pseudo_random.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Сгенерировать случайное число от 0 до N\n",
    "N = 100\n",
    "random.randint(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07734228275244748"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Сгенерировать случайное вещественное число от 0 до 1\n",
    "random.rand()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.414568048202824"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.rand() * N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([76, 26, 91, 29, 24])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Генерация случайного массива\n",
    "x = random.randint(100, size=5)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = random.randint(100, size=(3, 5))\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = random.rand(5)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Случайный выбор при помощи random\n",
    "x = random.choice([3, 5, 7, 9])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Составление нового массива из фиксированного набора элементов\n",
    "y = random.choice([3, 6, 7, 9, 2], size=(3, 5))\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Случайные распределения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal = random.normal(size=100)\n",
    "normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt \n",
    "\n",
    "plt.figure(figsize=(15, 12))\n",
    "plt.grid()\n",
    "hist = plt.hist(normal, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal = random.normal(size=10000)\n",
    "plt.figure(figsize=(15, 12))\n",
    "plt.grid()\n",
    "hist = plt.hist(normal, bins=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Изменить стандартное отклонение \n",
    "\"\"\"\n",
    "\n",
    "sigma = 10\n",
    "normal = random.normal(scale=sigma, size=10000)\n",
    "plt.figure(figsize=(15, 13))\n",
    "plt.grid()\n",
    "hist = plt.hist(normal, bins=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Треугольное вероятностное распределение \n",
    "\"\"\"\n",
    "triangular = random.triangular(0, 0.5, 1, size=1000000)\n",
    "\n",
    "plt.figure(figsize=(15, 13))\n",
    "plt.grid()\n",
    "hist = plt.hist(triangular, bins=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Экспоненциальное распределение \n",
    "\"\"\"\n",
    "lambd = 1\n",
    "exponential = random.exponential(lambd, size=1000000)\n",
    "plt.figure(figsize=(15, 13))\n",
    "plt.grid()\n",
    "hist = plt.hist(exponential, bins=200)"
   ]
  }
 ],
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   "display_name": "Python 3",
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  "language_info": {
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