yapay_zeka_giris_dart_isabet_hesaplama.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Yapay Zekaya Giriş"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "circle = plt.Circle((0, 0), 30, color='b', fill=False)\n",
    "ax.add_artist(circle)\n",
    "ax.set_xlim(-100, 100)\n",
    "ax.set_ylim(-100, 100)\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-26, -30)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "atis = (np.random.randint(-100, 100), np.random.randint(-100, 100))\n",
    "atis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# draw atis point on the plot\n",
    "\n",
    "def atisi_ciz(atis):\n",
    "    fig, ax = plt.subplots()\n",
    "    circle = plt.Circle((0, 0), 30, color='b', fill=False)\n",
    "    ax.add_artist(circle)\n",
    "    ax.set_xlim(-100, 100)\n",
    "    ax.set_ylim(-100, 100)\n",
    "    plt.gca().set_aspect('equal', adjustable='box')\n",
    "    plt.scatter(0,0, color='b')\n",
    "    plt.scatter(*atis, color='r')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "atisi_ciz(atis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "yari_cap = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tahmin_et(atis):\n",
    "  x = atis[0]\n",
    "  y = atis[1]\n",
    "\n",
    "  mesafe = np.sqrt(x**2 + y**2)\n",
    "\n",
    "  return mesafe < yari_cap\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "atislar = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>isabet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>-80</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-96</td>\n",
       "      <td>-71</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>78</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>92</td>\n",
       "      <td>-5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-26</td>\n",
       "      <td>-39</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>82</td>\n",
       "      <td>19</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>37</td>\n",
       "      <td>-87</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>64</td>\n",
       "      <td>-66</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>54</td>\n",
       "      <td>35</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      x   y  isabet\n",
       "0    63 -80   False\n",
       "1   -96 -71   False\n",
       "2    32  78   False\n",
       "3    92  -5   False\n",
       "4   -26 -39   False\n",
       "..   ..  ..     ...\n",
       "195  82  19   False\n",
       "196  37 -87   False\n",
       "197  64 -66   False\n",
       "198  54  35   False\n",
       "199   4   7    True\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "atislar_df = pd.DataFrame(atislar)\n",
    "atislar_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>isabet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>-15</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-6</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-21</td>\n",
       "      <td>-14</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>-15</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>-20</td>\n",
       "      <td>-8</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2</td>\n",
       "      <td>-12</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>-6</td>\n",
       "      <td>20</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>-26</td>\n",
       "      <td>13</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>-15</td>\n",
       "      <td>-25</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>-5</td>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>1</td>\n",
       "      <td>-22</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>11</td>\n",
       "      <td>-5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      x   y  isabet\n",
       "8     7 -15    True\n",
       "11    4  27    True\n",
       "25   -6   3    True\n",
       "30  -21 -14    True\n",
       "69  -15   4    True\n",
       "91  -20  -8    True\n",
       "95    2 -12    True\n",
       "98   -6  20    True\n",
       "101 -26  13    True\n",
       "112 -15 -25    True\n",
       "118  -5   7    True\n",
       "179   1 -22    True\n",
       "192  11  -5    True\n",
       "199   4   7    True"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# isabet eden atislar\n",
    "atislar_df[atislar_df['isabet'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(63, -80) False\n",
      "(-96, -71) False\n",
      "(32, 78) False\n",
      "(92, -5) False\n",
      "(-26, -39) False\n",
      "(83, 61) False\n",
      "(25, 82) False\n",
      "(-18, 53) False\n",
      "(7, -15) True\n",
      "(19, 74) False\n",
      "(-49, -53) False\n",
      "(4, 27) True\n",
      "(60, -19) False\n",
      "(-34, -96) False\n",
      "(-83, -48) False\n",
      "(59, 13) False\n",
      "(-24, 23) False\n",
      "(89, 85) False\n",
      "(-8, -83) False\n",
      "(-64, 57) False\n",
      "(-63, 48) False\n",
      "(72, 34) False\n",
      "(-50, 70) False\n",
      "(-94, 20) False\n",
      "(-2, -75) False\n",
      "(-6, 3) True\n",
      "(76, -31) False\n",
      "(-39, 66) False\n",
      "(89, 12) False\n",
      "(35, 57) False\n",
      "(-21, -14) True\n",
      "(-34, 88) False\n",
      "(4, -97) False\n",
      "(0, -66) False\n",
      "(-79, -66) False\n",
      "(42, 36) False\n",
      "(-62, -91) False\n",
      "(59, -12) False\n",
      "(-54, -93) False\n",
      "(26, -76) False\n",
      "(44, 69) False\n",
      "(-1, -74) False\n",
      "(-65, -96) False\n",
      "(-32, -65) False\n",
      "(72, -66) False\n",
      "(42, 62) False\n",
      "(-2, 54) False\n",
      "(73, 0) False\n",
      "(-33, 76) False\n",
      "(60, 8) False\n",
      "(-90, -91) False\n",
      "(-40, 77) False\n",
      "(45, 33) False\n",
      "(39, -32) False\n",
      "(-99, 32) False\n",
      "(66, -29) False\n",
      "(-100, 75) False\n",
      "(37, -100) False\n",
      "(5, 72) False\n",
      "(9, -58) False\n",
      "(-97, -54) False\n",
      "(-35, -66) False\n",
      "(-54, -40) False\n",
      "(28, -50) False\n",
      "(-93, 44) False\n",
      "(-75, -85) False\n",
      "(-98, 88) False\n",
      "(-26, 33) False\n",
      "(-61, 34) False\n",
      "(-15, 4) True\n",
      "(72, 24) False\n",
      "(-60, 36) False\n",
      "(-29, 16) False\n",
      "(-36, 80) False\n",
      "(-3, 84) False\n",
      "(68, -12) False\n",
      "(25, 92) False\n",
      "(67, 8) False\n",
      "(-13, 51) False\n",
      "(-66, -46) False\n",
      "(-23, 35) False\n",
      "(-57, -30) False\n",
      "(27, -29) False\n",
      "(-96, 98) False\n",
      "(-64, 65) False\n",
      "(-34, -94) False\n",
      "(-6, -53) False\n",
      "(74, -24) False\n",
      "(69, -49) False\n",
      "(-20, -47) False\n",
      "(-72, -31) False\n",
      "(-20, -8) True\n",
      "(39, 55) False\n",
      "(99, -72) False\n",
      "(76, 32) False\n",
      "(2, -12) True\n",
      "(58, -35) False\n",
      "(22, -73) False\n",
      "(-6, 20) True\n",
      "(81, -42) False\n",
      "(-100, 42) False\n",
      "(-26, 13) True\n",
      "(-22, 65) False\n",
      "(95, 78) False\n",
      "(40, 43) False\n",
      "(-71, -45) False\n",
      "(-55, -26) False\n",
      "(-48, 33) False\n",
      "(94, 25) False\n",
      "(-19, -92) False\n",
      "(-7, 46) False\n",
      "(4, -86) False\n",
      "(-15, -25) True\n",
      "(-93, -52) False\n",
      "(65, 72) False\n",
      "(72, -58) False\n",
      "(34, 41) False\n",
      "(6, 76) False\n",
      "(-5, 7) True\n",
      "(-48, -39) False\n",
      "(46, -17) False\n",
      "(-50, -35) False\n",
      "(-52, -7) False\n",
      "(44, 32) False\n",
      "(-79, 96) False\n",
      "(68, -9) False\n",
      "(57, -89) False\n",
      "(82, -93) False\n",
      "(-3, 56) False\n",
      "(-83, 61) False\n",
      "(89, 35) False\n",
      "(-50, -49) False\n",
      "(-46, 99) False\n",
      "(40, 81) False\n",
      "(90, -18) False\n",
      "(92, 20) False\n",
      "(-84, -99) False\n",
      "(-44, 63) False\n",
      "(80, -3) False\n",
      "(72, -71) False\n",
      "(23, -40) False\n",
      "(23, -22) False\n",
      "(74, -61) False\n",
      "(-54, 7) False\n",
      "(15, 36) False\n",
      "(32, 38) False\n",
      "(-23, 63) False\n",
      "(0, 54) False\n",
      "(-43, 81) False\n",
      "(-49, -4) False\n",
      "(-51, 76) False\n",
      "(60, 22) False\n",
      "(39, -20) False\n",
      "(-54, 17) False\n",
      "(-35, 57) False\n",
      "(-36, -25) False\n",
      "(0, -98) False\n",
      "(67, -26) False\n",
      "(80, -76) False\n",
      "(91, -11) False\n",
      "(-51, -51) False\n",
      "(79, 49) False\n",
      "(68, -24) False\n",
      "(-56, 95) False\n",
      "(-83, 80) False\n",
      "(-54, 39) False\n",
      "(90, 31) False\n",
      "(77, -54) False\n",
      "(78, 49) False\n",
      "(-57, 1) False\n",
      "(46, -93) False\n",
      "(68, -38) False\n",
      "(-24, 34) False\n",
      "(-34, -14) False\n",
      "(57, -54) False\n",
      "(8, -59) False\n",
      "(31, 72) False\n",
      "(-70, -6) False\n",
      "(-26, 62) False\n",
      "(1, -22) True\n",
      "(-96, 17) False\n",
      "(-31, 96) False\n",
      "(-91, 35) False\n",
      "(-76, -48) False\n",
      "(-77, -72) False\n",
      "(85, 85) False\n",
      "(21, 23) False\n",
      "(75, -74) False\n",
      "(-66, -64) False\n",
      "(-29, 71) False\n",
      "(-44, 78) False\n",
      "(-98, -25) False\n",
      "(11, -5) True\n",
      "(27, 32) False\n",
      "(0, -62) False\n",
      "(82, 19) False\n",
      "(37, -87) False\n",
      "(64, -66) False\n",
      "(54, 35) False\n",
      "(4, 7) True\n"
     ]
    }
   ],
   "source": [
    "atislar = []\n",
    "for i in range(200):\n",
    "  atis = (np.random.randint(-100, 100), np.random.randint(-100, 100))\n",
    "  # atisi_ciz(atis)\n",
    "  tahmin = tahmin_et(atis)\n",
    "  atislar.append({\n",
    "    \"x\": atis[0],\n",
    "    \"y\": atis[1],\n",
    "    \"isabet\": tahmin\n",
    "  })\n",
    "  print(atis, tahmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basarili = atislar_df[atislar_df[\"isabet\"] == True]\n",
    "plt.scatter(basarili[\"x\"], basarili[\"y\"], color=\"green\", label=\"Başarılı\")\n",
    "\n",
    "basarisiz = atislar_df[atislar_df[\"isabet\"] == False]\n",
    "plt.scatter(basarisiz[\"x\"], basarisiz[\"y\"], color=\"red\", label=\"Başarısız\")\n",
    "\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.legend(title=\"Başarı Durumu\", loc=\"upper left\", bbox_to_anchor=(1, 1), shadow=True, ncol=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Learning Rate = öğrenme miktari\n",
    "- Parametre = Weight, ağırlık\n",
    "- Loss = Hata büyüklüğü\n",
    "- Train, Fit = Eğitim\n",
    "- **Test = Test**\n",
    "- Overfitting = Aşırı öğrenme\n",
    "- Epoch = Tüm veri setinin ağ üzerinden bir kez geçirilmesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "random_yari_cap = np.random.randint(0, 100)\n",
    "type(random_yari_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "ogrenme_orani = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def yeni_tahmin_et(atis):\n",
    "  x = atis[0]\n",
    "  y = atis[1]\n",
    "\n",
    "  mesafe = np.sqrt(x**2 + y**2)\n",
    "\n",
    "  return mesafe < random_yari_cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. epoch sonrası yarı çap: 31.140000000002253\n",
      "2. epoch sonrası yarı çap: 31.140000000002253\n",
      "3. epoch sonrası yarı çap: 31.140000000002253\n",
      "4. epoch sonrası yarı çap: 31.140000000002253\n",
      "5. epoch sonrası yarı çap: 31.140000000002253\n",
      "6. epoch sonrası yarı çap: 31.140000000002253\n",
      "7. epoch sonrası yarı çap: 31.140000000002253\n",
      "8. epoch sonrası yarı çap: 31.140000000002253\n",
      "9. epoch sonrası yarı çap: 31.140000000002253\n",
      "10. epoch sonrası yarı çap: 31.140000000002253\n",
      "11. epoch sonrası yarı çap: 31.140000000002253\n",
      "12. epoch sonrası yarı çap: 31.140000000002253\n",
      "13. epoch sonrası yarı çap: 31.140000000002253\n",
      "14. epoch sonrası yarı çap: 31.140000000002253\n",
      "15. epoch sonrası yarı çap: 31.140000000002253\n",
      "16. epoch sonrası yarı çap: 31.140000000002253\n",
      "17. epoch sonrası yarı çap: 31.140000000002253\n",
      "18. epoch sonrası yarı çap: 31.140000000002253\n",
      "19. epoch sonrası yarı çap: 31.140000000002253\n",
      "20. epoch sonrası yarı çap: 31.140000000002253\n",
      "21. epoch sonrası yarı çap: 31.140000000002253\n",
      "22. epoch sonrası yarı çap: 31.140000000002253\n",
      "23. epoch sonrası yarı çap: 31.140000000002253\n",
      "24. epoch sonrası yarı çap: 31.140000000002253\n",
      "25. epoch sonrası yarı çap: 31.140000000002253\n",
      "26. epoch sonrası yarı çap: 31.140000000002253\n",
      "27. epoch sonrası yarı çap: 31.140000000002253\n",
      "28. epoch sonrası yarı çap: 31.140000000002253\n",
      "29. epoch sonrası yarı çap: 31.140000000002253\n",
      "30. epoch sonrası yarı çap: 31.140000000002253\n",
      "31. epoch sonrası yarı çap: 31.140000000002253\n",
      "32. epoch sonrası yarı çap: 31.140000000002253\n",
      "33. epoch sonrası yarı çap: 31.140000000002253\n",
      "34. epoch sonrası yarı çap: 31.140000000002253\n",
      "35. epoch sonrası yarı çap: 31.140000000002253\n",
      "36. epoch sonrası yarı çap: 31.140000000002253\n",
      "37. epoch sonrası yarı çap: 31.140000000002253\n",
      "38. epoch sonrası yarı çap: 31.140000000002253\n",
      "39. epoch sonrası yarı çap: 31.140000000002253\n",
      "40. epoch sonrası yarı çap: 31.140000000002253\n",
      "41. epoch sonrası yarı çap: 31.140000000002253\n",
      "42. epoch sonrası yarı çap: 31.140000000002253\n",
      "43. epoch sonrası yarı çap: 31.140000000002253\n",
      "44. epoch sonrası yarı çap: 31.140000000002253\n",
      "45. epoch sonrası yarı çap: 31.140000000002253\n",
      "46. epoch sonrası yarı çap: 31.140000000002253\n",
      "47. epoch sonrası yarı çap: 31.140000000002253\n",
      "48. epoch sonrası yarı çap: 31.140000000002253\n",
      "49. epoch sonrası yarı çap: 31.140000000002253\n",
      "50. epoch sonrası yarı çap: 31.140000000002253\n",
      "51. epoch sonrası yarı çap: 31.140000000002253\n",
      "52. epoch sonrası yarı çap: 31.140000000002253\n",
      "53. epoch sonrası yarı çap: 31.140000000002253\n",
      "54. epoch sonrası yarı çap: 31.140000000002253\n",
      "55. epoch sonrası yarı çap: 31.140000000002253\n",
      "56. epoch sonrası yarı çap: 31.140000000002253\n",
      "57. epoch sonrası yarı çap: 31.140000000002253\n",
      "58. epoch sonrası yarı çap: 31.140000000002253\n",
      "59. epoch sonrası yarı çap: 31.140000000002253\n",
      "60. epoch sonrası yarı çap: 31.140000000002253\n",
      "61. epoch sonrası yarı çap: 31.140000000002253\n",
      "62. epoch sonrası yarı çap: 31.140000000002253\n",
      "63. epoch sonrası yarı çap: 31.140000000002253\n",
      "64. epoch sonrası yarı çap: 31.140000000002253\n",
      "65. epoch sonrası yarı çap: 31.140000000002253\n",
      "66. epoch sonrası yarı çap: 31.140000000002253\n",
      "67. epoch sonrası yarı çap: 31.140000000002253\n",
      "68. epoch sonrası yarı çap: 31.140000000002253\n",
      "69. epoch sonrası yarı çap: 31.140000000002253\n",
      "70. epoch sonrası yarı çap: 31.140000000002253\n",
      "71. epoch sonrası yarı çap: 31.140000000002253\n",
      "72. epoch sonrası yarı çap: 31.140000000002253\n",
      "73. epoch sonrası yarı çap: 31.140000000002253\n",
      "74. epoch sonrası yarı çap: 31.140000000002253\n",
      "75. epoch sonrası yarı çap: 31.140000000002253\n",
      "76. epoch sonrası yarı çap: 31.140000000002253\n",
      "77. epoch sonrası yarı çap: 31.140000000002253\n",
      "78. epoch sonrası yarı çap: 31.140000000002253\n",
      "79. epoch sonrası yarı çap: 31.140000000002253\n",
      "80. epoch sonrası yarı çap: 31.140000000002253\n",
      "81. epoch sonrası yarı çap: 31.140000000002253\n",
      "82. epoch sonrası yarı çap: 31.140000000002253\n",
      "83. epoch sonrası yarı çap: 31.140000000002253\n",
      "84. epoch sonrası yarı çap: 31.140000000002253\n",
      "85. epoch sonrası yarı çap: 31.140000000002253\n",
      "86. epoch sonrası yarı çap: 31.140000000002253\n",
      "87. epoch sonrası yarı çap: 31.140000000002253\n",
      "88. epoch sonrası yarı çap: 31.140000000002253\n",
      "89. epoch sonrası yarı çap: 31.140000000002253\n",
      "90. epoch sonrası yarı çap: 31.140000000002253\n",
      "91. epoch sonrası yarı çap: 31.140000000002253\n",
      "92. epoch sonrası yarı çap: 31.140000000002253\n",
      "93. epoch sonrası yarı çap: 31.140000000002253\n",
      "94. epoch sonrası yarı çap: 31.140000000002253\n",
      "95. epoch sonrası yarı çap: 31.140000000002253\n",
      "96. epoch sonrası yarı çap: 31.140000000002253\n",
      "97. epoch sonrası yarı çap: 31.140000000002253\n",
      "98. epoch sonrası yarı çap: 31.140000000002253\n",
      "99. epoch sonrası yarı çap: 31.140000000002253\n",
      "100. epoch sonrası yarı çap: 31.140000000002253\n",
      "101. epoch sonrası yarı çap: 31.140000000002253\n",
      "102. epoch sonrası yarı çap: 31.140000000002253\n",
      "103. epoch sonrası yarı çap: 31.140000000002253\n",
      "104. epoch sonrası yarı çap: 31.140000000002253\n",
      "105. epoch sonrası yarı çap: 31.140000000002253\n",
      "106. epoch sonrası yarı çap: 31.140000000002253\n",
      "107. epoch sonrası yarı çap: 31.140000000002253\n",
      "108. epoch sonrası yarı çap: 31.140000000002253\n",
      "109. epoch sonrası yarı çap: 31.140000000002253\n",
      "110. epoch sonrası yarı çap: 31.140000000002253\n",
      "111. epoch sonrası yarı çap: 31.140000000002253\n",
      "112. epoch sonrası yarı çap: 31.140000000002253\n",
      "113. epoch sonrası yarı çap: 31.140000000002253\n",
      "114. epoch sonrası yarı çap: 31.140000000002253\n",
      "115. epoch sonrası yarı çap: 31.140000000002253\n",
      "116. epoch sonrası yarı çap: 31.140000000002253\n",
      "117. epoch sonrası yarı çap: 31.140000000002253\n",
      "118. epoch sonrası yarı çap: 31.140000000002253\n",
      "119. epoch sonrası yarı çap: 31.140000000002253\n",
      "120. epoch sonrası yarı çap: 31.140000000002253\n",
      "121. epoch sonrası yarı çap: 31.140000000002253\n",
      "122. epoch sonrası yarı çap: 31.140000000002253\n",
      "123. epoch sonrası yarı çap: 31.140000000002253\n",
      "124. epoch sonrası yarı çap: 31.140000000002253\n",
      "125. epoch sonrası yarı çap: 31.140000000002253\n",
      "126. epoch sonrası yarı çap: 31.140000000002253\n",
      "127. epoch sonrası yarı çap: 31.140000000002253\n",
      "128. epoch sonrası yarı çap: 31.140000000002253\n",
      "129. epoch sonrası yarı çap: 31.140000000002253\n",
      "130. epoch sonrası yarı çap: 31.140000000002253\n",
      "131. epoch sonrası yarı çap: 31.140000000002253\n",
      "132. epoch sonrası yarı çap: 31.140000000002253\n",
      "133. epoch sonrası yarı çap: 31.140000000002253\n",
      "134. epoch sonrası yarı çap: 31.140000000002253\n",
      "135. epoch sonrası yarı çap: 31.140000000002253\n",
      "136. epoch sonrası yarı çap: 31.140000000002253\n",
      "137. epoch sonrası yarı çap: 31.140000000002253\n",
      "138. epoch sonrası yarı çap: 31.140000000002253\n",
      "139. epoch sonrası yarı çap: 31.140000000002253\n",
      "140. epoch sonrası yarı çap: 31.140000000002253\n",
      "141. epoch sonrası yarı çap: 31.140000000002253\n",
      "142. epoch sonrası yarı çap: 31.140000000002253\n",
      "143. epoch sonrası yarı çap: 31.140000000002253\n",
      "144. epoch sonrası yarı çap: 31.140000000002253\n",
      "145. epoch sonrası yarı çap: 31.140000000002253\n",
      "146. epoch sonrası yarı çap: 31.140000000002253\n",
      "147. epoch sonrası yarı çap: 31.140000000002253\n",
      "148. epoch sonrası yarı çap: 31.140000000002253\n",
      "149. epoch sonrası yarı çap: 31.140000000002253\n",
      "150. epoch sonrası yarı çap: 31.140000000002253\n",
      "151. epoch sonrası yarı çap: 31.140000000002253\n",
      "152. epoch sonrası yarı çap: 31.140000000002253\n",
      "153. epoch sonrası yarı çap: 31.140000000002253\n",
      "154. epoch sonrası yarı çap: 31.140000000002253\n",
      "155. epoch sonrası yarı çap: 31.140000000002253\n",
      "156. epoch sonrası yarı çap: 31.140000000002253\n",
      "157. epoch sonrası yarı çap: 31.140000000002253\n",
      "158. epoch sonrası yarı çap: 31.140000000002253\n",
      "159. epoch sonrası yarı çap: 31.140000000002253\n",
      "160. epoch sonrası yarı çap: 31.140000000002253\n",
      "161. epoch sonrası yarı çap: 31.140000000002253\n",
      "162. epoch sonrası yarı çap: 31.140000000002253\n",
      "163. epoch sonrası yarı çap: 31.140000000002253\n",
      "164. epoch sonrası yarı çap: 31.140000000002253\n",
      "165. epoch sonrası yarı çap: 31.140000000002253\n",
      "166. epoch sonrası yarı çap: 31.140000000002253\n",
      "167. epoch sonrası yarı çap: 31.140000000002253\n",
      "168. epoch sonrası yarı çap: 31.140000000002253\n",
      "169. epoch sonrası yarı çap: 31.140000000002253\n",
      "170. epoch sonrası yarı çap: 31.140000000002253\n",
      "171. epoch sonrası yarı çap: 31.140000000002253\n",
      "172. epoch sonrası yarı çap: 31.140000000002253\n",
      "173. epoch sonrası yarı çap: 31.140000000002253\n",
      "174. epoch sonrası yarı çap: 31.140000000002253\n",
      "175. epoch sonrası yarı çap: 31.140000000002253\n",
      "176. epoch sonrası yarı çap: 31.140000000002253\n",
      "177. epoch sonrası yarı çap: 31.140000000002253\n",
      "178. epoch sonrası yarı çap: 31.140000000002253\n",
      "179. epoch sonrası yarı çap: 31.140000000002253\n",
      "180. epoch sonrası yarı çap: 31.140000000002253\n",
      "181. epoch sonrası yarı çap: 31.140000000002253\n",
      "182. epoch sonrası yarı çap: 31.140000000002253\n",
      "183. epoch sonrası yarı çap: 31.140000000002253\n",
      "184. epoch sonrası yarı çap: 31.140000000002253\n",
      "185. epoch sonrası yarı çap: 31.140000000002253\n",
      "186. epoch sonrası yarı çap: 31.140000000002253\n",
      "187. epoch sonrası yarı çap: 31.140000000002253\n",
      "188. epoch sonrası yarı çap: 31.140000000002253\n",
      "189. epoch sonrası yarı çap: 31.140000000002253\n",
      "190. epoch sonrası yarı çap: 31.140000000002253\n",
      "191. epoch sonrası yarı çap: 31.140000000002253\n",
      "192. epoch sonrası yarı çap: 31.140000000002253\n",
      "193. epoch sonrası yarı çap: 31.140000000002253\n",
      "194. epoch sonrası yarı çap: 31.140000000002253\n",
      "195. epoch sonrası yarı çap: 31.140000000002253\n",
      "196. epoch sonrası yarı çap: 31.140000000002253\n",
      "197. epoch sonrası yarı çap: 31.140000000002253\n",
      "198. epoch sonrası yarı çap: 31.140000000002253\n",
      "199. epoch sonrası yarı çap: 31.140000000002253\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(1, 200):\n",
    "  for i in range(atislar_df.shape[0]):\n",
    "    atis = atislar_df.iloc[i]\n",
    "    tahmin = yeni_tahmin_et((atis[\"x\"], atis[\"y\"]))\n",
    "    hata = int(atis[\"isabet\"]) - int(tahmin)\n",
    "    random_yari_cap = random_yari_cap + hata * ogrenme_orani  \n",
    "    # print(atis[\"x\"], atis[\"y\"], tahmin, atis[\"isabet\"])\n",
    "\n",
    "  print(f\"{epoch}. epoch sonrası yarı çap: {random_yari_cap}\")\n"
   ]
  }
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