{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7b1b80d5-d4c4-4057-887a-416342efdfa8",
   "metadata": {},
   "source": [
    "# Úloha 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f06203e0-39dc-43f3-b63e-031c397faf00",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef02437f-1f4e-4db0-9489-10e2b7be9d1e",
   "metadata": {},
   "source": [
    "Takhle lze počítá s binomickým rozdělením seriózně pomocí knihovní funkce. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b6c848fd-db39-4c15-9d62-753236d65d11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33830464814224426"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.binom.pmf(1,234,1/365)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a638e2a-274f-4a20-84b6-2a18d92c59ca",
   "metadata": {},
   "source": [
    "A takhle přímočaře podle vzorečku."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5d34185e-6d88-472b-a1ba-b42fc4d8b7bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3383046481422452"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "234*(1/365)*(1-1/365)**233"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef1debb4-7a35-4b7a-a11e-e01dbfc7aa2d",
   "metadata": {},
   "source": [
    "A teď poissonova aproximace, opět knihovní funkcí i přímo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b4d34428-4537-4ee8-a874-7716904f6650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3376747487359775"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.poisson.pmf(1,234/365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "56c28fff-c1fb-46d9-a4e2-bed7909bfc8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33767474873597747"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(-234./365)*234/365"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84b94b6e-3d89-49b7-9374-9f5f2e9dd84f",
   "metadata": {},
   "source": [
    "# Úloha 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b490d96a-0263-486e-9586-9d450a1c363f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "49acef4d-09ed-4ee6-bc9f-f78c5130712a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bff75553-fac2-49bf-ad18-640a4a792011",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = stats.poisson.pmf(x, 4.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c002027c-7d7c-43db-a8a2-0e0d4cfe557d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc4b2e20a90>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,p, 'o')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}