example-code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "83089a2f-a0b5-430f-8883-bdf77c3d720f",
   "metadata": {},
   "outputs": [],
   "source": [
    "__author__ = 'fengchen'\n",
    "\n",
    "from cvxopt import matrix, solvers, spmatrix\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from math import *\n",
    "\n",
    "def calc_Q_p_G_h(data):\n",
    "#     Q = spmatrix(2.0, range(3), range(3))\n",
    "    Q = matrix([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], (3,3))\n",
    "    Q[0,0] = 2\n",
    "    Q[1,1] = 2\n",
    "    p = matrix([0.0, 0.0, 0.0], (3,1))\n",
    "    G = []\n",
    "    h = []\n",
    "    for items in data:\n",
    "        row = []\n",
    "        if items[2] == 1:\n",
    "            row.extend([-1 * item for item in items[:2]])\n",
    "            row.append(-1)\n",
    "            G.append(row)\n",
    "            h.append(-1.0)\n",
    "        else:\n",
    "            row.extend(items[:2])\n",
    "            row.append(1)\n",
    "            G.append(row)\n",
    "            h.append(-1.0)\n",
    "    #print G\n",
    "    G = matrix(G).trans()\n",
    "    h = matrix(h)\n",
    "    print(Q, p)\n",
    "    print(G, h)\n",
    "    print(type(G))\n",
    "    return Q, p, G, h\n",
    "    \n",
    "    \n",
    "def plot(data, w1, w2, b, sol):\n",
    "    ### suppose you have obtained sol['x'] from CVXOPT QP minimization\n",
    "\n",
    "    x = [item[0] for item in data if item[2] == 1]\n",
    "    y = [item[1] for item in data if item[2] == 1]\n",
    "    plt.scatter(x, y, s=80, facecolors='none', edgecolors='r')\n",
    "    x = [item[0] for item in data if item[2] == -1]\n",
    "    y = [item[1] for item in data if item[2] == -1]\n",
    "    plt.scatter(x, y, s=80, facecolors='none', edgecolors='b')\n",
    "    x = [item[0] for item in data]\n",
    "    y = [item[1] for item in data]\n",
    "    plt.scatter(x, y, s=40, facecolors='none', edgecolors='k')\n",
    "    w1 = sol['x'][0]\n",
    "    w2 = sol['x'][1]\n",
    "    b  = sol['x'][2]\n",
    "\n",
    "    print('w: {0}; b: {1} \\n'.format([w1,w2], b))\n",
    "\n",
    "    print('w.x + b > 0 for positive class')\n",
    "    print('w.x + b < 0 for negative class') \n",
    "\n",
    "    x = [-1, 6]\n",
    "\n",
    "    y = [(w1 * x[i] + b)/(-1 * w2) for i in range(2)]\n",
    "\n",
    "    \n",
    "    plt.plot(x, y, color='red')\n",
    "\n",
    "    y = [(w1 * x[i] + b - 1)/(-1 * w2) for i in range(2)]\n",
    "\n",
    "    plt.plot(x, y, color='black')\n",
    "\n",
    "    y = [(w1 * x[i] + b + 1)/(-1 * w2) for i in range(2)]\n",
    "\n",
    "    plt.plot(x, y, color='black')\n",
    "    \n",
    "    plt.gca().set_aspect('equal', adjustable='box')\n",
    "\n",
    "    plt.grid()\n",
    "\n",
    "    plt.gcf().set_size_inches(5, 5)\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def svm_proc(data):\n",
    "    Q, p, G, h = calc_Q_p_G_h(data)\n",
    "    sol=solvers.qp(Q, p, G, h)\n",
    "    w1 = sol['x'][0]\n",
    "    w2 = sol['x'][1]\n",
    "    b  = sol['x'][2]\n",
    "    print('\\n\\nw1= {0}; w2={1}; b={2}'.format(w1, w2, b))\n",
    "    plot(data, w1, w2, b, sol)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc8aca41-83ee-4251-a06d-d8e06498c466",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.00e+00  0.00e+00  0.00e+00]\n",
      "[ 0.00e+00  2.00e+00  0.00e+00]\n",
      "[ 0.00e+00  0.00e+00  0.00e+00]\n",
      " [ 0.00e+00]\n",
      "[ 0.00e+00]\n",
      "[ 0.00e+00]\n",
      "\n",
      "[-5.00e-01 -1.00e+00 -1.00e+00]\n",
      "[-1.00e+00 -5.00e-01 -1.00e+00]\n",
      "[ 2.00e+00  2.00e+00  1.00e+00]\n",
      " [-1.00e+00]\n",
      "[-1.00e+00]\n",
      "[-1.00e+00]\n",
      "\n",
      "<class 'cvxopt.base.matrix'>\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.3319e-01  9.7293e-01  2e+00  2e+00  6e-16\n",
      " 1:  7.1732e-01  1.1991e+00  2e-02  3e-01  2e-16\n",
      " 2:  1.2831e+00  1.2800e+00  3e-03  1e-16  5e-14\n",
      " 3:  1.2800e+00  1.2800e+00  3e-05  1e-16  2e-15\n",
      " 4:  1.2800e+00  1.2800e+00  3e-07  3e-16  1e-15\n",
      "Optimal solution found.\n",
      "\n",
      "\n",
      "w1= -0.8000000954977434; w2=-0.8000000954977432; b=2.200000279372788\n",
      "w: [-0.8000000954977434, -0.8000000954977432]; b: 2.200000279372788 \n",
      "\n",
      "w.x + b > 0 for positive class\n",
      "w.x + b < 0 for negative class\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [[0.5,1,1], [1,0.5,1], [2,2,-1]]\n",
    "svm_proc(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f8a704f-a6c8-42d7-8b51-cdb4e6700acd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.00e+00  0.00e+00  0.00e+00]\n",
      "[ 0.00e+00  2.00e+00  0.00e+00]\n",
      "[ 0.00e+00  0.00e+00  0.00e+00]\n",
      " [ 0.00e+00]\n",
      "[ 0.00e+00]\n",
      "[ 0.00e+00]\n",
      "\n",
      "[-5.00e-01 -1.00e+00 -1.00e+00]\n",
      "[-1.00e+00 -1.00e+00 -1.00e+00]\n",
      "[ 2.00e+00  2.00e+00  1.00e+00]\n",
      " [-1.00e+00]\n",
      "[-1.00e+00]\n",
      "[-1.00e+00]\n",
      "\n",
      "<class 'cvxopt.base.matrix'>\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.1865e-01  1.1437e+00  2e+00  2e+00  6e-16\n",
      " 1:  7.3137e-01  1.5280e+00  2e-01  4e-01  3e-16\n",
      " 2:  1.9734e+00  1.9984e+00  1e-01  4e-02  3e-14\n",
      " 3:  1.9997e+00  2.0000e+00  1e-03  4e-04  2e-15\n",
      " 4:  2.0000e+00  2.0000e+00  1e-05  4e-06  2e-15\n",
      " 5:  2.0000e+00  2.0000e+00  1e-07  4e-08  3e-15\n",
      "Optimal solution found.\n",
      "\n",
      "\n",
      "w1= -0.9999999926367941; w2=-0.9999999916426239; b=2.9999999726858544\n",
      "w: [-0.9999999926367941, -0.9999999916426239]; b: 2.9999999726858544 \n",
      "\n",
      "w.x + b > 0 for positive class\n",
      "w.x + b < 0 for negative class\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [[0.5,1,1], [1,1,1], [2,2,-1]]\n",
    "svm_proc(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dd979da-d9fb-41bb-a95b-21fcc572dc0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.00e+00  0.00e+00  0.00e+00]\n",
      "[ 0.00e+00  2.00e+00  0.00e+00]\n",
      "[ 0.00e+00  0.00e+00  0.00e+00]\n",
      " [ 0.00e+00]\n",
      "[ 0.00e+00]\n",
      "[ 0.00e+00]\n",
      "\n",
      "[-5.00e+00 -3.00e+00 -1.00e+00]\n",
      "[-6.00e+00 -1.00e+00 -1.00e+00]\n",
      "[ 1.00e+00  2.00e+00  1.00e+00]\n",
      "[ 3.00e+00  1.00e+00  1.00e+00]\n",
      " [-1.00e+00]\n",
      "[-1.00e+00]\n",
      "[-1.00e+00]\n",
      "[-1.00e+00]\n",
      "\n",
      "<class 'cvxopt.base.matrix'>\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.3172e-01  1.4858e+00  7e+00  2e+00  8e+00\n",
      " 1:  1.1909e+00  4.0539e-01  8e-01  4e-16  1e-14\n",
      " 2:  6.0696e-01  5.4828e-01  6e-02  4e-16  6e-15\n",
      " 3:  5.5624e-01  5.5542e-01  8e-04  3e-16  2e-15\n",
      " 4:  5.5556e-01  5.5555e-01  8e-06  2e-16  3e-15\n",
      " 5:  5.5556e-01  5.5556e-01  8e-08  2e-16  5e-15\n",
      "Optimal solution found.\n",
      "\n",
      "\n",
      "w1= 0.6666667267107993; w2=0.33333331547659695; b=-3.3333335297147606\n",
      "w: [0.6666667267107993, 0.33333331547659695]; b: -3.3333335297147606 \n",
      "\n",
      "w.x + b > 0 for positive class\n",
      "w.x + b < 0 for negative class\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [[5.0,3.0,1], [6.0,1.0,1], [1.0,2.0,-1], [3.0, 1.0, -1]]\n",
    "svm_proc(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "582be7d3-af6c-4987-aa29-e9fd4772dc7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.00e+00  0.00e+00  0.00e+00]\n",
      "[ 0.00e+00  2.00e+00  0.00e+00]\n",
      "[ 0.00e+00  0.00e+00  0.00e+00]\n",
      " [ 0.00e+00]\n",
      "[ 0.00e+00]\n",
      "[ 0.00e+00]\n",
      "\n",
      "[-4.00e+00 -3.00e+00 -1.00e+00]\n",
      "[-5.00e+00 -2.00e+00 -1.00e+00]\n",
      "[ 1.00e+00  2.00e+00  1.00e+00]\n",
      "[ 3.00e+00  1.00e+00  1.00e+00]\n",
      " [-1.00e+00]\n",
      "[-1.00e+00]\n",
      "[-1.00e+00]\n",
      "[-1.00e+00]\n",
      "\n",
      "<class 'cvxopt.base.matrix'>\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.3801e-01  7.3342e-01  1e+00  1e+00  2e-15\n",
      " 1:  5.9131e-01  8.4389e-01  4e-02  2e-01  7e-16\n",
      " 2:  8.8373e-01  8.8873e-01  3e-03  5e-03  3e-14\n",
      " 3:  8.8884e-01  8.8889e-01  3e-05  5e-05  2e-15\n",
      " 4:  8.8889e-01  8.8889e-01  3e-07  5e-07  4e-15\n",
      " 5:  8.8889e-01  8.8889e-01  3e-09  5e-09  3e-15\n",
      "Optimal solution found.\n",
      "\n",
      "\n",
      "w1= 0.6666666658975391; w2=0.6666666635704114; b=-3.6666666575609805\n",
      "w: [0.6666666658975391, 0.6666666635704114]; b: -3.6666666575609805 \n",
      "\n",
      "w.x + b > 0 for positive class\n",
      "w.x + b < 0 for negative class\n"
     ]
    },
    {
     "data": {
      "image/png": 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I/YRUOWvnCalCP5HX5z/v/3bnnT5kyBDftWuXu7tXVVV5ly5dvFu3bv7W5MkNcwxZLiePCiNCA34KPBT0NmkMcf9+96Ii9/vue8oHDx7sV199tY8ePdoHDhzo9933Ky8sdN+7N9S7dPeGdeTj7HA5a0eMoYe4ZYv7gAH+owULvF+/fn799df7lVde6VdccYUXmHlJjx5+/O23w71Pj/4sbjcCtwKvmNlLjT/7O3dfH8LYiTFoEDz1FNxyy6cZOPAPFBe/gJmzY8dYfv7zbqxbBx/7WH7uW88zNjN2LDz2GH/z1a/yV6NHs3XECHrX13PtM8+w5qKLmPXGG5RNnx6rV+Dk/Nty92dp2Cpm3tVXw+9/D+vWdeWZZ/4cgHvvhc99Lv+fqaIYmykrg3376LlmDRPOvrLm4YcpHz8eGh9xjtXL4VrbVObzK427plFpvo4k7qZG8bvIx0mMo941lRjRljGYuL1QXCGmkGIMJk4xKsSUUozBxCVGhZhiiYkxYnGIUSGmXBJi7HLXXZFOB6KPUSFmQNxjHHXsGIwfn+ndVIWYEXGOsZ+OGRVilsQ1xt27dzOysjLTMSrEjIljjIemTmXkyJGZfjRVIWZQHGPM+lMbCjGjFGMwnRWjQswwxRhMZ8SoEDNOMQaT7xgVorQY444dXVm5Eo4cgcGDYfZsuOqqTpxUxmJUiAKcH+Nzz0FBwUo++9mdXHrpHzhy5E8ZP/4TTJ8O//iP0GmnxslQjKGexU2Sbf78+VxzTSUHD67hgw8G8vTT5bz1VhXr109m7Ngv8PLLJ/judzt5UnE5O1wT5eXl550drra2NucxFaKc85//CQcOzGfEiKs4evQoxcXFrF69mr1799KnTw+GD7+XH/4wtzOWd0gGYlSIcs4vfgFlZft4991jfP/732ft2rXMmjWLLl26sGzZMn75y5WMG3eSp56KYHIJiLGurq7DY+kYUc45fhx69HibIUOGsGDBArp27XruAZyVK1fSpUsXLrvsOO+/H9FxWsyPGc8G2REKUc4ZPhxeeukTvP7667z55pvnPYBz9OhRLrvsMl55pR/z5kU4yZjGOHr0aA4cONDhMbRrKufMmAHPP9+HW2+dz7Rp03j22WepqKhgzpw5bNq0iW7d+nPqVD3jx0c80Rjupo4YMSKn2ytEOad3b3jwQfiXf/k2Eyf+N+bOnUu/fv3YuXMnEyZ8lT17XqCoaBYffqjP2gibQpTzfO1rsGyZsXHjPM6c+R0lJXW8/fZWTp78CRUVlfzmN/pIuHzQMaJ8xBe/CNOnN3zi9dlX1nzykwDzGTZML4fLB4UoLTKDa6/96M/12tT8UIjSbooxfApROkQxhkshSocpxvAoRMmJYgyHQpScKcbcKUQJhWLMjUKU0CjGjlOIEirF2DEKUUKnGNtPIUpeKMb2UYiSN4oxOIUoeaUYg1GIkneKsW0KUTqFYrywUP4kzOwzwA+AAmCFu38/jHElXRRj63L+UzCzAuB/A38B7Ae2mtmv3P13uY4t6dNSjJGLQYxh/HM0FnjV3V8HMLMq4POAQpQWNY9xXqSnhWsUcYxhhDgIeLPJ9/uBcSGMKynWNMZ33nmHCRMmZHo3NYyVWws/849cyWwuMBegsLCQzZs3h3DXHVdbWxv5HMKQ5HUUFxdTUVHB8uXLmTJlCgsXLqSg0z7hphXDhlF0zz2MrKzk6MSJ1CxaRH337oFumtPvwt1z+gJuADY2+f5+4P4L3aa4uNijVl1dHfUUQpGGdVRUVDjgM2bM8NOnT0c9nQYrVriDe1mZe11doJu09bsAtnkrTYSxRdwKjDCzPwEOAF8GZoUwrmREeXk5w4YNy/SjqTmv1t3PmNntwEYanr74sbvX5DwzyZSsP7URykrdfT2wPoyxJLuyHKNeWSOxktUYFaLEThZjVIgSS1mLUSFKbGUpRoUosZaVGBWixF5iYsyBQpRESEKMXe66q8NDKURJjLjHOGD0aLjppg4NoxAlUWIb4+jRvP3ee3yig0MoREmcWMZ47bWQw7tgFKIkUixjzEFyZy6Zl6YYkzlrkUZpiTF5MxZpJg0xJmu2Iq1IeozJmalIG5IcYzJmKRJQUmOM/wxF2imJMcZ7diIdlLQY4zszkRwlKcZ4zkokJEmJMX4zEglZEmKM12xE8iTuMcZnJiJ5FucY4zELkU4S1xijn4FIJ4tjjApRMiluMSpEyaw4xagQJdPiEqNClMyLQ4wKUYToY1SIIo2ijFEhijQRVYwKUaSZKGJUiCIt6OwYFaJIKzozRoUocgGdFaNCFGlDZ8SoEEUCyHeMClEkoHzGqBBF2iFfMeY0gpn9L+BzwCngNWC2u7+b86xEYiwfMeaa8r8C97v7GTNbCtwPLMhxTJHYaynGXOQUors/3eTb3wJfzGk2IgnSPMZ58+Z1eKwwjxFvA1a3dqGZzQXmAhQWFrI5h485DkNtbW3kcwhDGtaR5DUUFxdTUVHB8uXLGTRoEAUFBR0ax9z9wlcw+zegqIWLvu3u/6fxOt8GxgC3eFsDAmPGjPFt27Z1YLrh2bx5MxMnTox0DmFIwzrSsIZNmzZhZpSWlrZ6HTPb7u5jWrqszS2iu0+50OVm9lXgZmBykAhF0mjSpEk5bdVzfdT0MzQ8ODPB3T/IZSyRLOuS4+3/CegD/KuZvWRmD4cwJ5HMyfVR0+FhTUQky3LdIopICBSiSAwoRJEYUIgiMaAQRWJAIYrEgEIUiYE2X2ualzs1Owzs6/Q7Pl9/4EjEcwhDGtaRhjVA2+v4mLsPaOmCSEKMAzPb1toLcJMkDetIwxogt3Vo11QkBhSiSAxkOcQfRT2BkKRhHWlYA+SwjsweI4rESZa3iCKxkckQzewzZrbHzF41s/uink97mdkQM6s2s11mVmNmd0Y9p44yswIz22FmT0Y9l44ys0vNbK2Z7W78ndzQ7jGytmtqZgXA74G/APYDW4GZ7v67SCfWDmY2EBjo7i+aWR9gO/CXSVrDWWZ2Nw3nO+rr7jdHPZ+OMLPHgH939xVm1h3o2d7z+2ZxizgWeNXdX3f3U0AV8PmI59Qu7v6Wu7/Y+P/HgV3AoGhn1X5mNhiYCqyIei4dZWZ9gfHAIwDufqojJ9nOYoiDgDebfL+fBP4lPsvMhgKjgS0RT6UjHgLuBeojnkcuPg4cBh5t3MVeYWa92jtIFkO0Fn6WyP1zM+sN/AK4y93fj3o+7WFmNwPvuPv2qOeSo67AtcBydx8NnADa/bhDFkPcDwxp8v1g4GBEc+kwM+tGQ4Qr3f2JqOfTATcC08xsLw2HB5PM7J+jnVKH7Af2u/vZPZK1NITZLlkMcSswwsz+pPHA+svAryKeU7uYmdFwTLLL3R+Mej4d4e73u/tgdx9Kw+9gk7t/JeJptZu7HwLeNLOrGn80GWj3g2aZ+1i2xg/MuR3YCBQAP3b3moin1V43ArcCr5jZS40/+zt3Xx/dlDLtDmBl4z/srwOz2ztA5p6+EImjLO6aisSOQhSJAYUoEgMKUSQGFKJIDChEkRhQiCIxoBBFYuD/ARNgsXlV+fiSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [[4.0,3.0,1], [5.0,2.0,1], [1.0,2.0,-1], [3.0, 1.0, -1]]\n",
    "svm_proc(data)"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}