第三讲-KNN算法/KNN算法-预测多个测试data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#录入数据集（此处为人模拟的数据集），X第一列为肿瘤大小，第二列为肿瘤时间，y为肿瘤性质良好还是恶性\n",
    "\n",
    "raw_data_X = [[3.393533211, 2.331273381],\n",
    "              [3.110073273, 1.786360121],\n",
    "              [1.343892307, 3.362874429],\n",
    "              [3.580243273, 4.671037091],\n",
    "              [2.274392744, 2.873335573],\n",
    "              [7.474390402, 4.673011339],\n",
    "              [5.772024290, 3.560262131],\n",
    "              [9.122354845, 2.568264233],\n",
    "              [7.722344298, 3.479979792],\n",
    "              [7.978408784, 0.773246244]\n",
    "             ]\n",
    "raw_data_y = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#转为向量传入训练集\n",
    "X_train = np.array(raw_data_X)\n",
    "y_train = np.array(raw_data_y)\n",
    "#print (X_train)\n",
    "#print (y_train)\n",
    "\n",
    "#绘制散点图\n",
    "plt.scatter(X_train[y_train == 0,0], X_train[y_train == 0,1], c='g', marker='o', label='0')\n",
    "plt.scatter(X_train[y_train == 1,0], X_train[y_train == 1,1], c='r', marker='x', label='1')\n",
    "plt.xlabel('size')\n",
    "plt.ylabel('time')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5 3]\n",
      " [6 4]]\n",
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#要预测的点\n",
    "x = [[5, 3],\n",
    "     [6, 4]\n",
    "    ]\n",
    "x_test = np.array(x)\n",
    "print (x_test)\n",
    "print (x_test.shape[0])\n",
    "#print (x_pre[0])\n",
    "#print (x_pre[:,0])\n",
    "\n",
    "#图中观测\n",
    "plt.scatter(X_train[y_train == 0,0], X_train[y_train == 0,1], c='g', marker='o', label='良性')\n",
    "plt.scatter(X_train[y_train == 1,0], X_train[y_train == 1,1], c='r', marker='x', label='恶性')\n",
    "plt.scatter(x_test[:,0], x_test[:,1])  #不能写成plt.scatter(x_pre[0], x_pre[1])\n",
    "plt.xlabel('size')\n",
    "plt.ylabel('time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN过程 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.740095064966033, 2.246050932042296, 3.6740714900551215, 2.1927321139070988, 2.728548843438044, 2.986900534321874, 0.9538947320237519, 4.144901113488964, 2.764333387560458, 3.718783561121629]\n",
      "[3.094885725050142, 3.6403128988217577, 4.699496551976164, 2.511078134061044, 3.8922387076112237, 1.620731723615076, 0.4953204193072049, 3.4349624575243984, 1.799136153152806, 3.7849757090988665]\n",
      "[[1.740095064966033, 2.246050932042296, 3.6740714900551215, 2.1927321139070988, 2.728548843438044, 2.986900534321874, 0.9538947320237519, 4.144901113488964, 2.764333387560458, 3.718783561121629], [3.094885725050142, 3.6403128988217577, 4.699496551976164, 2.511078134061044, 3.8922387076112237, 1.620731723615076, 0.4953204193072049, 3.4349624575243984, 1.799136153152806, 3.7849757090988665]]\n"
     ]
    }
   ],
   "source": [
    "#求距离\n",
    "\n",
    "from math import sqrt\n",
    "\n",
    "#distance = []\n",
    "all_distance = []\n",
    "for i in range(x_test.shape[0]):\n",
    "    #print (i)\n",
    "    distance = []\n",
    "    for x_train in X_train:\n",
    "        d = sqrt(np.sum((x_train - x_test[i])**2))\n",
    "        distance.append(d)\n",
    "    print (distance)\n",
    "    all_distance.append(distance)\n",
    "print (all_distance)\n",
    "\n",
    "\n",
    "        \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6 0 3 1 4 8 5 2 9 7]\n",
      "[6 5 8 3 0 7 1 9 4 2]\n",
      "[array([6, 0, 3, 1, 4, 8, 5, 2, 9, 7], dtype=int64), array([6, 5, 8, 3, 0, 7, 1, 9, 4, 2], dtype=int64)]\n"
     ]
    }
   ],
   "source": [
    "nearest_list = []\n",
    "for dis in all_distance:\n",
    "    nearest = np.argsort(dis)\n",
    "    nearest_list.append(nearest)\n",
    "    print (nearest)\n",
    "print (nearest_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 0, 0, 0, 0, 1]\n",
      "[1, 1, 1, 0, 0, 1]\n",
      "[[1, 0, 0, 0, 0, 1], [1, 1, 1, 0, 0, 1]]\n"
     ]
    }
   ],
   "source": [
    "#令k = 6 思考：如何求最优 k\n",
    "\n",
    "k = 6\n",
    "TOP_K = []\n",
    "for near in nearest_list:\n",
    "    top_k = []\n",
    "    for i in near[:6]:\n",
    "        top_k.append(y_train[i])\n",
    "    print (top_k)\n",
    "    TOP_K.append(top_k)\n",
    "print (TOP_K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 4, 1: 2})\n",
      "[(0, 4)]\n",
      "0\n",
      "Counter({1: 4, 0: 2})\n",
      "[(1, 4)]\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "#求投票结果（每种结果各有多少个，比例）\n",
    "from collections import Counter\n",
    "\n",
    "VOTES = []\n",
    "for top in TOP_K:\n",
    "    votes = Counter(top)\n",
    "    VOTES.append(votes.most_common(1))\n",
    "    print (votes)\n",
    "    print (votes.most_common(1))\n",
    "    #print (votes.most_common(2))\n",
    "    predict_y1 = votes.most_common(1)[0][0]\n",
    "    #predict_y2 = votes.most_common(2)[0][0]\n",
    "    print (predict_y1)\n",
    "    #print (predict_y1)\n",
    "    \n",
    "#print (VOTES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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