KNN self made.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from math import sqrt\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from collections import Counter\n",
    "from matplotlib import style\n",
    "style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset={'k':[[1,2],[2,3],[3,1]],'r':[[6,5],[7,7],[8,6]]}\n",
    "new_features=[5,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rx4Xa7bZWVlYmlAhA0hzoNZBmszkV64hTUvK3Wq2hxyVlm6TkfP6XQ/54JT2/dLDbsLCw0Pf9Ay2QQZMN0mw2x15HnJKUP5vNDj0uKduUpM//Usgfr6Tnlya/DdyneUSVy2VlMpm+YzKZjCqVyoQSAUgaCuSIKhQKyufzfcfk83ktLS1NKBGApBnmNt51SY9JutHMnjGz90cfC1FLpVKqVqtaXFy86Egkk8locXFR1WqVhwkBXNbAayDu/t5JBMHk5XI5bW1taWNjQ6urq+eeRK9UKlpaWqI8APTFk+hHXCqVUrFYVLFYPBQXEQFMDruYAIAgFAgAIAgFAgAIQoEAAIJQIACAIBQIACCIuftYK9jd3R1vBQCAqTc/P28XLuMIBAAQhAIBAAQZ+xQWAOBo4ggEABBkKgrEzL5gZs+Z2cm4s4zKzF5vZt8ys1Nm1jCzD8edaVRmljGzJ8zsqd42fDLuTKMysxkz+66ZJfKXuJvZj83se2b2pJn9e9x5RmVm15jZA2b2g973wu1xZxqWmd3Y+9zPfj1vZh+JO9cozOwve9+7J81s3cz6/7Kfg5p3Gk5hmdlbJb0o6cvuflPceUZhZtdKutbdT5jZVZLqkv7M3b8fc7ShmZlJutLdXzSzKyQ9KunD7v7tmKMNzcz+StKtkq5298T9EhMz+7GkW939v+POEsLM/knSv7n7fWY2K2nO3X8bd65RmdmMpJ9L+iN3/0nceYZhZq/T/vfsm9z9ZTP7F0lfd/cvRT33VByBuPu2pF/HnSOEu//C3U/0Xr8g6ZSk18WbajS+78XeH6/ofcW/ZzEkM7tO0nsk3Rd3lqPIzK6W9FZJ90uSu59JYnn03CnpR0kpj1dISzpmZmlJc5KencSkU1Egh4WZXS/pZkmPx5tkdL1TQE9Kek7SlrsnaRs+K+mjkrpxBxmDS3rYzOpm9oG4w4zoDZJ2JH2xdxrxPjO7Mu5QgUqS1uMOMQp3/7mkv5f0U0m/kLTr7g9PYm4K5ICY2askfUXSR9z9+bjzjMrdO+5+XNJ1km4zs0ScSjSzJUnPuXs97ixjusPdb5H0Lkkf7J3WTYq0pFsk/aO73yzpJUkfizfS6Hqn3gqS/jXuLKMws1dLKkr6fUmvlXSlmVUmMTcFcgB61w2+ImnV3R+MO884eqceHpF0V8xRhnWHpELvGkJV0tvMbCXeSKNz92d7/31O0kOSbos30UiekfTMK45aH9B+oSTNuySdcPdfxR1kRG+X9F/uvuPu/yvpQUl/PImJKZAx9S5A3y/plLt/Ju48IcwsZ2bX9F4f0/5fyB/Em2o47v5xd7/O3a/X/umHb7r7RPa+DoqZXdm7AUO9Uz/vlJSYOxLd/ZeSfmZmN/YW3SkpMTeRvMJ7lbDTVz0/lfQWM5vr/Xt0p/avxUZuKgrEzNYlPSbpRjN7xszeH3emEdwh6X3a3/M9exvgu+MONaJrJX3LzJ6W9B3tXwNJ5O2wCfUaSY+a2VOSnpD0NXf/RsyZRvUhSau9v0PHJX065jwjMbM5Se/Q/t57ovSO/B6QdELS97T/7/rnJzH3VNzGCwBInqk4AgEAJA8FAgAIQoEAAIJQIACAIBQIACAIBQIACEKBAACCUCAAgCD/Bylm9KMux5w+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in dataset:\n",
    "    for ii in dataset[i]:\n",
    "        plt.scatter(ii[0],ii[1],s=100,color=i)\n",
    "        plt.scatter(new_features[0],new_features[1])\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def k_nearest_neighbors(data,predict,k=3):\n",
    "    if(len(data)>=k):\n",
    "        warning.warn('k is set to a value less than total voting groups')\n",
    "    distances=[]\n",
    "    for group in data:\n",
    "        for features in data[group]:\n",
    "                eucleadian_distance=np.linalg.norm(np.array(features)-np.array(predict))\n",
    "                distances.append([eucleadian_distance,group])\n",
    "        votes=[i[1] for i in sorted(distances)[:k]]\n",
    "        print(Counter(votes).most_common(1))\n",
    "        vote_result=Counter(votes).most_common(1)[0][0]\n",
    "        return vote_result\n",
    "        result=k_nearest_neighbors(data,new_features,k=3)\n",
    "        print(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('k', 3)]\n",
      "The data belongs to k\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result=k_nearest_neighbors(dataset,[7,3],k=3)\n",
    "print(\"The data belongs to \" + result)\n",
    "for i in dataset:\n",
    "    for ii in dataset[i]:\n",
    "        plt.scatter(ii[0],ii[1],s=100,color=i)\n",
    "        plt.scatter(new_features[0],new_features[1],color=result)\n",
    "        \n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}