB)\n",
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    "  0.000078 seconds\n",
    "  0.000309 seconds (2 allocations: 390.688 KB)\n",
    "  0.000345 seconds (2 allocations: 390.688 KB)\n",
    "  0.000078 seconds\n",
    "  0.000424 seconds (2 allocations: 390.688 KB)\n",
    "  0.000303 seconds (2 allocations: 390.688 KB)\n",
    "  0.000114 seconds\n",
    "  0.000390 seconds (2 allocations: 390.688 KB)\n",
    "  0.000336 seconds (2 allocations: 390.688 KB)\n",
    "  0.000120 seconds\n",
    "  0.000386 seconds (2 allocations: 390.688 KB)\n",
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    "  0.000080 seconds\n",
    "  0.000335 seconds (2 allocations: 390.688 KB)\n",
    "  0.000248 seconds (2 allocations: 390.688 KB)\n",
    "  0.000077 seconds\n",
    "  0.000301 seconds (2 allocations: 390.688 KB)\n",
    "  0.000258 seconds (2 allocations: 390.688 KB)\n",
    "  0.000077 seconds\n",
    "  0.000588 seconds (2 allocations: 390.688 KB)\n",
    "  0.000394 seconds (2 allocations: 390.688 KB)\n",
    "  0.000118 seconds\n",
    "  0.000389 seconds (2 allocations: 390.688 KB)\n",
    "  0.000089 seconds (2 allocations: 390.688 KB)\n",
    "  0.000118 seconds\n",
    "  0.000164 seconds (2 allocations: 390.688 KB)\n",
    "  0.000192 seconds (2 allocations: 390.688 KB)\n",
    "  0.000139 seconds\n",
    "  0.000119 seconds (2 allocations: 390.688 KB)\n",
    "  0.000209 seconds (2 allocations: 390.688 KB)\n",
    "  0.000119 seconds\n",
    "  0.000190 seconds (2 allocations: 390.688 KB)\n",
    "  0.000120 seconds (2 allocations: 390.688 KB)\n",
    "  0.000073 seconds\n",
    "  0.000126 seconds (2 allocations: 390.688 KB)\n",
    "  0.000107 seconds (2 allocations: 390.688 KB)\n",
    "  0.000070 seconds\n",
    "  0.000163 seconds (2 allocations: 390.688 KB)\n",
    "  0.000116 seconds (2 allocations: 390.688 KB)\n",
    "  0.000099 seconds\n",
    "  0.000072 seconds (2 allocations: 390.688 KB)\n",
    "  0.000054 seconds (2 allocations: 390.688 KB)\n",
    "  0.000071 seconds\n",
    "  0.000355 seconds (2 allocations: 390.688 KB)\n",
    "  0.000344 seconds (2 allocations: 390.688 KB)\n",
    "  0.000118 seconds\n",
    "  0.000436 seconds (2 allocations: 390.688 KB)\n",
    "  0.000371 seconds (2 allocations: 390.688 KB)\n",
    "  0.000118 seconds\n",
    "  0.000401 seconds (2 allocations: 390.688 KB)\n",
    "  0.000355 seconds (2 allocations: 390.688 KB)\n",
    "  0.000128 seconds\"))\n",
    "\n",
    "plot(ss[1:3:120], label=\"element iteration\")\n",
    "plot(ss[2:3:120], label=\"elementwise\")\n",
    "plot(ss[3:3:120], label=\"index iteration\")\n",
    "legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "結局要素には配列でアクセスするほうが一番早いようです."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ついでにもう一つだけ速度の計測をします."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "usefor (generic function with 1 method)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function usewhile(max::Int)\n",
    "    c::Int = 1\n",
    "    while c <= max\n",
    "        1 + 2\n",
    "        c += 1\n",
    "    end\n",
    "end\n",
    "\n",
    "function usefor(max::Int)\n",
    "    for c in 1:max\n",
    "        1 + 2\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.035203 seconds\n",
      "  0.036081 seconds\n",
      "  0.046439 seconds\n",
      "  0.035142 seconds\n",
      "  0.041064 seconds\n",
      "  0.067063 seconds\n",
      "  0.048078 seconds\n",
      "  0.036587 seconds\n",
      "  0.041598 seconds\n",
      "  0.040113 seconds\n"
     ]
    }
   ],
   "source": [
    "for i in 1:10; @time usewhile(100000000); end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.000001 seconds\n",
      "  0.000000 seconds\n",
      "  0.000000 seconds\n",
      "  0.000000 seconds\n",
      "  0.000000 seconds\n",
      "  0.000000 seconds\n",
      "  0.000000 seconds\n",
      "  0.000000 seconds\n",
      "  0.000000 seconds\n",
      "  0.000000 seconds\n"
     ]
    }
   ],
   "source": [
    "for i in 1:10; @time usefor(100000000); end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "明らかにfor loopの方が最適化されてるようです."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "また気になったことがあったので比較します. 単に演算を行いたい場合（リストを返さなくて良い場合), リスト内包表記とforloopどっちが早いのでしょうか."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "useforloop (generic function with 1 method)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function uselistcomprehension(l)\n",
    "    Int[1+3 for i in 1:l]\n",
    "end\n",
    "\n",
    "function uselistcomprehension2(l)\n",
    "    [1+3 for i in 1:l]\n",
    "end\n",
    "\n",
    "function useforloop(l)\n",
    "    for i in 1:l\n",
    "        1+3\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.061237 seconds (2 allocations: 76.294 MB)\n",
      "  0.017674 seconds (2 allocations: 76.294 MB)\n",
      "  0.018529 seconds (2 allocations: 76.294 MB)\n",
      "  0.018085 seconds (2 allocations: 76.294 MB)\n",
      "  0.018240 seconds (2 allocations: 76.294 MB)\n",
      "  0.019275 seconds (2 allocations: 76.294 MB)\n",
      "  0.023673 seconds (2 allocations: 76.294 MB)\n",
      "  0.022596 seconds (2 allocations: 76.294 MB)\n",
      "  0.024667 seconds (2 allocations: 76.294 MB)\n",
      "  0.021204 seconds (2 allocations: 76.294 MB)\n",
      "  0.024639 seconds (2 allocations: 76.294 MB)\n",
      "  0.022118 seconds (2 allocations: 76.294 MB)\n",
      "  0.024306 seconds (2 allocations: 76.294 MB)\n",
      "  0.020537 seconds (2 allocations: 76.294 MB)\n",
      "  0.018549 seconds (2 allocations: 76.294 MB)\n",
      "  0.018955 seconds (2 allocations: 76.294 MB)\n",
      "  0.021216 seconds (2 allocations: 76.294 MB)\n",
      "  0.019180 seconds (2 allocations: 76.294 MB)\n",
      "  0.019217 seconds (2 allocations: 76.294 MB)\n",
      "  0.019380 seconds (2 allocations: 76.294 MB)\n",
      "  0.019432 seconds (2 allocations: 76.294 MB)\n",
      "  0.019005 seconds (2 allocations: 76.294 MB)\n",
      "  0.019543 seconds (2 allocations: 76.294 MB)\n",
      "  0.024634 seconds (2 allocations: 76.294 MB)\n",
      "  0.020661 seconds (2 allocations: 76.294 MB)\n",
      "  0.021535 seconds (2 allocations: 76.294 MB)\n",
      "  0.020270 seconds (2 allocations: 76.294 MB)\n",
      "  0.027304 seconds (2 allocations: 76.294 MB)\n",
      "  0.020120 seconds (2 allocations: 76.294 MB)\n",
      "  0.021851 seconds (2 allocations: 76.294 MB)\n",
      "  0.020975 seconds (2 allocations: 76.294 MB)\n",
      "  0.021745 seconds (2 allocations: 76.294 MB)\n",
      "  0.018803 seconds (2 allocations: 76.294 MB)\n",
      "  0.019773 seconds (2 allocations: 76.294 MB)\n",
      "  0.019087 seconds (2 allocations: 76.294 MB)\n",
      "  0.024007 seconds (2 allocations: 76.294 MB)\n",
      "  0.019811 seconds (2 allocations: 76.294 MB)\n",
      "  0.018926 seconds (2 allocations: 76.294 MB)\n",
      "  0.025049 seconds (2 allocations: 76.294 MB)\n",
      "  0.019534 seconds (2 allocations: 76.294 MB)\n",
      "  0.000003 seconds\n",
      "  0.000001 seconds\n",
      "  0.000001 seconds\n",
      "  0.000001 seconds\n",
      "  0.000001 seconds\n",
      "  0.000001 seconds\n",
      "  0.000002 seconds\n",
      "  0.000001 seconds\n",
      "  0.000001 seconds\n",
      "  0.000002 seconds\n",
      "  0.000001 seconds\n",
      "  0.000001 seconds\n",
      "  0.000001 seconds\n",
      "  0.000000 seconds\n",
      "  0.000000 seconds\n",
      "  0.000001 seconds\n",
      "  0.000000 seconds\n",
      "  0.000000 seconds\n",
      "  0.000000 seconds\n",
      "  0.000001 seconds\n"
     ]
    }
   ],
   "source": [
    "l = 10000000\n",
    "for i in 1:20; @time uselistcomprehension(l); end\n",
    "for i in 1:20; @time uselistcomprehension2(l); end\n",
    "for i in 1:20; @time useforloop(l); end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31d9c2978>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x31de10ac8>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss2 = map(x -> parse(Float64, x), get_seconds(\"0.061237 seconds (2 allocations: 76.294 MB)\n",
    "  0.017674 seconds (2 allocations: 76.294 MB)\n",
    "  0.018529 seconds (2 allocations: 76.294 MB)\n",
    "  0.018085 seconds (2 allocations: 76.294 MB)\n",
    "  0.018240 seconds (2 allocations: 76.294 MB)\n",
    "  0.019275 seconds (2 allocations: 76.294 MB)\n",
    "  0.023673 seconds (2 allocations: 76.294 MB)\n",
    "  0.022596 seconds (2 allocations: 76.294 MB)\n",
    "  0.024667 seconds (2 allocations: 76.294 MB)\n",
    "  0.021204 seconds (2 allocations: 76.294 MB)\n",
    "  0.024639 seconds (2 allocations: 76.294 MB)\n",
    "  0.022118 seconds (2 allocations: 76.294 MB)\n",
    "  0.024306 seconds (2 allocations: 76.294 MB)\n",
    "  0.020537 seconds (2 allocations: 76.294 MB)\n",
    "  0.018549 seconds (2 allocations: 76.294 MB)\n",
    "  0.018955 seconds (2 allocations: 76.294 MB)\n",
    "  0.021216 seconds (2 allocations: 76.294 MB)\n",
    "  0.019180 seconds (2 allocations: 76.294 MB)\n",
    "  0.019217 seconds (2 allocations: 76.294 MB)\n",
    "  0.019380 seconds (2 allocations: 76.294 MB)\n",
    "  0.019432 seconds (2 allocations: 76.294 MB)\n",
    "  0.019005 seconds (2 allocations: 76.294 MB)\n",
    "  0.019543 seconds (2 allocations: 76.294 MB)\n",
    "  0.024634 seconds (2 allocations: 76.294 MB)\n",
    "  0.020661 seconds (2 allocations: 76.294 MB)\n",
    "  0.021535 seconds (2 allocations: 76.294 MB)\n",
    "  0.020270 seconds (2 allocations: 76.294 MB)\n",
    "  0.027304 seconds (2 allocations: 76.294 MB)\n",
    "  0.020120 seconds (2 allocations: 76.294 MB)\n",
    "  0.021851 seconds (2 allocations: 76.294 MB)\n",
    "  0.020975 seconds (2 allocations: 76.294 MB)\n",
    "  0.021745 seconds (2 allocations: 76.294 MB)\n",
    "  0.018803 seconds (2 allocations: 76.294 MB)\n",
    "  0.019773 seconds (2 allocations: 76.294 MB)\n",
    "  0.019087 seconds (2 allocations: 76.294 MB)\n",
    "  0.024007 seconds (2 allocations: 76.294 MB)\n",
    "  0.019811 seconds (2 allocations: 76.294 MB)\n",
    "  0.018926 seconds (2 allocations: 76.294 MB)\n",
    "  0.025049 seconds (2 allocations: 76.294 MB)\n",
    "  0.019534 seconds (2 allocations: 76.294 MB)\n",
    "  0.000003 seconds\n",
    "  0.000001 seconds\n",
    "  0.000001 seconds\n",
    "  0.000001 seconds\n",
    "  0.000001 seconds\n",
    "  0.000001 seconds\n",
    "  0.000002 seconds\n",
    "  0.000001 seconds\n",
    "  0.000001 seconds\n",
    "  0.000002 seconds\n",
    "  0.000001 seconds\n",
    "  0.000001 seconds\n",
    "  0.000001 seconds\n",
    "  0.000000 seconds\n",
    "  0.000000 seconds\n",
    "  0.000001 seconds\n",
    "  0.000000 seconds\n",
    "  0.000000 seconds\n",
    "  0.000000 seconds\n",
    "  0.000001 seconds\"))\n",
    "plot(ss2[1:20], label=\"list comprehension\")\n",
    "plot(ss2[21:40], label=\"type specified list comprehension\")\n",
    "plot(ss2[41:60], label=\"for loop\")\n",
    "legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "forloopがグラフの端に張り付いて圧勝しています. 配列の結果が欲しい場合はどうでしょうか."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "useforloop2 (generic function with 1 method)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function uselistcomprehension2(l)\n",
    "    [1+3 for i in 1:l]\n",
    "end\n",
    "\n",
    "function useforloop2(l)\n",
    "    xs = Array(Int, l)\n",
    "    for i in 1:l\n",
    "        xs[i] = 1+3\n",
    "    end\n",
    "    xs\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.004035 seconds (2 allocations: 7.629 MB)\n",
      "  0.001935 seconds (2 allocations: 7.629 MB)\n",
      "  0.001956 seconds (2 allocations: 7.629 MB)\n",
      "  0.001909 seconds (2 allocations: 7.629 MB)\n",
      "  0.001925 seconds (2 allocations: 7.629 MB)\n",
      "  0.001918 seconds (2 allocations: 7.629 MB)\n",
      "  0.001921 seconds (2 allocations: 7.629 MB)\n",
      "  0.002021 seconds (2 allocations: 7.629 MB)\n",
      "  0.001970 seconds (2 allocations: 7.629 MB)\n",
      "  0.002035 seconds (2 allocations: 7.629 MB)\n",
      "  0.001942 seconds (2 allocations: 7.629 MB)\n",
      "  0.001968 seconds (2 allocations: 7.629 MB)\n",
      "  0.002015 seconds (2 allocations: 7.629 MB)\n",
      "  0.001735 seconds (2 allocations: 7.629 MB)\n",
      "  0.001984 seconds (2 allocations: 7.629 MB)\n",
      "  0.002046 seconds (2 allocations: 7.629 MB)\n",
      "  0.001750 seconds (2 allocations: 7.629 MB)\n",
      "  0.001881 seconds (2 allocations: 7.629 MB)\n",
      "  0.001843 seconds (2 allocations: 7.629 MB)\n",
      "  0.001734 seconds (2 allocations: 7.629 MB)\n",
      "  0.001845 seconds (2 allocations: 7.629 MB)\n",
      "  0.001854 seconds (2 allocations: 7.629 MB)\n",
      "  0.001741 seconds (2 allocations: 7.629 MB)\n",
      "  0.001847 seconds (2 allocations: 7.629 MB)\n",
      "  0.001849 seconds (2 allocations: 7.629 MB)\n",
      "  0.001721 seconds (2 allocations: 7.629 MB)\n",
      "  0.002079 seconds (2 allocations: 7.629 MB)\n",
      "  0.001934 seconds (2 allocations: 7.629 MB)\n",
      "  0.001792 seconds (2 allocations: 7.629 MB)\n",
      "  0.001940 seconds (2 allocations: 7.629 MB)\n",
      "  0.001998 seconds (2 allocations: 7.629 MB)\n",
      "  0.001876 seconds (2 allocations: 7.629 MB)\n",
      "  0.001850 seconds (2 allocations: 7.629 MB)\n",
      "  0.001844 seconds (2 allocations: 7.629 MB)\n",
      "  0.001712 seconds (2 allocations: 7.629 MB)\n",
      "  0.001842 seconds (2 allocations: 7.629 MB)\n",
      "  0.001920 seconds (2 allocations: 7.629 MB)\n",
      "  0.001725 seconds (2 allocations: 7.629 MB)\n",
      "  0.001844 seconds (2 allocations: 7.629 MB)\n",
      "  0.001911 seconds (2 allocations: 7.629 MB)\n",
      "  0.001816 seconds (2 allocations: 7.629 MB)\n",
      "  0.001940 seconds (2 allocations: 7.629 MB)\n",
      "  0.002059 seconds (2 allocations: 7.629 MB)\n",
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      "  0.001933 seconds (2 allocations: 7.629 MB)\n",
      "  0.001889 seconds (2 allocations: 7.629 MB)\n",
      "  0.001977 seconds (2 allocations: 7.629 MB)\n",
      "  0.001973 seconds (2 allocations: 7.629 MB)\n",
      "  0.001821 seconds (2 allocations: 7.629 MB)\n",
      "  0.001984 seconds (2 allocations: 7.629 MB)\n",
      "  0.001959 seconds (2 allocations: 7.629 MB)\n",
      "  0.001778 seconds (2 allocations: 7.629 MB)\n",
      "  0.001848 seconds (2 allocations: 7.629 MB)\n",
      "  0.001924 seconds (2 allocations: 7.629 MB)\n",
      "  0.001727 seconds (2 allocations: 7.629 MB)\n",
      "  0.001832 seconds (2 allocations: 7.629 MB)\n",
      "  0.001835 seconds (2 allocations: 7.629 MB)\n",
      "  0.001727 seconds (2 allocations: 7.629 MB)\n",
      "  0.001989 seconds (2 allocations: 7.629 MB)\n",
      "  0.002033 seconds (2 allocations: 7.629 MB)\n",
      "  0.001926 seconds (2 allocations: 7.629 MB)\n",
      "  0.001970 seconds (2 allocations: 7.629 MB)\n",
      "  0.001928 seconds (2 allocations: 7.629 MB)\n",
      "  0.001798 seconds (2 allocations: 7.629 MB)\n",
      "  0.002002 seconds (2 allocations: 7.629 MB)\n",
      "  0.002025 seconds (2 allocations: 7.629 MB)\n",
      "  0.001968 seconds (2 allocations: 7.629 MB)\n",
      "  0.001938 seconds (2 allocations: 7.629 MB)\n",
      "  0.001956 seconds (2 allocations: 7.629 MB)\n",
      "  0.002405 seconds (2 allocations: 7.629 MB)\n",
      "  0.002079 seconds (2 allocations: 7.629 MB)\n",
      "  0.002259 seconds (2 allocations: 7.629 MB)\n",
      "  0.002226 seconds (2 allocations: 7.629 MB)\n",
      "  0.002433 seconds (2 allocations: 7.629 MB)\n",
      "  0.002174 seconds (2 allocations: 7.629 MB)\n",
      "  0.002313 seconds (2 allocations: 7.629 MB)\n",
      "  0.002065 seconds (2 allocations: 7.629 MB)\n",
      "  0.002451 seconds (2 allocations: 7.629 MB)\n",
      "  0.002351 seconds (2 allocations: 7.629 MB)\n",
      "  0.001972 seconds (2 allocations: 7.629 MB)\n",
      "  0.001963 seconds (2 allocations: 7.629 MB)\n",
      "  0.001890 seconds (2 allocations: 7.629 MB)\n",
      "  0.001963 seconds (2 allocations: 7.629 MB)\n",
      "  0.001983 seconds (2 allocations: 7.629 MB)\n",
      "  0.001922 seconds (2 allocations: 7.629 MB)\n",
      "  0.001950 seconds (2 allocations: 7.629 MB)\n",
      "  0.001924 seconds (2 allocations: 7.629 MB)\n",
      "  0.002036 seconds (2 allocations: 7.629 MB)\n",
      "  0.002031 seconds (2 allocations: 7.629 MB)\n",
      "  0.002708 seconds (2 allocations: 7.629 MB)\n",
      "  0.001975 seconds (2 allocations: 7.629 MB)\n",
      "  0.001973 seconds (2 allocations: 7.629 MB)\n",
      "  0.001968 seconds (2 allocations: 7.629 MB)\n",
      "  0.001925 seconds (2 allocations: 7.629 MB)\n",
      "  0.001958 seconds (2 allocations: 7.629 MB)\n",
      "  0.001977 seconds (2 allocations: 7.629 MB)\n",
      "  0.002029 seconds (2 allocations: 7.629 MB)\n",
      "  0.002025 seconds (2 allocations: 7.629 MB)\n",
      "  0.002043 seconds (2 allocations: 7.629 MB)\n",
      "  0.002041 seconds (2 allocations: 7.629 MB)\n",
      "  0.002023 seconds (2 allocations: 7.629 MB)\n",
      "  0.002206 seconds (2 allocations: 7.629 MB)\n",
      "  0.002141 seconds (2 allocations: 7.629 MB)\n",
      "  0.001933 seconds (2 allocations: 7.629 MB)\n",
      "  0.001911 seconds (2 allocations: 7.629 MB)\n",
      "  0.001848 seconds (2 allocations: 7.629 MB)\n",
      "  0.001915 seconds (2 allocations: 7.629 MB)\n",
      "  0.001985 seconds (2 allocations: 7.629 MB)\n",
      "  0.001867 seconds (2 allocations: 7.629 MB)\n",
      "  0.001918 seconds (2 allocations: 7.629 MB)\n",
      "  0.001948 seconds (2 allocations: 7.629 MB)\n",
      "  0.002374 seconds (2 allocations: 7.629 MB)\n",
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      "  0.001898 seconds (2 allocations: 7.629 MB)\n",
      "  0.001982 seconds (2 allocations: 7.629 MB)\n",
      "  0.001907 seconds (2 allocations: 7.629 MB)\n",
      "  0.002623 seconds (2 allocations: 7.629 MB)\n",
      "  0.002333 seconds (2 allocations: 7.629 MB)\n",
      "  0.002231 seconds (2 allocations: 7.629 MB)\n",
      "  0.002395 seconds (2 allocations: 7.629 MB)\n",
      "  0.002427 seconds (2 allocations: 7.629 MB)\n",
      "  0.002347 seconds (2 allocations: 7.629 MB)\n",
      "  0.002189 seconds (2 allocations: 7.629 MB)\n",
      "  0.002238 seconds (2 allocations: 7.629 MB)\n",
      "  0.002313 seconds (2 allocations: 7.629 MB)\n",
      "  0.002045 seconds (2 allocations: 7.629 MB)\n",
      "  0.002203 seconds (2 allocations: 7.629 MB)\n",
      "  0.002302 seconds (2 allocations: 7.629 MB)\n",
      "  0.002135 seconds (2 allocations: 7.629 MB)\n",
      "  0.002220 seconds (2 allocations: 7.629 MB)\n",
      "  0.002224 seconds (2 allocations: 7.629 MB)\n",
      "  0.002139 seconds (2 allocations: 7.629 MB)\n",
      "  0.002205 seconds (2 allocations: 7.629 MB)\n",
      "  0.002277 seconds (2 allocations: 7.629 MB)\n",
      "  0.002812 seconds (2 allocations: 7.629 MB)\n",
      "  0.002137 seconds (2 allocations: 7.629 MB)\n",
      "  0.002199 seconds (2 allocations: 7.629 MB)\n",
      "  0.002302 seconds (2 allocations: 7.629 MB)\n",
      "  0.002166 seconds (2 allocations: 7.629 MB)\n",
      "  0.002549 seconds (2 allocations: 7.629 MB)\n",
      "  0.002143 seconds (2 allocations: 7.629 MB)\n",
      "  0.002936 seconds (2 allocations: 7.629 MB)\n",
      "  0.002123 seconds (2 allocations: 7.629 MB)\n",
      "  0.002314 seconds (2 allocations: 7.629 MB)\n",
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      "  0.002110 seconds (2 allocations: 7.629 MB)\n",
      "  0.002181 seconds (2 allocations: 7.629 MB)\n",
      "  0.002190 seconds (2 allocations: 7.629 MB)\n",
      "  0.002330 seconds (2 allocations: 7.629 MB)\n",
      "  0.003146 seconds (2 allocations: 7.629 MB)\n",
      "  0.002584 seconds (2 allocations: 7.629 MB)\n",
      "  0.002230 seconds (2 allocations: 7.629 MB)\n",
      "  0.002020 seconds (2 allocations: 7.629 MB)\n",
      "  0.001950 seconds (2 allocations: 7.629 MB)\n",
      "  0.002011 seconds (2 allocations: 7.629 MB)\n",
      "  0.004797 seconds (2 allocations: 7.629 MB)\n",
      "  0.004161 seconds (2 allocations: 7.629 MB)\n",
      "  0.003544 seconds (2 allocations: 7.629 MB)\n",
      "  0.001927 seconds (2 allocations: 7.629 MB)\n",
      "  0.002123 seconds (2 allocations: 7.629 MB)\n",
      "  0.002376 seconds (2 allocations: 7.629 MB)\n",
      "  0.003033 seconds (2 allocations: 7.629 MB)\n",
      "  0.002858 seconds (2 allocations: 7.629 MB)\n",
      "  0.003165 seconds (2 allocations: 7.629 MB)\n",
      "  0.003457 seconds (2 allocations: 7.629 MB)\n",
      "  0.002261 seconds (2 allocations: 7.629 MB)\n",
      "  0.002538 seconds (2 allocations: 7.629 MB)\n",
      "  0.002204 seconds (2 allocations: 7.629 MB)\n",
      "  0.002178 seconds (2 allocations: 7.629 MB)\n",
      "  0.002740 seconds (2 allocations: 7.629 MB)\n",
      "  0.002884 seconds (2 allocations: 7.629 MB)\n",
      "  0.002040 seconds (2 allocations: 7.629 MB)\n",
      "  0.001940 seconds (2 allocations: 7.629 MB)\n",
      "  0.002032 seconds (2 allocations: 7.629 MB)\n",
      "  0.002038 seconds (2 allocations: 7.629 MB)\n",
      "  0.002158 seconds (2 allocations: 7.629 MB)\n",
      "  0.001977 seconds (2 allocations: 7.629 MB)\n",
      "  0.002113 seconds (2 allocations: 7.629 MB)\n",
      "  0.002022 seconds (2 allocations: 7.629 MB)\n",
      "  0.002117 seconds (2 allocations: 7.629 MB)\n",
      "  0.001976 seconds (2 allocations: 7.629 MB)\n",
      "  0.001962 seconds (2 allocations: 7.629 MB)\n",
      "  0.002109 seconds (2 allocations: 7.629 MB)\n",
      "  0.001925 seconds (2 allocations: 7.629 MB)\n",
      "  0.002018 seconds (2 allocations: 7.629 MB)\n",
      "  0.001963 seconds (2 allocations: 7.629 MB)\n",
      "  0.002166 seconds (2 allocations: 7.629 MB)\n",
      "  0.001962 seconds (2 allocations: 7.629 MB)\n",
      "  0.002005 seconds (2 allocations: 7.629 MB)\n",
      "  0.001958 seconds (2 allocations: 7.629 MB)\n",
      "  0.002432 seconds (2 allocations: 7.629 MB)\n",
      "  0.003345 seconds (2 allocations: 7.629 MB)\n",
      "  0.001985 seconds (2 allocations: 7.629 MB)\n",
      "  0.002026 seconds (2 allocations: 7.629 MB)\n",
      "  0.002201 seconds (2 allocations: 7.629 MB)\n",
      "  0.003640 seconds (2 allocations: 7.629 MB)\n",
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      "  0.002230 seconds (2 allocations: 7.629 MB)\n",
      "  0.002923 seconds (2 allocations: 7.629 MB)\n",
      "  0.004151 seconds (2 allocations: 7.629 MB)\n",
      "  0.002809 seconds (2 allocations: 7.629 MB)\n",
      "  0.003419 seconds (2 allocations: 7.629 MB)\n",
      "  0.003098 seconds (2 allocations: 7.629 MB)\n",
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      "  0.002179 seconds (2 allocations: 7.629 MB)\n",
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      "  0.002880 seconds (2 allocations: 7.629 MB)\n",
      "  0.006988 seconds (2 allocations: 7.629 MB)\n",
      "  0.002961 seconds (2 allocations: 7.629 MB)\n",
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      "  0.003677 seconds (2 allocations: 7.629 MB)\n",
      "  0.002539 seconds (2 allocations: 7.629 MB)\n",
      "  0.002489 seconds (2 allocations: 7.629 MB)\n",
      "  0.002245 seconds (2 allocations: 7.629 MB)\n",
      "  0.002224 seconds (2 allocations: 7.629 MB)\n",
      "  0.003145 seconds (2 allocations: 7.629 MB)\n",
      "  0.003468 seconds (2 allocations: 7.629 MB)\n",
      "  0.002604 seconds (2 allocations: 7.629 MB)\n",
      "  0.002457 seconds (2 allocations: 7.629 MB)\n",
      "  0.003122 seconds (2 allocations: 7.629 MB)\n",
      "  0.002252 seconds (2 allocations: 7.629 MB)\n",
      "  0.002448 seconds (2 allocations: 7.629 MB)\n",
      "  0.002323 seconds (2 allocations: 7.629 MB)\n",
      "  0.004850 seconds (2 allocations: 7.629 MB)\n",
      "  0.003692 seconds (2 allocations: 7.629 MB)\n",
      "  0.002406 seconds (2 allocations: 7.629 MB)\n",
      "  0.002712 seconds (2 allocations: 7.629 MB)\n",
      "  0.003161 seconds (2 allocations: 7.629 MB)\n",
      "  0.002399 seconds (2 allocations: 7.629 MB)\n",
      "  0.002635 seconds (2 allocations: 7.629 MB)\n",
      "  0.002418 seconds (2 allocations: 7.629 MB)\n",
      "  0.002453 seconds (2 allocations: 7.629 MB)\n",
      "  0.005072 seconds (2 allocations: 7.629 MB)\n",
      "  0.002920 seconds (2 allocations: 7.629 MB)\n",
      "  0.003577 seconds (2 allocations: 7.629 MB)\n",
      "  0.002494 seconds (2 allocations: 7.629 MB)\n",
      "  0.002250 seconds (2 allocations: 7.629 MB)\n",
      "  0.007709 seconds (2 allocations: 7.629 MB)\n",
      "  0.003312 seconds (2 allocations: 7.629 MB)\n",
      "  0.002475 seconds (2 allocations: 7.629 MB)\n",
      "  0.002896 seconds (2 allocations: 7.629 MB)\n",
      "  0.004056 seconds (2 allocations: 7.629 MB)\n",
      "  0.002778 seconds (2 allocations: 7.629 MB)\n",
      "  0.002402 seconds (2 allocations: 7.629 MB)\n",
      "  0.002271 seconds (2 allocations: 7.629 MB)\n",
      "  0.002507 seconds (2 allocations: 7.629 MB)\n",
      "  0.002435 seconds (2 allocations: 7.629 MB)\n",
      "  0.002187 seconds (2 allocations: 7.629 MB)\n",
      "  0.003048 seconds (2 allocations: 7.629 MB)\n",
      "  0.002639 seconds (2 allocations: 7.629 MB)\n",
      "  0.004468 seconds (2 allocations: 7.629 MB)\n",
      "  0.002955 seconds (2 allocations: 7.629 MB)\n",
      "  0.002885 seconds (2 allocations: 7.629 MB)\n",
      "  0.002230 seconds (2 allocations: 7.629 MB)\n",
      "  0.002418 seconds (2 allocations: 7.629 MB)\n",
      "  0.003808 seconds (2 allocations: 7.629 MB)\n",
      "  0.004153 seconds (2 allocations: 7.629 MB)\n",
      "  0.002412 seconds (2 allocations: 7.629 MB)\n",
      "  0.002304 seconds (2 allocations: 7.629 MB)\n",
      "  0.003515 seconds (2 allocations: 7.629 MB)\n",
      "  0.005989 seconds (2 allocations: 7.629 MB)\n",
      "  0.010745 seconds (2 allocations: 7.629 MB)\n",
      "  0.002906 seconds (2 allocations: 7.629 MB)\n",
      "  0.002807 seconds (2 allocations: 7.629 MB)\n",
      "  0.002894 seconds (2 allocations: 7.629 MB)\n",
      "  0.002894 seconds (2 allocations: 7.629 MB)\n",
      "  0.002668 seconds (2 allocations: 7.629 MB)\n",
      "  0.003279 seconds (2 allocations: 7.629 MB)\n",
      "  0.002675 seconds (2 allocations: 7.629 MB)\n",
      "  0.002885 seconds (2 allocations: 7.629 MB)\n",
      "  0.011620 seconds (2 allocations: 7.629 MB)\n",
      "  0.003235 seconds (2 allocations: 7.629 MB)\n",
      "  0.002903 seconds (2 allocations: 7.629 MB)\n",
      "  0.011905 seconds (2 allocations: 7.629 MB)\n",
      "  0.002913 seconds (2 allocations: 7.629 MB)\n",
      "  0.013886 seconds (2 allocations: 7.629 MB)\n",
      "  0.003582 seconds (2 allocations: 7.629 MB)\n",
      "  0.002217 seconds (2 allocations: 7.629 MB)\n",
      "  0.003011 seconds (2 allocations: 7.629 MB)\n",
      "  0.002659 seconds (2 allocations: 7.629 MB)\n",
      "  0.002220 seconds (2 allocations: 7.629 MB)\n",
      "  0.002168 seconds (2 allocations: 7.629 MB)\n",
      "  0.002558 seconds (2 allocations: 7.629 MB)\n",
      "  0.002159 seconds (2 allocations: 7.629 MB)\n",
      "  0.005459 seconds (2 allocations: 7.629 MB)\n",
      "  0.002732 seconds (2 allocations: 7.629 MB)\n",
      "  0.002544 seconds (2 allocations: 7.629 MB)\n",
      "  0.002594 seconds (2 allocations: 7.629 MB)\n",
      "  0.002896 seconds (2 allocations: 7.629 MB)\n",
      "  0.002718 seconds (2 allocations: 7.629 MB)\n",
      "  0.003449 seconds (2 allocations: 7.629 MB)\n",
      "  0.002522 seconds (2 allocations: 7.629 MB)\n",
      "  0.003687 seconds (2 allocations: 7.629 MB)\n",
      "  0.003092 seconds (2 allocations: 7.629 MB)\n",
      "  0.002973 seconds (2 allocations: 7.629 MB)\n",
      "  0.002426 seconds (2 allocations: 7.629 MB)\n",
      "  0.002227 seconds (2 allocations: 7.629 MB)\n",
      "  0.002284 seconds (2 allocations: 7.629 MB)\n",
      "  0.003066 seconds (2 allocations: 7.629 MB)\n",
      "  0.002181 seconds (2 allocations: 7.629 MB)\n",
      "  0.002187 seconds (2 allocations: 7.629 MB)\n",
      "  0.002558 seconds (2 allocations: 7.629 MB)\n",
      "  0.002920 seconds (2 allocations: 7.629 MB)\n",
      "  0.003580 seconds (2 allocations: 7.629 MB)\n",
      "  0.003634 seconds (2 allocations: 7.629 MB)\n",
      "  0.002338 seconds (2 allocations: 7.629 MB)\n",
      "  0.002275 seconds (2 allocations: 7.629 MB)\n",
      "  0.003178 seconds (2 allocations: 7.629 MB)\n",
      "  0.005015 seconds (2 allocations: 7.629 MB)\n",
      "  0.002605 seconds (2 allocations: 7.629 MB)\n",
      "  0.002284 seconds (2 allocations: 7.629 MB)\n",
      "  0.004524 seconds (2 allocations: 7.629 MB)\n",
      "  0.003628 seconds (2 allocations: 7.629 MB)\n",
      "  0.002255 seconds (2 allocations: 7.629 MB)\n",
      "  0.002980 seconds (2 allocations: 7.629 MB)\n",
      "  0.003092 seconds (2 allocations: 7.629 MB)\n",
      "  0.002569 seconds (2 allocations: 7.629 MB)\n",
      "  0.003062 seconds (2 allocations: 7.629 MB)\n",
      "  0.002287 seconds (2 allocations: 7.629 MB)\n",
      "  0.002281 seconds (2 allocations: 7.629 MB)\n",
      "  0.002565 seconds (2 allocations: 7.629 MB)\n",
      "  0.002596 seconds (2 allocations: 7.629 MB)\n",
      "  0.003085 seconds (2 allocations: 7.629 MB)\n",
      "  0.002326 seconds (2 allocations: 7.629 MB)\n",
      "  0.002407 seconds (2 allocations: 7.629 MB)\n",
      "  0.004662 seconds (2 allocations: 7.629 MB)\n",
      "  0.002626 seconds (2 allocations: 7.629 MB)\n",
      "  0.003103 seconds (2 allocations: 7.629 MB)\n",
      "  0.005187 seconds (2 allocations: 7.629 MB)\n",
      "  0.003178 seconds (2 allocations: 7.629 MB)\n",
      "  0.003546 seconds (2 allocations: 7.629 MB)\n"
     ]
    }
   ],
   "source": [
    "l = 1000000\n",
    "for i in 1:200; @time uselistcomprehension2(l); end\n",
    "for i in 1:200; @time useforloop2(l); end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31e340ef0>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x3186136d8>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss3 = get_seconds(\"0.004035 seconds (2 allocations: 7.629 MB)\n",
    "  0.001935 seconds (2 allocations: 7.629 MB)\n",
    "  0.001956 seconds (2 allocations: 7.629 MB)\n",
    "  0.001909 seconds (2 allocations: 7.629 MB)\n",
    "  0.001925 seconds (2 allocations: 7.629 MB)\n",
    "  0.001918 seconds (2 allocations: 7.629 MB)\n",
    "  0.001921 seconds (2 allocations: 7.629 MB)\n",
    "  0.002021 seconds (2 allocations: 7.629 MB)\n",
    "  0.001970 seconds (2 allocations: 7.629 MB)\n",
    "  0.002035 seconds (2 allocations: 7.629 MB)\n",
    "  0.001942 seconds (2 allocations: 7.629 MB)\n",
    "  0.001968 seconds (2 allocations: 7.629 MB)\n",
    "  0.002015 seconds (2 allocations: 7.629 MB)\n",
    "  0.001735 seconds (2 allocations: 7.629 MB)\n",
    "  0.001984 seconds (2 allocations: 7.629 MB)\n",
    "  0.002046 seconds (2 allocations: 7.629 MB)\n",
    "  0.001750 seconds (2 allocations: 7.629 MB)\n",
    "  0.001881 seconds (2 allocations: 7.629 MB)\n",
    "  0.001843 seconds (2 allocations: 7.629 MB)\n",
    "  0.001734 seconds (2 allocations: 7.629 MB)\n",
    "  0.001845 seconds (2 allocations: 7.629 MB)\n",
    "  0.001854 seconds (2 allocations: 7.629 MB)\n",
    "  0.001741 seconds (2 allocations: 7.629 MB)\n",
    "  0.001847 seconds (2 allocations: 7.629 MB)\n",
    "  0.001849 seconds (2 allocations: 7.629 MB)\n",
    "  0.001721 seconds (2 allocations: 7.629 MB)\n",
    "  0.002079 seconds (2 allocations: 7.629 MB)\n",
    "  0.001934 seconds (2 allocations: 7.629 MB)\n",
    "  0.001792 seconds (2 allocations: 7.629 MB)\n",
    "  0.001940 seconds (2 allocations: 7.629 MB)\n",
    "  0.001998 seconds (2 allocations: 7.629 MB)\n",
    "  0.001876 seconds (2 allocations: 7.629 MB)\n",
    "  0.001850 seconds (2 allocations: 7.629 MB)\n",
    "  0.001844 seconds (2 allocations: 7.629 MB)\n",
    "  0.001712 seconds (2 allocations: 7.629 MB)\n",
    "  0.001842 seconds (2 allocations: 7.629 MB)\n",
    "  0.001920 seconds (2 allocations: 7.629 MB)\n",
    "  0.001725 seconds (2 allocations: 7.629 MB)\n",
    "  0.001844 seconds (2 allocations: 7.629 MB)\n",
    "  0.001911 seconds (2 allocations: 7.629 MB)\n",
    "  0.001816 seconds (2 allocations: 7.629 MB)\n",
    "  0.001940 seconds (2 allocations: 7.629 MB)\n",
    "  0.002059 seconds (2 allocations: 7.629 MB)\n",
    "  0.001766 seconds (2 allocations: 7.629 MB)\n",
    "  0.002067 seconds (2 allocations: 7.629 MB)\n",
    "  0.001933 seconds (2 allocations: 7.629 MB)\n",
    "  0.001889 seconds (2 allocations: 7.629 MB)\n",
    "  0.001977 seconds (2 allocations: 7.629 MB)\n",
    "  0.001973 seconds (2 allocations: 7.629 MB)\n",
    "  0.001821 seconds (2 allocations: 7.629 MB)\n",
    "  0.001984 seconds (2 allocations: 7.629 MB)\n",
    "  0.001959 seconds (2 allocations: 7.629 MB)\n",
    "  0.001778 seconds (2 allocations: 7.629 MB)\n",
    "  0.001848 seconds (2 allocations: 7.629 MB)\n",
    "  0.001924 seconds (2 allocations: 7.629 MB)\n",
    "  0.001727 seconds (2 allocations: 7.629 MB)\n",
    "  0.001832 seconds (2 allocations: 7.629 MB)\n",
    "  0.001835 seconds (2 allocations: 7.629 MB)\n",
    "  0.001727 seconds (2 allocations: 7.629 MB)\n",
    "  0.001989 seconds (2 allocations: 7.629 MB)\n",
    "  0.002033 seconds (2 allocations: 7.629 MB)\n",
    "  0.001926 seconds (2 allocations: 7.629 MB)\n",
    "  0.001970 seconds (2 allocations: 7.629 MB)\n",
    "  0.001928 seconds (2 allocations: 7.629 MB)\n",
    "  0.001798 seconds (2 allocations: 7.629 MB)\n",
    "  0.002002 seconds (2 allocations: 7.629 MB)\n",
    "  0.002025 seconds (2 allocations: 7.629 MB)\n",
    "  0.001968 seconds (2 allocations: 7.629 MB)\n",
    "  0.001938 seconds (2 allocations: 7.629 MB)\n",
    "  0.001956 seconds (2 allocations: 7.629 MB)\n",
    "  0.002405 seconds (2 allocations: 7.629 MB)\n",
    "  0.002079 seconds (2 allocations: 7.629 MB)\n",
    "  0.002259 seconds (2 allocations: 7.629 MB)\n",
    "  0.002226 seconds (2 allocations: 7.629 MB)\n",
    "  0.002433 seconds (2 allocations: 7.629 MB)\n",
    "  0.002174 seconds (2 allocations: 7.629 MB)\n",
    "  0.002313 seconds (2 allocations: 7.629 MB)\n",
    "  0.002065 seconds (2 allocations: 7.629 MB)\n",
    "  0.002451 seconds (2 allocations: 7.629 MB)\n",
    "  0.002351 seconds (2 allocations: 7.629 MB)\n",
    "  0.001972 seconds (2 allocations: 7.629 MB)\n",
    "  0.001963 seconds (2 allocations: 7.629 MB)\n",
    "  0.001890 seconds (2 allocations: 7.629 MB)\n",
    "  0.001963 seconds (2 allocations: 7.629 MB)\n",
    "  0.001983 seconds (2 allocations: 7.629 MB)\n",
    "  0.001922 seconds (2 allocations: 7.629 MB)\n",
    "  0.001950 seconds (2 allocations: 7.629 MB)\n",
    "  0.001924 seconds (2 allocations: 7.629 MB)\n",
    "  0.002036 seconds (2 allocations: 7.629 MB)\n",
    "  0.002031 seconds (2 allocations: 7.629 MB)\n",
    "  0.002708 seconds (2 allocations: 7.629 MB)\n",
    "  0.001975 seconds (2 allocations: 7.629 MB)\n",
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    "  0.003178 seconds (2 allocations: 7.629 MB)\n",
    "  0.003546 seconds (2 allocations: 7.629 MB)\")\n",
    "ss3 = map(x -> parse(Float64, x), ss3)\n",
    "plot(ss3[1:200], label=\"list comprehension\")\n",
    "plot(ss3[201:400], label=\"for loop\")\n",
    "legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00214382\n",
      "0.0030613550000000004\n"
     ]
    }
   ],
   "source": [
    "println(mean(ss3[1:200]))\n",
    "println(mean(ss3[201:400]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8146825889447233e-7\n",
      "2.4481314763567836e-6\n"
     ]
    }
   ],
   "source": [
    "println(var(ss3[1:200]))\n",
    "println(var(ss3[201:400]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for loopが遅いうえにばらついてるようです. 分散が等しくないとして帰無仮説: 両方の平均が等しい　に対し両側T検定を行うと,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Two sample t-test (unequal variance)\n",
       "------------------------------------\n",
       "Population details:\n",
       "    parameter of interest:   Mean difference\n",
       "    value under h_0:         0\n",
       "    point estimate:          -0.0009175350000000006\n",
       "    95% confidence interval: (-0.0011434712312310754,-0.0006915987687689258)\n",
       "\n",
       "Test summary:\n",
       "    outcome with 95% confidence: reject h_0\n",
       "    two-sided p-value:           6.154100229042786e-14 (extremely significant)\n",
       "\n",
       "Details:\n",
       "    number of observations:   [200,200]\n",
       "    t-statistic:              -8.001894365119986\n",
       "    degrees of freedom:       228.34062002695802\n",
       "    empirical standard error: 0.00011466472289355728\n"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UnequalVarianceTTest(ss3[1:200], ss3[201:400])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "p値がすごいです. 統計的に有意でした."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## まとめ\n",
    "※条件によって変わる可能性あり\n",
    "\n",
    "ループが本質的なとき(処理の大部分を占めるとき)には, \n",
    "* enumerateを使わないことで微妙に早くなる\n",
    "* python では早いlist内包表記は使わないほうが無難\n",
    "* while文をfor文でかけるときはfor文で書くべき\n",
    "* 列でアクセスすると良い\n",
    "* elementwiseの演算はそうでない演算のループに書き直すと良い\n",
    "\n",
    "\n",
    "* indmax, indmin(pythonでのargmax, argminに相当)は遅い#そうでもない\n",
    "* typeは指定する\n",
    "* 変数の型をInt64より小さくしても(Int8など)高速化にはほとんど意味が無い\n",
    "* @inbounds で微妙に早くなる#そうでもない\n",
    "* threadは読み込みに時間が掛かる(?)\n",
    "* 変数をいちいち作ると遅くなる\n",
    "* findfirstなど, リストを走査するような関数はおそいので, 関数内で何度も呼ぶ必要があるときは事前に何らかの形で簡単に出来ないか考慮する."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "findminindex (generic function with 1 method)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function findminindex(xs)\n",
    "    ind = 1\n",
    "    for i in 1:length(xs)\n",
    "        if xs[ind] > xs[i]\n",
    "            ind = i\n",
    "        end\n",
    "    end\n",
    "    return ind\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs = shuffle(collect(1:1000000))\n",
    "t1s = []\n",
    "t2s = []\n",
    "for i in 1:100\n",
    "    _, t1, _, _, _ = @timed indmin(xs)\n",
    "    _, t2, _, _, _ = @timed findminindex(xs)\n",
    "    push!(t1s, t1)\n",
    "    push!(t2s, t2)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x32c084d30>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x309dfdb38>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using PyPlot\n",
    "plot(t1s, label=\"indmin()\")\n",
    "plot(t2s, label=\"findminindex()\")\n",
    "legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "function noinbounds(xs)\n",
    "    for i in 1:length(xs)\n",
    "        xs[i] = xs[xs[i]]\n",
    "    end\n",
    "end\n",
    "\n",
    "@inbounds function inbounds(xs)\n",
    "    for i in 1:length(xs)\n",
    "        xs[i] = xs[xs[i]]\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs = shuffle(collect(1:1000000))\n",
    "t1s = []\n",
    "t2s = []\n",
    "for i in 1:100\n",
    "    _, t1, _, _, _ = @timed inbounds(xs)\n",
    "    _, t2, _, _, _ = @timed noinbounds(xs)\n",
    "    push!(t1s, t1)\n",
    "    push!(t2s, t2)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RhAkT1KlTJ3322WfKzc3VyJEjGz1H2F3Tp0/Xxo0btX79esXExCguLk7FxcV67733NGLECKWnpzv137Ztm2bOnKlhw4YpKipKXbt21dGjR5Weni4vLy/NnDnTqf/dd9+trKws/e1vf9Ntt93m8nzDMPThhx/qRz/6ka699tqLfp+2yvQrxVU1VZ6eAgAAbU5jH75o6kMYTfWXJLvdru3btysuLk5r167V66+/roCAAC1fvlzLly936b9w4UKlpqYqKipKb731llasWCHDMLRgwYJGP9wxatQopaena8CAAVq7dq1SUlIUHh6uvXv3qlevXo3OqbmPejT2nj4+Pvrwww81bdo0FRUVafny5dq1a5eeffZZvfzyyy73i4uL09SpU1VeXq5Nmzbp5Zdf1q5duxQXF6ddu3bprrvucrr/pEmT5O3trZSUlEbns23bNh0/flxTpkxpcs6QLEZj1esmkJWVpcGDB2vnZzs1YtiI8/YHAKA5Df+9kpmZqRgzHSeBK8Jvf/tbpaSkKDc312Uz3d13361du3YpJyfnok/daC0t/fflcv57ZfqV4uraxnecAgAAXC2ef/55+fj4aMGCBU7t+/fv18aNGzVv3rwrJhBfqUwfiimfAAAAV7uuXbtqzZo16t69u1N7QUGB5s+fr4cffthDM7t6mH6jXWVt5fk7AQAAXOFGjx6t0aNHO7XdeuutuvXWWz00o6sLK8WsFAMAAJgeoZhQDAAAYHqE4lpCMQAAgNmZPhRX1lBTDAAAYHamD8WsFAMAAIBQTE0xAACA6RGKCcUAAACmRyimfAIAAMD0CMWEYgAAANMjFFM+AQAAYHqmD8UcyQYAAADTh2LKJwAAuLrk5eXJarXqwQcfvOh79e7dW+Hh4a0wqytXa/68RowYoUGDBjm1LV26VD4+PsrOzr7o+3sSoZjyCQAArjoWi0UWi6VV7oOWSUtL0yeffKLnn3/eqX3KlCnq0qWLZsyY4aGZtQ6bpyfgaawUAwBwdQkNDdXBgwfVsWNHT0/FVJ5++mlFRUXpjjvucGpv166dHn/8cSUlJSkjI0NDhw710AwvjulXiiurqSkGAOBqYrPZFBUVpW7dunl6Kqaxbds2/etf/9IDDzzQ6PWJEyfKYrEoOTn58k6sFZk+FLNSDABA6zq7hjUvL0+/+tWv1KVLF/n6+mrIkCHavHmzy5jKykotXLhQAwcOVPv27dWxY0fddNNNeu+995q9/9keeOABWa1WHTlyRK+99poGDhwoX19fBQcH6+GHH9b333/f5Jy///57PfbYY+rRo4d8fX113XXXacWKFU32X7dunW666SYFBATIz89PAwcO1MKFC1VZ6brYZrVaNWrUqEbvc/acL+bnJ0mlpaV64okn1LNnT/n6+qp///5aunSpamtrG+3/3XffacaMGerXr5/8/f0VGBiofv36KTExUbm5uU59//SnP8lisWj8+PGN3iskJEQ33XST0tLSVFpa2mifK53pyyeqa6s9PQUAgImVVZXpUNGhy/7cfkH95Oftd0mfkZubq9jYWEVEROjXv/61Tp48qbVr1+rOO+/U9u3bNWLECElSVVWVfvGLX+jjjz9W//799dhjj6msrExpaWmaMGGC/vd//1fz588/7/Ma6oxnzpyprVu36vbbb1dcXJx27Nih119/XTk5Odq+fbvLuMrKSt1yyy0qKSlRQkKCKisrtX79ev3ud79Tdna2Szh+6qmntHDhQnXp0kX33nuv/P39tWXLFj311FPaunWrtm7dKputZRGrudrolv78Gt5h1KhR+vzzzxUdHa2JEyfq1KlTmj9/vj766COXe5eXl+vGG2/U4cOH9fOf/1xjxoyRYRjKy8vTpk2bNG7cOPXu3dvRf8eOHQoODlafPn2afJfhw4fro48+0scff6z4+PgWvf+VxPShmCPZAACedKjokAavHHzZn5v5UKZiQmIu6TM++ugjzZs3T88884yjLSEhQbfeeqsWL17sCHVLlizRxx9/rP/6r/9Senq6rNa6/0f23LlzNWTIEL344osaPXp0i2pVDcPQnj179OWXXyo0NFSSVFtbq5tvvlk7duzQ559/rhtuuMFpTH5+viIiIvTpp5/K29tbkjRv3jzdcMMNSk5O1oQJE/STn/xEkpSRkaGFCxeqV69e2rt3r7p06SJJevHFF3XnnXdq8+bNWrJkiZKSki7yp9fyn59U9zP8/PPPNXbsWK1bt87RnpSUpJiYGJfg/eGHH+qbb77RE088oSVLljhdq66uVkVFheOfv/rqK504cUK33357s/MdMmSIDMMgFF+tKJ8AAHhSv6B+ynwo0yPPvdR69eqlp59+2qntF7/4hcLCwrR3715H25///GdZrVa9/PLLjkAsSUFBQZozZ45+85vf6I033mhRKLZYLJo7d64jEEt15QuJiYnatWuX9u7d6xKKpbpQ2xCIJSkgIEBz5sxRYmKiVq1a5QjFDWUEzzzzjCMQNzzjpZde0vvvv6833nijVUJxS39+krRq1Sp5eXlp0aJFLveYOnWq5s2b1+gz2rVr59Jms9mcVrobSjtCQkKanW9wcLBT/6uN6UMxK8UAAE/y8/a75Cu2nhIdHd1oaUDPnj2VkZEhqa4ONicnRz169FDfvn1d+jbU4u7bt6/Fzx082HXlvWfPnpKk4uJil2s2m03Dhg1zaR85cqTLsxv+fvPNN7v079u3r3r06KHDhw/r9OnTuuaaa1o858a05Ocn/fAzDAsLa7S8YeTIkS6heMSIEQoNDdXChQuVmZmp+Ph4DR8+XNHR0U7/w0SS/v3vf0uSAgMDm51vp06dJElFRUUte8ErjOk32lFTDADApREQENBou81mc2z+KikpkdT0KmRD+6lTpy7quQ0rnzU1NS7XgoKCGg2fDSufDXO8VPNtSkt+fmfPqanTOBre42zXXHON9uzZo8TERGVlZenxxx/XDTfcoODgYD333HOqrv4hH/n6+kqSzpw50+x8y8vLnfpfbUwfilkpBgDAcxrOGi4oKGj0en5+vlO/S6GoqEiGYbi0N8zp7GdfyHwtFotTyDxba4TnhmcVFhY2er2puXbv3l2vv/66CgsL9eWXX2rFihUKCgrS888/rxdeeMHRr2vXrpJ+WDFuSsP1hv5XG9OHYr5oBwCA5/j7+ysiIkLHjh1TTk6Oy/W//e1vkhoviWgt1dXV+uyzz1zad+zYIUn68Y9/7Ghr+PvOnTtd+ufk5Ojo0aPq06ePOnTo4GgPDAzUt99+69K/trZW+/fvv9jpy9/fX5GRkTp27JgOHz7scr3hPZrTv39/Pfroo9q6daskaePGjY5r1113nby8vHToUPOnpDRcj46Odmf6VwxCMRvtAADwqAcffFC1tbWaOXOmU1lAUVGRXnjhBVksFiUmJl7SOcyePdvpjOGTJ09q/vz5Ls9+8MEHZRiG5s+f71Q7W1tbq+nTp8swDP3mN79xundsbKyOHDnichzcCy+8oLy8vFaZf2JiompqavTkk086rXofPnxYK1ascCkP+ec//6nvvvvO5T4Nq8rt27d3tHXo0EHR0dH6xz/+4XQqxbl2794ti8XSaL311cD0G+0IxQAAeNaMGTO0ZcsWpaena9CgQYqPj1dZWZnee+89nThxQk8++aRuvPHGS/b8kJAQVVRUaMCAARozZozjnOKCggI9+uijjpMnJGnYsGGaNWuWFi9erAEDBmjs2LFq3769tmzZogMHDuinP/2pZsyY4fJ+H3zwgcaMGaMJEyaoU6dO+uyzz5Sbm6uRI0c2eo6wu6ZPn66NGzdq/fr1iomJUVxcnIqLi/Xee+9pxIgRSk9Pd+q/bds2zZw5U8OGDVNUVJS6du2qo0ePKj09XV5eXpo5c6ZT/7vvvltZWVn629/+pttuu83l+YZh6MMPP9SPfvQjXXvttRf9Pp7ASjHlEwAAtLrmPkrRcL2Bt7e3tm/frgULFshiseiVV15RSkqKfvSjHyk1NVW///3vW3z/8z2zset2u13bt29XXFyc1q5dq9dff10BAQFavny5li9f7tJ/4cKFSk1NVVRUlN566y2tWLFChmFowYIFjX64Y9SoUUpPT9eAAQO0du1apaSkKDw8XHv37lWvXr2afI+W/vwkycfHRx9++KGmTZumoqIiLV++XLt27dKzzz6rl19+2eV+cXFxmjp1qsrLy7Vp0ya9/PLL2rVrl+Li4rRr1y7dddddTvefNGmSvL29lZKS0uh8tm3bpuPHj2vKlClNzvlKZzEaqyw3gaysLA0ePFh9Z/dV9u+zPT0dAMBVruG/VzIzMxUT0zaPWIO5/fa3v1VKSopyc3NdNtPdfffd2rVrl3Jyclp0FF1L/325nP9esVJM+QQAAMB5Pf/88/Lx8dGCBQuc2vfv36+NGzdq3rx5F302sycRiimfAAAAOK+uXbtqzZo16t69u1N7QUGB5s+fr4cffthDM2sdbLRjpRgAAKBFRo8erdGjRzu13Xrrrbr11ls9NKPWY/qV4spqPt4BAABgdqYPxXzmGQAAAKYPxZRPAAAAwPShuLKG8gkAAACzM30oNgxDNbU1np4GAAAAPMjt0ycqKys1Z84crVmzRsXFxRo4cKDmz5+vW2655bxjS0pKNHPmTG3cuFFlZWWKjY3VSy+9pB//+MeOPuXl5frzn/+sTZs26YsvvlBpaakiIyP10EMP6aGHHpLV+kOOz8vLU58+fVyeY7FYlJqaqvHjx7fonSpqKuRn9WtRXwAAmnPw4EFPTwG44l2J/564HYrvv/9+bdiwQdOmTVNkZKRWr16t+Ph47dy5s9nvkhuGofj4eH3xxReaNWuWOnfurOTkZI0cOVJZWVmKiIiQJH3zzTeaOnWqbrnlFk2fPl0dOnTQBx98oEceeUR79uzRqlWrXO59zz33KD4+3qlt2LBhLX6nyppK+XkTigEAFy4oKEh+fn6aOHGip6cCXBX8/PwUFBTk6Wk4uBWK9+7dq7Vr1+qll17StGnTJEn33XefBgwYoFmzZumTTz5pcux7772n3bt3a/369Y7vaY8bN05RUVGaO3eu1qxZI0kKDg7Wl19+qf79+zvGTp48WZMmTdLq1as1Z84chYeHO907JiZG99xzjzuv4qSiuuKCxwIAIElhYWE6ePCgioqKPD0V4KoQFBSksLAwT0/Dwa1QnJaWJpvNpsmTJzva7Ha7Jk2apKefflrHjh1TaGhoo2PXr1+v4OBgRyCW6n4Y48eP19tvv62qqip5e3urc+fO6ty5s8v4u+66S6tXr9bBgwddQrEklZWVydvbW97e3u68kiQ22wEAWkdYWNgV9V/yAFrOrY12+/fvV1RUlPz9/Z3aY2NjHdebsm/fPsXExLi0x8bGqqysTNnZ2c0+Oz8/X5IaXWafN2+e/P391a5dO8XGxmrbtm3nfZezVdSwUgwAAGBmboXi/Px8hYSEuLSHhITIMAwdP378gsZKanZsVVWVli1bpvDwcA0ZMuSHyVutiouL0+LFi/XXv/5Vy5Yt04kTJ3Tbbbdpy5YtLX4vVooBAADMza3yifLyctntdpf2du3aOa5fyFjDMJod++ijj+rQoUN6//33nU6f6Nmzp0v4nThxoq699lpNnz5dt91223nfSSIUAwAAmJ1bodjX11cVFa6lBmfOnHFcv5CxFoulybGLFy/WG2+8oQULFiguLu68cwwMDFRiYqIWLVqk48ePq3v37s0P+H/S//3m/yrQN9DRlJCQoISEhPM+CwAAAK0jNTVVqampTm0lJSWX7fluheKQkJBGyxwa6n2bC6AhISGOfi0du3r1aiUlJemRRx7R7NmzWzzPnj17SpJOnjx5/lB8q/T7p36vn/b6aYvvDwAAgNbV2KJkVlaWBg8efFme71ZNcXR0tLKzs1VaWurUnpGRIYvFoujo6GbHZmVlubRnZGTIz89PUVFRTu3p6emaPHmyxo4dq1deecWdaSonJ0eS1KVLlxb1Z6MdAACAubkViseOHavq6mqtXLnS0VZZWanVq1fUUhPjAAAgAElEQVRr6NChjuPYCgoK9NVXX6mmpsZpbGFhoTZs2OBoKyoqUlpamsaMGeN0lNrHH3+shIQEjRw50nF+cWMaOwvy2LFjWrVqlQYNGqRu3bq16L2oKQYAADA3t8onYmNjNW7cOM2ePVuFhYWOL9rl5eU5fWkuKSlJKSkpys3NdZzXOHbsWC1btkyJiYk6cOCAgoKClJycrNraWj333HOOsUeOHNGYMWNktVr1y1/+UuvWrXOaw8CBA3X99ddLkmbNmqWcnBz97Gc/U/fu3XX48GGtXLlSZWVl+uMf/9ji9+LjHQAAAObm9mee33rrLc2ZM0dr1qxRcXGxBg4cqM2bN2v48OGOPhaLxemUCKnu+LQtW7Zo5syZWrFihcrLyxUbG6uUlBT17dvX0e/w4cM6ffq0JOmxxx5zef7cuXMdoTguLk6vvvqqkpOTVVxcrICAAI0cOVJPP/10s6Uc52KlGAAAwNwshmEYnp6EJzgKtx+S3nzkTf160K89PSUAAACcpSGvZWZmNvoRuNbkVk1xW8VKMQAAgLmZPhTbvGyEYgAAAJMzfSj2tnqz0Q4AAMDkTB+Kfbx8WCkGAAAwOdOHYm+rNx/vAAAAMDnTh2KblZpiAAAAszN9KPax+VBTDAAAYHKmD8XeVm9WigEAAEzO9KHYx8uHmmIAAACTM30opqYYAAAApg/FHMkGAAAA04diby+OZAMAADA7QjEb7QAAAEyPUMxnngEAAEyPUOzFSjEAAIDZmT4UcyQbAAAATB+KqSkGAAAAoZjyCQAAANMjFLPRDgAAwPRMH4r5eAcAAABMH4ptVhsb7QAAAEzO9KGYlWIAAACYPhR7e1FTDAAAYHaEYo5kAwAAMD1CsZe3KmoqZBiGp6cCAAAADyEUW70lSdW11R6eCQAAADzF9KHYx8tHkiihAAAAMDHTh2Jvr7qVYo5lAwAAMC9CcX35BCvFAAAA5kUorg/FHMsGAABgXoRiL1aKAQAAzM70obhhox01xQAAAOZl+lBss9oksVIMAABgZqYPxY6VYmqKAQAATMv0oZjTJwAAAEAoZqMdAACA6RGK+XgHAACA6Zk+FPtY+cwzAACA2Zk+FDtWitloBwAAYFqEYjbaAQAAmJ7pQ7GX1UsWWagpBgAAMDHTh2KLxSIfLx9WigEAAEzM9KFYkuw2OzXFAAAAJkYollgpBgAAMDlCsSS7l51QDAAAYGKEYtWtFLPRDgAAwLwIxaqrKWalGAAAwLwIxapfKWajHQAAgGkRisVGOwAAALMjFKtuox01xQAAAOZFKBYrxQAAAGZHKFb9xztYKQYAADAtQrFYKQYAADA7QrH4eAcAAIDZEYrFkWwAAABmRygW5RMAAABmRygWR7IBAACYHaFYrBQDAACYHaFY9UeyUVMMAABgWoRisVIMAABgdoRicSQbAACA2RGKVX8kGxvtAAAATItQrLqaYlaKAQAAzItQLD7eAQAAYHaEYtWF4qraKhmG4empAAAAwAMIxarbaCeJEgoAAACTcjsUV1ZW6sknn1RoaKj8/Pw0dOhQbd++vUVjS0pK9NBDD6lr167y9/fXqFGjtG/fPqc+5eXl+u///m/FxcWpe/fu6tChg2JiYvTqq6+qtrbW5Z6GYegPf/iDwsPD5evrq0GDBundd9916518vHzq3o1QDAAAYEpuh+L7779fy5Yt03333afly5fLZrMpPj5en332WbPjDMNQfHy83n33XU2dOlWLFy/WiRMnNHLkSOXk5Dj6ffPNN5o6daokafr06XrppZcUHh6uRx55RJMmTXK571NPPaWkpCTFxcXplVdeUa9evXTPPfdo3bp1LX4nu61upZgTKAAAAEzKcMOePXsMi8VivPzyy462M2fOGJGRkcbw4cObHbt27VrDYrEYGzZscLSdOHHCCAwMNO69915HW1FRkfHPf/7TZfyDDz5oWK1WIycnx9F27Ngxw8fHx5g6dapT35tuuskICwszamtrm5xPZmamIcnIzMw0NmdvNvScjGPfH2v2HQAAAHD5nJ3XLjW3VorT0tJks9k0efJkR5vdbtekSZO0e/duHTt2rMmx69evV3BwsO666y5HW1BQkMaPH6/09HRVVVVJkjp37qz+/fu7jG8Yd/DgQUfbxo0bVV1drSlTpjj1nTJlio4ePardu3e36L2oKQYAADA3t0Lx/v37FRUVJX9/f6f22NhYx/Wm7Nu3TzExMS7tsbGxKisrU3Z2drPPzs/Pl1QXpM+eT/v27dWvXz+XexqG4VKv3JSGmmKOZQMAADAnt0Jxfn6+QkJCXNpDQkJkGIaOHz9+QWMlNTu2qqpKy5YtU3h4uIYMGeJ0z27dul3QPc/WUFPMSjEAAIA52dzpXF5eLrvd7tLerl07x/ULGWsYRrNjH330UR06dEjvv/++rNYfcvzFzOdsjpViNtoBAACYkluh2NfXVxUVrsHxzJkzjusXMtZisTQ5dvHixXrjjTe0YMECxcXFtdp8GkybNk1WX6t0WHr8k8fVybeTEhISlJCQcN6xAAAAaB2pqalKTU11aispKblsz3crFIeEhDRaktBQ79u9e/dmxzb0a+nY1atXKykpSY888ohmz57d6D137tx5QfNpsHTpUnXs3VGRKyL1wq9f0M19bj7vGAAAALSuxhYls7KyNHjw4MvyfLdqiqOjo5Wdna3S0lKn9oyMDFksFkVHRzc7Nisry6U9IyNDfn5+ioqKcmpPT0/X5MmTNXbsWL3yyitN3rOsrEyHDh1yez5n4+MdAAAA5uZWKB47dqyqq6u1cuVKR1tlZaVWr16toUOHKjQ0VJJUUFCgr776SjU1NU5jCwsLtWHDBkdbUVGR0tLSNGbMGHl7ezvaP/74YyUkJGjkyJFas2ZNk/O54447ZLPZlJyc7NT+6quvKjQ0VDfeeGOL3ouPdwAAAJibW+UTsbGxGjdunGbPnq3CwkJFRkZq9erVysvL06pVqxz9kpKSlJKSotzcXIWFhUmqC8XLli1TYmKiDhw4oKCgICUnJ6u2tlbPPfecY+yRI0c0ZswYWa1W/fKXv3T5Mt3AgQN1/fXXS5JCQ0P1+OOPa8mSJaqsrNSQIUP0l7/8RZ9++qneeecdWSyWFr0XK8UAAADm5lYolqS33npLc+bM0Zo1a1RcXKyBAwdq8+bNGj58uKOPxWJxOiVCkqxWq7Zs2aKZM2dqxYoVKi8vV2xsrFJSUtS3b19Hv8OHD+v06dOSpMcee8zl+XPnznWEYklatGiROnXqpNdee01vvvmm+vbtq7ffflsTJkxo8Tvx8Q4AAABzsxiGYXh6Ep7QULidmZmpQdGDZHvBpj+P+bMSf5zo6akBAABAznmtsY/AtSa3aorbKi+rl6wWKyvFAAAAJkUormf3srPRDgAAwKQIxfV8vHxYKQYAADApQnE9u82uimpWigEAAMyIUFyPlWIAAADzIhTXo6YYAADAvAjF9VgpBgAAMC9CcT27zU4oBgAAMClCcT0fLx822gEAAJgUobiej5ePKmtZKQYAADAjQnE9uxdHsgEAAJgVobgeG+0AAADMi1Bcz27jSDYAAACzIhTXY6UYAADAvAjF9exeHMkGAABgVoTiehzJBgAAYF6E4nqsFAMAAJgXobiej5cPG+0AAABMilBcj412AAAA5kUorme38fEOAAAAsyIU12OlGAAAwLwIxfXsXny8AwAAwKwIxfVYKQYAADAvQnE9u40j2QAAAMyKUFzPx8tH1bXVqjVqPT0VAAAAXGaE4np2L7sksVoMAABgQoTiej5ePpLEsWwAAAAmRCiu1xCKWSkGAAAwH0JxPbutrnyCY9kAAADMh1Bcj5ViAAAA8yIU12vYaEdNMQAAgPkQiuuxUgwAAGBehOJ6DTXFhGIAAADzIRTXcxzJxkY7AAAA0yEU16N8AgAAwLwIxfXYaAcAAGBepg/FVVV1f7JSDAAAYF6mD8XZ2XV/8vEOAAAA8zJ9KD5woO5PVooBAADMy/Sh+Msv6/50nD5BTTEAAIDpmD4UN6wUWy1W2aw2VooBAABMyPShODdXOnWq7u92LzuhGAAAwIRMH4ol6fPP6/708fJhox0AAIAJmT4U+/tLe/fW/d3Hy4eVYgAAABMyfSi+9tofQrHdZmejHQAAgAmZPhQPGCDt2SMZBivFAAAAZkUoHiAVFEhHj9ZttKOmGAAAwHxMH4qvvbbuz717WSkGAAAwK9OH4i5dpJ4960oo7DaOZAMAADAj04diSYqN/WGlmPIJAAAA8yEUS/o//6furGIfKyvFAAAAZkQoVt1K8X/+I1WW+3AkGwAAgAkRiiUNHixZrdLpU2y0AwAAMCNCseq+anfdddL3JzmSDQAAwIwIxfViY6WTRawUAwAAmBGhuF5srHSqyK7ySlaKAQAAzIZQXC82VjKqfVRSykoxAACA2RCK6w0YINlk1+kyQjEAAIDZEIrr2WxScBcflZ6hfAIAAMBsCMVn6dHdR2eqWCkGAAAwG0LxWXr3sKtGFfruO0/PBAAAAJcTofgsfcJ8JK9K/f3vnp4JAAAALidC8VmCu9glW4X27vX0TAAAAHA5EYrPYveqWynes8fTMwEAAMDlRCg+i91mlyy12vP3ahmGp2cDAACAy4VQfBYfLx9J0qnTlcrJ8fBkAAAAcNm4HYorKyv15JNPKjQ0VH5+fho6dKi2b9/eorElJSV66KGH1LVrV/n7+2vUqFHat2+fS79t27Zp0qRJuv7662Wz2RQeHt7o/fLy8mS1Wl3+4+XlpXXr1rn7arJ72ev+4lWp48fdHg4AAICrlM3dAffff782bNigadOmKTIyUqtXr1Z8fLx27typG2+8sclxhmEoPj5eX3zxhWbNmqXOnTsrOTlZI0eOVFZWliIiIhx933nnHa1bt04xMTEKDQ0975zuuecexcfHO7UNGzbM3VdzrBTLq0KnTrk9HAAAAFcpt0Lx3r17tXbtWr300kuaNm2aJOm+++7TgAEDNGvWLH3yySdNjn3vvfe0e/durV+/XnfddZckady4cYqKitLcuXO1Zs0aR98XX3xRb7zxhry8vHT77bfrwIEDzc4rJiZG99xzjzuv0qgfQnEloRgAAMBE3CqfSEtLk81m0+TJkx1tdrtdkyZN0u7du3Xs2LEmx65fv17BwcGOQCxJQUFBGj9+vNLT01VVVeVoDw4OlpeXlztTU1lZmdM9LoTdVlc+YW9foeLii7oVAAAAriJuheL9+/crKipK/v7+Tu2xsbGO603Zt2+fYmJiXNpjY2NVVlam7Oxsd6biZN68efL391e7du0UGxurbdu2XdB9GlaKrwlgpRgAAMBM3ArF+fn5CgkJcWkPCQmRYRg63szutObGSmp2bFOsVqvi4uK0ePFi/fWvf9WyZct04sQJ3XbbbdqyZYvb92vYaHdNIDXFAAAAZuJWTXF5ebnsdrtLe7t27RzXL2SsYRjNjm1Kz549XcLvxIkTde2112r69Om67bbb3Lpfw0qxf0dWigEAAMzErVDs6+uriooKl/YzZ844rl/IWIvF0uxYdwQGBioxMVGLFi3S8ePH1b1792b7T5s2TR07dpQklVaWSoel8k6bdcr+f1plPgAAADi/1NRUpaamOrWVlJRctue7FYpDQkIaLXPIz8+XpGYDaEhIiKOfu2Pd1bNnT0nSyZMnz3vfpUuXOmqdc0/lqs8f+yjs6E0q/lerTQcAAADnkZCQoISEBKe2rKwsDR48+LI8362a4ujoaGVnZ6u0tNSpPSMjQxaLRdHR0c2OzcrKcmnPyMiQn5+foqKi3JlKs3LqP0fXpUsXt8Y11BT7+lM+AQAAYCZuheKxY8equrpaK1eudLRVVlZq9erVGjp0qONDGwUFBfrqq69UU1PjNLawsFAbNmxwtBUVFSktLU1jxoyRt7e325MvKipyaTt27JhWrVqlQYMGqVu3bm7dr6GmuJ0/G+0AAADMxK3yidjYWI0bN06zZ89WYWGh44t2eXl5WrVqlaNfUlKSUlJSlJubq7CwMEl1oXjZsmVKTEzUgQMHFBQUpOTkZNXW1uq5555zes4XX3yhTZs2SZK+/vprlZSUaMGCBZKkQYMGafTo0ZKkWbNmKScnRz/72c/UvXt3HT58WCtXrlRZWZn++Mc/uv3DcITi9qwUAwAAmInbn3l+6623NGfOHK1Zs0bFxcUaOHCgNm/erOHDhzv6WCwWWa3Oi9BWq1VbtmzRzJkztWLFCpWXlys2NlYpKSnq27evU9+srCw9++yzTm0N/3z//fc7QnFcXJxeffVVJScnq7i4WAEBARo5cqSefvrpZks5mtLw8Q4fvwp9/71UWytZ3VpLBwAAwNXIYhiG4elJeEJD4XZmZqZjo51hGLI+b9Vvu7+uVx/6jU6elAIDPTxRAAAAk2osr10qrIOexWKxyMfLR7Z2dUfHUUIBAABgDoTic/h4+ci7XaUkQjEAAIBZEIrPYfeyy2YnFAMAAJgJofgcPl4+snpTPgEAAGAmhOJz1IXiupXi4mIPTwYAAACXBaH4HHabXVW1FfL3Z6UYAADALAjF5/Dx8lFlTaUCAwnFAAAAZkEoPofdy66KmgoFBBCKAQAAzIJQfI6GlWJCMQAAgHkQis9ht/2wUsxGOwAAAHMgFJ+DlWIAAADzIRSfw+5lJxQDAACYDKH4HD5ePqqoruD0CQAAABMhFJ+D8gkAAADzIRSf4+yNdqWlUlWVp2cEAACAS41QfA4f6w8rxZJUUuLZ+QAAAODSIxSfw26zq6K6whGKKaEAAABo+wjF5zj7M88SoRgAAMAMCMXnOPszzxIf8AAAADADQvE5zj59QmKlGAAAwAwIxeew2+o+3tGhg2SxEIoBAADMgFB8joaPd1itUocOhGIAAAAzIBSfo6F8QhJftQMAADAJQvE5GjbaGYahgAA22gEAAJgBofgcPl4+kqTq2mo+9QwAAGAShOJz2G12SXIcy0YoBgAAaPsIxedoWCluOJaNUAwAAND2EYrPYfeqXymurmCjHQAAgEkQis9x7koxG+0AAADaPkLxOSifAAAAMB9C8TnO3Wh35kzdfwAAANB2EYrP0c7WTpJUVlWmgIC6tpISD04IAAAAlxyh+Bw9OvSQJB0pOaLAwLo2SigAAADaNkLxOTr7dlYHewflnMxxrBSz2Q4AAKBtIxSfw2KxKLJTpHKKfwjFrBQDAAC0bYTiRkQERujrk18TigEAAEyCUNyIiMAI5RTnyN9fsloJxQAAAG0dobgRkZ0i9W3Jt6qsP5aNUAwAANC2EYobEdEpQoYMHT51WIGBbLQDAABo6wjFjYgIjJAkxwkUrBQDAAC0bYTiRoR2CJXdy+44gYJQDAAA0LYRihthtVgVHhjuOIGCUAwAANC2EYqbENEpgpViAAAAkyAUNyEyMFI5J3PYaAcAAGAChOImRHSK0DfF36hDxxpWigEAANo4QnETIgIjVFVbJXU4qlOnJMPw9IwAAABwqRCKmxDZKVKSVO6bo6oqqbzcwxMCAADAJUMobkKvgF6yWqw67f21JDbbAQAAtGWE4ib4ePkorGOYii05kthsBwAA0JYRipsR2SlS31XVhWJWigEAANouQnEzIgIjdKyc8gkAAIC2jlDcjIjACB05nSPJIBQDAAC0YYTiZkR2ilRpValsHU8QigEAANowQnEzIjpFSJKu6fU1G+0AAADaMEJxM8IDwyVJ9uAcVooBAADaMEJxM/x9/BXsHyxrF0IxAABAW0YoPo+IwAgZAV8TigEAANowQvF5RHaKVGV7VooBAADaMkLxeUQERug/9hw22gEAALRhhOLziOgUoTNeJ3TyP997eioAAAC4RAjF5xHZKVKSdFI5Hp4JAAAALhVC8XlEBNadVXzaliPD8PBkAAAAcEkQis+jk28n+Vk7ygj8WqWlnp4NAAAALgVC8XlYLBaF+kZKgWy2AwAAaKsIxS0Qdk2E1Ilj2QAAANoqQnELRARGSJ34gAcAAEBb5XYorqys1JNPPqnQ0FD5+flp6NCh2r59e4vGlpSU6KGHHlLXrl3l7++vUaNGad++fS79tm3bpkmTJun666+XzWZTeHh4k/c0DEN/+MMfFB4eLl9fXw0aNEjvvvuuu6/VrH7dIqUOR3XiZEWr3hcAAABXBrdD8f33369ly5bpvvvu0/Lly2Wz2RQfH6/PPvus2XGGYSg+Pl7vvvuupk6dqsWLF+vEiRMaOXKkcnKcjzt755139O677yogIEChoaHN3vepp55SUlKS4uLi9Morr6hXr1665557tG7dOndfrUkDQiIki6F/FR1utXsCAADgCmK4Yc+ePYbFYjFefvllR9uZM2eMyMhIY/jw4c2OXbt2rWGxWIwNGzY42k6cOGEEBgYa9957r1Pf/Px8o7q62jAMwxg9erTRp0+fRu957Ngxw8fHx5g6dapT+0033WSEhYUZtbW1Tc4nMzPTkGRkZmY2O2/DMIxvS7419JyMyYv/et6+AAAAaB3u5LWL5dZKcVpammw2myZPnuxos9vtmjRpknbv3q1jx441OXb9+vUKDg7WXXfd5WgLCgrS+PHjlZ6erqqqKkd7cHCwvLy8zjufjRs3qrq6WlOmTHFqnzJlio4ePardu3e783pN6n5Nd6m6nb79Dx/wAAAAaIvcCsX79+9XVFSU/P39ndpjY2Md15uyb98+xcTEuLTHxsaqrKxM2dnZ7kzF8bz27durX79+Lvc0DKPReuULYbVY5fOfcBVUEIoBAADaIrdCcX5+vkJCQlzaQ0JCZBiGjh8/fkFjJTU7trl7duvWrVXv2ZT2FRE6UfN1q90PAAAAVw63QnF5ebnsdrtLe7t27RzXL2SsYRjNjr0U83FXx5pIlVhZKQYAAGiLbO509vX1VUWF67FkZ86ccVy/kLEWi6XZsZdiPg2mTZumjh07OrUlJCQoISHBqa2zNUJ5PodVU1sjL+v5650BAADQcqmpqUpNTXVqKykpuWzPdysUh4SENFqSkJ+fL0nq3r17s2Mb+rk7trl77ty586LuuXTp0kZrnV2eZY+QYa3St99/q94Bvd2dKgAAAJrR2KJkVlaWBg8efFme71b5RHR0tLKzs1VaWurUnpGRIYvFoujo6GbHZmVlubRnZGTIz89PUVFR7kzFcc+ysjIdOnTI7fm4q4dfpCQp5yQlFAAAAG2NW6F47Nixqq6u1sqVKx1tlZWVWr16tYYOHer40EZBQYG++uor1dTUOI0tLCzUhg0bHG1FRUVKS0vTmDFj5O3t7fbk77jjDtlsNiUnJzu1v/rqqwoNDdWNN97o9j2b0qtjL6nWqm+Kv2m1ewIAAODK4Fb5RGxsrMaNG6fZs2ersLBQkZGRWr16tfLy8rRq1SpHv6SkJKWkpCg3N1dhYWGS6kLxsmXLlJiYqAMHDigoKEjJycmqra3Vc8895/ScL774Qps2bZIkff311yopKdGCBQskSYMGDdLo0aMlSaGhoXr88ce1ZMkSVVZWasiQIfrLX/6iTz/9VO+8844sFssF/2DO1TnQWzrRRfmnC1rtngAAALgyuBWKJemtt97SnDlztGbNGhUXF2vgwIHavHmzhg8f7uhjsVhktTovQlutVm3ZskUzZ87UihUrVF5ertjYWKWkpKhv375OfbOysvTss886tTX88/333+8IxZK0aNEiderUSa+99prefPNN9e3bV2+//bYmTJjg7qs1KyBAUmmwjpwkFAMAALQ1FsMwDE9PwhMaCrczMzNbtNFu+3bp52/eqrhR7fX/EtdfhhkCAACYm7t57WK4VVNsZnUrxd1UWFro6akAAACglRGKW6ihfOK7MsonAAAA2hpCcQsFBEj6TzedrCQUAwAAtDWE4hbq2FFSabDO1P5HpZWl5+0PAACAqwehuIW8vaUO1mBJoq4YAACgjSEUuyE0oJskqaCUEgoAAIC2hFDsht6d61eK/8NKMQAAQFtCKHZDRGigVOPNSjEAAEAbQyh2Q1hPqyxlXfnUMwAAQBtDKHZDWJhknA7Wt8WUTwAAALQlhGI3hIVJKg1WXhErxQAAAG0JodgNPXtKKu2m45RPAAAAtCmEYjeEhEiWsmAVlVM+AQAA0JYQit3g5SUFeAWrpKZAhmF4ejoAAABoJYRiN3Vt3001lgqVVJR4eioAAABoJYRiN/UM5FPPAAAAbQ2h2E19utSFYj7gAQAA0HYQit30o9BukqTj3xOKAQAA2gpCsZuienWQqtrp6wLKJwAAANoKQrGbwsIsUmmwvi5gpRgAAKCtIBS7KSxM0n+66chJQjEAAEBbQSh2U0CA5FUerAJOnwAAAGgzCMVuslikayzB+ncFK8UAAABtBaH4AnS2d9Npg1AMAADQVhCKL0A3/2Cd8fpOtUatp6cCAACAVkAovgBhnYIla7VOlp/09FQAAADQCgjFFyC8a90HPHKLKKEAAABoCwjFF6B/z7pPPR/I4wQKAACAtoBQfAEGRtStFH91jJViAACAtoBQfAGiereXKvz1zXeEYgAAgLaAUHwB2rWr+4DH0WLKJwAAANoCQvEF8q3tpsL/sFIMAADQFhCKL1AHa7CKqwjFAAAAbQGh+AIFtQtWqSifAAAAaAsIxRcopEM3VXgXyDA8PRMAAABcLELxBerVOVjyO6Gik9WengoAAAAuEqH4AvUNCZYshv7xdZGnpwIAAICLRCi+QP171n3A48tcNtsBAABc7QjFF+i6XnWfes7OJxQDAABc7QjFFyjkmrqV4twTnEABAABwtSMUXyC7zS5bVYCOl7BSDAAAcLUjFF8EPyNY35UTigEAAK52hOKLEGAL1qkqyicAAACudoTii9DFt5vKrAWqqfH0TAAAAHAxCMUXoUdAsORfoPx8T88EAAAAF4NQfBF6BwVL7Qv17beengkAAAAuBqH4IvTt3k3yO6lv8io9PRUAAL+KWVIAACAASURBVABcBELxRQjvUvcBj3/mfefhmQAAAOBiEIovQrB/XSj+uoBj2QAAAK5mhOKL0M2/7qt2ef8mFAMAAFzNCMUXoYtfF8mwKP/0/2/vvuObqt4/gH+SJk3Spi0dlE6WUDaUYQUKWIaWIUVlyVYRlCFDv4DioExlWJYCMgQBBdn+EFEpgoKUQmkLyN50752dPL8/rg2EtkCgg7bP+/XKq3DuOfeem5PePD333HN4rmLGGGOMscqMg+KnILWRwl7khnRe1Y4xxhhjrFLjoPgpOUuFBTxUqoquCWOMMcYYe1IcFD+lWvYegDIF8fEVXRPGGGOMMfakOCh+Sr7Owqp2d+9WdE0YY4wxxtiT4qD4KdWtWQtQJvOqdowxxhhjlRgHxU/J28kDImUK9xQzxhhjjFViHBQ/JQ+lB0iWi+t3+Ek7xhhjjLHKioPip1TLXljA48IdnquYMcYYY6yy4qD4KZmXek5KAVEFV4YxxhhjjD0RDoqfUuFSz3mUjLS0Cq4MY4wxxhh7IhwUPyVXhStsRDaAMhmXL1d0bRhjjDHG2JPgoPgp2Yht4G7vDpFDCgfFjDHGGGOVlKSiK1AVeCg9UOCVxEExY4wxxlglxT3FpcDb0RuKmgkcFDPGGGOMVVIcFJcCHwcfmBziOChmjDHGGKukrA6KdTodZsyYAW9vb9jZ2aF9+/YIDw9/rLI5OTkYO3Ys3N3doVQq0a1bN8TExBSb98SJE+jUqRPs7e3h6emJyZMno6CgwCLPnTt3IBaLi7xsbGywY8cOa0/tifk6+UItjcft24BaXW6HZYwxxhhjpcTqMcWjRo3Cnj17MHXqVDRo0ACbNm1C7969cfToUXTs2LHEckSE3r174/z585g+fTpcXV2xatUqBAUFITo6Gs8995w5b2xsLHr06IGmTZti6dKliI+Px+LFi3H9+nUcOHCgyL6HDh2K3r17W6R16NDB2lN7Yj6OPsg3ZQASFa5ds0PLluV2aMYYY4wxVgqsCopPnTqFn376CV999RWmTp0KABgxYgSaN2+O6dOn4/jx4yWW3blzJyIiIrB792689tprAICBAwfCz88Ps2bNwtatW815Z86cCRcXF/z111+wt7cHANSpUwdjx45FeHg4evToYbHvNm3aYOjQodacSqnydfQV/uGQgMuXG3JQzBhjjDFWyVg1fGLXrl2QSCQYM2aMOU0mk2H06NGIiIhAQkJCiWV3794NDw8Pc0AMAG5ubhg0aBB+/vln6PV6AEBeXh7Cw8MxYsQIc0AMACNHjoS9vX2JwyJUKpV5H+XNx9EHAOBUm8cVM8YYY4xVRlYFxbGxsfDz84NSqbRIDwgIMG8vSUxMDNq0aVMkPSAgACqVClevXgUAnD9/HgaDAW3btrXIJ5VK4e/vX+wY5NmzZ0OpVEIulyMgIACHDh2y5rSeWmFQXKthPAfFjDHGGGOVkFVBcVJSEjw9PYuke3p6goiQmJj4RGUBmMsmJSVBJBKVmPf+Y4jFYgQHB2Px4sXYv38/li1bhrS0NPTq1QsHDx605tSeikKqgKvCFY4+3FPMGGOMMVYZWTWmWK1WQyaTFUmXy+Xm7U9SlojMZQt/lpT3/mP4+voWCX6HDx+Opk2b4sMPP0SvXr0e46xKh6+TL6SGeFy8AphMgJgnu2OMMcYYqzSsCooVCgW0Wm2RdI1GY97+JGVFIpG5bOHPkvI+7BgA4OzsjLfeegsLFy5EYmIivLy8Hpp/6tSpcHJyskgbMmQIhgwZ8tByD/Jx9EGKOg4qFRAfD9SubVVxxhhjjLFqbdu2bdi2bZtFWk5OTrkd36qg+MHhC4WSkpIA4KEBqKenpznfw8oWDsUoKe+jglxA6EEGgMzMzEfmX7p0abFjna3l4+CDG2kRAIDLlzkoZowxxhizRnGdktHR0UWeMysrVt3k9/f3x9WrV5Gfn2+RfvLkSYhEIvj7+z+0bHR0dJH0kydPws7ODn5+fgCA5s2bQyKRICoqyiKfXq9HbGzsQ49R6MaNGwCAmjVrPjJvafF18kWKJg4yGXhcMWOMMcZYJWNVUDxgwAAYDAasXbvWnKbT6bBp0ya0b98e3t7eAIDk5GRcuXIFRqPRomxKSgr27NljTktPT8euXbsQEhICqVQKAHB0dESPHj2wdetWixXsNm/ejIKCAgwaNMii/IMSEhKwceNGtGrVCrVq1bLm9J6Kj6MPMtWZaNBEhStXyu2wjDHGGGOsFFg1fCIgIAADBw7Exx9/jJSUFPOKdnfu3MHGjRvN+T766CNs3rwZt2/fRu3/xhEMGDAAy5Ytw1tvvYULFy7Azc0Nq1atgslkQmhoqMVx5s+fj8DAQHTp0gVjx45FXFwcwsLCEBwcjJdeesmcb/r06bhx4wa6d+8OLy8v3Lp1C2vXroVKpcLy5cuf4m2xXuECHr7NhAU8GGOMMcZY5WH1HAlbtmzBlClTsHXrVkyePBlGoxEHDhxAYGCgOY9IJIL4gekXxGIxDh48iMGDB2PlypWYPn063N3dceTIETRsaBlEtm7dGuHh4bCzs8MHH3yA9evXY8yYMdi5c6dFvuDgYIjFYqxatQoTJkzA+vXrERQUhIiICHTu3NnaU3sqhXMVu9XnadkYY4wxxiobERFRRVeiIhQO3D5z5kypPGin1qtht8AO79X6HmvGjURODuDoWAoVZYwxxhirpko7XnsYnk23lBQu4CGuEQcAPK6YMcYYY6wS4aC4FPk6+UIrjwfAM1AwxhhjjFUmHBSXIh9HHySr4uDry0ExY4wxxlhlwkFxKfJ19EV8bjwaN+agmDHGGGOsMuGguBT5OPogLjcOjRpxUMwYY4wxVplwUFyKfB19kanORP1GKly7BhgMFV0jxhhjjDH2ODgoLkWFcxW71I2HXg/culXBFWKMMcYYY4+Fg+JS5OskrGqnqMUzUDDGGGOMVSYcFJcibwdvAIBKGgelkoNixhhjjLHKgoPiUlS4gEcCz0DBGGOMMVapcFBcynydfBGXG8dBMWOMMcZYJcJBcSnzcfQxz1V86RJAVNE1Yowxxhhjj8JBcSnzdbzXU5yVBaSnV3SNGGOMMcbYo3BQXMru7ykGeAgFY4wxxlhlwEFxKStcwMOrjgpiMQfFjDHGGGOVAQfFpaxwAY80bTzq1+egmDHGGGOsMuCguJQVLuBx/8N2jDHGGGPs2cZBcSkrXMAjLicOrVoBZ87wDBSMMcYYY886DopLmUKqgJudG+Jz4xEUBKSmAhcvVnStGGOMMcbYw3BQXAZ8HH0QlxuHjh0BqRQ4cqSia8QYY4wxxh6Gg+Iy4Ovoi/jceNjZAe3bc1DMGGOMMfas46C4DBT2FANA167A0aOAyVSxdWKMMcYYYyXjoLgMFC7gAQhBcWYmcO5cBVeKMcYYY4yViIPiMlC4gIdKr0L79oBMxkMoGGOMMcaeZRwUl4HCBTzic+MhlwOBgRwUM8YYY4w9yzgoLgOFC3jE5dwbV/z334DRWJG1YowxxhhjJeGguAwULuBx/7jinBwgJqYia8UYY4wxxkrCQXEZKFzAo3AGiuefB+zseAgFY4wxxtizioPiMnL/DBS2tkCnThwUM8YYY4w9qzgoLiO+jr7mnmJAGEJx7Big11dgpRhjjDHGWLE4KC4j9/cUA0JQnJ8PREVVYKUYY4wxxlixOCguI76OvubZJwCgbVvAwYGHUDDGGGOMPYs4KC4jPo4+yNJkoUBXAACQSIDOnTkoZowxxhh7FnFQXEYK5ypOyEswp3XtCvzzD6DVVlStGGOMMcZYcTgoLiOFq9rdP4Sia1dArQZOnaqoWrHSkFqQar4DwBhjjLGqgYPiMnL/Us+F/P2BGjV4CEVldi3jGpp80wT9tvcDEVlsS00FLl+uoIoxxhhj7KlwUFxG5BK5xQIeAGBjA3TpUnpBMRGwaZMw1Rsre2kFaej1Qy9IxBIcvnUY+6/uN28zGIDgYKBjRyAjowIryUrdmjXAb79VdC0YY4yVNQ6Ky9CD07IBQLduQEQEoNE83b4NBmD8eOCtt4C+fYFbt55uf+zhNAYNXv3pVeTp8nBy9El0r9cd//vjf9AZdQCAlSuBs2cBnQ4IDa3YurLSc/s28P77wMSJgNFY0bVhjDFWljgoLkMPLuABCOOKtVohMH5S+flAv37AunXA8uWAqysweLAQkLHSZyITRu0bhZikGOwfsh/1nOshLDgMN7JuYPXp1YiLAz77DJgwQQiIV68GLl6s6Fqz0vDll8KKlDduAL/+WtG1Yaz8nYg7gY0xG4sMF2OsKuKguAw1dmuMf+7+Y9Fb3Ly5EMT++eeT7TMxURiCceyY8CU9aRKwY4fQSzl9eilV/AHXrlXvlfhmHp6JnRd24ofXf0CAdwAAoGWtlhjdejRm/zUb4z7IhKMjMG+e0KtYty7wwQfC8BZWecXFAd99B3z+OfDCC8CKFRVdI8bKl96ox/A9w/H2/72NaYemcWDMqjwOisvQR50+gtJWiZF7R8JEJgCAWAwEBT3ZuOLz54H27YG0NGFqt5dfFtLbtgW++kroNd67t/TqDwD79wONGwPvvVe6+31W5OYKwc6BA8K/H7T2zFos/GchwoLD8FqT1yy2zek6BxqdHgfy52DZMsDJCZDJgCVLgN9/Bw4eLKeTYGVi0SJhwZ3x44U/PsPDgQsXKrpWjJWfzWc341b2LUxtPxVfRXyFMfvHwGjicUSs6uKguAy5KFyw+bXNOHr7KL468ZU5vUcP4ORJ64ZQhIcDnToJvcwnTwItWlhunzAB6N9fGGNcWuOLz5wB3ngD8PMTesz27Sud/T4r7t4FAronYfLvE/HK11NQ45UvUK//BvT/+Bcs23Eam07vwPgD4zHx+YmY/MLkIuUdRB6wPTUTooBv0KrbVXN6v37CMJkPPii/Hvajt49i8T+Lka/LL58DVnFJScLwpKlThcB4wADA05N7i1n5K9AV4J+7/+B8yvlyPa7OqMO8Y/PQv0l/hAWHYfOrm7EpdhOG7B5ifpaCO45ZlUPV1JkzZwgAnTlzpsyP9b/f/0fSOVKKTowmIiKtlqhTJ6JatYju3n10+V9/NZFNk/3UtW8S5eaWnC87m6h+faLnnxeO8TTu3CHy8CAKCCAqKCB69VUiNzeipKSn2++zIiqKqGb9BJJM9aMaC1ypwdKmpJzjSgiFxav2jL6UkGgodh/TphHJ7NXkvbgOhWwLsdh29iyRWEy0fHnZn0tKfgq5LhTq7rHEg1afWkvxiXo6d47o8GGio0eJjMayr0dVMnUqkZOT8DtVaO5cIoWCKCOj4urFqjaNXkMRcRG0MnIljdo7ipqvak7i2WLz9eidn9+h9IL0J97/5ctES5cSXbv26Lxro9YSQkHnks+Z0/Ze2ku2c22p59aetGJ1Abm7E61bx9eX6iAvj+jChYo5dnnGaxwUl8ObrNFryH+NPzX5ugkV6AqIiCglhah2baLWrYny80sue+SIiWxenkkIBbktdKP/u/x/Dz1WVBSRrS3R5MlPXt/sbKLmzYnq1iVKThbSUlOJ3N2J+vQhMpmefN+l4eZNojFjiMaPJ4qNtb78zz8Tyd3jSTatIXkt9qFrGfe+IXQGHSXkJNK+yBgat/Ao1XDVkqMj0ZIlln9onD1LZGNDNH8+0bbz2wihoMM3D1scZ+xYImdnovQn/w57LAN2DCDlbDeqF3iKZEOGCl+g45sRGh4gwEQAUdOmRJs2Eel0ZVuXqiAlRQh+P//cMj01lUgmI1q4sGLqxZ5NKhVRSV8jWsPj906kFaRR468bE0JB0jlSare2Hb27/11ad2YdRSdG06pTq8jpCydyXehK68+sJ6Pp8SLR1FSiFSuEzhKhb5fI1ZXoxImSy2gNWqq9tDYN3DGwyLbwG+GkmGtPotGdyK9lNgFEXboQXbr02KfKKpG8POGa5+YmfHZefln4/itPHBSXg/J8k4mILqReIPk8OU04MMGcFhtLZG9PNGBA8X9pnzxpImnv6YRQ0GfhsylkWwghFDTul3Hm4PpBRpORRi/ZRXi3Nfl80ZROxZ+yqp46HVGPHkQ1ahBdvGi57ZdfhF+KNWus2mWpSU8XevAkvtFk+25nkkxoQ3jpf9TolV9pzXd5pFI9eh/LlxPBMY7sP25APl/50vWM64885oQJQq9vw4bCe2A0ErVvT9SkiRAom0wm6rC+A7Va3YoMxnu9yikpRA4ORBMnPu2Zl2zHvzuEILj5dnr9dSFI/+Sb09Rs8YuEUFCHNd1oxc9/00uvJhNEBvL1JVq27OF/iFV306cL7VZcj/CbbxL5+hLp9eVfr+ridmo61ZjyIjWeNpb0hme7C1KnE4IEgGjjRsttYSfCSDpHSqtPr37kflQ6FXVY34FqLqpJx+4cI41eU2y+5LxkGrFnBCEU1OCLDtTv3Vjq2JHolVeI3npLuHu1aJFQl02biPr2JZJIhFffvkQ7dhAlJBB17kwklxPt3Vt8fdacXkOiUBH9m/JvkW1qNdFzL0aQeGYN8vnKl1qEdSbFu91JNLw3NQ59lQZsH0Rj/m9MsWXZ08nV5NLRm8fL5Vh5eURffikEwxK5hgZMjKZP1x6jBg1NJBIJ18K4uHKpSrnGayKi6jkqKDo6Gm3btsWZM2fQpk2bcjnmN6e+wcSDE3Fg6AH0btgbgPBg3OuvC1N5zZp1L+/Zs4QXPv8ftG3CsLDrMkzvMhlEhDVRa/DBHx+gXo162NZ/G1p5tAIAGE1G7Ly4E/P+nocLaRfgnt8DqTk5EHvFYHaXBZjZ9UOIRSUPIdcatDgZH4l1C5phxyZX/P67MC72Qe+9B2zZAsTGAg0blua7UzK1WpgHeP6iAqg7zIKx3TI0cWsKfw9/HLzyJzL1CYBRApuUALR17oZB7XqgtVtHyKRSSCSARCIsnPLdd8DK7+NQY0pXODkbcGTUEdRzrvdYdTh/HpgyRZg1pHlz4N9/gb/+EmYCAYCT8SfRYUMHrOmzBu+2e9dcbvFi4OOPgXPngKZNS/d9SStIQ+OVzZBzvjNeUe3Cnt0iiP9rYiLC/qv7MSN8Bi6nC8vsiSGGraEmNBm1INHWQmNvLzR3egGNFJ3hK28KW6kYEonwsGCnToC7e+nW91kWlxOHiQcnoqfvQEwLHo7Jk4H584vmi40FWrcGdu4Uxhmz0nU7IwnNF76EApsEQJaDFuqJiP1iOcRiUUVXrQgiYMwY4PvvhedEDh0Srue9+xjx4R8fYnnkcrzg/QIiEyKxvOdyTHphUrH7MZqMGLBzAP648QeOjjqK572fL5InN1d4cPfIEeF1VfsX0Gc8UPMy6ueOgjy3BXTpXlCleiE33hv5SZ6AQYGAAGDECGHKzpo17+1PowFGjgR27xaurePH39umNWjRcGVDBNYOxLb+24rUZepUYNUq4Mfwf3Eo52uo9CqodTqcu6jDtRs6KBy0cKhzDRm6BAz0+RCvOH4GVbY9MjOBnBxhrv5u3QBROTZpVhawYIHwXduhw9PtKzlZ+B7o31+4VpaX9IJMNPsyGKmSKHTRLcCeDz6Gq+ujy93NuYu0grQSt4tEIoggMv9UqUT4bnsafvzzLNROsXBuEotc20swkAEA0KX2i+iYuwzr5/mjoED4PMyYATg6Wu73+N3jWB21GiNbjkRwg+CnOfXyjdfKPOx+RpV3TzGR0KPY+4fe5L7YnVLyU8zpc+cKPQ27dgn/v3zZRIrXJxFCQYuOfl1kPxdSL1DL1S3Jdq4thZ0Ioy1nt1CjlY0IoaBeW3vRibsnSK8n+mKRliS9phFCQa3Dgik5L7nIvjJUGTT/7/nkscRD6HGcJaL6CwLosz8/o+N3jpPeaNkllpalJt+256lhyG6a/9cXNOnXSTR8z3Dq80Mf6rC+AzX+ujG5L3anwA2BdDX96lO+X0Rbtgg9c+JGv5LDZ3VIPldOXxz7gnQGnfk9vZJ+heb8upoafTqQRDP+Gxf8kRNhwCBCq+8JdqkEEIldbpPr3HpUd1ldupV164nqs3s30XPPCUM3HjRy70hCKOjFjS/Szgs7SWfQkUYj5O/Uieiff0p3+MJrP7xB4o9dqHG7ZMrLKz6P3qink3Enad+lffRt1Lc0++hsGrF9PD33cX+yea8d4TOJ8H5NdyG80Y/QYQnB6xRJZDp64w2iI0cqfrhMWTudcJo8l3iScoFSuHXdazolpxQ/jpxIuFXcuXM5VrCauJ5+m+w/bkD4wJu+P3CJhoWtIYSCOn8+q6KrVqzC6/b33wt3Dl57jUimVFHQqv4kni2mb059QyaTiab/IdztW3R8UZF9mEwmmnBgAolni2n/lf1FthuNRBs2CEPXAKLGjYnGjRN6fOMTdbTw+EJquKIhKeYpijwP4bawJr217y0KvxFucQfr/n1PmSLsd8aMe3crV51aRaJQEV1MvVikzO+/C/nDwop/T86dE+6iwUZD6DKH8KmMMKU2odHPVKOG8BwNQNSypdCbrSm+Q7xU/f238B0CCMOffvzxyfcVGUnk5SXsq04doe0NJV8qSKUSxlx37Uq0ePHD8z5MQnYKOX/ckjDNjRpMHkcIBdm+HEqffW6yeO7hfiaTicJOhJHNbJsin43HeUk+t6PW37xA7+5/l1adWkUn7p6gX678Qo2/bkyiUBGN2jWGJs9MIbmcqGZNopUriTQaE/127Tfq/F1nQijIZaEL2cy2oW+jvn2yE/8PD58oBxURFBMJt79qLqpJXTZ2oZ/+/YmuZ1wno9FEgwcT2dkR7dlrJOWg8cJF9M+Sb7up9WqacnCK+QPc98e+xQ6VuHmTqN3g3wn/cyfZJ7Xox8jfiYjoesZ1GrtvIsnm2JHNLBk5DhtD8DlBfT/fQIN2DiLnL50JoSCnL5woZFsIvbzlZaq7rC6JQkXmY8pCnajZN82o83edqd+2fvTmvjfpg98+oDlH51DDFQ1JuUBJK//eTAcOEM2eLdy+CwoiOnTo0e/TnTv/3Za0TybfKUMIoaAem3s8criDWmOkvZGnaeKuWdR8eTtCKEgUKqLmy14gr8W1qf7y+nQn+86jK/AIxQWKeqOedvy7g7ps7EIIBXl/5U1zjs6h7b8kUY0awoXUwUG41blsGdG//z55wLnj/B5CKEjZ8Qe6devJzyNfm0+HrofTp4c/pxe/60ryeXLhghgqI/nE9oRe75NH8Gb6eMllSkt/tm9lP4ndF3eTYp6CXlj3Al2KSyLZi2EkmiWmPj/0oRxNTrFldu0S2jI6upwrW4VdTrtC9p/6ECbVp7U7b5rTe877ghAK6vfFsgqsXVFbtgifgTlz7qXFZ6aT45RAwicKWv77PnO6yWSiz/78jBAKmvvXXIv9LDq+iBAKWnO66Ji0yMh744CHDSO6fbvk+phMJspWZ9PF1IsUfiOcNsdupo/DP6bnlj9HCAV5LvGkqb9NpaiEKDI9cNEJCyMSiYRj5BZoyCfMh4buHlrkGGlpwsPXL7308AfrDAaiv/4iioggOhR1nbp/15MQCgrZFkK3Mm9TeLjwbAogBMmzZwvjnkubXk/02WfC0LfOnYWHC4cPF447d+7Dr73xOfE0M3wmDdo5iGYfnU17Lu6hxRuuka3MSB06COf3+uvCvpo3J9q/33J/iYlEn3763zhckYla97hMkKqoc2fhO9kaN1ITSPlRY8KHHrRoo/Ck2ycHFxBCQTYvz6QaziZasIAsOkbytHk0eOdgQiho2h/TKCYpptjXydvRNG/DGfLvFUXwOk2uLU7RW59G0pHzl4v9Q4pIeO5m+cnlVOPLGuT4hSN98utiGv6WmtBkN8neb0sIBQWsDaCfL/9MOoOOJh6YSAgFfXToo8ceB/8gDorLQUUFxUREf1z/g2ovrW0OLmt8WYNe/K4b1Ro+jfD6MMIsES0+vO6x9hWVEEVnkx8+6t1kIlq9JZls336ZEAry+qQzYZaYMM2NEPQ51WmWQuPGWf5iG4wGOhl3kmYfnU3dvu9Gr25/lab/MZ02RG+gY3eO0Yefp5LYxkQLFwoX1QULhAeTpk8nmjSJ6JX+uWQ3TOg5xWsjyLlWLr388n+9CCAKCSn+CWiTSRizbOd1i5T9PyDFHAdyW+RGm2M3F7mYP46kvCT6Lvo76v9Tf+r+fXe6m/0Y032UgrPJZ2ns/40lxTwFSedI6fXt/WnK9qU0du5x6tI9n2xthffB01N4v06ffvwAOb0gnRSf1yLRkBA6cqR0u3G1Bi2duHuClkUso6G7hpLPwob3eg8+cqLaHw6kL3cdJJ3+Cbs8nkBODlF4uPBwR2n1WptMJvry2JeEUNCgnYNIpVNRaKjwgN0Pp34lxy8cqek3TelG5o0iZfV64SHZUaNKpy7VXUxiLNl97k4Y35RWbEyw2GYymeiFT4W7XW+v2FRBNbR0+DCRVEr09tv3Po83M29So5WNyHWhGzUIiiAvr6JB7Ny/5hJCQZ8e/pRMJhP9eO5HQijok8OfWORLThbGBwNE/v5Ex449eV1NJhOdjDtJ7//6PrkvdieEghqtbERfHPuCEnMTzfl27BAe0K7Z52sShYopJu7yA/sh6tdPeEAvIeHBozy6Djsv7CTvr7zJbr4d9dzak6YcnEKh+9dQyPt/kdw1hWxlJurcWXi+ZsIE4Y+Nb78l2rdPeIDP2t/7W7eIOnYUHoaeM+deD63JJPwfIBoxomhPdWR8JA3ZNYQkcyTksMCBOn/X2TyzT2Hvabtvn6f39r9Hf1z/g/6J0FPXrsL+OnUi+uknYb9SKZG9o5Ze/t8WarFC6Jxxnu9ONV5ZQPYu2bRhw+Od08WEO6SY8RzhAx/6dpflndcl/ywhhIL8p39IEqmJXF2Fa9JXG69So+XNSLlASTsv7Cyyz8REop07id5/X+jhBYTOqh07rLuTmV6QThMOTCCb2TbmOxWuH3Ql1Aunds+b6MiRwvdc6LEWhYrojV1vkFqvfvyDYM9kzgAAHGBJREFU/IfHFJeDihhT/KDUglREJ0XjTOIZnEk6g8i4KCTnp+CLwDWY/tJbpX689AwT+swLQ4zqZzQ1DsPbbUeiz8t2eO456/el1wO9ewvjauVyy5dMBtSqBbRrBxQ8txUbU8fB09ED2/tvRxvPttixQ1h9LykJmDwZ+PRTYeGLW7eAgVMjcMY2DKKme+Akd8K459/DBx0+gJudW6m/H+UhS52FjbEbsfPiTsQkxUBr1EIsEqOxa1P4ip+H7nY7xP7eClmXW6BRXUeMGAEMGyasileS9gtHIDLrFyyqcxHTxnmWyzmEX4zCut8i8Vf6duhqXIBNvi86Kd/EvAFvoVOz4sdla7VAQQHg7Pz44weJgJs3gRMn7r3On4d5PtQmTYS5s/u8nguVw1lEJ0VDY9CgjWcbtPVqCxeFCwDAYBCO7ehY9Ng6ow7jfhmH72K/w4QWn8IvYTZ+/02MQ4eEsZXLlgGX0i4hZHsIstRZ2DVoF4LqBlnsY/Fi4XN7967wWWfWIyL8E/cPXtrYF5rE+ljQ9Hd8PLno77nJRGj28Vhclm/EtDq7sOjtVyugtkJ9I2KzEdw/CX5tk/D+zCSkq5ORlJeEH87/AKWtEgeHHYSDviECA4VnGY4ftxzLu/ifxZgePh2vNhiMAzf3oLv7ELxbaxMyM0VITxeuid99J5SdNw8YO1Z4HqI0GEwGHL55GFvObcHuS7uhN+rxit8reKfNO+jZoCeiog3ouu85aC51g8vRLXjvPWH+ey8vYO1a4N13hbnq+/V7suPnafOwPHI5TieexqW0S7iZdRNGEhYCUcAZTqq2sEvrArrdBQVXApCerIBJWPMKDRoAISHCsTt2FN6f4uTmCgtOTZgA1KgB/PijkP9B27YBb74pLIa1Y5cBf6XuwbKTyxARH4HnnJ/DpBcm4U3/N2FUOWLwG4TDkcl497PzqN/hPP5NPY9jd4/hZtZNuCpc8VqT11FfPQg/fRmEszES+DRKRfNR3yJGsgopqmS8VP8lvNPmHfx5609sjN0I0suh/2cCgp2mYPNq9xKf3Thz6wY6rukOvU6MbT0PY3Bw0evsysiVmPTbJLzZ5H24RS3HrrO/4Hab4UC+Bxqf3YuQDk0RFAQkJAifxePHhSXrAaB+feCVV4TnhJo0sa4t73ch9QK2ntuKkEYh6ODbAUePCt/vp08DvXoJq4ECwCXsxm4MhzfaYZjNPjTwdkXr1sKzNra2Dz9GecZrHBRXYFBcHIPJAIm4hN/4SupaxjW8sfsNnE85j+mB01G3Rl3AaIs/Dtri5z22UNhKERCUicM5q2HyioSP3A8zu03ByFYjYW9rX9HVLzV6ox4X0i7gdMJpRCVG4XTiaZxPPQ+DSXiAQamvB/XtljAmtEIj5xZ4zscRSqUIDkoRlEoxHJQi3Mq7hB+yJ6B77vcI/2pkuZ+DyURYf/A0wo5swBXbbYAsDy7Z3dHWqScKcuTIy7ZFdoYtsjKkyM+2BUxSyG2l8KolhbeHLXy8pKjtLYVHLQkys/VIStUhOU2LlAwd0jKFV0G+cEny9hYWjvFrBDRoQLiQcAdHLkfjri4G5HINACCBDFKxLdSmPACAQlsXNintoLreFqbURlA4aFHTuwAuHgWo4aaC0rkA141HcK3gNGqeXI/k30dAKgU6dxb+yBs3DrCzE841U52JQTsH4a87f+H1Jq/DVeEKZ7kznBXOsDU5Y9pEZwQF2iHgeRFEYmG1SrEI5ocdtTpAqyXo/vup1QnBukwqhkJuAzu5DewUwk+FXIwMdQbicxOQXJCINE0iMnQJyDElgmCCndgJ9hInKCVOcLB1gpPcCQ62jlDY2EMmtoNcbA+5jT1kYnvAZIOU3Eyk5WcgQ52BLE0GcvQZUJtyYWfjBBdZTbgo3FBL6QZPJzd41XCDGBIUqI1Qa0zQaE1Qa0xQa4wwQAtIVTDZFMBko4LJRgWjWAWR2FTkuHKxPaRiW+hMGmhJBb1JDS2poDWpoDHlI1OXjAxtEjJ0icjQJSJTnwQD6YC7gfio9gF8McupxM+dTm9Eg4+GIM7uZwyw+wZOIh/kZEmRkylBTpYUWelS5OUDNjI1xPICiGUqiGUFENmqIJIVQGavhq29ClI7FWzkaohsVRBLNVBI7aCU1ICDpAaU0hpQSmrAUVoDalM+UjR3kKq9i2TNHaRq7iJFcxcak8qiXo4yR3goPdDCvQVW9VkFd3shwrlxAwgMFB5WbdNGeDgrJUV4JddZAeo5GbjRA/jxAGAUIgInJ2Fxpp49gTlz8FgPUT2pbE02fjz/I9ZFr0Nsciy8HLzg7+GP367/hj/6XcKB7/2wfr3wQN6AAUIwPHIksGZN6dVBa9DiRtYNXE6/jItpFxGZEInjd48jW5MNWxtbtPN8Hu1qdoE0uwnORBNiYk3IyTXBXklo0dKE+vWAvGxbpKfYIi3FFimJtsjLsgXIBoFd8zHs7RzoxNnI0eYgR5ODHG0OsjXZ5ldydjYSs7JAtjmA2Igm8q54yWEKWsr7QAQbGI3CH7/p6cKDtd2736s7ESEmOQY7LuzAzos7cTPrJtzs3NDEMQCRaeGwEdlgZKuRmPTCJDStee8J68S8RCyNWIqvI9dAozNAfnE0XmrQHXI7I+RyE2RyI2zlJoilWqy5+hlMWnvs7/8nenfyKfF9XHtmLd795V108OmAiPgI9Kz7Kl4XfY9j4Y44dEj47InFQKtWwgPUhS8vr9JrywcRAbt2CQ8rp6UVTgQI6NxPIqtXX4g1LjD+NRMQ62Ej08LDRwMPby3cPbVoVs8diwdOsNgfB8Xl4FkNiqsqrUGLmYdnYlXUKmgMmmLzeGu7Y+ngqejfstdDZ8qoSrQGLS6lX8K5lHM4l3IOMYnnEBV/FrnG1BLL1Mzoh/gle2FrW7FP5KdkFeCTrbuw88YG5CmjAbEeZKMrs+MpbZVo7dEaLWu2gSilNa4ebYO/9zaGRmUDaa3rqNnyDOT1o6BzO4N0aTQ0lGcuKzbKAb09TFo7QOUGt6jleK1NZ/TuLXzZOTgUf0y9UY85f83B8bjjyFJnIVuTjSxNFnK1xawJXhpMYiDfA6J8b0g1XpDrPQGygU6UA71NDoySHECWC8j/+ylVAVJ18fsiEUQaZ0j0rrA1uEJKjtCJcqCTpMMgSxPKW4NEgN5OeJFIOLZtASB6xFeIXgHo7IF8DyDP694r3xPI9cHEnsFYEaZ45N2EfLUWDT59DSmOj79+uojEsDHZQWy0B/QKkM4ORq1C+BwY5IBEDciz771k960IWeAG5NQBcmoLr+w6cJH6YF2YJ1rV94SH0uOhf7THxAi9cBKJcDehVi3Aw0P4me9wBq28m8Cnlh1cXQEXl5J7P8tadFI01kevxw/nf8CgpoOwLmQdAGGmiA0bgOXLAaUSOHUKsC/jPgoTmfBv6r/4+87f5ldKQUrRjCR69OcOgEKigJPcCU4y4Q9JZ7kzashrmF/Gghr4YYMTkk51BFJaFSnfqpUwQ8fD7qYSEaKTorHz4k78E/cP+vr1xTtt3jHftSpOpjoTi458g6URy6GTZBSbR5ruj/C3f0WX1o++G7gxZiPG/zoen3f5HDM6zTB/fxIB164Jn7sHZ4eoKDcyb6Dvtr64lH4JACCBLUQmGUx6GYxaOZxUrZG9+v8synBQXA44KK44RASDyQCdUWd+gcSo5VDz0YWribSCNKj0KhAIRAQTEfILCLl5hA6N6kNi82z+0UBEMJIROqMOeqNe+GnSQ2/Um3+qdHqkpRtQ08UWDna2sLURXjIbGaQ20vsu6PcuTQSC0lZZ5I+l/HwgIwPw8bG8zWwiEzJUGVBIFVBIFLARCxs1GqHnwsfn6aaEMpgMyFbnICVLBZMJMBmFZVIK/00gKOSi/4YTCVPliSACgWAiE4wmI9RaI/JVRhSojChQG1HLwRX13WvBydGmxACJSJiisKBAGJ5SeK4agxoqQwFUhgKQyIDaNV3gWaMGJA+5956v1uFGUjpup6ZDJDbB3k7osbZXiCGX2cBGLIbMRgY7qR3kNvaAQQadTgSNRujxFomENtIYNdAYC6DSF0Bn0kFuo4BMrIDcxg62YjkAEYiE/IUvsVj4KZVaN/UfEeFOZjJsJAboTXoYTAbojcJPAsFOagc7qR3spfawk9rB1sYWomIaWqsFMjOFYWD0X7sRATqDATnaHMhthPrf/74DwudGqXz8+lYmBpMBYpG4yO+Y0Si8HnWLuywQEXRGnble908fZjAQIBauNVqD1vxdojfp4WDrACe5E2xtHl1pk0n4HDz42Sx8lSWdUYc8bR7EIrFwjSIxtGobqAps4O4qhULx+BWoTHeZTWSCzqiDrY2txedNq713fb4fB8XlgINixhhjjLFnW3nGa89mdxNjjDHGGGPliINixhhjjDFW7XFQzBhjjDHGqj0OihljjDHGWLXHQTFjjDHGGKv2OChmjDHGGGPVHgfFjDHGGGOs2uOgmDHGGGOMVXscFDPGGGOMsWqPg2LGGGOMMVbtcVDMGGOMMcaqPQ6KGWOMMcZYtWd1UKzT6TBjxgx4e3vDzs4O7du3R3h4+GOVzcnJwdixY+Hu7g6lUolu3bohJiam2LwnTpxAp06dYG9vD09PT0yePBkFBQVF8hERFi1ahPr160OhUKBVq1bYvn27tafFGGOMMcaqMauD4lGjRmHZsmUYMWIEVqxYAYlEgt69e+PEiRMPLUdE6N27N7Zv345JkyZh8eLFSEtLQ1BQEG7cuGGRNzY2Fj169IBGo8HSpUsxZswYrF27FoMGDSqy35kzZ+Kjjz5CcHAwvv76a9SpUwdDhw7Fjh07rD01VsVt27atoqvAyhG3d/XC7V29cHuzMkFWiIyMJJFIRGFhYeY0jUZDDRo0oMDAwIeW/emnn0gkEtGePXvMaWlpaeTs7EzDhg2zyNurVy/y9vam/Px8c9r69etJLBbToUOHzGkJCQlka2tLkyZNsijfpUsXql27NplMphLrc+bMGQJAZ86cefhJsyqjb9++FV0FVo64vasXbu/qhdu7+ijPeM2qnuJdu3ZBIpFgzJgx5jSZTIbRo0cjIiICCQkJJZbdvXs3PDw88Nprr5nT3NzcMGjQIPz888/Q6/UAgLy8PISHh2PEiBGwt7c35x05ciTs7e0teoD37dsHg8GAcePGWRxr3LhxiI+PR0REhDWnxxhjjDHGqimrguLY2Fj4+flBqVRapAcEBJi3lyQmJgZt2rQpkh4QEACVSoWrV68CAM6fPw+DwYC2bdta5JNKpfD397cYgxwbGwt7e3s0bty4yD6JqMTxyowxxhhjjN3PqqA4KSkJnp6eRdI9PT1BREhMTHyisgDMZZOSkiASiUrMe/8xkpKSUKtWrUfukzHGGGOMsYeRWJNZrVZDJpMVSZfL5ebtT1KWiMxlC3+WlPf+YzxtfQDg0qVLJeZhVUtOTg6io6MruhqsnHB7Vy/c3tULt3f1URinPSymKy1WBcUKhQJarbZIukajMW9/krIikchctvBnSXnvP8bT1Of27dsAgOHDh5eYh1U9Dw7LYVUbt3f1wu1dvXB7Vy+3b99GYGBgmR7DqqD4weELhZKSkgAAXl5eDy1bmO9hZQuHYpSU9/5jeHp64ujRo09Un+DgYGzduhV169Z9aPDMGGOMMcYqhlqtxu3btxEcHFzmx7IqKPb398fRo0eRn59v8bDdyZMnIRKJ4O/v/9Cyx48fL5J+8uRJ2NnZwc/PDwDQvHlzSCQSREVFYcCAAeZ8er0esbGxGDx4sMU+N2zYgMuXL1s8bPc49XFzc8OwYcMe78QZY4wxxliFKOse4kJWPWg3YMAAGAwGrF271pym0+mwadMmtG/fHt7e3gCA5ORkXLlyBUaj0aJsSkoK9uzZY05LT0/Hrl27EBISAqlUCgBwdHREjx49sHXrVosV7DZv3oyCggKLBTz69esHiUSCVatWWdRzzZo18Pb2RseOHa05PcYYY4wxVk2JiIisKTB48GDs27cPU6ZMQYMGDbBp0yZERUXhzz//NEfyb775JjZv3ozbt2+jdu3aAACTyYROnTrhwoUL+N///gc3NzesWrUKcXFxOH36NBo2bGg+RkxMDAIDA9GkSROMHTsWcXFxCAsLQ1BQEH799VeL+syYMQNLlizBmDFj8Pzzz2Pv3r04ePAgfvzxR4teZcYYY4wxxkpidVCs0+nw2WefYevWrcjKykLLli0xb9489OjRw5znrbfewpYtW3Dz5k1zUAwIT4tOmzYN+/btg1qtRkBAAJYsWYLWrVsXOc6JEycwY8YMREdHw8HBAYMHD8aCBQssFvQotHDhQnz77bdISkpCw4YNMXPmTLzxxhvWnBZjjDHGGKvGrA6KGWOMMcYYq2qsGlPMGGOMMcZYVVTtgmKdTocZM2bA29sbdnZ2aN++PcLDwyu6WuwpRUVFYeLEiWjevDmUSiXq1KmDwYMH49q1a0XyXr58GT179oSDgwNcXV0xcuRIpKenV0CtWWmZP38+xGIxWrZsWWQbt3fVER0djZCQELi6usLe3h4tWrTA119/bZGH27tquH79Ot544w34+vrC3t4eTZo0wdy5c4ss4MDtXbkUFBRg1qxZ6NWrF1xdXSEWi7F58+Zi81rTths2bEDTpk2hUCjg5+dX5LrwuKrd8IkhQ4Zgz549mDp1qvlBwVOnTuHo0aM8W0UlNnDgQJw4cQIDBw5Ey5YtkZycjJUrVyI/Px+RkZFo2rQpACAhIQH+/v5wdnbG5MmTkZeXh8WLF6NOnTo4deoUJBKrZilkz4CEhAQ0atQIYrEYdevWxblz5yy2cXtXDX/88QdCQkLQpk0bDB48GEqlEjdu3IDJZMKXX34JgNu7qoiPj0eLFi3g7OyM9957Dy4uLoiIiMDGjRvRr18/7N27FwC3d2V0584d1KtXD3Xq1EH9+vVx9OhRbNy4ESNHjrTIZ03bfvvttxg3bhwGDhyIl19+GceOHcPmzZuxcOFCTJs2zboKUjUSGRlJIpGIwsLCzGkajYYaNGhAgYGBFVgz9rQiIiJIr9dbpF27do3kcjmNGDHCnDZu3Diyt7en+Ph4c1p4eDiJRCJat25dudWXlZ7BgwdTjx49KCgoiFq0aGGxjdu7asjNzSUPDw8aMGDAQ/Nxe1cN8+fPJ7FYTJcuXbJIHzVqFInFYsrOziYibu/KSKfTUUpKChERRUVFkUgkou+//75IvsdtW7VaTW5ubhQSEmJRfvjw4eTg4GD+rDyuajV8YteuXZBIJBgzZow5TSaTYfTo0YiIiEBCQkIF1o49jfbt2xfpFWjQoAGaNWtmXjcdAPbs2YNXXnnFPKc2AHTv3h1+fn7YsWNHudWXlY6///4be/bswbJly4rdzu1dNfzwww9ITU3F/PnzAQAqlQpUzE1Obu+qIS8vDwDg7u5uke7h4QGxWAxbW1sA3N6VkVQqLdKuxXnctj1y5AgyMzMxfvx4i/ITJkxAfn4+Dhw4YFX9qlVQHBsbCz8/P4vV+AAgICDAvJ1VLSkpKXBzcwMAJCYmIjU1Fe3atSuSLyAgADExMeVdPfYUTCYTJk2ahDFjxqBZs2ZFtnN7Vx2HDx+Go6Mj4uLi0LhxYyiVSjg6OmL8+PHQarUAuL2rkqCgIBAR3n77bZw9exbx8fH46aefsGbNGkyePBkKhYLbuwqzpm0L/922bVuLfG3btoVYLLb6c1CtguKkpCR4enoWSff09AQRITExsQJqxcrK1q1bkZCQYJ6zOikpCQBK/AxkZmZCr9eXax3Zk1u9ejXu3r2LuXPnFrud27vquHbtGvR6Pfr164devXphz549GD16NNasWYO3334bALd3VRIcHIy5c+fi0KFDaN26NWrXro2hQ4di0qRJWLJkCQBu76rMmrZNSkqCjY2NufOrkFQqhaurq9VxXbUaha5WqyGTyYqky+Vy83ZWNVy+fBkTJ05EYGCgeQB/Yfs+6jNQuOQ4e3ZlZmZi1qxZ+Pzzz+Hi4lJsHm7vqiM/Px9qtRrjxo3D0qVLAQCvvvoqtFot1q5dizlz5nB7VzF169bFiy++iAEDBsDFxQUHDhzA/Pnz4eHhgfHjx3N7V2HWtK1arTYPpykur7VxXbUKihUKhflW2/00Go15O6v8UlJS0KdPHzg7O2Pnzp0QiUQA7rUvfwYqv08++QSurq6YOHFiiXm4vauOwnZ6cKXSoUOH4ttvv0VERASaNGkCgNu7Kti+fTvGjh2L69evm3sLX331VRiNRsyYMQNDhgzh3+8qzJq2VSgU0Ol0xe5Ho9FY/RmoVsMnPD09zd3y9ytM8/LyKu8qsVKWm5uLnj17Ijc3F7/99hs8PDzM2wovriV9BlxcXLhXoRK4fv061q1bh0mTJiEhIQF37tzB7du3odFooNfrcefOHWRlZXF7VyGF1+ZatWpZpBc+sMPtXbWsXr0abdq0KXL7PCQkBCqVCjExMdzeVZg1bevp6Qmj0Vhk/mK9Xo+MjAyr47pqFRT7+/vj6tWryM/Pt0g/efIkRCIR/P39K6hmrDRotVq88soruH79Og4cOIBGjRpZbPfy8kLNmjURFRVVpOypU6e4/SuJhIQEEBEmTZqEevXqoV69eqhfvz4iIyNx5coV1K9fH3PnzuX2rkIKH6J5cIagwvGC7u7u3N5VSEpKCoxGY5F0vV4PIoLBYOD2rsKsaVt/f38QUZG8p0+fhslksvpzUK2C4gEDBsBgMGDt2rXmNJ1Oh02bNqF9+/YWU3+wysVkMmHQoEGIjIzErl27zDOKPKh///745ZdfLL5cDx8+jKtXr2LQoEHlVV32FJo3b469e/di79692Ldvn/nVrFkz1KlTB/v27cPo0aMBcHtXFYMGDQIRYcOGDRbp69atg1QqxYsvvgiA27uq8PPzQ0xMDK5fv26R/uOPP8LGxsa8ciW3d9X1uG3brVs3uLi4YPXq1RblV69eDXt7e/Tp08eq41a7Fe0GDx6Mffv2YcqUKeYV7aKiovDnn38iMDCwoqvHntCUKVOwYsUKhISEYODAgUW2Dxs2DICwUlKbNm3g5ORkXiVnyZIlqF27Nk6dOsW32yqxrl27IiMjw2JFO27vquOdd97Bxo0bMXDgQLz44os4cuQIdu/ejZkzZ5pnIOH2rhqOHTuG7t27w8XFBRMnToSrqyv279+P33//HWPGjMGaNWsAcHtXVt988w2ys7ORkJCANWvW4PXXX0fr1q0BAJMmTYKDg4NVbbt69WpMnDgR/fv3R3BwMP7++29s3boVCxYswIwZM6yrnFVLfVQBWq2Wpk+fTl5eXqRQKOiFF16gQ4cOVXS12FMKCgoisVhc4ut+Fy9epJ49e5JSqSQXFxcaOXIkpaamVlDNWWkJCgqili1bFknn9q4aDAYDzZkzh+rVq0cymYz8/PxoxYoVRfJxe1cNp0+fpj59+pCXlxfJZDJq3Lgxffnll2Q0Gi3ycXtXPnXr1i3xu/rOnTvmfNa07fr166lJkyYkl8upYcOGxV4bHke16ylmjDHGGGPsQdVqTDFjjDHGGGPF4aCYMcYYY4xVexwUM8YYY4yxao+DYsYYY4wxVu1xUMwYY4wxxqo9DooZY4wxxli1x0ExY4wxxhir9jgoZowxxhhj1R4HxYwxxhhjrNrjoJgxxhhjjFV7HBQzxhhjjLFqj4NixhhjjDFW7f0/F3PH613FCcwAAAAASUVORK5CYII=",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x309e274a8>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x319644c18>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using PyPlot\n",
    "plot(t1s, label=\"inbounds()\")\n",
    "plot(t2s, label=\"noinbounds()\")\n",
    "legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nouseuselessvar (generic function with 1 method)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function useuselessvar(xs)\n",
    "    for x in xs\n",
    "        j = x\n",
    "        x += j\n",
    "    end\n",
    "end\n",
    "\n",
    "function useuselessvar2(xs)\n",
    "    for x in xs\n",
    "        j::Int = x\n",
    "        k::Int = j\n",
    "        l::Int = k\n",
    "        m::Int = l\n",
    "        n::Int = m\n",
    "        o::Int = n\n",
    "        p::Int = o\n",
    "        q::Int = p\n",
    "        x += q\n",
    "    end\n",
    "end\n",
    "\n",
    "function nouseuselessvar(xs)\n",
    "    for x in xs\n",
    "        x += x\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xs = shuffle(collect(1:1000000))\n",
    "t0s = []\n",
    "t1s = []\n",
    "t2s = []\n",
    "for i in 1:100\n",
    "    _, t0, _, _, _ = @timed useuselessvar(xs)\n",
    "    _, t1, _, _, _ = @timed useuselessvar2(xs)\n",
    "    _, t2, _, _, _ = @timed nouseuselessvar(xs)\n",
    "    push!(t0s, t0)\n",
    "    push!(t1s, t1)\n",
    "    push!(t2s, t2)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x316cda978>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x3170b7828>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(t0s, label=\"unnecessary variables\")\n",
    "plot(t1s, label=\"unnecessary variables2\")\n",
    "plot(t2s, label=\"no unnecessary variables\")\n",
    "legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "function useuselessvar(xs)\n",
    "    for i in 1:length(xs) \n",
    "        x[i] *= 2\n",
    "    end\n",
    "end\n",
    "\n",
    "function useuselessvar2(xs)\n",
    "    for x in xs\n",
    "        ys = xs\n",
    "        x += q\n",
    "    end\n",
    "end\n",
    "\n",
    "function nouseuselessvar(xs)\n",
    "    for x in xs\n",
    "        x += x\n",
    "    end\n",
    "end"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.4.6",
   "language": "julia",
   "name": "julia-0.4"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.4.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}