Chapter2/perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 原始形式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 感知机原始形式\n",
    "def perceptron(X, y, eta):\n",
    "    w, b = np.zeros(X.shape[1]),0\n",
    "    while ((y*(w.dot(X.T)+b))<=0).any():\n",
    "        index = np.random.randint(X.shape[0])\n",
    "        if y[index]*(w.dot(X[index])+b) <= 0:\n",
    "            w = w + eta * y[index] * X[index]\n",
    "            b = b + eta * y[index]\n",
    "    def f(x):\n",
    "        return np.sign(w.dot(x)+b)\n",
    "    return w, b, f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把结果打印出来\n",
    "def plotModel(X, w, b, f):\n",
    "    for x in X:\n",
    "        if f(x) == -1:\n",
    "            plt.scatter(x[0],x[1], color='red')\n",
    "        else:\n",
    "            plt.scatter(x[0],x[1], color='blue')\n",
    "    line_x0 = np.array([1, 5])\n",
    "    if w[1] == 0:\n",
    "        line_x0 = [-b/w[0], -b/w[0]]\n",
    "        line_x1 = [-1, 3]\n",
    "    else:\n",
    "        line_x1 = np.array((-b - w[0]*line_x0) / w[1])\n",
    "    # plt.scatter(X[index,0],X[index,1], s=150, c='none', alpha=0.7, linewidth=1.5, edgecolor='#AB3319')\n",
    "    plt.plot(line_x0, line_x1)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "# 书上的例子\n",
    "X = np.array([[3,3],[4,3],[1,1]])\n",
    "w, b, f = perceptron(X, np.array([1,1,-1]), eta=1)\n",
    "plotModel(X, w, b, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打一份线性可分的数据\n",
    "我选择了skearn.datasets的iris数据第1类和第2类的后面两个特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAW8ElEQVR4nO3dfYwdV3nH8d8vdx3IBto68pbGdrwbqgjVRBSSlVuRKgRsUhNeQtVKTbogoEEua9JSUFvRWipqqkj9q0VVScqWui3y1qlESJum5TWAKC2BrKnzCgkmtcNihDcESlNXgM3TP2au9u71fZm9bzN77vcjje6dc+7MOTN/PB6fc/YZR4QAAOk6r+wOAACGi0APAIkj0ANA4gj0AJA4Aj0AJG6i7A60smXLlpiZmSm7GwCwYRw5cuSpiJhqVVfJQD8zM6OlpaWyuwEAG4btE+3qGLoBgMQR6AEgcQR6AEgcgR4AEkegB4DEEegBIHEEegAoYHFRmpmRzjsv+1xcLLtHxVVyHT0AVMniorRvn3T6dLZ/4kS2L0lzc+X1qyie6AGgiwMHVoN83enTWflGQKAHgC6efHJ95VVDoAeALnbsWF951RDoAaCLW2+VJifXlk1OZuUbQddAb/ug7VO2H25T/7u2j+bbw7bP2r4orztu+6G8jixlADakuTlpYUGanpbs7HNhYWNMxEqSu70c3PbVkp6R9MGIuLzLb18r6Z0R8Yp8/7ik2Yh4aj2dmp2dDbJXAkBxto9ExGyruq5P9BHxWUlPF2zrRkmH19E3AMCQDWyM3vakpL2S7mwoDkkft33E9r4ux++zvWR7aWVlZVDdAoCxN8jJ2NdK+veIaHz6vyoirpD0Kklvz4eBWoqIhYiYjYjZqamWL0kBAPRgkIH+BjUN20TEyfzzlKS7JO0aYHsAgAIGEuht/7ikl0n6p4ayC20/t/5d0rWSWq7cAYCUlZ0np2uuG9uHJV0jaYvtZUnvkbRJkiLiL/Of/ZKkj0fE/zYc+jxJd9mut/P3EfHRwXUdAKqvCnlyui6vLAPLKwGkYmYmC+7Npqel48cH105fyysBAL2rQp4cAj0ADFEV8uQQ6AFgiKqQJ4dADwBDVIU8ObxhCgCGbG6u3ARoPNEDQOII9ACQOAI9ACSOQA8gKf2kGygjVcEo2mQyFkAy+kk3UEaqglG1SQoEAMnoJ93AqFIVDKtNUiAAGAv9pBsoI1XBqNok0ANIRj/pBspIVTCqNgn0AJLRT7qBMlIVjKpNAj2AZPSTbqCMVAWjapPJWABIAJOxADDGCPQAkDgCPQAkrmugt33Q9inbD7epv8b2f9s+mm9/2FC31/Zjto/ZfvcgOw4AKKbIE/3fStrb5Tf/FhEvzrdbJMl2TdL7JL1K0k5JN9re2U9nAaRjWDletm3LVrDUt23birfbT5/275cmJrI2Jyay/aromusmIj5re6aHc++SdCwinpAk23dIul7Soz2cC0BChpXjZds26eTJtWUnT2bl3/hG53al3vu0f790++2r+2fPru7fdlvv1zMohZZX5oH+noi4vEXdNZLulLQs6aSk34mIR2z/iqS9EfHW/HdvlPRzEXFzt/ZYXgmkbVh5Zez2dRGd25V679PERBbcm9Vq0pkznY8dlE7LKweRvfJLkqYj4hnb10n6R0mXSWp1y9v+q2J7n6R9krRjlK9HBzByZeSV6bXdIn1qFeQ7lY9a36tuIuJ7EfFM/v1fJW2yvUXZE/4lDT/druyJv915FiJiNiJmp6am+u0WgAorI69Mt3b76VOttr7yUes70Nv+KTv7D5PtXfk5vy3pfkmX2b7U9vmSbpB0d7/tAdj4hpXjZevWzuWd2u2nT43j/EXKRy4iOm6SDkv6pqQfKntKv0nS2yS9La+/WdIjkh6QdJ+klzYce52kxyV9TdKBbm3VtyuvvDIApO3QoYjp6Qg7+zx0aDDn3bo1IhuRz7atW4u320+f5ucjarWszVot2x8lSUvRJqaS6wYAEkCuGwAYYwR6AEgcgR4AEkegB9DRsFIV9KOfdAOdrqfTeat4HwprN0tb5saqG6AaDh2KmJxcu4plcnJwK2R6MT+/tj/1rcgql07X0+m8VbwPzcSqGwC9GFaqgn70k26g0/UsL7c/7/bt1bsPzYadAgFAospKVdBJP+kGOl1Pu2fes2ereR/WgzF6AG2Vlaqgk37SDXS6nk7nreJ9WA8CPYC2hpWqoB/9pBvodD2dzlvF+7Au7Qbvy9yYjAWqY1ipCvrRT7qBTtfT6bxVvA+NxGQsAKSNFAgAMMYI9ACQOAI9ACSOQA8AiSPQA4koIxfLnj1ZXpj6tmfP2vpOuWN6rZM6X2u3+7Chc9b0qt1ynDI3llcC61NGLpbdu1vnhtm9O6vvlDum17pu19rtPmyEnDW9EssrgbSVkZMme1N0axGdc9JIvdWdOdP5WqXO96GKuXsGhVw3QOKqmIull5w0Rep6udZ6XRXv0ygwRg8koIq5WDrljum1Tup8rd3uQxXv0yh0DfS2D9o+ZfvhNvVzth/Mt/+w/bMNdcdtP2T7qG3GYoAhKSMXy+7dncs75Y7ptU7qfK3d7sOGz1nTq3aD9/VN0tWSrpD0cJv6l0ranH9/laQvNNQdl7SlWxvNG5OxwPqVkYuleUK2PhFb1yl3TK91EZ2vtdt9qHrOml6p38lY2zOS7omIy7v8bnP+D8K2fP+4pNmIeGo9//gwGQsA6zPKXDc3SfpIw35I+rjtI7Y7JhG1vc/2ku2llZWVAXcLAMbXwFbd2H65skD/Cw3FV0XESds/KekTtr8SEZ9tdXxELEhakLIn+kH1CwDG3UCe6G2/SNIHJF0fEd+ul0fEyfzzlKS7JO0aRHsAgOL6DvS2d0j6sKQ3RsTjDeUX2n5u/bukayW1XLkDoLr6STfQ63mHeWwZ5y1du1na+ibpsKRvSvqhpGVlwzNvk/S2vP4Dkr4j6Wi+LeXlz5f0QL49IulAt7bqG6tugGroJ91Ar+cd5rFlnHdURAoEAL3oJ91Ar+cd5rFlnHdUOq26IdADaOu887Jn22b1PDft6n70o97PO8xjyzjvqPAqQQA96SfdQK/nHeaxZZy3Cgj0ANrqJ91Ar+cd5rFlnLcS2g3el7kxGQtURz/pBno97zCPLeO8oyAmYwEgbYzRA8AYI9ADQOII9ACQOAI9ACSOQA+0kGzOkxa6Xes43YtU8XJwoMniYvbautOns/0TJ1ZfYzc3V16/hqHbtY7TvUgZyyuBJhs958l6dLvWcboXGx3LK4F1ePLJ9ZVvZN2udZzuRcoI9ECTlHOeNOt2reN0L1JGoAeaJJ3zpEm3ax2ne5EyAj3QZG5OWljIxqHt7HNhIc3Jx27XOk73ImVMxgJAApiMBYAxRqAHgMQR6AEgcYUCve2Dtk/ZfrhNvW3/ue1jth+0fUVD3ZtsfzXf3jSojgMb0f790sRENrE5MZHtj+LYYaUxIH3CBtHujSSNm6SrJV0h6eE29ddJ+ogkS/p5SV/Iyy+S9ET+uTn/vrlbe7xhCiman4/IXj+9dpufH+6xhw5FTE6uPW5ysv+3J3U777DaRWsaxBumbM9IuiciLm9R935Jn4mIw/n+Y5KuqW8R8RutftcOq26QookJ6ezZc8trNenMmeEdO6w0BqRPqJZRrLrZJunrDfvLeVm78lad3Gd7yfbSysrKgLoFVEerQN2pfFDHDiuNAekTNo5BBXq3KIsO5ecWRixExGxEzE5NTQ2oW0B11GrrKx/UscNKY0D6hI1jUIF+WdIlDfvbJZ3sUA6MnXp636Llgzp2WGkMSJ+wgbQbvG/eJM2o/WTsq7V2MvaLsToZ+1/KJmI3598v6tYWk7FI1fx8RK2WTUzWasUmUwdx7KFDEdPTEXb2OagJ0W7nHVa7OJf6nYy1fVjZxOoWSd+S9B5Jm/J/KP7StiX9haS9kk5LektELOXH/rqkP8hPdWtE/E239piMBYD16TQZW+gNUxFxY5f6kPT2NnUHJR0s0g4AYPD4y1gASByBHgASR6AHgMQR6AEgcQR6AEgcgR4AEkegB4DEEegBIHEEegBIHIEeABJHoAeAxBHoASBxBHoASByBHgASR6AHgMQR6AEgcQR6AEgcgR4AEkegB4DEEegBIHGFAr3tvbYfs33M9rtb1P+Z7aP59rjt7zbUnW2ou3uQnQcAdDfR7Qe2a5LeJ+mVkpYl3W/77oh4tP6biHhnw+9/U9JLGk7xfxHx4sF1GQCwHkWe6HdJOhYRT0TEDyTdIen6Dr+/UdLhQXQOANC/IoF+m6SvN+wv52XnsD0t6VJJn2oofrbtJdv32X59u0Zs78t/t7SyslKgWwCAIooEercoiza/vUHShyLibEPZjoiYlfRrkt5r+6dbHRgRCxExGxGzU1NTBboFACiiSKBflnRJw/52SSfb/PYGNQ3bRMTJ/PMJSZ/R2vF7AMCQFQn090u6zPalts9XFszPWT1j+wWSNkv6fEPZZtvPyr9vkXSVpEebjwUADE/XVTcRccb2zZI+Jqkm6WBEPGL7FklLEVEP+jdKuiMiGod1fkbS+23/SNk/Kn/SuFoHADB8XhuXq2F2djaWlpbK7gYAbBi2j+TzoefgL2MBIHEEegBIHIEeABJHoAeAxBHoASBxBHoASByBHgASR6AHgMQR6Mu0uCjNzEjnnZd9Li6W3SMACeqaAgFDsrgo7dsnnT6d7Z84ke1L0txcef0CkBye6Mty4MBqkK87fTorB4ABItCX5ckn11cOAD0i0Jdlx471lQNAjwj0Zbn1Vmlycm3Z5GRWDgADRKAvy9yctLAgTU9Ldva5sMBELICBY9VNmebmCOwAho4negBIHIEeABJHoAeAxBUK9Lb32n7M9jHb725R/2bbK7aP5ttbG+reZPur+famQXY+aaRHADAgXSdjbdckvU/SKyUtS7rf9t0R8WjTT/8hIm5uOvYiSe+RNCspJB3Jj/3OQHqfKtIjABigIk/0uyQdi4gnIuIHku6QdH3B8/+ipE9ExNN5cP+EpL29dXWMkB4BwAAVCfTbJH29YX85L2v2y7YftP0h25es81jZ3md7yfbSyspKgW4ljPQIAAaoSKB3i7Jo2v9nSTMR8SJJn5T0d+s4NiuMWIiI2YiYnZqaKtCthJEeAcAAFQn0y5IuadjfLulk4w8i4tsR8f18968kXVn0WLRAegQAA1Qk0N8v6TLbl9o+X9INku5u/IHtixt2Xyfpy/n3j0m61vZm25slXZuXoRPSIwAYoK6rbiLijO2blQXomqSDEfGI7VskLUXE3ZJ+y/brJJ2R9LSkN+fHPm37j5X9YyFJt0TE00O4jvSQHgHAgDii5ZB5qWZnZ2NpaansbgDAhmH7SETMtqrjL2MBIHEEegBIHIEeABJHoC+i17wze/Zkq2bq2549xc/bT64b8uQAaBQRlduuvPLKqIxDhyImJyOk1W1yMivvZPfutcfUt927u5+31zb76S+ADU3ZKsiWMZVVN93MzGRJxZpNT0vHj7c/zq3+KDgX0fm8Um9tSr33F8CG1mnVDa8S7GZYeWd6OW+RNsmTA6AJY/TdDCvvTKfz9tMmeXIANCHQd9Nr3pnduzuXdzpvP7luyJMDoFm7wfsyt0pNxkZkE5nT0xF29ll0YrN5QrY+EVvkvL222e+xADYkMRkLAGkjBQIAjDECPQAkjkAPAIkj0Bexf780MZH9EdTERLZf1ynNAWkMAFQAfzDVzf790u23r+6fPbu6//jj0r33rv39vfdmwf4tb5H27ZNOn87KT5zI9qXuLxRZXOz9WABowqqbbiYmsuDerFZrXV43PU0aAwAjw6qbfrQL5p2CvNRfKgLSGAAYIAJ9N7Xa+srrSGMAoCII9N3Ux8ZblXdKc0AaAwAVUSjQ295r+zHbx2y/u0X9u2w/avtB2/fanm6oO2v7aL7dPcjOj8Rtt0nz86tP8LVatn/bbdInP3lusN+9Oyufm5MWFrJxdTv7XFgoNpnaz7EA0KTrZKztmqTHJb1S0rKk+yXdGBGPNvzm5ZK+EBGnbc9LuiYifjWveyYinrOeTlVqMhYANoB+J2N3SToWEU9ExA8k3SHp+sYfRMSnIyJfC6j7JG3vp8MAgMEpEui3Sfp6w/5yXtbOTZI+0rD/bNtLtu+z/fp2B9nel/9uaWVlpUC3AABFFPmDqVbvxGs53mP7DZJmJb2soXhHRJy0/XxJn7L9UER87ZwTRixIWpCyoZsC/QIAFFDkiX5Z0iUN+9slnWz+ke09kg5Iel1EfL9eHhEn888nJH1G0kv66C8AYJ2KBPr7JV1m+1Lb50u6QdKa1TO2XyLp/cqC/KmG8s22n5V/3yLpKkmPahj6yQ3TKZeNJL3whWvz2bzwhat155+/tu7881frJifX1jUvmdy2bW39toYRMfLkABiUdm8kadwkXads5c3XJB3Iy25RFtgl6ZOSviXpaL7dnZe/VNJDkh7IP28q0t663zB16FDE5OTatzlNThZ7s9L8/Nrj6tv8fFa/c2fr+p07IzZtal23aVPEBRe0rrvgguy8W7e2rt+6tb/r6edYABuWkn/DVD+5YTrlsjlzJnvSHrSIzuclTw6AdUo/100/uWF6zWUzTOTJATBAaQT6fnLD9JrLZpjIkwNggNII9P3khumUy0aSdu5sXb9zp7RpU+u6TZukCy5oXVcv37q1df3WreTJATBY7Qbvy9zWPRkbkU02Tk9H2NnneiYf5+cjarVs4rJWW52IrWuekN25c7WueUJ206bVuuYJ2fpEbF3zhOzWrYO5nn6OBbAhKfnJWAAYc+lPxgIA2iLQA0DiCPQAkLjxCfTDSgvQKX1Ct9QKADACRbJXbnyLi9lyydN5yvwTJ1aXT/bz1qb9+6Xbb1/dP3t27X67uttu671NAFin8Vh1M6y0AJ3SJ0idUysAwAB1WnUzHk/0w0oL0Ev6hDJTKwAYS+MxRj+stACd0idUMbUCgLE0HoF+WGkBOqVP6JZaAQBGZDyGbuoTrgcOZMM1O3ZkQb6fiVhpdVJ1YSEbkqnVskDeONnaqQ4ARmA8JmMBIHGkQACAMUagB4DEEegBIHEEegBIHIEeABJHoAeAxBHoASBxlVxHb3tFUossZIVskfTUALuTKu5TMdynYrhPxQzzPk1HxFSrikoG+n7YXmr3RwNYxX0qhvtUDPepmLLuE0M3AJA4Aj0AJC7FQL9Qdgc2CO5TMdynYrhPxZRyn5IbowcArJXiEz0AoAGBHgASl0ygt33Q9inbD5fdlyqzfYntT9v+su1HbL+j7D5Vke1n2/6i7Qfy+/RHZfepqmzXbP+n7XvK7kuV2T5u+yHbR22P9IUbyYzR275a0jOSPhgRl5fdn6qyfbGkiyPiS7afK+mIpNdHxKMld61SbFvShRHxjO1Nkj4n6R0RcV/JXasc2++SNCvpxyLiNWX3p6psH5c0GxEj/8OyZJ7oI+Kzkp4uux9VFxHfjIgv5d//R9KXJW0rt1fVE5ln8t1N+ZbGU9EA2d4u6dWSPlB2X9BeMoEe62d7RtJLJH2h3J5UUz4kcVTSKUmfiAju07neK+n3JP2o7I5sACHp47aP2N43yoYJ9GPK9nMk3SnptyPie2X3p4oi4mxEvFjSdkm7bDMk2MD2aySdiogjZfdlg7gqIq6Q9CpJb8+Hm0eCQD+G8jHnOyUtRsSHy+5P1UXEdyV9RtLekrtSNVdJel0+9nyHpFfYPlRul6orIk7mn6ck3SVp16jaJtCPmXyS8a8lfTki/rTs/lSV7SnbP5F/v0DSHklfKbdX1RIRvx8R2yNiRtINkj4VEW8ouVuVZPvCfPGDbF8o6VpJI1shmEygt31Y0uclvcD2su2byu5TRV0l6Y3Knr6O5tt1ZXeqgi6W9GnbD0q6X9kYPcsH0avnSfqc7QckfVHSv0TER0fVeDLLKwEArSXzRA8AaI1ADwCJI9ADQOII9ACQOAI9ACSOQA8AiSPQA0Di/h9h0FQQVflsbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "X = X[y<2,2:]\n",
    "y = y[y<2]\n",
    "plt.scatter(X[y==0,0],X[y==0,1], color='red')\n",
    "plt.scatter(X[y==1,0],X[y==1,1], color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[y==0]=-1\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于感知机要求的y的标签为-1和1，当前y的标签为0，1\n",
    "需要把标签0全部改成标签-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "# iris数据\n",
    "w, b, f = perceptron(X, y, eta=0.01)\n",
    "plotModel(X, w, b, f)"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}