surekli_olasilik_dagilimlari.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sympy import *\n",
    "import math\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#UNIFORM DAGILIM\n",
    "#x a ve b arasında olmalı a<x<b\n",
    "def uniform_olasilik(a,b,c):\n",
    "    if(a<b):\n",
    "        x=Symbol('x')                    \n",
    "        f=1/(b-a)\n",
    "        sonuc= integrate(f,(x,b-c,b))\n",
    "        plt.plot(sonuc,'y')\n",
    "        plt.legend('uniform')\n",
    "        plt.show()\n",
    "        \n",
    "    else:\n",
    "        print(\"yanlıs girdiniz:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23eeb182cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uniform_olasilik(0,30,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#NORMAL DAĞILIM\n",
    "class normal:\n",
    "    def __init__(self, mu, sigma):\n",
    "        self.mu = mu\n",
    "        self.sigma = sigma\n",
    "        \n",
    "    def get(self,x):\n",
    "        return normal.n_dagilim(x, self.mu, self.sigma)\n",
    "\n",
    "    @staticmethod\n",
    "    def n_dagilim(x, mu, sigma):\n",
    "        return ( 1. / np.sqrt(2*np.pi) * sigma ) * np.exp( -0.5 * (x-mu/sigma)**2 ) \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vwMwckskW8fgI3d0/pabmY3Yzmsu83hJqan6f7u6H7JHYeWzKgiAiXuBuYBWw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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23eeb1825f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-5,5,0.01)\n",
    "\n",
    "g1 = normal(0,0.5)\n",
    "\n",
    "y1 = [g1.get(i) for i in x]\n",
    "plt.plot(x,y1,'y')\n",
    "plt.legend(['ilk','ikinci','sonuc'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#USTEL DAGILIM\n",
    "#ustel dağılım lamda>0 olmalı\n",
    "\n",
    "def ustel_dagilim(lamda,x):\n",
    "    if(lamda>0):\n",
    "        beta = 1.0/lamda\n",
    "\n",
    "        Y = np.random.exponential(beta, 5000)\n",
    "        plt.hist(Y, normed=True, bins=200,lw=0,alpha=.8)\n",
    "        plt.plot([0,max(Y)],[lamda,lamda],'r--')\n",
    "        plt.ylim(0,lamda*1.1)\n",
    "        plt.show()\n",
    "    else:\n",
    "        print(\"lamda 0 dan buyuk olmalı\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23eeb3a3908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ustel_dagilim(5,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}