Source_Code.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Assignment (1).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python [default]",
      "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": "3.6.8"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gnitish18/Pattern-Audio-Slides/blob/master/Source_Code.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "GX7ICBnxHmwh",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "DATA1 = np.matrix([[1.08,0.75,0.85,0.94,0.40,1.25,1.19,0.99,0.69,1.32],[0.08,-0.19,-0.11,0.01,-0.09,-0.21,0.07,0.04,-0.02,0.02]])\n",
        "DATA2 = np.matrix([[0.01,-0.01,0.09,-0.05,-0.45,0.07,-0.33,-0.06,-0.33,-0.24],[0.85,1.05,0.93,1.41,1.45,1.20,0.88,1.08,1.10,1.01]])\n",
        "\n",
        "data = np.matrix([[1.08,0.75,0.85,0.94,0.40,1.25,1.19,0.99,0.69,1.32,0.01,-0.01,0.09,-0.05,-0.45,0.07,-0.33,-0.06,-0.33,-0.24],[0.08,-0.19,-0.11,0.01,-0.09,-0.21,0.07,0.04,-0.02,0.02,0.85,1.05,0.93,1.41,1.45,1.20,0.88,1.08,1.10,1.01]])\n",
        "\n",
        "t1 = np.full((10),1)\n",
        "t2 = np.full((10),-1)\n",
        "t = np.append(t1,t2)\n",
        "t = t.reshape(-1, 20)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1IkikNiqHmwo",
        "colab": {}
      },
      "source": [
        "kernel_matrix = np.zeros((20,20))\n",
        "\n",
        "def K(x1,x2,t1,t2):\n",
        "    output = np.matmul(x1.T,x2)\n",
        "    #print(output)\n",
        "    return output*t1*t2\n",
        "\n",
        "def krn(x1,x2):\n",
        "    output = np.matmul(x1.T,x2)\n",
        "    #print(output)\n",
        "    return output[0,0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oLsO6aGAHmwr",
        "colab": {}
      },
      "source": [
        "for i in range(20):\n",
        "    for j in range(20):\n",
        "        kernel_matrix[i,j] = K(data[:,i],data[:,j],t[:, i],t[:, j])\n",
        "kernel_matrix = np.concatenate((kernel_matrix, t), axis=0)\n",
        "print(kernel_matrix.shape)\n",
        "print(kernel_matrix)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fFoqHpyGHmwv",
        "colab": {}
      },
      "source": [
        "b = np.ones((21, 1))\n",
        "b[20] = 0\n",
        "\n",
        "print(b.shape)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LKUzl8gZHmwz",
        "colab": {}
      },
      "source": [
        "x = np.linalg.pinv(kernel_matrix)\n",
        "\n",
        "a = np.matmul(x, b)\n",
        "\n",
        "print(a)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZIpjTngnINY7",
        "colab": {}
      },
      "source": [
        "m1 = []\n",
        "for i in range (20):\n",
        "  if a[i,0] > 0 and a[i,0] < 10:\n",
        "    m1.append(i)\n",
        "\n",
        "m2 = []\n",
        "for i in range (20):\n",
        "  if a[i,0] > 0 and a[i,0] < 10:\n",
        "    m2.append(i)\n",
        "\n",
        "print (m1)\n",
        "print (m2)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eyq_s9i-OHLF",
        "colab": {}
      },
      "source": [
        "w1=w2=0\n",
        "\n",
        "for i in range(len(m2)):\n",
        "  #print (data[0,m2[i]]) \n",
        "  w1 += a[m2[i],0]*t[0,m2[i]]*data[0,m2[i]] \n",
        "  w2 += a[m2[i],0]*t[0,m2[i]]*data[1,m2[i]]\n",
        "w = np.array([[w1],[w2]])\n",
        "print(w)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cVMtnopmQWHp",
        "colab": {}
      },
      "source": [
        "si = sj = 0\n",
        "for i in range(len(m1)):\n",
        "  for j in range(len(m2)):\n",
        "    \n",
        "    sj += a[m2[j],0]*t[0,m2[j]]*krn(data[:,j],data[:,i])\n",
        "  si += (t[0,m1[i]]-sj)\n",
        "  sj = 0\n",
        "b = si/len(m1)\n",
        "print(b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "r2xTKEqDeFXG",
        "colab": {}
      },
      "source": [
        "plt.plot(DATA1[0,:], DATA1[1,:], 'ro')\n",
        "plt.plot(DATA2[0,:], DATA2[1,:], 'bo')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkK4p4VTltSP",
        "colab_type": "code",
        "outputId": "d05e8526-0215-4c01-ae0e-9dffea0f9dbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "a = -w[0] / w[1]\n",
        "xx = np.linspace(-1, 1.5)\n",
        "yy = a * xx - (b) / w[1]\n",
        "yy1 = a * xx - (1+b) / w[1]\n",
        "yy2 = a * xx - (-1+b) / w[1]\n",
        "\n",
        "plt.plot(xx, yy, 'k-')\n",
        "plt.plot(xx, yy1, 'b--')\n",
        "plt.plot(xx, yy2, 'r--')\n",
        "plt.plot(DATA1[0, :], DATA1[1, :], 'mo')\n",
        "plt.plot(DATA2[0, :], DATA2[1, :], 'co')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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M+Ysv4L/+C+bPl5Pl27aFcePkbMsGDaB791O3oyZvJvGwGSqeVBYNe+utt5Kenu75aFgv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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9iXSRCP1e0Tq",
        "colab_type": "text"
      },
      "source": [
        "y = np.zeros((2,20))\n",
        "\n",
        "for i in range(20):\n",
        "  y[0,i] = w1*data[0,i]+b\n",
        "  y[1,i] = w1*data[1,i]+b\n",
        "\n",
        "p1 = []\n",
        "p2 = []\n",
        "\n",
        "for i in range(len(m2)):\n",
        "  p1.append(data[0,m2[i]])\n",
        "  p2.append(data[1,m2[i]])\n",
        "p1=np.matrix(p1)\n",
        "p2=np.matrix(p2)\n",
        "u1=u2=v1=v2=[]\n",
        "for i in range(20):\n",
        "  u1.append(data[0,i])\n",
        "for i in range(20):\n",
        "  u2.append(data[1,i])\n",
        "for i in range(20):\n",
        "  v1.append(y[0,i])\n",
        "for i in range(20):\n",
        "  v2.append(y[1,i])\n",
        "\n",
        "plt.plot(data[0,m2[:]], data[1,m1[:]], 'ro')\n",
        "# plt.plot(u1,v1, 'g-')\n",
        "# plt.plot(u2,v2, 'b-')\n",
        "plt.plot(v1,v2, 'b-')\n",
        "plt.show()\n",
        "for i in range(20):\n",
        "  v1[i]+=1\n",
        "  v2[i]+=1\n",
        "plt.plot(data[0,m2[:]], data[1,m1[:]], 'ro')\n",
        "plt.plot(u1,v1, 'r-')\n",
        "# plt.plot(v1-1,v2-1, 'r-')\n",
        "# plt.plot(y[1,:], data[1,:], 'b-')\n",
        "# plt.plot(p1, y[0,:], 'b-')\n",
        "plt.show()"
      ]
    }
  ]
}