part 17.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from math import sqrt\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from matplotlib import style\n",
    "from collections import Counter\n",
    "\n",
    "%matplotlib inline\n",
    "style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "dataset = {\n",
    "    'k': [\n",
    "        [1, 2], \n",
    "        [2, 3], \n",
    "        [3, 1]\n",
    "    ],\n",
    "    'r': [\n",
    "        [6, 5],\n",
    "        [7, 7],\n",
    "        [8, 6]\n",
    "    ]\n",
    "}\n",
    "new_features = [5, 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def k_nearest_neighbors(data, predict, k=3):\n",
    "    if len(data) >= k:\n",
    "        warnings.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",
    "            euclidian_distance = np.linalg.norm(np.array(features)-np.array(predict))\n",
    "            distances.append([euclidian_distance, group])\n",
    "            \n",
    "    votes = [i[1] for i in sorted(distances)[:k]]\n",
    "    vote_result = Counter(votes).most_common(1)[0][0]\n",
    "    return vote_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'r'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = k_nearest_neighbors(dataset, new_features, k=3)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9bdbce5ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "[[plt.scatter(ii[0], ii[1], s=100, color=i) for ii in dataset[i]] for i in dataset]\n",
    "plt.scatter(new_features[0], new_features[1], s=100, color=result)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}