tek_hucreli_sinir_agi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>borc</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   borc  risk\n",
       "0     1   1.0\n",
       "1     2   2.2\n",
       "2     4   5.0\n",
       "3     6   7.5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'borc': [1, 2, 4, 6],\n",
    "    'risk': [1, 2.2, 5, 7.5],\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Borç 15 olunca risk ne olur?\n",
    "\n",
    "agirlik1 = 0.5\n",
    "agirlik2 = 0.7\n",
    "\n",
    "bias = 0.1\n",
    "\n",
    "def risk_hesapla(borc):\n",
    "    return agirlik1 * borc + agirlik2 * borc ** 2 + bias # f(x) = 0.5x + 0.7x^2 + 0.1 f(x) = ax + bx^2 + c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.543"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "borc_15 = risk_hesapla(15)\n",
    "borc_15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.3, 3.9, 13.299999999999999, 28.3]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ilk_tahminler = []\n",
    "for borc in df['borc']:\n",
    "    risk_tahmini = risk_hesapla(borc)\n",
    "    ilk_tahminler.append(risk_tahmini)\n",
    "ilk_tahminler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def grafigi_ciz(tahminler = ilk_tahminler):\n",
    "    line = [risk_hesapla(i) for i in np.linspace(0, 6, 1000)]\n",
    "    plt.plot(np.linspace(0, 6, 1000), line, c = 'red')\n",
    "    plt.scatter(df['borc'], df['risk'], s = 50, c = 'green', label = \"Doğru Değerler\")\n",
    "    plt.scatter(df['borc'], tahminler, s = 50, c = 'blue', label = \"İlk Tahminler\")\n",
    "    plt.show()\n",
    "\n",
    "grafigi_ciz()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[1.2770000000000001, 2.067, 4.445, 7.8870000000000005]"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agirlik1 = 0.391\n",
    "agirlik2 = 0.133\n",
    "bias = 0.753\n",
    "\n",
    "# 0.39146656776604805 0.1332867793581989 0.7530513052953026\n",
    "# float64 olarak kaydedersek model boyutumuz 24 byte olur\n",
    "# float32 olarak kaydedersek model boyutumuz 12 byte olur\n",
    "# parametre sayımız da 3 olur\n",
    "ikinci_tahminler = []\n",
    "\n",
    "for borc in df['borc']:\n",
    "    risk_tahmini = risk_hesapla(borc)\n",
    "    ikinci_tahminler.append(risk_tahmini)\n",
    "grafigi_ciz(ikinci_tahminler)\n",
    "ikinci_tahminler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "class YapaySinirHucresi():\n",
    "  def __init__(self, veriler, ogrenme_orani = 0.01, epoch = 1000):\n",
    "    self.veriler = veriler\n",
    "    self.ogrenme_orani = ogrenme_orani\n",
    "    self.epoch = epoch\n",
    "    np.random.seed(42)\n",
    "    self.agirlik1 = np.random.randn()\n",
    "    self.agirlik2 = np.random.randn()\n",
    "    self.bias = np.random.randn()\n",
    "    \n",
    "    self.tahminler = [self.risk_hesapla(borc) for borc in self.veriler['borc']]\n",
    "    self.hata = self.hata_hesapla(self.tahminler, self.veriler['risk'])\n",
    "    print(\"Başlangıç Ağırlıkları: \", self.agirlik1, self.agirlik2, self.bias)\n",
    "    print(\"İlk Tahminler: \", self.tahminler)\n",
    "    print(\"Başlangıç Hatası: \", self.hata)\n",
    "\n",
    "  def risk_hesapla(self, borc):\n",
    "    return self.agirlik1 * borc + self.agirlik2 * borc ** 2 + self.bias\n",
    "  \n",
    "  def tahminleri_ve_hatayi_guncelle(self):\n",
    "    self.tahminler = [self.risk_hesapla(borc) for borc in self.veriler['borc']]\n",
    "    self.hata = self.hata_hesapla(self.tahminler, self.veriler['risk'])\n",
    "\n",
    "  def hata_hesapla(self, tahminler, gercekler):\n",
    "    # MSE Hesaplama (Mean Squared Error)\n",
    "    # Türev ve gradyan hesaplamaları ile backpropagation için bu hata hesaplaması kullanılacak\n",
    "    hata = 0\n",
    "    for tahmin, gercek in zip(tahminler, gercekler):\n",
    "      hata += (tahmin - gercek) ** 2\n",
    "    return hata / len(tahminler)\n",
    "  \n",
    "  def yonlu_hata_hesaplama(self, tahmin, gercek):\n",
    "    return tahmin - gercek\n",
    "\n",
    "  def ogren(self):\n",
    "    # random delta verisi ile ağırlıkları güncelleme\n",
    "    for _ in range(self.epoch):\n",
    "      hatalar = []\n",
    "      for borc, risk in zip(self.veriler['borc'], self.veriler['risk']):\n",
    "        tahmin = self.risk_hesapla(borc)\n",
    "        hata = self.yonlu_hata_hesaplama(tahmin, risk)\n",
    "        hatalar.append(hata)\n",
    "        \n",
    "      hatalarin_ortalamasi = sum(hatalar) / len(hatalar)\n",
    "      random_delta1 = np.random.randn()\n",
    "      self.agirlik1 += (hatalarin_ortalamasi * random_delta1 * self.ogrenme_orani)\n",
    "      random_delta2 = np.random.randn()\n",
    "      self.agirlik2 += (hatalarin_ortalamasi * random_delta2 * self.ogrenme_orani)\n",
    "      random_delta3 = np.random.randn()\n",
    "      self.bias += (hatalarin_ortalamasi * random_delta3 * self.ogrenme_orani)\n",
    "    print(\"Ağırlıklar: \", self.agirlik1, self.agirlik2, self.bias)\n",
    "    self.tahminleri_ve_hatayi_guncelle()\n",
    "    print(\"Hata: \", self.hata)\n",
    "\n",
    "  def grafigi_ciz(self):\n",
    "    line = [self.risk_hesapla(i) for i in np.linspace(0, 6, 1000)]\n",
    "    plt.plot(np.linspace(0, 6, 1000), line, c = 'red')\n",
    "    plt.scatter(self.veriler['borc'], self.veriler['risk'], s = 50, c = 'green', label = \"Doğru Değerler\")\n",
    "    plt.scatter(self.veriler['borc'], self.tahminler, s = 50, c = 'blue', label = \"İlk Tahminler\")\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Başlangıç Ağırlıkları:  0.4967141530112327 -0.13826430117118466 0.6476885381006925\n",
      "İlk Tahminler:  [1.0061383899407406, 1.0880596394384192, 0.4223163314066687, -1.3495413859945589]\n",
      "Başlangıç Hatası:  25.126504889353374\n"
     ]
    },
    {
     "data": {
      "image/png": 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OT51KzwUAnI7f7QIQOXNeek8PP/q1DmwaLDnnSZ6QumUV6l/ua6spN/d3u7z6HEc6ftxMcPj6a+mbb6TKSvMOf6bb6q4Gj6f29nRf+/1SXJzUpo1e+ec4XeGNU3mojY4rrqZVynxfrkTJ10azZ0vZ2ZF/SQAg2nkcx3Ei+YTBYFApKSkqLS1VIBCI5FO3arfet1ZLfj1C8oakcJvaO7yVUtinW+8v0OK8Uef+RJWV0pdfSqWl9VswWP9YWZkJDV9/XRsgTvy6OiBEkeNqo2Nqp/Zp7eRJaie1O0VLSpJSUqT27Wtbhw51v2/f3gQaAIgBjX3/pseiFZjz0nsmVMgrhU8a/fo2ZDw/a4RGZr1Xv+eivFw6cEA6eFD6/HPp8OGG21dfNc8v4fNJCQlSmzam+f0N33q9psdDMrcNfR0KScePq6r8uD7dc1xtVFnTV1H9tV+hmlLiVKk4fSUVW/hdExNN4OjcWerSRTrvvLrt5GPt25vfDQCiFMGiFXj40a+/7amofUOK1zdK16c6X58pTcXq5vlUmrZVyveYIHHggFRcbHoVmsrjkZKTzSf26hYI1P2++lggYD7ht21r3mTbtq3/ddu2Jiw0s8py6cKkU3eSeBVSnI4rUeVqp2NK9hzTlnXHlBA6Jh07RTt61Lx2X31Vvx05YnpvJBPcysvNa90YbdpI3bpJ558vpaWZVv31ibfJyVZeEwBoKoJFS+Q40hdfSEVFKvtwl8ZvPKBM/VHdtU+ZKlKmipSqkpMeI+mgpOdO8fMSEsybWdeu5pN1p07m9nStQwfTwxBjEhOlnByz+uPkiZth+fSNEvWNElXm76ihOVLCucyxCIVMuPjqKzN0dPiwdOiQ6RU6sZ14LBg0Q01FRaY1JClJSk+XevQwrXv3urddu9LzAaBZMMciVlVWSvv2SR9/LO3eXff2k0/M/IQzOKa2+lTpOqBuOqBuKlaafnB/srr1622CRHVLSamd7NjCFRSYpaYN/a/weKR161yYvFlRYXbrKi427bPP6t5Wf13dG9KQ+HgpM7M2bFxwgdS7t2kXXmiCCQCcoLHv3wSLaBYKSXv3Sjt2SDt31g0P+/aZ+xuSmqqq89P1v5t7qEjdVaRM7fv2tkiZ+lIdJZ0QGDwhffHVcXUMJDbrrxXt5s0zS0p9vro9F36/ecnnzJEmT3avvjM6etSEjP37Tcjct6/u7aefnnlSbLduJmT06lUbOHr1Mq1duwj8EgCiDcEilpSXm+CwY4f04Yem7dgh7dplPqWeTmKi+XRZ3Xr1MrcXXCBlZJhPpZLShhTqwOZBdVeDnMxbqbSsTfrs7eGWf7nYtH69NHu2lJ9v3oO9XmnsWGn69BawzLSy0vRsVIeNvXulPXukjz4y7YsvGn58RoZ0ySV128UXmyEwAC0WwSIaHT9uAsN775m2fbsJEfv2nb7vPSFB6tPHtOpPjNUholu3Rg1RzHnpPeX+XT81vB9aWHNe3B6d+1m4qLzcjCwEAibHtQpHjpieseqgceLXX355+selptYPHJddJnXsGLnaATQbgoWbHMd8InzvPWnbttogsWNH/VmB1Tp1Mp/6+vate9u9u5VJdj94YK2enxWBfSzQsn3xhfl3/MEHte3DD82wy+mkp0sDBkiXX25uBwww4TgGJ/gCrRnBIlJCITNksXmzaX/9qwkRR46c+vz27aX+/c0nucsuM5/q+vY1exQ0s7kvvaeHf31MxRuHSI5P8oSUNvgd/fMv2tFTgXMTDNYPHNu3m964U2nb1vz7rw4a1aGD+RtA1CJYNIdQyMyFqA4R1UHi2LH65/p8JjBUh4j+/U1LT3d9hcWXwXIVHz6qtM5JrX6iJppZaakJ2u++W9u2bz/1dei9XunSS6WsrNrWv78ZDgTgOoLFuXIcM7mtsFB6+21p0yZp69ZTh4i2bc0nrkGDpCuuMF9ffHHN5EkAJwiFzHyNE8PGX/9qNmU7md9vgnlWljR4sLnt1y8iG6YBqItg0VRHj5rwUFhY20pK6p/Xtq00cKAJEdWtb1/Gi4FzVVxs/g9Wt40bzcZhJ0tIMAFj+PDalpoa+XqBVoZg0RDHMfMiTgwR771Xf22/329CxLBh5tPSoEFmdQYhAmh+jmN2GD0xaGzadOpt5nv2lK680oSMK680vRx+NhYGbCJYnKiqynS3rlsnrV1rtlf8/PP656Wnmz9Mw4aZNnBgK1pjCMSAcNgMo2zYIL31lrl9//36y7XbtTMfBq68Uho50mw+wvVTgHPSuoNFRYX5dLN2rQkT69eby3OfqLo7tTpEDB1qggWA2FJaauZBVYeNwsL625r7fGb+01VXmT3bR440K7QANFrrChZVVdIbb5ggsXat+SNz8o6VgYA0YkTtH5VBg5hcCbRE4bBZ7rphg+mdXLvWTMQ+kcdjlrdedZVpI0eaC+gBOK3WFyzat6+7YqNLl9oQMWqUGXNlbgTQOhUVmYCxZo1pH31U/5xLL5WuvloaPVr6znfMhxEANVpXsJCkO+4wy9iqg0Tv3q7vFwEgShUXm2HS6qDxwQd17/f5zPDo6NGmDR0qxcW5UysQJVpfsACAs/X556ZHY/VqadUqc32UE7VrZ4ZM/uZvTNC49FI+uKDVIVgAwNn65BMTMv7yF9NO3k8jNdUEjBtukK691lzrp4la5QXuENMIFgBgQzhs9rmpDhlr19bdktzrNUMlN9wgXX+9WabewIUDCwqkxx+Xli0zP9rrlXJypJ/9zKyKBaIVwQIAmkNFhVnWunKl9Prr5grGJ+ra1QSM6683QycdOtTcNXeulJtrpnCceKFjv99MEZszR5o8OUK/B9BEBAsAiIT9+6U//cmEjL/8xVweoJrPZzbdu+EGbUn/f8r6h0vk6PRzMzweM6eUngtEI4IFAETa8eNmrOP116UVK+qtNtmtC7VMOVqmHL2lKxVS3W3H/X4zLPLii5EsGmgcggUAuG3fPmnFCoVeXq7KlauVoNqN+w6rk17TGC1TjlbqOn2tdpLMnIujR5nQiehDsACAKFFSIl2YelTXaaVytExj9Jo66cua+79RvP6i0VqmHC3XjXr3YKq6dnWxYOAUCBYAECXKy6WkpNoLKPtUpWyt/3ZQZJku1J6ac8PyyBl+pXy3jJfGjZMyMlyqGqiLYAEAUWTcOGn58rqrQQxHl+p95WiZbtIyDdbGuncPGyaNHy/dfLPUvXukygXqIVgAQBQpKDBXG2joL67HI7390qcavP9/zQzOgoK6Dxg8uDZkXHBB8xcNnIBgAQBRZt48aerUJuxjUVws5eebkLF2be1YimQuAz9+vGkXXhix3wGtF8ECAKLQ+vXS7NkmL1TvvDl2rDR9+hn2rygpqQ0Zb7xRN2QMGSLdeqt0yy1St27N/jugdSJYAEAUO6drhXz+ufTyy9If/2iuaVIdMrxe6bvfNSFj3Lg6u34C54pgAQCtQUmJCRiLF0sbNtQej4sz1y+59Vbpe9+T2rZ1r0a0CAQLAGht9u6VliwxIWP79trjSUnSTTdJP/iBuX6J33/aHwGcDsECAFqzbduk5583IWPfvtrjqanSbbdJEydK/fq5Vx9iDsECAGCWqxYWSs89Z3ozvvii9r4rrjAB49ZbpfPOc69GxASCBQCgruPHzQXSFi2SXntNqqw0x/1+acwYEzLGjDHzM4CTNPb923s2P/yZZ55Rjx49lJCQoKFDh+qdd94560IBABESF2fmWuTnmz0ynnxSGjTIbKqxbJlZSZKWJt15p7RpU8O7eQGn0eRgsXTpUs2YMUMzZ87Uli1bNGDAAF133XU6dOhQc9QHAGgOnTvXBojt26V77zV7YHzxhfT002aXz8svl556SjpyxO1qEUOaPBQydOhQDR48WE8//bQkKRwOKyMjQ3feeafuv//+Mz6eoRAAiFJVVWZfjEWLTK/GN9+Y4/Hx0t/9nfSTn5h9yT0ed+uEK5plKOT48ePavHmzRo8eXfsDvF6NHj1aG05cP32CiooKBYPBOg0AEIX8fum668xKkuJi01vRv79UUWEmf37nO1KfPtKjj5r9M4BTaFKwOHz4sEKhkLp27VrneNeuXXXw4MFTPiYvL08pKSk1LYNLAANA9OvQQZo2Tdq6VXrnHemnPzX7YXz0kXT//VJ6upmTsWKFudAJ8K2zmrzZFA888IBKS0tr2v79+5v7KQEAtng8Zr7Fs89KBw5Iv/uduZR7VZUZLrnhBqlHD+mhh0wvB1q9JgWLzp07y+fzqeSkLrCSkhKlpqae8jHx8fEKBAJ1GgAgBiUlSXfcYbYO37ZNuvtuqWNH6dNPpQcflDIzzdVW33iDFSWtWJOCRVxcnAYNGqTVq1fXHAuHw1q9erWGDx9uvTgAQJTq10964gnps8/M/IsRI8yQyIsvSldfLV1yiVnO+tVXbleKCGvyUMiMGTO0YMECLVq0SB9++KGmTJmiY8eOadKkSc1RHwAgmiUkmGuQrFsnvfeeNGWK6dnYscP0aJx/vpmf8de/ul0pIuSsdt58+umn9Zvf/EYHDx7U5ZdfrieffFJDhw5t1GNZbgoALVwwKP3hD9KcOdL779ceHzZMmjrVDJckJLhXH84KW3oDANzlOFJBgQkYL71Uu4V4587SP/6jCRlpae7WiEZr1i29AQA4I49HGjnSXGW1qEh6+GEpI0M6fFj693+Xunc3wyhvv+12pbCIYAEAaH6pqdIvfynt2WMmeI4caZasPv+8GSIZPtxcfbW6VwMxi2ABAIgcv1+6+WZp7Vpp82bpH/7BXBytsNBcvr1nT+mRR0yvBmISwQIA4I4rrjDXJSkqMvtgdO1qlq/+8pdmyOTHPzb7ZSCmECwAAO7q2lWaOVPat0/67/82geObb8wun/37m+uXrFrFplsxgmABAIgO8fHSD39oLuW+bp25oqrXK/35z9K110oDB5plrMzDiGoECwBAdPF4zE6ef/yjtHu3dOedUtu20rvvmuBxwQXSY4+Z/TIQdQgWAIDo1bOn2Rp8/36zRLVrV3Ntkp//3MzDuO8+My8DUYNgAQCIfh07Sv/0T9Inn0i//a3Ut6/psfj1r034uP12JnpGCYIFACB2JCRIP/qR2Sp8+XLpqqvMnItFi8xEzzFjpPXr3a6yVSNYAABij9crfe970ptvmp07v/99c+z11838jKuuklauZCWJCwgWAIDYNmSItHSptHOn9JOfSG3amA24/vZvpawsc52ScNjtKlsNggUAoGXo1UuaP1/au1eaPt2sJNmyxSxbveQS6fe/Z6lqBBAsAAAty/nnS48/bjbc+td/ldq3N70ZkyaZ8PH001J5udtVtlgECwBAy9S5s/TQQyZg/PrXZqlqUZHZF6NHD+nRR6WyMrerbHEIFgCAli0QkO691wyRzJljQsWhQ9L995ulqnl5BAyLCBYAgNYhMVGaMkXatcssT+3dW/riC7M/Ro8e5qqq7OZ5zggWAIDWpU0bc7n2Dz6Q/ud/pIsukr780lxVtUcP6eGHCRjngGABAGid/H7ptttMwPjDH6Q+faQjR6R/+RcTMH71K6m01O0qYw7BAgDQuvl80oQJZjfPxYvNduFHjpgVJT16SP/2b9JXX7ldZcwgWAAAIJmAceut0vbt0vPPSxdfbALFzJm1PRhM8jwjggUAACfy+aS//3tzUbMlS8zmWqWlpgej+pLt7INxWgQLAABOxeeTbrmlNmBcdJF0+LC5ZHuvXtLcudLx425XGXUIFgAANMTrNQHj/fel3/1OysyUioulqVPNhM9Fi6SqKrerjBoECwAAGsPvl+64w+yD8dRTUmqq9Mkn0u23S5ddJr3wAhc7E8ECAICmiY+Xpk2TPv7YbAvesaO0Y4fp1Rg0SHr11VZ9uXaCBQAAZ6NtW+kXv5D27DErR5KTpa1bpRtvlLKzzaXbWyGCBQAA5yIlRXrwQXMtknvvNVuHb9ggXXWVCRnbt7tdYUQRLAAAsKFTJ3MV1Y8/liZPNqtKXn1VGjDAzM3Yv9/tCiOCYAEAgE3dupmlqO+/L918s5nQuXChWa56331mV88WjGABAEBz6NNHevFFMywyapT0zTemR+PCC6X/+A/zfQtEsAAAoDkNGya9+aa0fLl06aWmx+Lee00PxqJFUijkdoVWESwAAGhuHo/0ve9J774r/dd/SenpZs7F7bdLAwdKr7/eYpaoEiwAAIgUn0+aNMlssvXoo1L79mbL8DFjpOuuM1/HOIIFAACRlpho9sD4+GNz7ZG4OGnVKunyy6Wf/lQ6eNDtCs8awQIAALd07Cj95jfShx9K48ebFSQLFki9e0uPPBKTV1ElWAAA4LYLLjDXGikokAYPlo4elX75S7OyZPHimJp/QbAAACBaZGdLhYXSc89JGRlmgueECdLw4dJbb7ldXaMQLAAAiCZer/SDH0g7d0oPPyy1aye9/bYJHbfcYrYOP43ycqmkxN0RFIIFAADRKDHRDIfs3i39+MdmyeoLL0h9+5odPMvKak4tKJDGjZOSkszV3JOSzPfr10e+bI/jRHbgJhgMKiUlRaWlpQoEApF8agAAYte770o/+5m0erX5PjVVmjVLc4/+ULl3euXzSVVVtaf7/WbvrTlzzKVLzlVj37/psQAAIBYMGGCWpL7yitSrl1mSevvtumLacA123q4TKiQTMhxHmjo1sj0XBAsAAGKFx1N7KfZHH1W5P0lD9Y7e1jD9XhOVqgP1HuLzSbNnR65EggUAALEmPl7ld/5CF4Y+0kLdLkmaqP/WLl2k+zRLcaqoObWqSsrPj9yEToIFAAAxKBiUDjipukMLNVjvaIOGKVlHNUsPaLA21jk3HDbnR4I/Mk8DAABsCgTMytRwWNqkwcrWek3QcxqsjVqvEXXO9XrN+ZFAjwUAADEoMVHKyTGrPyTJkVd/0A91t56sc57fL40da86PBIIFAAAxasYMs6S0IaGQNH16ZOqRCBYAAMSsESPMPhUeT23PRTW/3xyfM8ds2hkpBAsAAGLY5MnSunVmWMT77bu612u+X7fOzuZYTcHkTQAAYlx2tmnl5Wb1RyAQuTkVJyNYAADQQiQmuhcoqjEUAgAArCFYAAAAawgWAADAGoIFAACwhmABAACsIVgAAABrCBYAAMAaggUAALAm4htkOY4jSQpG6sLwAADgnFW/b1e/j59OxINFWVmZJCkjIyPSTw0AAM5RWVmZUlJSTnu/xzlT9LAsHA6ruLhYycnJ8ng81n5uMBhURkaG9u/fr0AgYO3ntkS8Vo3Ha9U0vF6Nx2vVeLxWjdecr5XjOCorK1NaWpq83tPPpIh4j4XX61V6enqz/fxAIMA/vEbitWo8Xqum4fVqPF6rxuO1arzmeq0a6qmoxuRNAABgDcECAABY02KCRXx8vGbOnKn4+Hi3S4l6vFaNx2vVNLxejcdr1Xi8Vo0XDa9VxCdvAgCAlqvF9FgAAAD3ESwAAIA1BAsAAGANwQIAAFjTYoLFM888ox49eighIUFDhw7VO++843ZJUWft2rW68cYblZaWJo/Ho5dfftntkqJWXl6eBg8erOTkZHXp0kU33XSTdu7c6XZZUWnu3Lnq379/zYY8w4cP14oVK9wuKybMmjVLHo9H99xzj9ulRKUHH3xQHo+nTuvbt6/bZUWtzz77TLfddps6deqkxMREXXbZZdq0aVPE62gRwWLp0qWaMWOGZs6cqS1btmjAgAG67rrrdOjQIbdLiyrHjh3TgAED9Mwzz7hdStRbs2aNcnNzVVhYqFWrVqmyslLXXnutjh075nZpUSc9PV2zZs3S5s2btWnTJl199dXKycnR+++/73ZpUW3jxo169tln1b9/f7dLiWqXXnqpDhw4UNMKCgrcLikqHTlyRNnZ2WrTpo1WrFihDz74QI899pg6dOgQ+WKcFmDIkCFObm5uzfehUMhJS0tz8vLyXKwqukly8vPz3S4jZhw6dMiR5KxZs8btUmJChw4dnN/+9rdulxG1ysrKnN69ezurVq1yrrrqKufuu+92u6SoNHPmTGfAgAFulxET7rvvPmfEiBFul+E4juPEfI/F8ePHtXnzZo0ePbrmmNfr1ejRo7VhwwYXK0NLUlpaKknq2LGjy5VEt1AopCVLlujYsWMaPny42+VErdzcXI0ZM6bO3y2c2kcffaS0tDRdcMEFmjBhgoqKitwuKSq98sorysrK0vjx49WlSxcNHDhQCxYscKWWmA8Whw8fVigUUteuXesc79q1qw4ePOhSVWhJwuGw7rnnHmVnZ6tfv35ulxOVtm3bpqSkJMXHx2vy5MnKz8/XJZdc4nZZUWnJkiXasmWL8vLy3C4l6g0dOlS///3v9ac//Ulz587V3r17NXLkSJWVlbldWtTZs2eP5s6dq969e2vlypWaMmWK7rrrLi1atCjitUT86qZArMnNzdX27dsZ221Anz59tHXrVpWWlurFF1/UxIkTtWbNGsLFSfbv36+7775bq1atUkJCgtvlRL3rr7++5uv+/ftr6NCh6t69u1544QX96Ec/crGy6BMOh5WVlaVHHnlEkjRw4EBt375d8+bN08SJEyNaS8z3WHTu3Fk+n08lJSV1jpeUlCg1NdWlqtBSTJs2Ta+++qreeOMNpaenu11O1IqLi1OvXr00aNAg5eXlacCAAfrP//xPt8uKOps3b9ahQ4d0xRVXyO/3y+/3a82aNXryySfl9/sVCoXcLjGqtW/fXhdddJF2797tdilRp1u3bvWC/MUXX+zK0FHMB4u4uDgNGjRIq1evrjkWDoe1evVqxnhx1hzH0bRp05Sfn6//+7//U8+ePd0uKaaEw2FVVFS4XUbUueaaa7Rt2zZt3bq1pmVlZWnChAnaunWrfD6f2yVGtaNHj+rjjz9Wt27d3C4l6mRnZ9dbEr9r1y5179494rW0iKGQGTNmaOLEicrKytKQIUP0xBNP6NixY5o0aZLbpUWVo0eP1kn6e/fu1datW9WxY0dlZma6WFn0yc3N1eLFi7Vs2TIlJyfXzNdJSUlRYmKiy9VFlwceeEDXX3+9MjMzVVZWpsWLF+vNN9/UypUr3S4t6iQnJ9ebp9OuXTt16tSJ+Tun8POf/1w33nijunfvruLiYs2cOVM+n0+33nqr26VFnenTp+vKK6/UI488ou9///t65513NH/+fM2fPz/yxbi9LMWWp556ysnMzHTi4uKcIUOGOIWFhW6XFHXeeOMNR1K9NnHiRLdLizqnep0kOQsXLnS7tKhzxx13ON27d3fi4uKc8847z7nmmmucP//5z26XFTNYbnp6t9xyi9OtWzcnLi7OOf/8851bbrnF2b17t9tlRa3ly5c7/fr1c+Lj452+ffs68+fPd6UOLpsOAACsifk5FgAAIHoQLAAAgDUECwAAYA3BAgAAWEOwAAAA1hAsAACANQQLAABgDcECAABYQ7AAAADWECwAAIA1BAsAAGANwQIAAFjz/wG4JoTHULKPswAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sinir_hucresi = YapaySinirHucresi(df, epoch=20)\n",
    "sinir_hucresi.grafigi_ciz()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ağırlıklar:  0.39146656776604805 0.1332867793581989 0.7530513052953026\n",
      "Hata:  0.138821859249491\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sinir_hucresi.ogren()\n",
    "sinir_hucresi.grafigi_ciz()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w11 = 0.5875\n",
    "b11 = 0.175\n",
    "second_predictions = [0.23375, 0.35125, 0.5275, 0.58625]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.axline((0, b11), slope=w11, label=\"Updated\")\n",
    "ax.axline((0, init_b11), slope=init_w11, c='grey', label='Initial')\n",
    "ax.scatter(df['x'], df['y'], label=\"True\")\n",
    "ax.scatter(df['x'], second_predictions, zorder=3, c='orange', label=\"Prediction\")\n",
    "ax.scatter(df['x'], init_predictions, zorder=3, c='orange')\n",
    "ax.set_title(\"MSE after 1 Epoch: {}\".format(0.0202925))\n",
    "ax.set_xlim([0,1])\n",
    "ax.set_ylim([0,1])\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SinirHucresi():\n",
    "  def __init__(self, X, y):\n",
    "    self.agirlik1 = np.random.rand()\n",
    "    self.agirlik2 = np.random.rand()\n",
    "    self.bias = np.random.rand()\n",
    "\n",
    "    self.tahminler = np.zeros(len(X))\n",
    "    self.ogrenme_orani = 0.1\n",
    "\n",
    "    self.X = X\n",
    "    self.y = y\n",
    "\n",
    "  def tahmin_et(self, X):\n",
    "    return self.agirlik1 * X + self.agirlik2 * (X ** 2) + self.bias\n",
    "    # f(x) = ax + bx^2 + c\n",
    "\n",
    "  def hata_hesapla(self):\n",
    "    # MSE Hesaplama\n",
    "    return np.mean((self.y - self.tahminler) ** 2)\n",
    "    # MSE = 1/n * sum((y - y_pred)^2)\n",
    "\n",
    "  def parametreleri_guncelle(self): # geriye yayılım ve gradyan iniş algoritması (backpropagation and gradient descent)\n",
    "    for i in range(len(self.X)):\n",
    "      # gradyan hesaplama\n",
    "      gradyan_agirlik1 = -2 * self.X[i] * (self.y[i] - self.tahminler[i])\n",
    "      gradyan_agirlik2 = -2 * (self.X[i] ** 2) * (self.y[i] - self.tahminler[i])\n",
    "      gradyan_bias = -2 * (self.y[i] - self.tahminler[i])\n",
    "      # gradyan = -2 * x * (y - y_pred) yani MSE fonksiyonunun türevi\n",
    "\n",
    "      # agirlik ve bias guncelleme\n",
    "      self.agirlik1 -= self.ogrenme_orani * gradyan_agirlik1\n",
    "      self.agirlik2 -= self.ogrenme_orani * gradyan_agirlik2\n",
    "      self.bias -= self.ogrenme_orani * gradyan_bias\n",
    "\n",
    "  def egit(self, epoch):\n",
    "    for _ in range(epoch):\n",
    "      for i in range(len(self.X)):\n",
    "        self.tahminler[i] = self.tahmin_et(self.X[i])\n",
    "      self.parametreleri_guncelle()\n",
    "      print(f\"Epoch: {_} Hata: {self.hata_hesapla()}\")\n",
    "\n",
    "  def plot_model(self, epoch, plot_num, nrows, ncols):\n",
    "      line = [self.agirlik1 * i + self.agirlik2 * (i * i) + self.bias for i in np.linspace(0, 1, 100)]\n",
    "      ax = plt.subplot(nrows, ncols, plot_num + 1)\n",
    "      ax.scatter(np.linspace(0, 1, 100), line, zorder = 3, s = 1 / 2, c = 'red', label = \"Model\")\n",
    "      ax.scatter(self.X, self.y, s = 50, c = 'green', label = \"True\")\n",
    "      ax.scatter(self.X, self.tahminler, zorder = 3, s = 50, c = 'orange', label = \"Tahmin\")\n",
    "      ax.set_title(\"Epoch: {}, Error: {:.5f}\".format(epoch, self.hata_hesapla()))\n",
    "      ax.set_xlim([0, 1])\n",
    "      ax.set_ylim([0, 1])\n",
    "      ax.grid()\n",
    "      ax.legend(loc = \"upper left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 Hata: 0.554244167636092\n",
      "Epoch: 1 Hata: 0.008284987167370255\n",
      "Epoch: 2 Hata: 0.007923287375840724\n",
      "Epoch: 3 Hata: 0.007613653342904715\n",
      "Epoch: 4 Hata: 0.007340009182280173\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hucre = SinirHucresi(df['x'], df['y'])\n",
    "hucre.egit(50000)\n",
    "hucre.plot_model(5, 0, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3910349730310442, 0.6594435776731177, 0.05924802669990848)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hucre.agirlik1, hucre.agirlik2, hucre.bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}