7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import numpy as np\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "x = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class KNN:\n",
    "  def __init__(self, k=3):\n",
    "    self.k = k\n",
    "\n",
    "  def fit(self, x, y):\n",
    "    self.x_train = x\n",
    "    self.y_train = y\n",
    "\n",
    "  def predictOne(self, x):\n",
    "    distances = [np.linalg.norm(x - trainInst) for trainInst in self.x_train]\n",
    "    k_indices = np.argsort(distances)[:self.k]\n",
    "\n",
    "    k_labels = [self.y_train[i] for i in k_indices]\n",
    "    self.ctr = Counter(k_labels)\n",
    "    return self.ctr.most_common(1)[0][0]\n",
    "\n",
    "  def predict(self, x):\n",
    "    return [self.predictOne(xInst) for xInst in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "knn = KNN(k=3)\n",
    "knn.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "y_pred = knn.predict(x_test)\n",
    "np.mean(y_pred == y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "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",
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