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@tok41
Created January 28, 2018 03:37
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 目的\n",
"- エンゼルがある回数で出現した確率、今後エンゼルが出現する回数の予測をする"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sys, os\n",
"import sqlite3\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from datetime import datetime as dt\n",
"\n",
"import scipy.stats as stats\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# データの取得\n",
"- データはDB(sqlite)に保存されている\n",
" - テーブル定義は、ブログに載っている通り"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def get_data(db_file='../data/choco-ball.db', table_name='measurement', filter_str=None):\n",
" \"\"\"\n",
" dbファイルから計測データを取得する\n",
" \n",
" TODO:\n",
" エラー処理を入れる\n",
" \"\"\"\n",
" con = sqlite3.connect(db_file)\n",
" sql = 'SELECT '\n",
" sql += 'measure_date,best_before,prd_number,weight,box_weight,ball_number,factory,shop,angel,campaign,taste '\n",
" sql += ', (weight - box_weight), (weight - box_weight)/ball_number '\n",
" sql += 'FROM ' + table_name + ' '\n",
" if filter_str is not None:\n",
" sql += 'WHERE ' + filter_str\n",
" sql += ';'\n",
" sql_result = con.execute(sql)\n",
" res = sql_result.fetchall()\n",
" con.close()\n",
" data = pd.DataFrame(res, columns=['measure_date','best_before','prd_number','weight','box_weight','ball_number','factory','shop','angel','campaign','taste','net_weight','mean_weight'])\n",
" print 'Shape of MeasurementData(record_num, n_columns) : {}'.format(data.shape)\n",
" return data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 幾何分布"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
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iyaEU9iP9SHaFiPg2qSH60aQbj0dJpayla+YC0o/WI6Tvy2VLH6Xhcz0L/DspyH6MFDDP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\nZp2gr7OnD5WSn4i4Cbhp0E9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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc629fec8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"p = 0.1\n",
"ns = np.arange(1,20,1)\n",
"\n",
"fig = plt.figure(figsize=(9,3))\n",
"ax1 = fig.add_subplot(1,2,1)\n",
"ax1.plot(ns, stats.geom.pmf(ns, p), '.')\n",
"ax1.set_title(\"Probability mass function. (p={})\".format(p))\n",
"ax1.set_xlabel(\"Number of trials\")\n",
"ax1.set_ylabel(\"Probability\")\n",
"ax1.set_ylim([0, max(stats.geom.pmf(ns, p))*1.2])\n",
"\n",
"ax2 = fig.add_subplot(1,2,2)\n",
"ax2.plot(ns, stats.geom.cdf(ns, p), '.')\n",
"ax2.set_title(\"Cumulative distribution function. (p={})\".format(p))\n",
"ax2.set_xlabel(\"Number of trials\")\n",
"ax2.set_ylabel(\"Cumulative probability\")\n",
"ax2.set_ylim([0, 1.0])\n",
"\n",
"fig.savefig('geom_dist.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 試行回数の推定"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"def getAngelTryNumber(n, p=0.001, minN=1, maxN=1000, fname=None):\n",
" cdf = stats.geom.cdf(np.arange(minN, maxN), p=p)\n",
" \n",
" plt.figure()\n",
" plt.plot(cdf, color=\"blue\", label=\"CDF\")\n",
" plt.xlabel(\"number of sample\")\n",
" plt.title(\"Cumulative distribution function. p={}\".format(p))\n",
" plt.vlines(x=n, ymin=0, ymax=1.0, color=\"red\", label=\"Number of Trial\")\n",
" plt.legend()\n",
" if fname is not None:\n",
" plt.savefig(fname)\n",
" \n",
" pmf = stats.geom.pmf(n, p=p)\n",
" cdf = stats.geom.cdf(n, p=p)\n",
" print(\"p={}, n={} -> pmf={:.4f}, cdf={:.4}\".format(p, n, pmf, cdf))\n",
" return {\"p\":p, \"n\":n, \"pmf\":pmf, \"cdf\":cdf}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ベイズ推定を利用したエンゼルの出現確率\n",
"- (第29回計測)[http://chocolate-ball.hatenablog.com/entry/2018/01/22/202355:embed:cite]を参照\n",
"- 予測確率は以下の通り\n",
" - 下限:0.07%\n",
" - 上限:3.77%\n",
" - 期待値:1.29%"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p=0.0129, n=78 -> pmf=0.0047, cdf=0.6368\n",
"p=0.0007, n=78 -> pmf=0.0007, cdf=0.05315\n",
"p=0.0377, n=78 -> pmf=0.0020, cdf=0.9501\n"
]
},
{
"data": {
"image/png": 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j2M7j7we8nO3HHB3be9E0tyRX1eVnW90PiIhkoUxrlhERkRQouYuI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\nZCEldxGRLPT/RUSIHdMTj50AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc629fc2350>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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lSeoHtbuPAkYVWjYwYd6B/tEkWWTo0DD03ZZbwhtvhLtORST96Q5VKVJuLlx+\nOZxwQujNcepUJXaRTKJTYfI7y5eHyxrfeQf69Qt3mm65ZdxRiUhZKLlLATNnQs+eYei7p54KJ1FF\nJPOoWUY2GjYsnDhduxbee0+JXSSTKbkLGzaELnmPPTbcbTp5ckjyIpK51CxTxa1ZA6efHmrtvXuH\nrnpr1ow7KhEpLyX3Kuzbb+HII8OVMHfeCZdeClZUZxMiknGU3KuoTz+Fv/4VfvwRRoyAI46IOyIR\nSSW1uVdBo0dD586Qlwfvv6/ELpKNlNyrmEceCTX2Fi3g44+hQ4e4IxKRiqDkXkVs2ABXXgnnnRfu\nNH3/fWjcOO6oRKSiKLlXAb/9Fq6Iuf12uPDC0Mau0ZJEsptOqGa51avhuONgzBi4+WYYMEBXxIhU\nBUruWWzZstC+PmUKPPFE6N1RRKoGJfcs9dVX0K0bLFoUblA66qjS1xGR7KHknoVmzgwnTX/5JTTH\n/OUvcUckIpVNJ1SzzIQJm5L5+PFK7CJVlZJ7FnnvPTj0UGjQAP73P2jbNu6IRCQuSu5ZYvRo6N4d\nmjQJNfZmzeKOSETipOSeBUaODCdMW7cOtfeddoo7IhGJm5J7hnvxxXAde/v2MHYsNGoUd0Qikg6U\n3DPY00/DKafAfvvBW29BvXpxRyQi6ULJPUM99RSceSYcfDC88QZss03cEYlIOtF17hno6aehb99w\nZcyIERo5SUR+TzX3DPPcc5tq7MOHK7GLSNGU3DPI4MFhnNOuXUONvVatuCMSkXSl5J4hXnoJTjst\n3HE6ciT84Q9xRyQi6UzJPQP897/hqpjOneG112DrreOOSETSnZJ7mhs1Cnr1gn33hddfh9q1445I\nRDKBknsae//9cINSu3YhyWv0JBFJlpJ7mvrkEzjiCNhlF3jzTahbN+6IRCSTJJXczexwM/vczOaa\n2VUllDvezNzMclIXYtXz+edw+OGw7bbhzlN1KSAiZVVqcjezLYAHgO5AG+BkM2tTRLk6wMXAx6kO\nsipZtCjcnGQWEnuTJnFHJCKZKJmaeydgrrvPd/ffgCFAzyLK3QjcCqxLYXxVytKlIbH//HMYQal1\n67gjEpFMlUxy3xlYnPB4SbRsIzPrADRx99dK2pCZnWtmk81s8rJly8ocbDZbtSr0x754cbgqZq+9\n4o5IRDJZMsndiljmG580qwbcBVxe2obc/VF3z3H3nEZqSN5o/Xo44QSYMQOGDg3Xs4uIlEcyyX0J\nkNjy2xj4JuFxHWBPYJyZLQT+BIzUSdXkuMM554SRlB59FHr0iDsiEckGyST3SUArM2tuZlsCvYCR\n+U+6+0p3b+juzdy9GTABOMr6jUtLAAAQ30lEQVTdJ1dIxFlm4MDQy+N118FZZ8UdjYhki1KTu7vn\nAv2A0cAc4CV3n2VmN5jZURUdYDZ75BH417/g7LNDkhcRSZWk+nN391HAqELLikxH7n5g+cPKfq++\nChdcEJphHnooXPooIpIqukM1BhMnwkknwd57h94eq2vIFBFJMSX3SrZoERx1FOywg3p4FJGKozpj\nJVq1Co48EtauhXffhe22izsiEclWSu6VJC8PTj0VZs0KNym1+V0HDiIiqaPkXkkGDAgnUe+/Hw47\nLO5oRCTbqc29Ejz+ONxxB/TrBxdeGHc0IlIVKLlXsLFj4fzzQ239rrvijkZEqgol9wo0b14YSalV\nK3jxRV3yKCKVR8m9gqxZA8ccE+ZffVUjKYlI5VJdsgK4h35iZs2CN96Ali3jjkhEqhol9wpw223h\nztNbboFu3eKORkSqIjXLpNiYMXD11XDiifD3v8cdjYhUVUruKTR/PvTqBXvsAU8+qc7ARCQ+Su4p\nsmYNHH10mB82TH3GiEi81OaeAvmjKc2aBaNG6QSqiMRPyT0FHnkEBg8OA2+oawERSQdqlimnKVPg\nkkuge/dwIlVEJB0ouZfDTz/BCSfA9tvDs89CNR1NEUkTapbZTO5w5pmweDGMHw8NGsQdkYjIJkru\nm+muu2D48PB3v/3ijkZEpCA1JGyGDz8M/bMfe2xobxcRSTdK7mX0ww/h7tOmTXWjkoikLzXLlIE7\n9O0Ly5bBRx+pp0cRSV9K7mXw0EMwcmRoZ+/YMe5oRESKp2aZJM2cCZdfDocfDhdfHHc0IiIlU3JP\nwtq1oUOwunVh0CBdzy4i6U/NMkm44orQb8ybb4YblkRE0p3qoKUYMQIefBD691e/MSKSOZTcS/D1\n12G4vA4d4Kab4o5GRCR5Su7F2LABeveGdetCj49bbRV3RCIiyUsquZvZ4Wb2uZnNNbOrini+v5nN\nNrMZZvaOme2S+lAr1wMPwDvvwJ13wm67xR2NiEjZlJrczWwL4AGgO9AGONnM2hQqNhXIcfd2wFDg\n1lQHWpk+/zx0L9C9O5x7btzRiIiUXTI1907AXHef7+6/AUOAnokF3H2su/8SPZwANE5tmJUnNxfO\nOANq1YInnlD3AiKSmZJJ7jsDixMeL4mWFacv8EZRT5jZuWY22cwmL1u2LPkoK9Ett8DEieFu1B13\njDsaEZHNk0xyL6ru6kUWNDsNyAFuK+p5d3/U3XPcPadRo0bJR1lJPvkErr8eTj45dA4mIpKpkrmJ\naQnQJOFxY+CbwoXM7BDgH0AXd/81NeFVnnXrQnPMdtvB/ffHHY2ISPkkk9wnAa3MrDnwNdALOCWx\ngJl1AB4BDnf371MeZSX45z/DXahvvAH168cdjYhI+ZTaLOPuuUA/YDQwB3jJ3WeZ2Q1mdlRU7Dag\nNvCymU0zs5EVFnEF+PBDuOMO+NvfQsdgIiKZLqm+Zdx9FDCq0LKBCfOHpDiuSrNuXbgLtWlTuP32\nuKMREUmNKt9x2A03hOvaR4+G2rXjjkZEJDWqdPcDn3wCt94KZ54J3brFHY2ISOpU2eS+fn1ojmnU\nKLS3i4hkkyrbLPOf/8D06TB8ONSrF3c0IiKpVSVr7rNnw403wkknQc+epZcXEck0VS655+WF5pg6\ndeC+++KORkSkYlS5Zpn77oOPP4bnnw/t7SIi2ahK1dwXL4ZrroEePUL/MSIi2apKJfeLLw4jLD3w\ngLryFZHsVmWaZUaMCFfG3HILNGsWdzQiIhWrStTcV6+Giy6CPfeE/v3jjkZEpOJViZr7tdeG9vYh\nQ6BGjbijERGpeFlfc586Fe6+O4yF+uc/xx2NiEjlyOrknpcXuvFt2DC0tYuIVBVZ3Szz8MMwaRI8\n95y6GBCRqiVra+5Ll8L//R8cfDCcckrp5UVEsknWJverr4ZffgnjoeqadhGparIyuU+YAE89BZdd\nBn/8Y9zRiIhUvqxL7nl50K8f7LhjGPRaRKQqyroTqk8+CVOmhJOoderEHY2ISDyyqub+4/o6XH01\n7L+/TqKKSNWWVcl94MKzWLFCJ1FFRLImuU9f3ZKHvjmK88+HvfaKOxoRkXhlRXJ3h4vmXkz9Gqu4\n4Ya4oxERiV9WJPeXXoL3V+7FTc0fo379uKMREYlfxif3detgwADYa+u5nLXDG3GHIyKSFjL+Ush7\n7oGvvoIn2j3IFrYh7nBERNJCRif3pUvh3/+GI4+Eg3/+JO5wRETSRkY3y1x7LaxdC7fdFnckIiLp\nJWOT+8yZ8NhjcMEFsNtucUcjIpJeMjK5u8Pll0PdujBwYNzRiIikn6SSu5kdbmafm9lcM7uqiOe3\nMrMXo+c/NrNmqQ400ZtvwpgxIbE3aFCRexIRyUylJncz2wJ4AOgOtAFONrM2hYr1BVa4+67AXcB/\nUh1ovtzcUGvfddfQJCMiIr+XTM29EzDX3ee7+2/AEKBnoTI9gaej+aHAwWYV07vLY4/BnDnhJOqW\nW1bEHkREMl8yl0LuDCxOeLwE2Le4Mu6ea2YrgQbAD4mFzOxc4FyApk2bblbA++4L/ftDz8JfL+PG\nbdb2RESyUTLJvagauG9GGdz9UeBRgJycnN89n4yOHcMkIiLFS6ZZZgnQJOFxY+Cb4sqYWXWgLvBj\nKgIUEZGySya5TwJamVlzM9sS6AWMLFRmJNA7mj8eeNfdN6tmLiIi5Vdqs0zUht4PGA1sATzp7rPM\n7AZgsruPBJ4AnjWzuYQae6+KDFpEREqWVN8y7j4KGFVo2cCE+XXACakNTURENldG3qEqIiIlU3IX\nEclCSu4iIllIyV1EJAtZXFcsmtky4KvNXL0hhe5+TROKq2zSNS5I39gUV9lkY1y7uHuj0grFltzL\nw8wmu3tO3HEUprjKJl3jgvSNTXGVTVWOS80yIiJZSMldRCQLZWpyfzTuAIqhuMomXeOC9I1NcZVN\nlY0rI9vcRUSkZJlacxcRkRIouYuIZKGMS+6lDdZdCftfaGafmtk0M5scLatvZm+Z2ZfR33rRcjOz\ne6NYZ5hZyoYZMbMnzex7M5uZsKzMcZhZ76j8l2bWu6h9pSCu68zs6+iYTTOzHgnPXR3F9bmZHZaw\nPKXvs5k1MbOxZjbHzGaZ2SXR8liPWQlxxXrMzKymmU00s+lRXNdHy5ub2cfRa38x6gYcM9sqejw3\ner5ZafGmOK5BZrYg4Xi1j5ZX2mc/2uYWZjbVzF6LHsd3vNw9YyZCl8PzgBbAlsB0oE0lx7AQaFho\n2a3AVdH8VcB/ovkewBuEkar+BHycwjgOADoCMzc3DqA+MD/6Wy+ar1cBcV0HXFFE2TbRe7gV0Dx6\nb7eoiPcZ2BHoGM3XAb6I9h/rMSshrliPWfS6a0fzNYCPo+PwEtArWv4wcH40fwHwcDTfC3ixpHgr\nIK5BwPFFlK+0z3603f7AC8Br0ePYjlem1dyTGaw7DokDhD8NHJ2w/BkPJgDbmtmOqdihu4/n96Nd\nlTWOw4C33P1Hd18BvAUcXgFxFacnMMTdf3X3BcBcwnuc8vfZ3b9190+i+VXAHMLYv7EesxLiKk6l\nHLPoda+OHtaIJgcOAoZGywsfr/zjOBQ42MyshHhTHVdxKu2zb2aNgb8Cj0ePjRiPV6Yl96IG6y7p\nH6EiODDGzKZYGPAbYHt3/xbCPyuwXbS8suMtaxyVGV+/6Gfxk/lNH3HFFf0E7kCo9aXNMSsUF8R8\nzKImhmnA94TkNw/4yd1zi9jHxv1Hz68EGlRGXO6ef7z+HR2vu8xsq8JxFdp/RbyPdwN/BzZEjxsQ\n4/HKtOSe1EDcFayzu3cEugMXmtkBJZRNh3ih+DgqK76HgJZAe+Bb4I644jKz2sB/gUvd/eeSilZm\nbEXEFfsxc/c8d29PGDe5E7B7CfuILS4z2xO4GvgjsA+hqWVAZcZlZkcA37v7lMTFJeyjwuPKtOSe\nzGDdFcrdv4n+fg8MI3zol+Y3t0R/v4+KV3a8ZY2jUuJz96XRP+QG4DE2/cys1LjMrAYhgT7v7q9E\ni2M/ZkXFlS7HLIrlJ2Acoc16WzPLH8EtcR8b9x89X5fQPFcZcR0eNW+5u/8KPEXlH6/OwFFmtpDQ\nJHYQoSYf3/Eqz8mDyp4IwwLOJ5xoyD9ptEcl7n9roE7C/IeEdrrbKHhS7tZo/q8UPJkzMcXxNKPg\nicsyxUGo4SwgnFCqF83Xr4C4dkyYv4zQpgiwBwVPHs0nnBhM+fscvfZngLsLLY/1mJUQV6zHDGgE\nbBvN1wLeB44AXqbgCcILovkLKXiC8KWS4q2AuHZMOJ53A7fE8dmPtn0gm06oxna8UpZoKmsinP3+\ngtD+949K3neL6MBPB2bl75/QVvYO8GX0t37CB+2BKNZPgZwUxjKY8HN9PeHbvu/mxAGcRThpMxc4\ns4Liejba7wxgJAUT1z+iuD4HulfU+wzsT/h5OwOYFk094j5mJcQV6zED2gFTo/3PBAYm/A9MjF77\ny8BW0fKa0eO50fMtSos3xXG9Gx2vmcBzbLqiptI++wnbPZBNyT2246XuB0REslCmtbmLiEgSlNxF\nRLKQkruISBZSchcRyUJK7iIiWUjJXTKWmY0zswof/NjMLo56bXy+ovdVShyrSy8lElQvvYhI9jGz\n6r6pz4/SXEC43nhBRcYkkkqquUuFMrNmUa33saj/7TFmVit6bmPN28waRrduY2Z9zGy4mb0a9dHd\nz8z6R/1kTzCz+gm7OM3MPjSzmWbWKVp/66izrUnROj0Ttvuymb0KjCki1v7Rdmaa2aXRsocJN6KM\nNLPLCpXfw0Lf4tOiDqtaRcuHRx3LzUroXA4zW21m/4mee9vMOkXHYL6ZHZUQ4wgzezPqz/vaYo7r\nldHrm2FRn+YiBaTqrixNmoqaCF0R5ALto8cvAadF8+OI7hgEGgILo/k+hDv36hBuN18JnBc9dxeh\nc6389R+L5g8g6vIAuClhH9sS7trcOtruEoq4zRzYm3AH49ZAbcIdyB2i5xZSqA//aPl9wKnR/JZA\nrWg+/y7XWoQ7JhtEj53ojkNCv0RjCF3W7gVMS3jt3xLunM1fP/8YrY7+diMMsGyECtprwAFxv9ea\n0mtSs4xUhgXuPi2an0JI+KUZ66F/81VmthJ4NVr+KeEW9HyDIfQjb2bbmNm2hOR3lJldEZWpCTSN\n5t9y96L6m98fGObuawDM7BXgL4Rb3YvzEfCPqB/vV9z9y2j5xWZ2TDTfBGgFLAd+A95MeB2/uvt6\nM/uUgsfkLXdfnhDH/sDkhOe7RVN+bLWjfYwvIVapYpTcpTL8mjCfR6iRQqjR5zcN1ixhnQ0JjzdQ\n8HNbuP+M/G5Tj3P3zxOfMLN9gTXFxFhUV6slcvcXzOxjQudUo83s7Ci+Q4D93P0XMxvHpte23t3z\n4934mtx9Q0LPgcW9psKx3uzuj5Q1Zqk61OYucVpIaA4BOH4zt3ESgJntD6x095XAaOCiaGQbzKxD\nEtsZDxxtZn8ws62BYwg9DhbLzFoA8939XkLnXu0IXbeuiBL7Hwk9EZbVoRbGdq1FGLnnf4WeHw2c\nFfUBj5ntbGbbFd6IVG2quUucbgdeMrPTCb36bY4VZvYhsA2hlz+AGwndvs6IEvxCQrewxXL3T8xs\nEKGHPoDH3b2kJhkIXyynmdl64DvgBsIvg/PMbAahV78JZX5F8AGhV8hdgRfcPbFJBncfY2a7Ax9F\n31+rgdPY1Be9iHqFFEknZtaHcAK1X9yxSGZTs4yISBZSzV1EJAup5i4ikoWU3EVEspCSu4hIFlJy\nFxHJQkruIiJZ6P8BPynnDm/qpT4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc62a57c950>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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LFi1it91247DDDuOXv/xlw/Jef/31Rh8q2ey333489dRTrFy5kvr6eiZPnsyB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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc62a007210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 出現確率\n",
"p = 0.0129\n",
"# 試行回数\n",
"n = 78\n",
"\n",
"ret = getAngelTryNumber(n=n, p=p, minN=1, maxN=500, fname=\"cdf_bayese.png\")\n",
"ret = getAngelTryNumber(n=n, p=0.0007, minN=1, maxN=4000)\n",
"ret = getAngelTryNumber(n=n, p=0.0377, minN=1, maxN=4000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 様々な人の集計結果\n",
"- 以下の3記事を参考にさせていただきました\n",
" - [【1,000箱目】森永チョコボール 金・銀のエンゼルが出る確率を出す(鳥取の社長日記)](http://blog.livedoor.jp/remsy/archives/51931218.html)\n",
" - [ヒカルが検証! チョコボールで金のエンゼルが当たる確率は?(YouTuberのネタ宝庫 アイデア&アイテム集結)](http://www.youtuber-item.com/entry/2017/01/19/212219)\n",
" - [915分の1って!「月曜から夜ふかし」が金のエンゼルが出る確率を検証した結果](http://mystock-abc.xyz/?p=476)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p=0.0028, n=78 -> pmf=0.0023, cdf=0.1964\n"
]
},
{
"data": {
"image/png": 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zCMlektx8c+gL9Y47dNmjiNSedJL7VKCLmXUys8bAqcCEpGWeAg4BMLO2hGqa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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc629fc2c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 出現確率\n",
"p = 0.0014*2 # 現在はエンゼル出現率が2倍\n",
"# 試行回数\n",
"n = 78\n",
"\n",
"ret = getAngelTryNumber(n=n, p=p, minN=1, maxN=2000, fname=\"cdf_likelihood.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## N個買ったら当たる確率"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"p=0.0028, n=357 -> pmf=0.0010, cdf=0.6325\n",
"p=0.0028, n=250 -> pmf=0.0014, cdf=0.5039\n"
]
},
{
"data": {
"image/png": 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Skr0kufnm0BfqHXfATjtVvryISHWkk9ynAZ3NrKOZNQZOAyYlLfMkcCiAmbUm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\n5CEldxGRPKTkLiKSh/4/oLZripjJOqcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff906b5acd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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HP/6Re++9l3POOQeAKVOmsDKp0XD+/Pn8/ve/5/zz07qZvEgZ7qE2/fnnqYcvv9y0pt2k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\nkoeU3EVE8pCSu4hIHlJyFxHJQ/8PWR5q6xbA9HcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff906af6090>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 出現確率(様々な人の集計結果を利用)\n",
"p = 0.0014*2\n",
"\n",
"ret = getAngelTryNumber(n=357, p=p, minN=1, maxN=2000)\n",
"ret = getAngelTryNumber(n=250, p=p, minN=1, maxN=2000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.14"
},
"toc": {
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"toc_cell": false,
"toc_position": {},
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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