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Merge pull request #29 from Jingjing092/main
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numpy-random
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@luweizheng
luweizheng authored Nov 15, 2023
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 随机数\n",
" `numpy.random` 模块用于生成随机数（Random）。\n",
" 电脑产生随机数是由一定的计算方法计算出的数值。只要计算方法一定，随机种子一定，那么产生的随机数就不会改变。"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 随机数种子\n",
" `seed(s)` 函数，随机数种子， `s` 是给定的种子值，用于保证每次随机生成的数组或数与上一次相同。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"现在我们规定随机种子为 `10` ，使用 `.randint` 函数在 `[100,400)` 之前随机生成 `2` 个维度为 `3` ，维度下元素为 `4` 的随机整数数组,则，在规定了随机种子后，每次运行下方随机数生成代码生成的随机数相同。"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[365, 225, 115, 223],\n",
" [256, 321, 108, 173],\n",
" [356, 140, 116, 339]],\n",
"\n",
" [[154, 222, 162, 133],\n",
" [300, 277, 279, 154],\n",
" [177, 113, 343, 241]]])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(10) \n",
"np.random.randint(100,400,(2,3,4)) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 随机数生成\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 均匀分布\n",
" `rand(d0,d1,..,dn)` 函数，根据 `d0-dn` 创建随机数数组，生成一个 `[0,1)` 之间的随机浮点数或 `N` 维浮点数组。\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"随机生成 `2` 个，维度为 `2` ，维度下元素为 `3` 的浮点数组，服从 `[0,1)` 均匀分布。"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[0.58390137, 0.18263144, 0.82608225],\n",
" [0.10540183, 0.28357668, 0.06556327]],\n",
"\n",
" [[0.05644419, 0.76545582, 0.01178803],\n",
" [0.61194334, 0.33188226, 0.55964837]]])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.rand(2,2,3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 正态分布\n",
" `randn(d0,d1,..,dn)` 函数，根据 `d0-dn` 创建随机数数组，生成一个的随机浮点数或 `N` 维浮点数组，服从标准正态分布。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"随机生成 `2` 个，维度为 `2` ，维度下元素为 `3` 的浮点数组，服从标准正态分布。"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[-0.94245806, -1.74554298, -1.02094527],\n",
" [-0.16206903, -0.6579709 , 0.42211306]],\n",
"\n",
" [[ 0.62402334, 0.5016401 , 0.84143473],\n",
" [-1.64024998, 1.05800516, -0.09178444]]])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.randn(2,2,3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 制定随机生成范围\n",
" `randint(low[,low,shape]))` 函数，根据 `shape` 创建随机整数或整数数组，范围为 `[low,high)` 。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"在 `[100,400)` 之前随机生成 `2` 个维度为 `3` ，维度下元素为 `4` 的随机整数数组。"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[234, 346, 185, 334],\n",
" [250, 111, 212, 368],\n",
" [320, 162, 285, 179]],\n",
"\n",
" [[332, 142, 337, 335],\n",
" [145, 301, 265, 228],\n",
" [123, 103, 385, 372]]])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.randint(100,400,(2,3,4)) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 排列\n",
"将给定对象进行随机排列。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" `shuffle(x)` 函数，打乱对象 `x` （矩阵或者列表），其中，多维矩阵按照第一维打乱。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"现在，我们在 `[100,400)` 之前随机生成 `1` 个维度为 `6` ，维度下元素为 `2` 的随机整数数组，并将该矩阵按照行打乱。"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[212 114]\n",
" [151 179]\n",
" [343 373]\n",
" [278 153]\n",
" [334 373]\n",
" [333 141]]\n",
"[[212 114]\n",
" [151 179]\n",
" [278 153]\n",
" [333 141]\n",
" [334 373]\n",
" [343 373]]\n"
]
}
],
"source": [
"x = np.random.randint(100,400,(6,2)) \n",
"print(x)\n",
"np.random.shuffle(x)\n",
"print(x)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`permutation(x)` 函数，打乱并返回对象 `x` （整数或者矩阵），其中，多维矩阵按照第一维打乱。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"现在，我们在 `[100,400)` 之前随机生成 `1` 个维度为 `6` ，维度下元素为 `2` 的随机整数数组，并将该矩阵按照行打乱。"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[244 228]\n",
" [259 301]\n",
" [344 354]\n",
" [122 196]\n",
" [167 195]\n",
" [182 162]]\n",
"[[167 195]\n",
" [259 301]\n",
" [244 228]\n",
" [122 196]\n",
" [344 354]\n",
" [182 162]]\n"
]
}
],
"source": [
"x = np.random.randint(100,400,(6,2)) \n",
"print(x)\n",
"y = np.random.permutation(x)\n",
"print(y)\n"
]
}
],
"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": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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