arghyadeep99/svm.ipynb

## svm.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SVM.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "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/gist/arghyadeep99/53e9477b6c00b058b7a12cc9d6129638/svm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "furp1T9bz0kj",
        "colab_type": "text"
      },
      "source": [
        "# Support Vector Machine (SVM)\n",
        "A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.\n",
        "\n",
        "Lets dissect this line by line. \n",
        "First, let us work with linearly separable dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIi605LGJntV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import datasets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LxKFSBiKIYH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iris = datasets.load_iris()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_t_REGhpDmw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_setosa = iris['data'][:50, (2)]\n",
        "y_setosa = iris['data'][:50, (3)]\n",
        "x_versicolor = iris['data'][50:100, (2)]\n",
        "y_versicolor = iris['data'][50:100, (3)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sMFAZUP0Nomt",
        "colab_type": "code",
        "outputId": "32ab70de-e124-47ac-ea46-be7361486290",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.xlabel('petal_length')\n",
        "plt.ylabel('petal_width')\n",
        "plt.plot(x_setosa,y_setosa, 'ro') #plotting setosa flower\n",
        "plt.plot(x_versicolor,y_versicolor, 'go')  #plotting versicolor flower\n",
        "\n",
        "plt.plot([2.5,2.5], [0, 1.75], 'k-', lw=2)  #lw: linewidth, k-: line design\n",
        "plt.plot([0,4], [0.9, 0.25], 'k-', lw=2)\n",
        "plt.plot([3,1.9], [1.5, 0], 'k-', lw=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2fa5c26b00>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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r0rdvX3/hvE2bNh5Geq4ZM2YwdOhQ2rZty6ZNm8os6lvCMPEi4pekVHVbea+q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\nY0xI/j8aWczaNmMN6QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9xgDNVczdwhl",
        "colab_type": "code",
        "outputId": "d7f06e81-1e6c-4fed-c0ae-b4638a7d21ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "X = iris['data'][:100, 2:]\n",
        "y = iris['target'][:100]\n",
        "\n",
        "svm_clf = LinearSVC(C=1, loss='hinge')\n",
        "svm_clf.fit(X,y)\n",
        "y"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFGon9yFlFG4",
        "colab_type": "code",
        "outputId": "7f05bf68-e8e1-474e-a2b3-574184251577",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "svm_clf.predict([[5.5,1.7]])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VIh6dPz0BOTo",
        "colab_type": "text"
      },
      "source": [
        "# Importance of Feature Scaling\n",
        "SVM is very sensitive to your data normalisation. It is esential to eep data normalised. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PANpvK6eGINT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "canc = datasets.load_breast_cancer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PwNUcIh38T8k",
        "colab_type": "code",
        "outputId": "46ce1394-b713-4926-9656-f18dd6b30ee9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(canc.DESCR)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".. _breast_cancer_dataset:\n",
            "\n",
            "Breast cancer wisconsin (diagnostic) dataset\n",
            "--------------------------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 569\n",
            "\n",
            "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
            "\n",
            "    :Attribute Information:\n",
            "        - radius (mean of distances from center to points on the perimeter)\n",
            "        - texture (standard deviation of gray-scale values)\n",
            "        - perimeter\n",
            "        - area\n",
            "        - smoothness (local variation in radius lengths)\n",
            "        - compactness (perimeter^2 / area - 1.0)\n",
            "        - concavity (severity of concave portions of the contour)\n",
            "        - concave points (number of concave portions of the contour)\n",
            "        - symmetry \n",
            "        - fractal dimension (\"coastline approximation\" - 1)\n",
            "\n",
            "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
            "        largest values) of these features were computed for each image,\n",
            "        resulting in 30 features.  For instance, field 3 is Mean Radius, field\n",
            "        13 is Radius SE, field 23 is Worst Radius.\n",
            "\n",
            "        - class:\n",
            "                - WDBC-Malignant\n",
            "                - WDBC-Benign\n",
            "\n",
            "    :Summary Statistics:\n",
            "\n",
            "    ===================================== ====== ======\n",
            "                                           Min    Max\n",
            "    ===================================== ====== ======\n",
            "    radius (mean):                        6.981  28.11\n",
            "    texture (mean):                       9.71   39.28\n",
            "    perimeter (mean):                     43.79  188.5\n",
            "    area (mean):                          143.5  2501.0\n",
            "    smoothness (mean):                    0.053  0.163\n",
            "    compactness (mean):                   0.019  0.345\n",
            "    concavity (mean):                     0.0    0.427\n",
            "    concave points (mean):                0.0    0.201\n",
            "    symmetry (mean):                      0.106  0.304\n",
            "    fractal dimension (mean):             0.05   0.097\n",
            "    radius (standard error):              0.112  2.873\n",
            "    texture (standard error):             0.36   4.885\n",
            "    perimeter (standard error):           0.757  21.98\n",
            "    area (standard error):                6.802  542.2\n",
            "    smoothness (standard error):          0.002  0.031\n",
            "    compactness (standard error):         0.002  0.135\n",
            "    concavity (standard error):           0.0    0.396\n",
            "    concave points (standard error):      0.0    0.053\n",
            "    symmetry (standard error):            0.008  0.079\n",
            "    fractal dimension (standard error):   0.001  0.03\n",
            "    radius (worst):                       7.93   36.04\n",
            "    texture (worst):                      12.02  49.54\n",
            "    perimeter (worst):                    50.41  251.2\n",
            "    area (worst):                         185.2  4254.0\n",
            "    smoothness (worst):                   0.071  0.223\n",
            "    compactness (worst):                  0.027  1.058\n",
            "    concavity (worst):                    0.0    1.252\n",
            "    concave points (worst):               0.0    0.291\n",
            "    symmetry (worst):                     0.156  0.664\n",
            "    fractal dimension (worst):            0.055  0.208\n",
            "    ===================================== ====== ======\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
            "\n",
            "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
            "\n",
            "    :Donor: Nick Street\n",
            "\n",
            "    :Date: November, 1995\n",
            "\n",
            "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
            "https://goo.gl/U2Uwz2\n",
            "\n",
            "Features are computed from a digitized image of a fine needle\n",
            "aspirate (FNA) of a breast mass.  They describe\n",
            "characteristics of the cell nuclei present in the image.\n",
            "\n",
            "Separating plane described above was obtained using\n",
            "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
            "Construction Via Linear Programming.\" Proceedings of the 4th\n",
            "Midwest Artificial Intelligence and Cognitive Science Society,\n",
            "pp. 97-101, 1992], a classification method which uses linear\n",
            "programming to construct a decision tree.  Relevant features\n",
            "were selected using an exhaustive search in the space of 1-4\n",
            "features and 1-3 separating planes.\n",
            "\n",
            "The actual linear program used to obtain the separating plane\n",
            "in the 3-dimensional space is that described in:\n",
            "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
            "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
            "Optimization Methods and Software 1, 1992, 23-34].\n",
            "\n",
            "This database is also available through the UW CS ftp server:\n",
            "\n",
            "ftp ftp.cs.wisc.edu\n",
            "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
            "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
            "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
            "     San Jose, CA, 1993.\n",
            "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
            "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
            "     July-August 1995.\n",
            "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
            "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
            "     163-171.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZvTuLog83Gx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = canc['data']\n",
        "y = canc['target']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3fI8kkB9mjx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, Xtest, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=20)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgRl-Mlm9_EX",
        "colab_type": "code",
        "outputId": "a5871608-f64e-4cd6-8ce0-c3a93c5119cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(X_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(455, 30)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OK0Yhoev-JIX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "svc_model = SVC()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_upwWdVw-Rjm",
        "colab_type": "code",
        "outputId": "c4d39964-446b-4ee6-f67e-ab200c16d7bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "source": [
        "svc_model.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
              "    kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
              "    shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pxZxxhs5-cof",
        "colab_type": "code",
        "outputId": "575f2b30-ae83-4a37-c407-51db3cd2565a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "y_pred = svc_model.predict(Xtest)\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5789473684210527"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Da_ky91z_9JJ",
        "colab_type": "code",
        "outputId": "e9a977da-0f88-42cf-fa68-20c27d110b65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        }
      },
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "model = Pipeline((\n",
        "(\"scaler\", StandardScaler()),\n",
        "(\"svm_clf\", SVC())\n",
        "))\n",
        "model.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('scaler',\n",
              "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
              "                ('svm_clf',\n",
              "                 SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
              "                     decision_function_shape='ovr', degree=3,\n",
              "                     gamma='auto_deprecated', kernel='rbf', max_iter=-1,\n",
              "                     probability=False, random_state=None, shrinking=True,\n",
              "                     tol=0.001, verbose=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ekqK7BCLAlWH",
        "colab_type": "code",
        "outputId": "3cba57ec-8956-4283-80c3-3cdc282654ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "y_pred = model.predict(Xtest)\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9912280701754386"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tg4g8eUKLbDL",
        "colab_type": "text"
      },
      "source": [
        "#Kernel Trick\n",
        "[A video explaining kernel trick in a concise manner.](https://www.youtube.com/watch?v=wBVSbVktLIY)\n",
        "\n",
        "[For a deeper understanding check out this](https://www.youtube.com/watch?v=ffF8UnbheLk&feature=youtu.be)\n",
        "\n",
        "Lets visualise Kernel Trick for a dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rhg21cHLMLLg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import make_moons\n",
        "moon = make_moons(n_samples=100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u5G9WUDNfrMt",
        "colab_type": "code",
        "outputId": "27979795-a20e-41a4-e25c-49a5b50527ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "x1, x2, y1, y2 = list(),list(),list(),list()\n",
        "for i in range(100):\n",
        "  if moon[1][i]==0:\n",
        "    x1.append(moon[0][i][0])\n",
        "    y1.append(moon[0][i][1])\n",
        "  else:\n",
        "    x2.append(moon[0][i][0])\n",
        "    y2.append(moon[0][i][1])\n",
        "  \n",
        "plt.plot(x1,y1,'ro')\n",
        "plt.plot(x2,y2,'go')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2fa5d19080>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ng/2mc05z6TnfvyDuG6VRdB/IdcJX0keAGeALKc/PStovaf/Ro0eXv6NWC06cOHXbiRPt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\n/M3MGsjB38ysgRz8zcwa6P8D0xAR38RdRW0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACoud_9MisxQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "model = SVC() # rbf,gamma,C\n",
        "\n",
        "X = moon[0]\n",
        "y = moon[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dI2S04hgi4uA",
        "colab_type": "code",
        "outputId": "d85869ff-83d2-4a97-dc5d-c9816776c331",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "source": [
        "model.fit(X,y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
              "    kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
              "    shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-0XOHM0jJF9",
        "colab_type": "code",
        "outputId": "926f980c-f363-4aa6-96f3-e0f301304a51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "model.predict([[0.5, -0.4]])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y1Ugtltxg97u",
        "colab_type": "text"
      },
      "source": [
        "# Gaussian Kernel\n",
        "<figure>\n",
        "<center>\n",
        "<img src='https://drive.google.com/uc?id=1hks8Ev1FqYFoGkep9biiSlKjTzSSyGYU'/>\n",
        "  </center>\n",
        "  <figcaption><b></b></figcaption>\n",
        "</figure>\n",
        "\n",
        "# Gaussian Radial Basis Function(RBF) Kernel\n",
        "\n",
        "\n",
        "<figure>\n",
        "<center>\n",
        "<img src='https://drive.google.com/uc?id=1KCnVQyBXnfEd7nKBpI32NhavD1KnktvZ'/>\n",
        "  </center>\n",
        "  <figcaption><b></b></figcaption>\n",
        "</figure>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PKPfFta-k8Md",
        "colab_type": "code",
        "outputId": "5f402b6b-4582-4a63-e17b-d4b7dac35ab6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def make_meshgrid(x, y, h=.02):\n",
        "    x_min, x_max = x.min() - 1, x.max() + 1\n",
        "    y_min, y_max = y.min() - 1, y.max() + 1\n",
        "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
        "    return xx, yy\n",
        "\n",
        "def plot_contours(ax, clf, xx, yy, **params):\n",
        "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "    Z = Z.reshape(xx.shape)\n",
        "    out = ax.contourf(xx, yy, Z, **params)\n",
        "    return out\n",
        "  \n",
        "fig, ax = plt.subplots()\n",
        "X0, X1 = X[:, 0], X[:, 1]\n",
        "xx, yy = make_meshgrid(X0, X1)\n",
        "plot_contours(ax, model, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\n",
        "\n",
        "plt.plot(x1,y1,'ro')\n",
        "plt.plot(x2,y2,'go')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2fa5cb3cc0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sks2bVfX7Jd7nZlVNish8AD8SkTdU1TZtlA0QVpB4/683feDVEu8RdfMAvOd3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\nsU0mOQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hawTq5Ikj-Xj",
        "colab_type": "text"
      },
      "source": [
        "# Task for Students\n",
        "1. Look into SVM for Regression Task\n",
        "2. Explore different Kernel Tricks"
      ]
    }
  ]
}