{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "numpy.random模块提供了很多生成随机数的函数，可以选择生成符合某种概率分布的随机数。比如我们可以用normal得到一个4 x 4的，符合标准正态分布的数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.5382462 , -0.79452471, -0.07993797,  0.72243531],\n",
       "       [ 0.87180676,  1.61663011, -0.62169955,  1.73880636],\n",
       "       [ 1.88294218,  0.07432438,  1.63474848,  0.23519213],\n",
       "       [ 0.92847885, -0.45791646,  0.63965317, -0.23654448]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "samples = np.random.normal(size=(4, 4))\n",
    "samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相对的，python内建的random模块一次只能生成一个样本。在生成大量样本方法，numpy.random是非常快的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from random import normalvariate\n",
    "\n",
    "N = 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 1.25 s per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit sample = [normalvariate(0, 1) for _ in range(N)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 49.1 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit np.random.normal(size=N)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "之所以称之为伪随机数，是因为随机数生成算法是根据seed来生成的。也就是说，只要seed设置一样，每次生成的随机数是相同的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当然，这个seed是全局的，如果想要避免全局状态，可以用numpy.random.RandomState来创建一个独立的生成器："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.47143516, -1.19097569,  1.43270697, -0.3126519 , -0.72058873,\n",
       "        0.88716294,  0.85958841, -0.6365235 ,  0.01569637, -2.24268495])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng.randn(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面是是写numpy.random里的函数：\n",
    "\n",
    "![](http://oydgk2hgw.bkt.clouddn.com/pydata-book/rzcuf.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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