06. 확률분포 - 베르누이 이항분포.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "controlling-rwanda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from random import random\n",
    "from scipy.special import comb\n",
    "import scipy as sp\n",
    "\n",
    "sns.set(style='ticks', color_codes = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "interpreted-manufacturer",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_coin(p):\n",
    "    return random() > p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "pediatric-probe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_coin(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "skilled-examination",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb(10, 3) # 10 combination 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "prepared-safety",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_binom(m, n, p):\n",
    "    return comb(m, n) * ( p ** n ) * (1-p) **(m-n)\n",
    "\n",
    "# 0과 1 두 가지 중 True가 몇 번 나오는가?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "important-transmission",
   "metadata": {},
   "source": [
    "10번 동전을 던졌을 때의 이항분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "parallel-stationery",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.10737418240000006,\n",
       "  0.2684354560000001,\n",
       "  0.3019898880000002,\n",
       "  0.2013265920000001,\n",
       "  0.08808038400000005,\n",
       "  0.02642411520000001,\n",
       "  0.005505024000000003,\n",
       "  0.0007864320000000004,\n",
       "  7.372800000000005e-05,\n",
       "  4.096000000000002e-06,\n",
       "  1.0240000000000006e-07],\n",
       " 1.0000000000000004)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = 10 # try \n",
    "p = 0.2\n",
    "\n",
    "result = [get_binom(m, n, p) for n in range(0, m+1)]\n",
    "result, sum(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dated-wheel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 11 artists>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(m+1), result)\n",
    "# 이산확률분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "lesser-roots",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6, 0.7000000000000001, 0.8, 0.9]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0.1, 1, 0.1).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "painted-material",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9OnXqlCRp3759Q+7wAQAPRtgdflJSkioqKlRSUiKfz6eioiKlpaWpvLxcK1euVGpqqrZu3arXXntNV69e1bRp01RSUiKLxaIdO3Zo/fr16u/v16RJk7Rly5YHsSYAwBDCBr4kORwOORyOkL6amprgz1OnTtV777036Lz09HTV19ffZ4kAgEjgk7YAYAgCHwAMQeADgCEIfAAwBIEPAIYg8PFQuunzj8q5gYfZsN6WCTxocbEWOV5tHJG5m7blj8i8wMOOHT4AGILABwBDEPgAYAgCHwAMQeADwD0Yje8k4106AHAPRuM7ydjhA4AhCHwAMASBDwCGIPABwBAEPgAYgsAHAEMQ+ABgCAIfAAxB4AOAIQh8ADAEgQ8AhiDwAcAQBD4AGILAx7CMxkfBAgjF45ExLKPxUbAAQrHDBwBDEPgAYIhhBX5TU5Nyc3OVnZ2turq6Qcc/+eQTFRYWKicnR+vWrdOtW7ckSZcuXZLT6dS8efO0dOlSXbt2LbLVAwCGLWzgezweuVwu7d+/Xw0NDTpw4IDOnz8fMmb16tV6/fXXdfToUQUCAR08eFCStGHDBhUXF6u5uVkpKSnatWvXyKwCABBW2D/atra2KiMjQ4mJiZKknJwcNTc3a8WKFZKkixcvqr+/XzNmzJAkFRYW6u2339YLL7ygEydOaOfOncH+l156SatXrw6Zv6enRz09PSF9Fy9elCS53e77WtyjzHdrQLFjIn9H7m7z+q53R/x6knThwgUjrzdS1+R6kfcw/Zu5m9uZ6fcP/c63sIHf1dUlq9UabNtsNnV0dNzxuNVqlcfj0eXLlzVu3DiNGTMmpP9/1dbWqrq6eshrO53OcOXhEfDssc1cj+s9tNeLxjXv93per1dPPvnkoP6wgT8wMKCYmJhgOxAIhLTvdPx/x0ka1Jak0tJSFRQUhPTdvHlTX3zxhSZNmiSLxRKuxPvidrvldDpVV1cnu90+oteKBtY3+j3qa3zU1yc9uDX6/X55vV6lpKQMeTxs4NvtdrW3twfbXq9XNpst5LjX6w22v/zyS9lsNo0fP169vb3y+/2yWCyDzrstISFBCQkJg/q/973vhSstoux2u5KTkx/oNR8k1jf6PeprfNTXJz2YNQ61s78t7E3gzMxMtbW1qbu7W319fWppaVFWVlbw+MSJEzV27FidPHlSktTY2KisrCzFxsYqPT1dR44ckSQ1NDSEnAcAeLDCBn5SUpIqKipUUlKi559/Xnl5eUpLS1N5eblOnz4tSdq6dat+85vfaN68ebp+/bpKSkokSVVVVTp48KByc3PV3t6uV155ZUQXAwC4s2E9WsHhcMjhcIT01dTUBH+eOnWq3nvvvUHnTZw4UXv37r3PEgEAkWD8J20TEhK0YsWKIf+O8ChgfaPfo77GR3190sOzxphAIBCIagUAgAfC+B0+AJiCwAcAQxgd+OEeCjfaVVdXa8GCBVqwYIG2bNkS7XJG1FtvvaXKyspolxFxx44dU2FhoebPn6833ngj2uWMiMbGxuC/07feeiva5UTM1atXlZeXF3xMQmtrqxwOh7Kzs+VyuaJTVMBQbrc78MwzzwQuX74cuHbtWsDhcAQ+/fTTaJcVMcePHw/87Gc/C9y4cSNw8+bNQElJSaClpSXaZY2I1tbWwKxZswK//vWvo11KRH3++eeBOXPmBDo7OwM3b94MvPjii4G//e1v0S4roq5fvx546qmnAv/+978DPp8vUFRUFDh+/Hi0y7pv//jHPwJ5eXmBadOmBb744otAX19f4Omnnw58/vnnAZ/PF1i8eHFU/lsau8P/+kPh4uPjgw+Fe1RYrVZVVlYqLi5OsbGx+v73v69Lly5Fu6yI+89//iOXy6UlS5ZEu5SI+8tf/qLc3FzZ7XbFxsbK5XJp+vTp0S4rovx+vwYGBtTX16dbt27p1q1bGjt2bLTLum8HDx5UVVVV8OkCHR0devLJJ/Xd735XY8aMkcPhiEreGPsVh+EeCjfa/eAHPwj+/K9//Ut//vOf9cc//jGKFY2M119/XRUVFers7Ix2KRH32WefKTY2VkuWLFFnZ6d+9KMfPXIfXhw3bpx+9atfaf78+frWt76lp556SjNnzox2Wfdt06ZNIe2h8maoh0mONGN3+OEeCveo+PTTT7V48WKtWbNGkyZNinY5EfWnP/1JEyZM0OzZs6Ndyojw+/1qa2vTm2++qQMHDqijo0P19fXRLiuizp07p0OHDumvf/2rPvroIz322GN69913o11WxD0seWNs4P/vQ9/u9HC30ezkyZNatGiRXn311UFPJH0UHDlyRMePH1d+fr7efvttHTt2TG+++Wa0y4qY73znO5o9e7bGjx+vxx9/XD/5yU8eqf8LlaSPP/5Ys2fP1re//W3FxcWpsLBQf//736NdVsQ9LHljbOCHeyjcaNfZ2anly5dr69atWrBgQbTLGRG///3v9cEHH6ixsVErV67Uj3/8Y61duzbaZUXMM888o48//lg9PT3y+/366KOPNG3atGiXFVFTp05Va2urrl+/rkAgoGPHjik1NTXaZUXc9OnT9c9//lOfffaZ/H6/Pvjgg6jkjbH38L/+UDifz6eioiKlpaVFu6yIeffdd3Xjxg1t3vz/X6SwcOFCvfjii1GsCt/E9OnTVVZWpuLiYvl8Pv3whz/UT3/602iXFVFz5szR2bNnVVhYqNjYWKWmpuoXv/hFtMuKuLFjx2rz5s365S9/qRs3bujpp5/WvHnzHngdPFoBAAxh7C0dADANgQ8AhiDwAcAQBD4AGILABwBDEPgAYAgCHwAMQeADgCH+D85efWCI+p2ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = 10\n",
    "result = list()\n",
    "\n",
    "# 확률값이 올라가면서 보이는 그래프의 변화\n",
    "# 0.5일 때는 종모양의 정규분포를 그림\n",
    "# 0.5는 10번 던졌을 때 5번 나오는 확률이 젤 많다는 것\n",
    "for p in np.arange(0.1, 1, 0.2).tolist():\n",
    "    for n in range(0, m+1):\n",
    "        result.append(get_binom(m, n, p))\n",
    "    plt.bar(range(m+1), result)\n",
    "    plt.show()\n",
    "    result = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ecological-chemical",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD7CAYAAABpJS8eAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAc00lEQVR4nO3de0xUZ94H8C/MxZU1hLo7wxhp6ro2mixSs0sjoqHbtYKgUwrS1UoXfC1svJUt8bK80hR169b6aseuqFm1aYziRlsEnMZFtu022xaSFbMRiW1S03pBmWEsrqCAc33/oOfIyOAMwzAzzPP9JMRzHZ5zynzn6Znn/E6Uy+VygYiIIl50qBtARETBwcAnIhIEA5+ISBAMfCIiQTDwiYgEoQx1Azzp6+tDa2srNBoNFApFqJtDRDQmOBwOWCwWJCYm4kc/+tGg9WEZ+K2trcjPzw91M4iIxqSqqiokJycPWh6Wga/RaAD0N1qn04W4NUREY4PJZEJ+fr6coQ8Ly8CXLuPodDokJCSEuDVERGPLUJfCffrS1mg0IisrC+np6aiqqhq0/uOPP0Z2djaef/55rFmzBnfu3AEA1NTUYN68ecjOzkZ2djYMBsMIDoGIiEbCaw/fbDbDYDDg1KlTUKvVWLZsGWbPno1p06YBAO7evYstW7aguroa8fHxePfdd7F37168/vrraG1tRVlZGRYvXjzqB0JERI/mtYff2NiIlJQUxMXFISYmBhkZGaivr5fX22w2VFRUID4+HgAwffp0tLe3AwAuXryImpoa6PV6bNiwQe75ExFR8HkN/I6ODrcvALRaLcxmszz/2GOPYcGCBQD6h1MePHgQzz33HID+L1/XrFmD06dPY9KkSdi2bdug1+/q6kJbW5vbj8lkGvGBERGRO6+XdJxOJ6KiouR5l8vlNi/p7u7G2rVrMWPGDOTk5AAA9u3bJ68vKiqSPxgGOnLkCCorK/1qPBER+c5r4Ot0OjQ3N8vzFosFWq3WbZuOjg688sorSElJwebNmwH0fwBUV1djxYoVAPo/KDx9c1xYWCh/QEikoUVERBQ4Xi/ppKamoqmpCZ2dnejt7UVDQwPS0tLk9Q6HA6tWrUJmZibKy8vl3n9MTAwOHz6MCxcuAACOHTvmsYcfGxuLhIQEtx+OvSciCjyvPfz4+HiUlpaioKAANpsNeXl5SEpKQnFxMUpKSmAymXDp0iU4HA6cPXsWAJCYmIjt27djz5492LJlC/r6+jBlyhTs3Llz1A8oUKw2B9QqxaBpIqKxKiocn3jV1taG+fPn45NPPgnpjVf69XUAAOPu7JC1gYjIV96yk9UyiYgEwcAnIhIEA5+ISBAMfCIiQTDwiYgEwcAnIhIEA5+ISBAMfCIiQTDwiYgEwcAnIhIEA5+ISBAMfCIiQTDwiYgEwcAnIhIEA5+ISBAMfCIiQTDwiYgEwcAnIhIEA5+ISBAMfCIiQTDwiYgEwcAnIhIEA5+ISBAMfCIiQTDwiYgEwcAnIhIEA5+ISBAMfCIiQTDwiYgEwcAnIhIEA5+ISBAMfCIiQTDwiYgE4VPgG41GZGVlIT09HVVVVYPWf/zxx8jOzsbzzz+PNWvW4M6dOwCAmzdvIj8/HwsXLsTq1atx7969wLY+wKw2h9d1j9qGiCiceQ18s9kMg8GA48ePo7a2FidOnMDly5fl9Xfv3sWWLVtw8OBBnD59GtOnT8fevXsBAFu3bsXy5ctRX1+PxMRE7N+/f/SOJADUKgX06+ugX1835Dq1ShGClhERjZzXwG9sbERKSgri4uIQExODjIwM1NfXy+ttNhsqKioQHx8PAJg+fTra29ths9lw7tw5ZGRkAAByc3Pd9iMiouBSetugo6MDGo1GntdqtWhpaZHnH3vsMSxYsAAA0NfXh4MHD+J3v/sdbt++jQkTJkCp7P8VGo0GZrN50Ot3dXWhq6vLbZnJZPLvaIiIaEheA9/pdCIqKkqed7lcbvOS7u5urF27FjNmzEBOTg7MZvOg7Tztd+TIEVRWVvrTdiIiGgavga/T6dDc3CzPWywWaLVat206OjrwyiuvICUlBZs3bwYATJw4Ed3d3XA4HFAoFB73A4DCwkLk5OS4LTOZTMjPz/frgIiIyDOv1/BTU1PR1NSEzs5O9Pb2oqGhAWlpafJ6h8OBVatWITMzE+Xl5XIvXqVSITk5GWfOnAEA1NbWuu0niY2NRUJCgtuPTqcL1PEREdEPvPbw4+PjUVpaioKCAthsNuTl5SEpKQnFxcUoKSmByWTCpUuX4HA4cPbsWQBAYmIitm/fjoqKCpSVleHAgQOYNGkS3nnnnVE/ICIi8sxr4AOAXq+HXq93W3bo0CEAwMyZM/H111973G/y5Mk4evToCJtIRESBwDttiYgEwcAnIhIEA5+ISBAMfCIiQTDwiYgEwcAnIhIEA5+ISBAMfCIiQTDwiYgEwcAnIhIEA5+ISBAMfCIiQTDwiYgEwcAnIhIEA5+ISBAM/FHktFs9ThMRhYJPD0Ah/0Qr1fh2+xIAwNTy6hC3hohExx4+EZEgGPhERIJg4BMRCYKBT0QkCAY+EZEgGPhERIJg4BMRCYKBT0QkCAY+EZEgGPhERIJg4BMRCYKBT0QkCAZ+gElVMR+ujsnKmUQUagz8AJMqZEYr1R6Xe1pHRBQMDHwiIkEw8ImIBOFT4BuNRmRlZSE9PR1VVVVDbrdp0yacOnVKnq+pqcG8efOQnZ2N7OxsGAyGkbeYiIj84vWJV2azGQaDAadOnYJarcayZcswe/ZsTJs2zW2biooKNDU1ISUlRV7e2tqKsrIyLF68eHRaT0REPvPaw29sbERKSgri4uIQExODjIwM1NfXu21jNBoxf/58ZGZmui2/ePEiampqoNfrsWHDBty5cyewrSciIp95DfyOjg5oNBp5XqvVwmw2u21TVFSEF198cdC+Go0Ga9aswenTpzFp0iRs27Zt0DZdXV1oa2tz+zGZTP4cCxERPYLXSzpOpxNRUVHyvMvlcpt/lH379snTRUVFWLBgwaBtjhw5gsrKSp9ej4iI/Oc18HU6HZqbm+V5i8UCrVbr9YW7u7tRXV2NFStWAOj/oFAoFIO2KywsRE5Ojtsyk8mE/Px8r7+DiIh85/WSTmpqKpqamtDZ2Yne3l40NDQgLS3N6wvHxMTg8OHDuHDhAgDg2LFjHnv4sbGxSEhIcPvR6XR+HAoRET2K1x5+fHw8SktLUVBQAJvNhry8PCQlJaG4uBglJSWYOXOmx/0UCgX27NmDLVu2oK+vD1OmTMHOnTsDfgBEROQbr4EPAHq9Hnq93m3ZoUOHBm23Y8cOt/nk5GTU1NSMoHlERBQovNM2zFkdNrd/iYj8xcAPc2qFCr89sRpqhSrUTSGiMY6BT0QkCAY+EZEgGPhERIJg4BMRCYKBT0QkCAZ+mBk4/PLhoZiPWkdE5I1PN15R8EjDMAHg5NIDPq8jIvKGPXwiIkEw8ImIBMHAJyISBAOfiEgQDHwiIkEw8ImIBMHAJyISBAM/wjmtVo/TRCQe3ngV4aLVanyZvQQAMLeuOsStIaJQYg+fiEgQDHwiIkEw8EfIabd6nPZ1v+HsQ0Q0Egz8EYpWqvHt9iX4dvsSRCvVw95vOPv46lFfzkrr+AUukXgY+BFI+qJW+rLW07podeA/aIgovAkd+Fabw+P0cPYjIhorhB6WqVYpoF9fBwAw7s4e9f2IiEJJ6B4+EZFIGPhERIJg4IcBf55PG8zn29oHfGdh5/cXRGOW0Nfww4U/z6oN5vNtlSoFtq3/CADwxu7Fo/q7iGj0sIdPRCQIBj559KhLN9I6Xt4hGlsY+OSRdBlHupTjaZ1SpQhBy4jIXwx8IiJB+BT4RqMRWVlZSE9PR1VV1ZDbbdq0CadOnZLnb968ifz8fCxcuBCrV6/GvXv3Rt5iIiLyi9fAN5vNMBgMOH78OGpra3HixAlcvnx50DarVq3C2bNn3ZZv3boVy5cvR319PRITE7F///7Atp6IiHzmNfAbGxuRkpKCuLg4xMTEICMjA/X19W7bGI1GzJ8/H5mZmfIym82Gc+fOISMjAwCQm5s7aD8KHH+qYAbz8Yd2m83jNBEFj9dx+B0dHdBoNPK8VqtFS0uL2zZFRUUAgPPnz8vLbt++jQkTJkCp7P8VGo0GZrN50Ot3dXWhq6vLbZnJZBrGIRDwoArmcB5jGMzHHypVKlT+7/8AANa99f6o/i4i8sxr4DudTkRFRcnzLpfLbX4onrbztN+RI0dQWVnpS1uJiGgEvAa+TqdDc3OzPG+xWKDVar2+8MSJE9Hd3Q2HwwGFQjHkfoWFhcjJyXFbZjKZkJ+f70v7KYzZbTYoVSr534eXPzxNRKPL6zX81NRUNDU1obOzE729vWhoaEBaWprXF1apVEhOTsaZM2cAALW1tR73i42NRUJCgtuPTqfz41Ao3EiXcR4OdGm5p3VENHq8Bn58fDxKS0tRUFCAF154AYsXL0ZSUhKKi4tx8eLFR+5bUVGBkydPIisrC83NzXjttdcC1W4iIhomn4qn6fV66PV6t2WHDh0atN2OHTvc5idPnoyjR4+OoHkUTHabA0qVQv53OPs8PD0anHYnopXR8r9ENDx815DMn5IJA0swjHaphWhlNL7Z9QXDnshPfOcQEQmCgU9hy2l3epz2to6IPOMDUChsSZdwAODJDfN8XkdEnrGHTwHjb/kEadvRLrlgt9s9ThOJgoFPAePv+PqhxusHmlKpxO7du7F792655AeRSBj4FNEe1ZNnj59Ew24ORTSpVw8A69ev93kdUSRiD5/Cij8jbjhih8g3DHwKK9LoG2kEznD3Ge2bspwOm8dporGAgU/kxcBgj1aocL5hI843bES0QiWvY/jTWMBr+GOU02rFyaUH5OlotTrELYpcUsgDwK/S/8/juoeXE4Uj9vBDxOqwYWp5Nax+9gylp1V9mb2EYf8QacTNcEbecMQOiYCBHyJqhQq/PbEaakXo6sHbbQ7MravG3Lpq2G2OkLUj0KTRN8MZa88x+iQC/mULTKp0CQBv7F4c4tYQ0Whj4NOI2G22iPywcDps8pey0T7+X9jAbYezH1Gw8JIOjcjAcgqRRPoydjih/fAIHqJwwx6+H5x2K4y7s0PdjIjhtDux7q33+SQrolHGd5cfopVqfLt9Cb7dviTUTYkIfJIVUXCwh09jnt1ul2vi2+12jrIhGgLfGTTmsQgakW8Y+EToH1UjfVgEYoQNR+xQOGLgE+HR5RPC4fWIAoHfkhERCYKBT0QkCAY+EZEgGPhERIJg4BMRCYKjdGjYpIJpdpsNStXwhxtKpRSkad5hSxQcfKcJRqqBP5L691LBNH/CHgjMM2ilu2ulO2yJyDv28AUj1cAf6yWNA3F37cCbrUabP+WWiQKNgT+GDHyOLY1cMG+O4rNvKRzwks4YMvA5tkREw8XAJyIShE+BbzQakZWVhfT0dFRVVQ1a/9VXXyE3NxcZGRkoLy+H3W4HANTU1GDevHnIzs5GdnY2DAZDYFtPREQ+83oN32w2w2Aw4NSpU1Cr1Vi2bBlmz56NadOmydts3LgRb775JmbNmoXNmzfj5MmTWL58OVpbW1FWVobFi8f2F4RERJHAaw+/sbERKSkpiIuLQ0xMDDIyMlBfXy+vv3HjBvr6+jBr1iwAQG5urrz+4sWLqKmpgV6vx4YNG3Dnzp1Br9/V1YW2tja3H5PJFKDDCx9Whw1Ty6sxtbwaVoct1M0hIgF57eF3dHRAo9HI81qtFi0tLUOu12g0MJvN8vTKlSvxy1/+Eu+88w62bdsmD6WTHDlyBJWVlSM+kHCnVqjw2xOrAYAjbYgoJLwGvtPpRFRUlDzvcrnc5h+1ft++ffLyoqIiLFiwYNDrFxYWIicnx22ZyWRCfn7+MA6DiIi88Rr4Op0Ozc3N8rzFYoFWq3Vbb7FY5Plbt25Bq9Wiu7sb1dXVWLFiBYD+DwKFQjHo9WNjYxEbGzuSYyBBSHfX+vvc2kA/1cpffBoWhYrXa/ipqaloampCZ2cnent70dDQgLS0NHn95MmTMW7cOJw/fx4AUFdXh7S0NMTExODw4cO4cOECAODYsWMee/gkBql+jlRDxx/S3bX+PqRcuvnpfMPGkIZsuLSDxOP1nRMfH4/S0lIUFBTAZrMhLy8PSUlJKC4uRklJCWbOnIldu3bh9ddfx927d/GLX/wCBQUFUCgU2LNnD7Zs2YK+vj5MmTIFO3fuDMYxUYBJxdKkaX9q6Ej1cwCw/g1RiPjUVdLr9dDr9W7LDh06JE/PmDEDH3744aD9kpOTUVNTM8ImBpbV5oBaNfjSEg1NKpYGYEQ9dCIKLeHutFWrFNCvr4N+fV2om0JEFFQsnhbhpHLI0rSS/3dDJCwGfoSTyiEDGPMlkYloZIS7pENEJCr28CmiBfMhJ/7gmHwKJvbwfeS0W2HcnQ2n3RrqptAwDBzzHo44Jp+CiYHvo2ilGt9uX4JopTqov1d6ypXTKt4HzcDn1kolt4nIfwz8MCc95SpaHdwPmkCQ7q512p1+7S/dWTuSu2uJ6AEGPo0a6e7aaCX/zIjCAd+JRESC4P8nk0cD6+cQUWRgD588kurnSDV0iGjsY+BTxJHG3jvH4KMkpTaPxbZT+GPgj6KBz7Gl4JHGto/Fce1jue0U/ngNfxSF6jm2AwumjTXS2HsiCjz28COQVDBNKpo2lgwce09EgcUePgWMdKOVNM3x90ThhYFPAcPHGBKFN3bBiIgEwR5+mJGKpUnTwayhI91s5e+DykNlYAnkSCoxzNLJFGjs4YcZqVhaKAqmSTdbjaWwByK3xHCkHheFDgOfQk4aiskSyESji4FPIScNxWQJZKLRxXdYhJButrLbHFCqFEH7vQOHYhJReGMPfwjSIw3HymMNpZutghn2wIOhmNJwTCIKX+zhD0F6pCGAYdXCkernWB02qPlF26gJ94eTBxpH7FAgMPADTKqfE8zaOf4aWPM+mEMxB9bLsdvtfl27l0awAMCv0v8voO0LR6IdL40OBn4YGDj2PpikYZgAgnodXvqSFoBQvXSiUBPiGr7V5nD7N9wMHHs/Foz04eREFBpCBL5apYB+fR3UQf5CczRJo3KkkTnBFKqHk4/lB5sEGh+UQv4QIvAj0cASyMMZmTOwfEIwSdftR1JUjQ8HeYDngvzBa/iCka7bB3vsvD/X7SO1Rk6gcQQP+YqBP4A09n44pGGY0rSvQzGlL2qDXSDNH6Gqc8+RKb7heSJf+RT4RqMRBw4cgN1uR2FhIfLz893Wf/XVVygvL8e9e/eQnJyMrVu3QqlU4ubNm9i4cSO+//57/OxnP8OuXbvw4x//eFQOJBD8GXvv72MMpS9qh/MoQn8eXRiIoZf+1rkfWCPH16GXoo2vDzT29ulRvHbVzGYzDAYDjh8/jtraWpw4cQKXL19222bjxo144403cPbsWbhcLpw8eRIAsHXrVixfvhz19fVITEzE/v37R+coBOHPowulSzjDrYIp9eqHc+ln4HV6KeSHWyNnYIVIGj5W2KRH8fpObGxsREpKCuLi4gAAGRkZqK+vx7p16wAAN27cQF9fH2bNmgUAyM3NxV/+8he8+OKLOHfuHPbt2ycvf/nll7Fxo/sbuaurC11dXW7Lbty4AQAwmUx+HZTN7oTqh8sO0rStpxNtbW39y3o6AQDXr36HA6W/kqejFCqY7/aXUVC3tblNA4D5rhXqtjbYnHaoC98FALS1tcF6u1eeBgDr7V63aQC4/t13eGduuTwdpVLhltUqb3fLapVfY+C0w+7EE/vexdUr16BQRuPuD21va2tzmwaAuz8co8Nux8rSWfK6rnt9btt13etzm5bWuRwuvLD2T7h+9TqiFFG4cqgZADClOBnm7lsAgPHSuei+hfFtbXA4HBi/bAqA/v9ehw8fBgAUFRUBALq7u+Xf1d3d3X/8165g6dKl8jQALF26FNevXUFUtBKW73vkNg2cBgDL9z1u04HYLpi/K9jbuZx2REX3v82HmqbIIWWmw+F55F6Uy+VyPeoF/vrXv6KnpwelpaUAgA8++AAtLS3405/+BAD4z3/+g507d+Jvf/sbAODq1av4/e9/j6NHjyIvLw//+te/APT3/mbNmoXW1la319+7dy8qKytHcIhERDRQVVUVkpOTBy33+hHvdDoRFRUlz7tcLrf5odY/vB2AQfMAUFhYiJycHLdlVqsV169fx5QpU6BQ+Dd23mQyIT8/H1VVVdDpdH69RqTguXiA5+IBnosHIuVcOBwOWCwWJCYmelzvNfB1Oh2am5vleYvFAq1W67beYrHI87du3YJWq8XEiRPR3d0Nh8MBhUIxaD9JbGwsYmNjBy2fOnWqt6b5RKfTISEhISCvNdbxXDzAc/EAz8UDkXAunnjiiSHXef3SNjU1FU1NTejs7ERvby8aGhqQlpYmr588eTLGjRuH8+fPAwDq6uqQlpYGlUqF5ORknDlzBgBQW1vrth8REQWX18CPj49HaWkpCgoK8MILL2Dx4sVISkpCcXExLl68CADYtWsX3nrrLSxcuBA9PT0oKCgAAFRUVODkyZPIyspCc3MzXnvttVE9GCIiGppPX9Pr9Xro9Xq3ZYcOHZKnZ8yYgQ8//HDQfpMnT8bRo0dH2EQiIgqEiK2lExsbi3Xr1nn8fkA0PBcP8Fw8wHPxgCjnwuuwTCIiigwR28MnIiJ3DHwiIkFEZOAbjUZkZWUhPT0dVVVVoW5O0FVWVmLRokVYtGgRdu7cCaC/RIZer0d6ejoMBkOIWxh8b7/9NsrKygCIey4+/fRT5ObmIjMzE2+++SYAcc9FXV2d/B55++23AQhyLlwRxmQyuZ599lnX7du3Xffu3XPp9XrXN998E+pmBc2XX37pWrp0qev+/fsuq9XqKigocBmNRtczzzzjunbtmstms7lWrlzp+uyzz0Ld1KBpbGx0zZ492/XHP/7R1dvbK+S5uHbtmmvevHmu9vZ2l9Vqdb300kuuzz77TMhz0dPT43r66add33//vctms7ny8vJcn3zyiRDnIuJ6+AOLvcXExMjF3kSh0WhQVlYGtVoNlUqFn//857hy5QqeeOIJPP7441AqldDr9cKck//+978wGAxYtWoVAKClpUXIc/GPf/wDWVlZ0Ol0UKlUMBgMGD9+vJDnwuFwwOl0ore3F3a7HXa7HRMmTBDiXERcubyOjg5oNBp5XqvVoqWlJYQtCq4nn3xSnr5y5Qr+/ve/4+WXXx50TsxmcyiaF3RvvPEGSktL0d7eDsDz34cI5+Lq1atQqVRYtWoV2tvb8etf/xpPPvmkkOdiwoQJ+MMf/oDMzEyMHz8eTz/9tDB/FxHXw/dW7E0U33zzDVauXIlNmzbh8ccfF/KcfPDBB5g0aRLmzJkjLxP178PhcKCpqQl//vOfceLECbS0tOD69etCnouvv/4a1dXV+Oc//4nPP/8c0dHRuHLlihDnIuJ6+N6KvYng/PnzKCkpwebNm7Fo0SL8+9//ditwJ8o5OXPmDCwWC7Kzs3Hnzh309PTgxo0bbhVYRTkXP/3pTzFnzhxMnDgRAPDcc8+hvr5eyHPxxRdfYM6cOfjJT34CoP9ZHe+9954Q5yLievjeir1Fuvb2dqxduxa7du3CokWLAABPPfUUvvvuO1y9ehUOhwMfffSREOfk/fffx0cffYS6ujqUlJTgN7/5DQ4fPizkuXj22WfxxRdfoKurCw6HA59//jkWLlwo5LmYMWMGGhsb0dPTA5fLhU8//VSY90jE9fAHFnuz2WzIy8tDUlJSqJsVNO+99x7u37+PHTt2yMuWLVuGHTt24NVXX8X9+/fxzDPPYOHChSFsZeiMGzdOyHPx1FNPoaioCMuXL4fNZsPcuXPx0ksvYerUqcKdi3nz5uHSpUvIzc2FSqXCzJkz8eqrr2Lu3LkRfy5YWoGISBARd0mHiIg8Y+ATEQmCgU9EJAgGPhGRIBj4RESCYOATEQmCgU9EJAgGPhGRIP4fXBOogkr6H/EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = 0.5\n",
    "result = list()\n",
    "\n",
    "# 횟수의 증가\n",
    "# 그래프 모양이 점차 단순한 종모양으로 변하는 걸 볼 수 있음\n",
    "for m in np.arange(10, 100, 10).tolist():\n",
    "    for n in range(0, m+1):\n",
    "        result.append(get_binom(m, n, p))\n",
    "        \n",
    "    plt.bar(range(m+1), result)\n",
    "    result = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "material-moses",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1171875, 0.11718750000000014)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Probability Mass Function -> PMF\n",
    "get_binom(m=10, n=3, p=0.5), sp.stats.binom.pmf(k=3, n=10, p=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "canadian-terry",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "developing-offering",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " Counter({3: 201604,\n",
       "          1: 268691,\n",
       "          2: 301428,\n",
       "          0: 107157,\n",
       "          4: 88162,\n",
       "          5: 26596,\n",
       "          7: 737,\n",
       "          6: 5545,\n",
       "          8: 74,\n",
       "          9: 6}))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binomial = sp.stats.binom(n=10, p=0.2) # 10번 실행, 확률 0.2\n",
    "\n",
    "rvs_binomial = binomial.rvs(size=1000000) # rvs 랜덤 샘플 제작\n",
    "np.unique(rvs_binomial), Counter(rvs_binomial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "arabic-permit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = np.arange(0, 10, 1) # 0부터 10까지 늘려가면서 pmf를 구함\n",
    "pmf_binomial = binomial.pmf(k=m) # 이항분포를 만들어 줄 수 있음\n",
    "plt.bar(m, pmf_binomial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fluid-purse",
   "metadata": {},
   "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": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}