ch02-KNN/01-kNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## kNN近邻算法 \n",
    "\n",
    "**欧式距离**：二维空间，两点间距离公式A(x1,y1)，B(x2,y2)\n",
    "\n",
    "$$\\sqrt{(x_{2}-x_{1})^{2}+(y_{2}-y_{1})^{2}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 原始数据 list\n",
    "raw_data_X = [[3.393533211, 2.331273381],\n",
    "              [3.110073483, 1.781539638],\n",
    "              [1.343808831, 3.368360954],\n",
    "              [3.582294042, 4.679179110],\n",
    "              [2.280362439, 2.866990263],\n",
    "              [7.423436942, 4.696522875],\n",
    "              [5.745051997, 3.533989803],\n",
    "              [9.172168622, 2.511101045],\n",
    "              [7.792783481, 3.424088941],\n",
    "              [7.939820817, 0.791637231]\n",
    "             ]\n",
    "raw_data_y = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.array(raw_data_X)\n",
    "y_train = np.array(raw_data_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x107efed68>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NRHxI0qlVpu2V9Hhz+0lJ1/chGwBgDfp1jH2npIXm9llJF7dOsF2yPW17en5+vk+7BQC06lexn5Y02twebT5fJiKqEVGIiMLY2FifdgsAaNWvYp+SdGNze6+kp/r07wIAetRzsdt+v+2DLcM1SZfZfkHSGTWKHgCQgW3dToyIy5uPP5d0d8trb0q6ub/RAABrwQVKAJAYih0AEkOxA0BiKHYAg69Wk/J5aWSk8VirZZ1ooHV98hQAMlGrSaWSVK83ns/ONp5LUrGYXa4BxoodwGArl98p9UX1emMcbVHsAAbb3Fxv46DYAQy48fHexkGxAxhwlYqUyy0fy+Ua42iLYgcw2IpFqVqVJiYku/FYrXLidBX8VQyAwVcsUuQ9YMUOAImh2AEgMRQ7ACSGYgeAxFDsAJAYih0AEkOxA0BiHBGbv1N7XtKspEsknd70AN0h29oMarZBzSWRba0GNdtG5pqIiLFOkzIp9rd3bk9HRCGzAKsg29oMarZBzSWRba0GNdsg5OJQDAAkhmIHgMRkXezVjPe/GrKtzaBmG9RcEtnWalCzZZ4r02PsAID+y3rFDgDos0yL3fZ220ezzNDKDd+y/ZztR2wPzFcb295m+7u2n7H9zazztLL9FdtPZJ1jKds32T5l++nmzxVZZ1rK9t/Y/pHt79u+IOs8i2x/csl/s/+xfVvWmSTJ9h/Y/rfme+Cfss6zlO2LbP+wme3eLLNkVuy2d0g6LumGrDKs4BpJ2yLiY5LeI+nGjPMs9TlJJyLiGkm7bH8460CLbE9I+mLWOVbwYERc2/x5Oeswi2z/saQPRsTHJX1f0h9lHOltEfHDxf9mkl6Q9JOsMzUVJT3XfA980PafZh1oiQOSftrMdo3t92cVJLNij4jfRMSHJJ3KKsMKXpd0uLn9uyyDtPEDSfc3P0W8V9LZjPMsdVjSPVmHWMGtto/Zfsi2sw6zxKSki2z/h6SPS/p5xnnOYzsn6fKIeCHrLE1vSso1/z9eqMF7j767mc2SMlt4cYy9RUS8EhHHbN8i6QJJj2WdaVFEvBERdUnPSHo9Il7LOpMk2T4g6YSkl7LO0sarku6NiKsl7ZJ0XcZ5lhqTNB8Rn1BjtX5txnnauUHSVNYhlvhXSZ+W9F+S/jsiXs04z1I1NRZcD6nxC2hHVkEo9jZsf1bSlyXti4i3ss6zyPZO2++StEeNld71WWdqulmN1ed3JH3E9p0Z51nqjKTF4/4nJV2aXZTznJW0eGjoNUmXZZhlJfskPZp1iCXukfSNiPgTSRfb3pN1oBZfioi/UKPY/zerEBR7C9vvk/Q1SZ+JiF9nnafFVyV9vvnLpq4MVwRLRcSB5rHY/ZKOR8SRrDMtcZek/bZHJF0p6cWM8yx1XNJHm9uXq1HuA6N5SOF6SU9mnWWJd0v6bXP7TUl/mGGWVp+Q9I3m4usqSc9lFYRiP99tanxkf6z5FwF3ZB1oiQck3WH7WUm/0gAdJhpgRyTdLul5SQ9HxMAcLoqIZyWdtv2fkl6OiGNZZ2rxUTVOBv6248zN84Ckv2q+B3ZosA4TfV+N4/4/kvT3EfFGVkG4QAkAEsOKHQASQ7EDQGIodgBIDMUOAImh2AEgMRQ7ACSGYgeAxPw/0XoU4nQ75OYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107ea0668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 每一个点在特征平面中的位置\n",
    "plt.scatter(X_train[y_train==0, 0], X_train[y_train==0, 1], color='g')\n",
    "plt.scatter(X_train[y_train==1, 0], X_train[y_train==1, 1], color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 属于哪一类(良性、恶性肿瘤)\n",
    "x = np.array([8.093607318, 3.365731514])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x107fb47f0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107ea0128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单独绘制\n",
    "plt.scatter(X_train[y_train==0, 0], X_train[y_train==0, 1], color='g')\n",
    "plt.scatter(X_train[y_train==1, 0], X_train[y_train==1, 1], color='r')\n",
    "plt.scatter(x[0], x[1], color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### kNN的过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4.8125669076098774,\n",
       " 5.2292708272353048,\n",
       " 6.7497989991600642,\n",
       " 4.6986266144110695,\n",
       " 5.8346001455685697,\n",
       " 1.4900114024329525,\n",
       " 2.354574897431513,\n",
       " 1.3761132675144652,\n",
       " 0.30643199929749998,\n",
       " 2.5786840957478887]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance = []\n",
    "for x_train in X_train:\n",
    "    # 欧拉距离\n",
    "    d = np.sqrt(np.sum((x - x_train)**2))\n",
    "    distance.append(d)\n",
    "distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [np.sqrt(np.sum((x - x_train)**2)) for x_train in X_train]\n",
    "# [np.linalg.norm(x - x_train) for x_train in X_train]\n",
    "# 以后计算欧拉距离 直接拿去用！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 7, 5, 6, 9, 3, 0, 1, 4, 2])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对结果进行排序，返回排序结果的索引\n",
    "nearest = np.argsort(distance)\n",
    "# 最近4个点 8，7，5，6\n",
    "nearest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1, 1, 1, 0]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 6\n",
    "topK_y = [y_train[i] for i in nearest[:k]]\n",
    "topK_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 1, 1: 5})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "# 统计频数\n",
    "Counter(topK_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 5)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 投票\n",
    "votes = Counter(topK_y)\n",
    "votes.most_common(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最终结果\n",
    "predict_y = votes.most_common(1)[0][0]\n",
    "predict_y"
   ]
  },
  {
   "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": "3.6.4rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}