{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import scipy.stats" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "12局中4局以上で高い一致率(10%で発生とする)を示す確率" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "r = 0.1\n", "num = 12\n", "lim = 3\n", "s = 1.0 - scipy.stats.binom.cdf(lim, num, r)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.5637470165\n" ] } ], "source": [ "print(100 * s)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "42局中に、一致率の4局を含む12局が現れる確率" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "num_ext = 42\n", "num_iter = 10000" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "np.random.seed(20170226)\n", "res = np.zeros(num_iter)\n", "for ii in range(num_iter):\n", " samp = np.random.choice([0, 1], num_ext, replace=True, p=[1.0-r, r])\n", " seq = pd.Series(samp).rolling(window=num).sum()\n", " res[ii] = seq.max()\n", "s = (res > lim).mean()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "17.79\n" ] } ], "source": [ "print(100 * s)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "念のため他のseedも試す" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "np.random.seed(20170227)\n", "res = np.zeros(num_iter)\n", "for ii in range(num_iter):\n", " samp = np.random.choice([0, 1], 42, replace=True, p=[0.9, 0.1])\n", " seq = pd.Series(samp).rolling(window=12).sum()\n", " res[ii] = seq.max()\n", "s = (res > 3).mean()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "17.48\n" ] } ], "source": [ "print(100 * s)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }