3. Makine Öğrenimi/1. Begin.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gerekli kütüphaneleri ekleme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noktaları oluşturma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nokta sayısı tanımlama\n",
    "n_pts = 100\n",
    "\n",
    "# Noktarları oluşturma\n",
    "\n",
    "# Her defasında aynı rastgele değerlerin atanamasını sağlar. \n",
    "# İnceleme işlemi için aynı değerler lazım\n",
    "np.random.seed(0)\n",
    "\n",
    "# Dikey ters parabol oluşturma\n",
    "rx_ust = np.random.normal(10, 2, n_pts)\n",
    "# Yatay ters parabol oluşturma\n",
    "ry_ust = np.random.normal(12, 2, n_pts)\n",
    "\n",
    "# Dikey ters parabol oluşturm\n",
    "rx_alt = np.random.normal(5, 2, n_pts)\n",
    "# Yatay ters parabol oluşturma\n",
    "ry_alt = np.random.normal(6, 2, n_pts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noktaların bölgelere eklenmesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# İlk satır x, ikinci satır y verisi olur ([x,x], [y,y]). Matrix işlemleri yapmak için bize \n",
    "# ([x,y], [x, y]) tipinde veri lazım bu sebeple transpozunu alıyoruz.\n",
    "ust_bolge = np.array([rx_ust, ry_ust]).T\n",
    "alt_bolge = np.array([rx_alt, ry_alt]).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Koordinat sistemine noktaların eklenmesi ve sistemin ekrana çizilmesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Koordinat sistemi parçası oluşturma\n",
    "# figsize (keyword): x,y uzunlukları\n",
    "_, ax = plt.subplots(figsize=(4, 4))\n",
    "\n",
    "# 0. ve 1. sütundaki her (:) satırı alma ve bu verileri koordinat sisteminde gösterme\n",
    "ax.scatter(ust_bolge[:, 0], ust_bolge[:, 1], color='r')\n",
    "ax.scatter(alt_bolge[:, 0], alt_bolge[:, 1], color='b')\n",
    "\n",
    "# Koordinat sistemini gösterme\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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