GuzinDeneme1 (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Guzin\n"
     ]
    }
   ],
   "source": [
    "x='Guzin'\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "4\n",
      "9\n",
      "16\n",
      "25\n",
      "36\n",
      "49\n",
      "64\n",
      "81\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for i in range(11):\n",
    "    b=i**2\n",
    "    print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#a cellin üstüne yeni cell açıyor;\n",
    "#b de cellin altına yeni cell açıyor (code olarak);\n",
    "#markdown a dönmek için m ye tıklıyoruz\n",
    "#code için y ye tıkla\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "x\n"
     ]
    }
   ],
   "source": [
    "print(1)\n",
    "print('x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random walk:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.randint(low=0, high=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#tab ile seçeneklere geçebiliyosun o kütüphanedeki fonksiyonların seçenekleri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x=10\n",
    "for i in range (101):\n",
    "    w=2*np.random.randint(high=2, low=0)-1\n",
    "    x += w\n",
    "    if x==0:\n",
    "        print(i+1)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86\n"
     ]
    },
    {
     "data": {
      "image/png": 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rkmWCHkAZ2FZE5HJu4cCtm4i4MSKOjYi1kq6QdF97yA+r13n0fHEKALJZNufRt7duCHoA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\negAoOYIeAEru/wEj5VK6CIYaEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x81079e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "X = np.zeros(1000)\n",
    "\n",
    "x = 10\n",
    "for i in range (10001):\n",
    "    w = 2*np.random.randint(high=2, low=0)-1\n",
    "    x += w\n",
    "    X[i] = x\n",
    "    \n",
    "    if x==0:\n",
    "        print(i+1)\n",
    "        break\n",
    "        \n",
    "plt.plot(X)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VRgDwDh6WzbKB2H770klcekq81wMQ1AjEUQJAcCWg95u/1U0J6MXdy6XVRs9O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\nERAEQWhixAgIgiA0MWIEBEEQmpj/D8xnftsDfBOnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7e325c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.zeros(1000)\n",
    "\n",
    "x = 100\n",
    "for i in range (1000):\n",
    "    w = 2*np.random.randint(high=2, low=0)-1\n",
    "    x += w\n",
    "    X[i] = x\n",
    "    \n",
    "    if x==0:\n",
    "        print(i+1)\n",
    "        break\n",
    "        \n",
    "plt.plot(X)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "EPOCHS =10000\n",
    "MAXTURN = 1000\n",
    "INITIALCAPITAL = 10\n",
    "default = 0\n",
    "\n",
    "for epochs in range (EPOCHS):  \n",
    "    CAPITAL = INITIALCAPITAL\n",
    "    for TURN in range (MAXTURN):\n",
    "        GAIN = 2*np.random.randint(high=2, low=0) - 1\n",
    "        CAPITAL += GAIN\n",
    "           \n",
    "        if CAPITAL == 0:\n",
    "            default += 1\n",
    "            break\n",
    "print(default/EPOCHS)        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3264\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "\n",
    "EPOCHS = 10000\n",
    "\n",
    "MAX_TURN = 100\n",
    "INITIAL_CAPITAL = 10\n",
    "lost = 0.\n",
    "\n",
    "for epoch in range (EPOCHS):\n",
    "    capital = INITIAL_CAPITAL\n",
    "    for turn in range(MAX_TURN):\n",
    "        gain = 2*np.random.randint(high=2, low=0)-1\n",
    "        capital += gain\n",
    "\n",
    "        if capital==0:\n",
    "            lost+=1\n",
    "            break\n",
    "        \n",
    "print(lost/EPOCHS)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}