SVM_Task3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SVM_Task3.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMlRFBWf2Q0q2NZH82cg3rW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shyney7/colabAI-lec-py/blob/main/SVM_Task3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZqIhdAyPsYZh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "outputId": "f345a137-8312-479c-e3c6-64e8adab6cd7"
      },
      "source": [
        "!pip install qpsolvers\n",
        "import numpy as np\n",
        "import qpsolvers\n",
        "from qpsolvers import solve_qp, sparse_solvers, dense_solvers\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# import data from csv file\n",
        "M = np.loadtxt('weather_data_not_separable.csv', delimiter=',', skiprows=0)\n",
        "\n",
        "# extract X & Y values\n",
        "X = M[:, :2]\n",
        "Y = M[:, 2]\n",
        "\n",
        "# input- & datasize variables\n",
        "n = np.size(X, 0)\n",
        "m = np.size(X, 1)\n",
        "\n",
        "# plot data points\n",
        "for i in range(n):\n",
        "  if(Y[i] < 0):\n",
        "    plt.plot(X[i,0], X[i,1], 'xb')\n",
        "  else:\n",
        "    plt.plot(X[i,0], X[i,1], 'or')\n",
        "plt.xlabel('x_1: av. wind speed w [bft]')\n",
        "plt.ylabel('x_2: av. solar radiation S [100/m²]')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: qpsolvers in /usr/local/lib/python3.6/dist-packages (1.4)\n",
            "Requirement already satisfied: quadprog in /usr/local/lib/python3.6/dist-packages (from qpsolvers) (0.1.8)\n",
            "Requirement already satisfied: Cython in /usr/local/lib/python3.6/dist-packages (from quadprog->qpsolvers) (0.29.21)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4JCcunINtUww",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "b0511f7b-e0a7-44dd-e6e2-afe40eac7f09"
      },
      "source": [
        "# create cost func. matrix\n",
        "H = np.zeros((m+1, m+1))\n",
        "for i in range(np.size(H,0)-1):\n",
        "  H[i,i] = 1.0\n",
        "\n",
        "# contraint matrix A\n",
        "A = np.zeros((n, m+1))\n",
        "for i in range(n):\n",
        "  for j in range(m):\n",
        "    A[i,j] = -X[i,j] * Y[i]\n",
        "  A[i,m] = -Y[i]\n",
        "\n",
        "# contraint vec. b\n",
        "b = -np.ones((n,1))\n",
        "\n",
        "# cost func. vec.\n",
        "f = np.zeros((m+1, 1))\n",
        "\n",
        "# reshape f & b to fit opt. func. format\n",
        "f = f.reshape((3,))\n",
        "b = b.reshape((100,))\n",
        "\n",
        "# solve QP\n",
        "v = solve_qp(H, f, A, b, solver=sparse_solvers[0])\n",
        "print('v: ', v)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-039b3d3c8915>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;31m# solve QP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolve_qp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mH\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msparse_solvers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'v: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/qpsolvers/__init__.py\u001b[0m in \u001b[0;36msolve_qp\u001b[0;34m(P, q, G, h, A, b, lb, ub, solver, initvals, sym_proj, verbose, **kwargs)\u001b[0m\n\u001b[1;32m    248\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcvxopt_solve_qp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0msolver\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'cvxpy'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcvxpy_solve_qp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0msolver\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'ecos'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mecos_solve_qp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/qpsolvers/cvxpy_.py\u001b[0m in \u001b[0;36mcvxpy_solve_qp\u001b[0;34m(P, q, G, h, A, b, initvals, solver, verbose)\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0mprob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mProblem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconstraints\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0mprob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msolver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m     \u001b[0mx_opt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mx_opt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 1 into shape (3,)"
          ]
        }
      ]
    }
  ]
}