Assignment_10.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Assignment 10.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ft-abhx/Machine-Learning-Worksheets/blob/main/Assignment_10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGnxUGtRBB-4"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YE82WMz9p2o1"
      },
      "source": [
        "# Topics\n",
        "1.   Support Vector Machine Classification (Implementation Only)\n",
        "2.   Parameter Tuning\n",
        "3. Visualization\n",
        "4. Importance of Preprocessing\n",
        "5. Advantages and Disadvantages of SVM\n",
        "6. Comparison of Different Classifiers\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2m24ctauWE7v"
      },
      "source": [
        "#1.Support Vector Machine Classification (Implementation Only)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLLWtY-T4t2I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bae421b5-6335-4d5b-ced7-27c579616e04"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data[:, :2]\n",
        "y = iris.target\n",
        "svc = svm.SVC().fit(X, y) \n",
        "print(\"Accuracy: {:.3f}\".format(svc.score(X, y)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.820\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RonObtAZIim2"
      },
      "source": [
        "#2.Parameter Tuning\n",
        "Important parameters having higher impact on model performance are **kernel, gamma and C.**\n",
        "\n",
        "**kernel**: we have various options available with kernel like, “linear”, “rbf”,”poly” and others (default value is “rbf”).  “rbf” and “poly” are useful for non-linear hyper-plane.\n",
        "\n",
        "**gamma**: Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. Higher the value of gamma, will try to exact fit the as per training data set, which will results in  generalization error and cause over-fitting problem.\n",
        "\n",
        "**C**: Penalty parameter C of the error term. It also controls the trade-off between smooth decision boundaries and classifying the training points correctly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XEAnm8UqK9C2"
      },
      "source": [
        "##kernel=\"linear\" and C=1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LM8uGQnVKDpJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "078e69df-4a3c-461a-c572-b817597d7dda"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data[:, :2]\n",
        "y = iris.target\n",
        "svc = svm.SVC(kernel=\"linear\").fit(X, y)\n",
        "print(\"Accuracy: {:.3f}\".format(svc.score(X, y)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.820\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lnxdLtk3LGE2"
      },
      "source": [
        "##kernel=\"linear\" and C=100"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T_i5VqdeK6Lw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d96fc13-b20f-4f30-e76c-f525cb5cb57d"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data[:, :2]\n",
        "y = iris.target\n",
        "svc = svm.SVC(kernel=\"linear\",C=100).fit(X, y)\n",
        "print(\"Accuracy: {:.3f}\".format(svc.score(X, y)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.820\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GPUEIeTiLIQP"
      },
      "source": [
        "##kernel=\"linear\" and C=1000"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nhkYYxoVK3TY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72de33b2-9fe1-46e7-c320-c0b481657386"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data[:, :2]\n",
        "y = iris.target\n",
        "svc = svm.SVC(kernel=\"linear\",C=1000).fit(X, y)\n",
        "print(\"Accuracy: {:.3f}\".format(svc.score(X, y)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.820\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_UWt-NqyMIMv"
      },
      "source": [
        "##kernel=\"rbf\", C=1, gamma=1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ecMT6pegLY1x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a5d31ff-9b2b-40e0-a102-7aabee7ef988"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data[:, :2]\n",
        "y = iris.target\n",
        "svc = svm.SVC(kernel=\"rbf\").fit(X, y)\n",
        "print(\"Accuracy: {:.3f}\".format(svc.score(X, y)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.820\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OyK4AYlaMR7W"
      },
      "source": [
        "##kernel=\"rbf\", C=100, gamma=1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VaQWS3GaLY9j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d346a04-17d9-4e77-b3b9-94ec7b179427"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data[:, :2]\n",
        "y = iris.target\n",
        "svc = svm.SVC(kernel=\"rbf\",C=100).fit(X, y)\n",
        "print(\"Accuracy: {:.3f}\".format(svc.score(X, y)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.833\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "43kOMvvKMWfX"
      },
      "source": [
        "##kernel=\"rbf\", C=1000, gamma=1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w1abVy6PLZAV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0561af0b-61c0-4c3c-a850-c65469f11a4a"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data[:, :2]\n",
        "y = iris.target\n",
        "svc = svm.SVC(kernel=\"rbf\",C=1000).fit(X, y)\n",
        "print(\"Accuracy: {:.3f}\".format(svc.score(X, y)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.820\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uz7JVpyVMYbA"
      },
      "source": [
        "##kernel=\"rbf\", C=1, gamma=100"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXOLycMxLZDJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71b331b2-7c81-493d-920d-5e9319d7e9ff"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data[:, :2]\n",
        "y = iris.target\n",
        "svc = svm.SVC(kernel=\"rbf\",gamma=100).fit(X, y)\n",
        "print(\"Accuracy: {:.3f}\".format(svc.score(X, y)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.920\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dRS6NjeeMbMA"
      },
      "source": [
        "##kernel=\"rbf\", C=100, gamma=1000"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJx8xh0ILt7R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e4240cb-f686-4b4f-99c1-2b43f4aff772"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data[:, :2]\n",
        "y = iris.target\n",
        "svc = svm.SVC(kernel=\"rbf\",gamma=1000).fit(X, y)\n",
        "print(\"Accuracy: {:.3f}\".format(svc.score(X, y)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.927\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VFzsxhi5MwfC"
      },
      "source": [
        "#3.Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZv-VfqJNET6"
      },
      "source": [
        "##kernel=\"linear\" and C=1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VNhDVzZlLt_-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "ea6ac528-0f26-4e10-bc9f-c373fff1d37d"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "# Select 2 features / variable for the 2D plot that we are going to create.\n",
        "X = iris.data[:, :2]  # we only take the first two features.\n",
        "y = iris.target\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 = clf.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",
        "model = svm.SVC(kernel='linear')\n",
        "clf = model.fit(X, y)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "# title for the plots\n",
        "title = ('Decision surface of linear SVC, C=1 ')\n",
        "# Set-up grid for plotting.\n",
        "X0, X1 = X[:, 0], X[:, 1]\n",
        "xx, yy = make_meshgrid(X0, X1)\n",
        "\n",
        "plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\n",
        "ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')\n",
        "ax.set_xlabel('Sepal Length')\n",
        "ax.set_ylabel('Sepal Width')\n",
        "ax.set_xticks(())\n",
        "ax.set_yticks(())\n",
        "ax.set_title(title)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lKV71AcjyPFEjSLEWVbJ32xw2r/wfbbq9hF9gQ5J2v8t3k27nD39x37hyUEgMfUc86bZ4FbE9OMBWqGX4Q9QUMsEoqmT35hk0aHIjEbG9CAxuTMtOT5N+/qi306q0J8ekEvbsLSikUIuaRXrWokp8/YLJzDxX8r05P6XW3gp1vGECJ5/dLTsTRY0kxVpUycDRzzP1ves4sO0N/IMSOJ00k449bvV2Wi4bb5jAyZWy6kPUXFKs67Efpj7E8UNr0dqKj28wf/jzTwSHNXApRnhUE37/53ks/fElCnK303/U3yt9K9SDuxewcsFbFJqzadZmCENvfBmTT/Us05NVH563+MhR3t2wmZyiIgY2SuS5vr3xNzkuQa62r+tkzLqeWrPoLY4fWku7nm/QbdAUAkNa8NUH11cqVkR0M265fyp3Pj670oX6zIltLJ7zAo3b/oUOvT8k+cxplv74SqViuaJtt0TuWf0AIKs+PGlncgqvrVrLowUR/McSz+ljyby5Zr3b2tcHUqzrqX3bf6RBk5sJi+qCX0AczTv+DXNBdsVv9JCj+1cQk3BtST5N2j1B0r6lHj3nTWPC5V4f1WT1yVMMs4ZwlQokVvnwkI5mxYmTbmtfH0ixrqd8fAPJzzlR8n1B7hmU8t6Pg59/COb8s5fk4+sX7LHzvdJlmaz6qEbBvj6cM1hKvj+rCwnxcXwzLlfb1wf1dwConht1+7tMn3gL+za/iH9QAmePzaVp635ey6d999vYvv4bDm5/DV//BqSc+oURY1/3yLmKt5DLqo/qc1OrVsz4bR9vFZwl1mpiiSGLF3v1dVv7+kBprd0eNC6xk77rCXm6SE23cfnHrP11AlpbiYxpwbjHZuPrF+Sw/d7tP7BqwX8w2ycAh9/8Or5+QQ6Pu6ogP5PftnxPQX4mzVoPpEGjLlX5eGWSVR/ek2U288PBQ2SZzfRPTOCqmBi3tq8LOn4+ZYvWukdZr1VYrJVSY4F/A7GAsn9prXWoo/dIsa75Th3bzNypj9Gm22v4BzbkyJ73iYqJ4Nrfve1S+0697nApjjcVbyGXHrWoqcor1s4MUv4HuEFrHaa1DtVah5RXqEXtcOzgKmITRxES0Q4fv3CatH2EYwdXutze1Tje0rZbotzrQ9RqzhTrc1rrvR7PRFQr/4Bw8nMvzq7nZZ/Azz/M5fauxvGGJ8ekyqoPUes5nGC0D38AbFZKfQf8ABQUv661nu3h3OodrTVKKY/FKX28Y4/b2LXxO/ZveQHfgAZcOP0r197+X4cxHbVPaNrTpTjV7ZUuy0h69mt5cEAZ3PXzJqqHwzFrpdQX5bxPa63vd/SijFm7Zvem71j5y38oNGfTtPVgRt32VqVuMeoojqPj5oIc9u2Yizk/iyat+hPTsH258R21dzWOO900JhyAeauMFGVcuOS14slEGaO+1Oz9B5iwcQs5RUX0j4/n9cH9CfaVJ9jXBFWdYOyrtV5T0bHSpFg772TSen765i+07fEv/AMbcOS39wkN92XMuPfcEqdLr3FuiV/TmMKiGNPfUtIz3HsqiH1bLw7HyKqPsm06c5Z/LF7GS7ohcfgwSaXgmxDOf4YO8nZqgqpPMH7g5DFRCccPryU6YSRBoc0xmgJp1Pp+Tiatc1scd8WvSdp2S2RMfwu+JkV0CPgYoW18NqawKMBWyOVeH2XbcOYMQ6zBNFV+BCgDd+lINp45W/EbhdeVN2bdB7gGiFFKlb7zeyggfwvcJDA4ivzs7SXjh7lZR/APiHBbHHfFrynadkukbXw2viZFWKDtWFggZOQqWrYIABJLJhNl6ONKkf7+7DAUlvw8HNNmwn39vJ2WcEJ5Oxh9gWB7m9IDqJlA7bsHZg3Vofvv2LVpFvs2P4NfQAMunF3JmDtd/8XFUZz4xt3ZtWkWezY+jV9ALGnJay+Jb7VaKSrKx9c38JJ4Wmus1iKMxqpt8XVXHCgen84mJvTKSbGwQBinvyLpA5lMLM/NrVvxw76DvJxzhljtw1pDNm/3HeTttIQTHBZrrfUKYIVS6kut9bFqzKle8fENYNxjMzmwez7m/Cwa3fQnomJbujVOWGQiSXt/RWsr/oFRhEc2AeCnb57g8G+L0NpKQHAM4x6dSVhkI7atncLqhW9hsRTQqHlfRo97D/8A15fjuSsOXJxILKtQFyvesSWF2rEAk4mpN1zHoqPHyDabeTS+IS3Cw72dlnBCeatBfgIczj5qrW9w9JpMMNYcm1Z8woZlk7iq74f4BcRxeNc75GbupkvvO8s8PuKWN1jw3T9od/Vb+AXEceS39wkKsnDD3R+5dN5jh1a7JU7picRoJxbImB8fKwVb1FqVnWB8G/gvcATIAybbv7KBw+5OUnjGkf1LiWt8PQFBiRgMPjRufR+52SkOj586spGo+OElxxNb/p5TRze6fF53xLl8ItEZcW+95biHIUQtVtEwCEqp/15W6X9SSm32eGbCLYJC4jh3Zk/JhFJ2xkGMRr9yjseQl7XlkuOBwdGVOG/V4pQ1keiMtKAWJA7oyKmVu6V3LeoUZ9ZZ7wVGa62T7N83A+Zrrds5eo8Mg7jOainCYjHj4+tcZSoy52M2Z1dYAPNzM/ns7UH4+sXiFxhPWvI6Box6mnZdb+Wztwfh4xuDX0AD0s9vZMCop7nq6ruYMXkc+flW/PxjyDi/hRvunkhi894u5VNUWMCMyeMoKND4BzQgLXmDU3HAufHpiuQ+ehOFykBIQ+cKdpHVitliIfCyeyY7Oi6EJ5Q3DOLM/az/BixXSiVhm79pAjzixvzqvc0rJ7P213fQWtMgsQs33D2RgKBIh+3nTHmIYweWAeDrH8Edj35LZEyLMtv6B4bSIKEzxw+vIDtjPz6+oTRpPRD/wFC697uPDUs/IDc7iajYNrTtchMmHz9atB/O+iXvkqX3ENOwI1FxrcvN31E+v3t4Okl7l2AuyCKx2bOERzUpN07x+DRUrVB/8ekHfLhsHVarlc4xUfx30EAi/B0/y/GLnbv5cOs2tIZOUVFMGDGECH9/h8eF8IYKN8VorX8BWgF/Af4MtNFaL/R0YvXF0QMr2LJ6Cl0HTqX3qPlgTGTh9885bL9h6QecOrqN7kOm0+e6RUTE9mPWZ46fe7hh6QecObmLHkO/45rRS4hqOIRZn93D0QMr2LH+O7oNnkafaxfiG9iWhd8/x9EDK9i+dhrdBn1N72t/wejXvNL5mEx+tL7qOjr2uN3pQq2UqlKhXrX8V779ejKLFi9m1+7dtBs8lFc2bnHYfvXJU3yzfTefqCbMNLQgPq2Il1ascXhcCG9xWKyVUkPs/x0LjAZa2L9Gl7rJk6ii08e2ENlwKH4BsShlJL7Z7Zw5vtVh+2OHVhHX6LqS9okt7yQv54LL7R2d19P5lKUyE4mObN+6kRtvuIH4+HiMRiMPPfoo28863qG3PTmZgdYgYpQPRqUYSzg7klMcHhfCW8obBhkILAXKeuS1BuSue24QHNqA3MwNaG1FKQNZaXsICol13D4snjMnd17S3mhyvAPNUXtH5/V0Ppcrb6NLZcTGNWTpL3OwWq0YDAa2b99ObIDjoYvYwEA2GcxYtcagFPt0PjEB/g6PC+Et5a2z7gLs0JV47pdMMDqvqKiA7z+7h5zsLPwC4si4sJ2b7vkf8Y27ldm+ID+Lz94ajNEUil9AAzIubGXw9S/R6epx5bY3GIPx9Y8hM3UnQ254mfbdxvL9Z/eQnZWFf0AcGam288bGd/BoPqW5YyLxcuaCAh659xbycrNIiE9g44b1vNOmGV0iwspcHWK2WHh4/kKy03OIUT7ssuby4ahhtI+K4uH5C8lKzyEaE7/pPD4cNYwusY7/4aqM9Px80vILaBIagsEgz6+u7yp11z378rzmwBZgLbAGWKe1zqrohFKsXWOxFHLs4CrM+VkkNO1JSHj597Qwm3PZvHIS+TlptOt6Mw0bdy23/ZpF77B55Scog4nQiEbc9uDXBIXEsGbxBDav+BilDIRHN+eW+6cQFBLj8Xxc3ejiqsLCQtauWkpWVibde/bh6oyNJH3wtcOlfIVWK2tPnSbbbKZbXBwNg23Pj3xg/kI2nT2LAgKNJqZcfx2tI913X5UHf/6FTefOYUQRYDTyhZvji9qn0rdIVUoFAldju6HTNUBP4CywRmv9uKP3SbGuOZL2LeHXOa/SvtcEfPwiOb5vEkZDMl363Fnm8bH3febRfDxdqB2J/fZFl9Zef7BlG9/v3MsEQyPCMfIZF1jvm8+v425zSz4fbNnG9zv2MMHY2Bbfep71Pnn8evftbokvaqdK3yJVa52rtV4OvAdMAD4CgoBR7k5SeMbZEzuIbDAYX/8olFI0aDqWc6d2ODzuSe6cSHRV4fX3oAFLymmn2m8+e5ZhKoQIZUIpxU2EkZaX77Z8Np05yzAVejG+IZw0c0HFbxT1VnmrQe5USn2olFoN/AgMB3YB/bTWzasrQVE1IeHxZGfsRluLAMhM3UlIWLzD455y05hw2sbbJhJd2ZHoLsU7G7FYnGofHxzMDvKx2H/z3K3zCDC5787ACSHB7CD3kvj+BrnzsHCsvNUgk4D9wCfASq31gepJqW7Iz8uksCCb4NAGKCcmjhy1z0g7SW5WCnHxV2EwObOH6VLtu93CgV0L2LnmEfwCYsjJOMDN931BTMN2HNi1gF1rH8EvII7s9H3cfF95T3KrPE9MJBbLzEgnJyebuAbxFU7QJd/xKnrlWCwppyu81/ULffsw5tRsHi04RozRl/3WHF7sd03J66eysjmfl0eH6ChMpc6bWWAmp7CQuKBADOU83/CFvn0YfWIWj5iPEqtM7Nf5vDjgmgrjuHrcEVfbC+8r729/ONAZ21j1y0qpNsAZYB22ical1ZBfraO1ZvXCt9i+bgpGUwDBIbGMve9zgsMauNQ+MCSW7ybdzrlTOzAYfDEYjNz24FfEJXZyKR+DwURMw3acPLKB/LxzhIQ0JDgkFqPRh5vv/ZyTRzZgzs+iYeNuBIXEuOMSlPDk+LTWmvfeeo2vp0wiKDCI6JhYJn4+g7gG5RfhFn+6m8MffF1hwQ40mfjld2P5Zu8+UvPzeal5c9pERWK1Wrl3/kJ2Jafgi8JgNDD5upF0iI7ivU1bmLZnHwHKQHRQIB9fO5y4oCCH8RfecesV8bXWZcaJDQx0ePz97TuZtncfQf7+RPn58dGg/g7P6yi+o/ai5qjw3iAlDZWKA24D/go001o7/J2tPk8wHtqziGU//Yv2vSZg8gnjxMEvwHKUWx+Y4lL7uMQO7No4h879P8HkE8ax/Z+RemYxj45f79F83KX4Rkyemkj8deE83n/7FaZPm0ZERATvvvceO3bvY9KXsyp8r6uTjaVN2LyFebsO8I6hEaEY+IpUVvjk8s++vXlnxTre0AmEYmAaqZyI9mHS6JEuxV9y7HiZcX7XsZ3D4x8cSmL6zJm26/DOO+xY+AsfDxrgUnxX8xSeUakJRqVUJ6XUo0qpqUqpQ8AmoB+25y/28kyqtV/yqd1ExA7AxzccpRRxja4n5cxvLrc/c3QzMYkjSo43bHIjBfkZHs/HHUrfMc9TE4l7d+9g1MiRREZGopRi3B13sHe3cxOkrk42lrb9XDKDCSZMGVFKcR2hpOcXsOf8Ba6xBJYcH0Uoe1NTXY7vKI6j43svXGDU9ddfvA533cXelPMuxxc1X3mDfF8C7YEFwBCtdWOt9R1a6/e01nKLVAfCIhuRlb4Dq7UQgPTzmwkNT3S5fXh0U9JTNl48nrIZk0+Ax/OpquqaSExo1IQNGzZiNpsBWL1mDQmNyr//SLG0oBa0+NPdTk82ltY4NIStKo9C+2+k28glwGQiMSSY3caCkuPbdS4JwcEux3cUx9HxhOBgNqxeffE6rF5NQmioy/FFzef0MIgr6vMwiNVSxI/THif59H78AmLIyz7OLfd/SUzD9uW2P3dqH37+0eTnnuSW+78kIqo5n78zlEJzIb7+0eRmHeHa296idacxAORkpWAuyCI0otElzzdMObOXnOxk4pv0xNc30OV8qsKTE4mXKyoq4skn7uHwgb00aNCAQ4cPM+nLWbRtf5XTMSrzVJn8oiJumPkD+XkFhBlMnLIW8Mag/gxr2vmwfksAACAASURBVIQnFy/lwLkLRBt8OImZT64dQdsox3dPBFh/6jSns7MZ1qwpob6+FFmtZcZpGRHOk4uXsutMMsHKSLqyMHn0KFpGhPP31Ws5nJdPXFwshw8d5uPBAx2e11H8ivIU1aPSm2Iqqz4XawBttXLm5HbM+VnEJXYiINDxrjStNct+epXdm2dgMvnjFxDKbQ9+RWhEItaiInZvnUle9nladxpDRHQztNYsn/cav22ZhY9vCL5+Adxy/5cEh8XzzUc3ceHcAYw+gWhrIWPv+4KEpj1cyqcyvLXRxWq1snPbZrKyMrmqczfCI1wrOI32zubwB19jcqFYa63519oNzDlwkEBlJMDfl8/GjCI+OBir1uxMSSHbXEjH6CjCy7mdqsViYcTM2aTl5hGojORj5c1B/RnerGmZcSwWCyO+nUVaQQGBGGztBw9w2L48rrYX1UeKdQ12YNd8Vi6YYJ8ADOHkoa+xFOzltoe+dql9bHxb9mxfSOd+H2PyCeHEwakkn5jHY89v8Gj+lX2iS03h6mTjoiNH+XDVRt7Q8QQrIzN0KgeiDPxvzLUunfeJRUs4eCqZdwyNCFZGvtWpzCWDdffc6bD9gZPnmGBsbGtvucBc0ll3390unVfUbJXewSg87/zZfYTH9MXkY+uSxiSM5Py5fS63P3dyJ9HxQ0qOxyZei7kg06O51/ZCDa5PNu5PTaOXJYBgZVsMNYQQDqSlu3zeA2lpDCC4JM4wQjBbihy3T01loAq52N4QillbXT6vqL3KWw3yk1LqR0df1ZlkXRYe1YTM1K1YLbatxmnJ6wmLdDxR5qh9RGwL0s6tKzmemrwOHx/PVVBv70h0F1cnGxuHhrLDVFBSKDeTQ6MQ18d+EkOC2aRyS+JsIhefcnYwJoaGsFHnXGxvzcEkfa16pbxNMW9XWxa1WEbqCcwFWUREt8Dk41fh8cu17XITSfuWs33lvfgFRGPOT+aWB6ZW2H7L8rvw9QnFYsnm1ge/IjyqKccODmfzktvx8YskP/c0o8e953I+zihrIvHkiaNkZ2XRrHkr/JwYA920YQ3nU5Lp238woWHh1RanrPYn2o1F8zWWsycqHA4Z06IZq44d57HTJ4gymDivLHw6cETJ6wuSkjiTncP1LVsSE3hx9c7JrCyyzYU0CwvDz2Rk4rAhDP9uNvcUJhGiTJzXhTzVq0e57YdNn8l9liOEY+QshTzZq8zfli9xeZyKjruLt85bl1X4dHNRNq01v855ngO7F+DrF45SFm59YCphkY3LPO7osVYGg5HR494n5cxezPmZxMR3wM+/vJ6a4sK5g1gKcynEQFFhNrnZ54mKbUWj5tewf+ePFBWmExAUSXRcG4d5VvSYrbKUNZGotebV559k8YIfiYyKpNBcxKSps2ncpFmZMaxWK7eO7s+J40cJCwvjpcxM3v/0G3r16e9ynLFDe3HiyGFCDCaylZUPv5pNrz79eeXvf2TRvDmE+fhhDQpg8oyfSWzUpMz2va+xbR5xdmej0WDgraGD2J+aRpbZTLuoSIJ9fbFYLAyZNoPcokKCMTJx8zZeGnANY1q04LVNm/n1+Ekiw8Mw5+TwyeCBJAYHExPoz8mMQgrRGJWBlhHhtuu5ai2LjxwjzGDC6mNg0nUjaBwaysq77+CL3Xs4l5PDuHZtaR4R7jBPR3EahYQ4jO8O3jpvfVDhzSaUUq2AN7GtuS7potT3mzkd2DmP44e30HXQNEymQE4lfcfC75+lS687yzx++8PTHcZSShEb79xSuhU/v0peXj49hs3CZArk5OFv+Xn6Xxly/QucPLKt5HhV8imLo/HpBT/NZvf2TSxbuoTg4GAm/+9/vPzcn/n8m5/KjPPmK89SaM5n7Zo1tvaTJ/PsXx/iH8+/4XKcvGPHmWJsTqAy8L1O4x8P382zb0xg24Kf+bQonkCLgdn56bz454do1aVbme1X7j4O2HrXiQO2c2rl7gqvhVLqiqVujy1eSkCR5mNjC1t8Syqvr1qP0WDkN3Mhy1auLPm8r3w7nRaBgeRn5V+SzzNLV/GPa65m+9FTfEoTArWB2flpvLR8NV/ccB1Go5EHOzu3NHHBkaNlxrmtYzuH8d3BW+etD5wZ9PoC+BgoAgYDU4GylyrUIxeSDxIW3RuTyVa5ohsOITX5oMPj7pJyeg9RDQeWxI+JH4q5IMuj+ZQ3kXj44D4GDx5EsH1jxfVjxnDogOMJ0n17dnHttddebH/99WRlZVYizk766iACle1HeCDBZOVkc+jgXnrkG0uOD9BBHDp8wGH70qqys/FIejr9VMjF+IYQCrWVpPR0Bo8YecnnPZyaxv7U1CvzMZs5nJZOD0vAxfxVMIczXN+56iiOu+LXtPPWB84U6wCt9RJsy/yOaa1fxvYA3XotMqYFGRc2YrHY7nF84ewqIqKbOzzuLtEN2pJ6dtXF+GdW4OMb5LF8KppIbNayNStXriIvLw+AhYsW0axFK4fxWrftwKJFi0ra//LLLwQHh1QiTkfWqVzy7RNua3UOwYFBNG/Zhq3+1pLj61QuzZq2cNi+tKrsbGwSFsY6nX0xvjUbHxTNwsJY+euvFz/XL7/QLCKcVhERrFM5l+bj40Pz8DC2mvIv5q+zaVaJoQJHcdwVv6adtz5w5p6bBUopA3BQKfVH4BRQ7/entul0PUcPrmbb8rvx84/Easnh1genEh7ZlKMHVrNt+V34+IWjrXnc9uBXbjvvoNEvcuzQtWz69VZ8fMMoLEjlht9/TJMW/R3nU8bxihSPT0P5OxKvu/4W1q9ZzpChQ4mKjiYzM4vJU+c4bP/cS/9i7LV9uaZvXyIiIkhJOc+7H0/lmv6DWbd6GQMGDiQ0NJQCcyGffT233Dg3r1jCvSeOEKpMpFHEexNncE3/waz9dSEPL5pPmNGH/ABfPnt/MomNm3Lz8iXcc/IIYaXag22c9fCh/eRkZZHbdiQm+2SjIS6RpPQMsgsLaRURTqCPT0n7y49PHDGUYV9/xz2WJEIxkkoR4/v25trmzVifnMKg/v2JjIggOy2NT4cOIjEkhLGnz3JvZhKhBhNpuoh3Bw+hT0I8606c4pFjx4gw+JBj0kwe5PpNlq5t3qzMOI1DQx3GL+tzVcd5hXOcKdZ/AQKBPwOvAUOAezyZVG2gDAZG3vof0i8coSA/i6jY1vj4BqCtVqzWIrTWaGsRWltx58Yjg8nEPX9byPHDq8jJTKFZmyEEBtvGT8vKp7zjjriyI9FgMPDavz/k2JHDZGVl0rJ1WwICHK/lMxgMtO/YhfPnkyksLCIkNJSExMaAbdLQZPIhICAQszmz3OtmMpmYu3wLa1ctIyXlHAOHjCAyMhqr1Yr2MaFCAlGhYei8fLTWGAwGOvS4mgvZ6RAQSKjVSkJiY6xWK88//QTr1iwjJjqG1LR0fnr5UdTsX3n+l0WsS0kl0mAiy6D5dPRImoSG8sK6jaxPSSYmMorUlBQmDR1Es7Awlt19O3MPHeZ0Tg63tG5FQ/uuRotVo9EUFRVh1RoNmAwGfhh7A+tOnyElL4+BjRKJsK9aeW1gP45lZpJlLqRlRDgBlbiPuUEph3HKOm7VmvHLVrHu5KlLPm+zsDCPnlc4r8KrpbXeBGDvXf/ZmQfm1hdKqSuGFPbt/JGzJw/SbfA3GI3+nDk6h4WznmXcYzPddl6DwUDTVgOdyqe842WpzEYXpRRNm7d0qu3PP87iWNJ+Vq9cSUBAAFO/+oqXnvszt915H0cP7WPpkl8vOT51xgKHsQwGA/0GDr0i/tHD+1ixbNkV8R2d98ihvSxZvLjk+COT5/FAsxAOJmfwCY3xtxqYV5TOS8tX87uO7ThqMLBk+Qpb+6lTeeWLL/hy+BCMRiNj27S+JJ/5SUc4ZjSyYtXqK9obDAb6JiaUfT1dLJJlcRSnrOPzk45w8NQ5PtGXft6pN7o+4unKeYXzKhyzVkr1UErtAnYCu5RSO5RS3T2fWu2UlpJEaFQPjEZbLykyrh/pF454OSvnVMdGl6OHD9K/fz8CAmy9+xHDh3M06ZDD456O7+j4/vBGdLUG4G+fEOutgjiamcnRjAz6Dxlysf2IERxNd7yD0dX23nIkPYOuRf5XfF5Rczgzwfg58LjWuqnWuinwBLYVIqIMUXGtST+/jqLCHADOn15CVGzrCt7lfdV1x7yWrduxdOkysrJsv6D9+NNPtGzTzuFxT8d3dLxRx75s9jOTq23j9ivIpmV4OC3Cw1m6cOHF9nPn0jLS8Q2kXG3vLS0jwtlkzLvi84qaw5lBI4vWelXxN1rr1UopxzcxqOdaXzWaE0kb2Lb8Tnz8wjEaKXdHordV9x3zRo25mc0b1tBvwACCg0PwMfnwv2lzSWzUhE0bVtOvf3+CgoLw8fXls2m2NdZaa/bs3kF2VibtOnS6ZKdiWfG3bFzLoCFDiIiIxGrVfDp1ti3++tX06dsXf/8AAgMC+Xz6TyQ2auIwn40rl3HfrGkEKiN+fj5MHjiKxJBgNqek0KdPb/x9/QhUiv8NH3oxzwup5BSaaRsZRaifL6OaNWXrhQsMGtCfiNAwrPn5fDL4yiGs0sqK42mjmjVl86kzPJRkm7BVvkYmDbw48eiOfLzxueoSZ4r1CqXUJGA6oIHbgeVKqW4AWuutHsyv1lFKMeym1+g16DEK8rMIj26KyVS17d2e4o1bm1qtVlLPnCa4SBOZmU+KKY/8vFwKCwtZu2opAQEBREZGcvToMfbv3UV8QiP+/vDd7Fq3hkijL8lGK5O/m0erNmVvIlJK8fxrb/PAY38lOyuTJk1b4Ovnh9lsZu3qpQQHBRETE8PRI0dL4peVj9VqJT31PKF+AUQUWUkpKiLfUkShxcLqw8fwzy8iIh9OY2Z/WirxwUE8s2Ydv2VlExsdzcm1G/hkyEBaRUQwvmcP7m/XjuxCM01CQ/E1Ot5mbbFaHcbxJKUUL/S/hge7dr4kT4vVytNLlrPrTDJRBh/OqSI+vW6Ey/m4K0595kyx7mz/70uXHe+KrXgPcWtGdURIeDzVeGtnl3nrjnk/zfmOk+vW87G5AT7KwML8TF78yyO06tyVkOAgfpn/M35+fnz77be88s+/kZ2Vxcl16/moINbWXtvaT/9lVbnnaRh/6dNw/u+Fv5cb//J8bn/wMU6uW8/Ewoa245YMXl6+mpYxUfjnW/jQ2AwfZeAXSzqvLF9DzjVXk+IfwMIf5l6M/8nHfD1imC2f4CCg4ofSzjucVG4cT7s8z3mHkzh1JpWJuhE+VgMLte06TLv5epfiuitOfVbhmLXWenA5X1KoayFv3jHv+LEkOuUb8LFPZHUngBMnj3Ek6SCDBw3Cz8/2W8igQYPIzc1x2N5Vrsa/4rgK5ERWFkfSM+ipgkqO9zAEkWsp4kRWFn0GDLgk/skM1yfo3BXHXY5nZtHJ6lfq+gRyIju7gnd5Lk595sxqkDil1GdKqQX279srpR7wfGrCE6rz0Vtladv+Ktb7F5GtLbabTBlyaNOmPZ26dOenefPIyMhAa813M2YQGhbusL2rXI1/xXGVRevgIDrFxrBCZ5UcX2TNINTkQ5vISBbPn18Sf+aMGbSOinI5T3fFcZe2UZGsN+SVfN7FZNK6EkMX7opTnzkzDPIlttUf4+3fHwC+Az7zUE7CA1wZn7ZarWzbsoHs7Cw6de5ORKT7isXwUTewbd0a7v/mSwKNJkKio5n8/mQaNExg6+b19LnmGgIDA7FYLHz8xSy6dOvJ5jUr+cO0L/BVBkLDo5j6/mSX83/quVcdxt++fi0PTP+SYJMvwVHRTHp/Mg3jE6/Ic87vh6K3H2HLqTP8Pi2JAAxYFUwaOYJOsTHsuJDKgH79CA0KIsigmDhogMvXZ1iTxm6J4y7DmjRme6tzPLD/AMEGE8H+fnwyaJDX4tRnFT7WSym1SWvdUym1TWvd1X5su9a6i6P3yGO9apbi8WlnCnVRURF/efRujh89RIO4hhw4sJ+Pv5hJ+46dy3+jk4rjHzm8n8iIKI4fP8YnX84qiX/40H5Sks/RpdvV+Pv7k56ezvCuLQjUiihMHKGAsX94gBdeLft26xXlf3n8YqkXzpOdnUV8QiNMJhNFRUX8+Z7bOLRtM2HKhzOqiB/f/Bv+P6/gqc272J+WRYgycp4iPh09knb23m9qXj7ZhWbig4MxGSr/cAB3xXGXuvq5apryHuvlTM86RykVhW0yEaVUb0Bul1VLuDqROHf2dHIy0/j5p5/w8fFh9pw5vPr8k3z7wxK35FMc/5f588uM36JlG1q0bFPS/o6bBpOgffiPsREmpVhiyWDS1M8dFuuK8r88frHIqGgio6IviZO6bTsfFjSwnVdn8uj7M3moU0NS0/L4hCaYUCyxZvLKijV8O/YGW5wAfyIDqv4AWnfFcZe6+rlqE2f+aXsS+BFooZRag+0WqX/yaFbCLSozkXj6xHGuvronPvab+PTp3Zszp064LSdX46emnKebCsSkbGPsnQ2BWHD826C78j914hgd8g0Xz0sAZ86e4kh0Uzpa/S8eVwGcycl1Ob4QrnJmNchWYCBwDfAI0EFrvdPTiYmqqexEYodOXVnwyy9cuHABrTXTvvmG9le5ZwikMvFbtm3HUp1JhrbdHGueNR2Tcvxj6678O3bqxhr/opLzzjdk0759J1r2GMo6v4KLx8mgXVTN25Eo6h6HwyBKqZ7ACa31Wa11kf1+ILcAx5RSL2utU6stS+G0qm50GTL8On7buZVBgwfj7x9Aw4REPvrft5XKxWKxsGHtSrKyMunWoxcxsQ3KjV9W+2++X0TfDo24JycJHxSgeOXdSZXKv6z45cXZ/Yf7ePCzifgZTDRMbMTH739KbFxDdt/7EA9Oeh8/ZaBhSDAfDepXqetT31isVjacOUu22UzXuFhiAmvxk5a9wOEEo1JqKzBMa52qlBoAfItt+KML0E5rfaujoDLB6B3u3OiSnZVJbm4O0TFxGCoxEWQ2m3nigdtJvXCO+IbxbN++nY8++45OXbqXGd9R+7btr+KJB27nzKnjREZGcPhwEh9/MbMkjrP5m81mHrvzRpL37iPW4MM+ax4fT/vB5TjFTOd2cPS5fxITGIBP7JV3zhOXKrRYeHzBYpLTMolRPuzX+UwcNYyrYmK8nVqNUtkJRmOp3vPtwKda6++B75VS292dpKgad+9IDA4JJTik8k/ymPv9dLTVzNw5czAajfw0bx7/9+JTzPhxeZnxHbW/bdx9aKuZXxbMLzOOs/nP/X46+Xv2805BNEalWGE18OpTjzNryYZKXYeiuM50G9HdqWc2Cph76DD5qTm8oxPt1z+T11auZcYtN3o7tVqjvC6TUSlVXMyHAktLvSZ3Da9BvLkj0ZGzp0/SrWtXjPb7YPTo3p1zZxw/29BRe1fjOHLm9Ana5iuM9onBDiqAc8lnXY5TWvIdr1b6mY31zZnsHNpafS+9/rl5Xs6qdimvWE/HdhOnuUAesApAKdUSWbpXY3h7R6Ijnbr1ZN68nzl37hxWq5Uvvvyy3CEHR+1djeNI525Xs8q/kAva9rSWucZsOnXqVpWPCECLP92NrsQzG+ubzrExrDTkXLz+ZHBVjPd2ZtZGDnvIWuvXlVJLgIbAIn1xcNuALN3zurImEgsLC1m5bBE52Vn06NWX+IRG5cZwtb0rBg4ewd7dOxg8ZAgGo5GWLdsw8fMZDs87cPAI9uzazsBBg1AGA61bt+PjL2YSFR3D/j27GDJ0KEaTiTZtO/DeJ19XKp99j/yRhz94C6NStG7Zlg/e+7Tc9+Tm5jLpw7fISE/jxlvupGv3q69oc6LdWBIHbOfUyt0YG7h2/QqtVladOEl2YSE9G8TRMLjuPtp0QKNE9l3Vjod37MSIolV4OO8PlIlZV1S4g7EyZILRs8oanzYXFPDAbdeRe/gIscrEDmsuH06dRbcevcuMYS4o4KE/3ExhQR4JCQmsW7+O9ydNc9jeVcXxC/JyaNAgjs1btvD+pGl0vKprmefteFXXkvxjMLJT512Sv7mggLz8PMLKuZe1s3k5Eyc7K5NRvTsQlldInMGXbZZsnnntLe64+8rb4jTaO5vDH3yNMhoxxsQ7l4fFwkM/LyQ3I5dYTOzQubw/chjd4mIr9blqC7PFQl5REWF+NfO2wd5W1R2MogaxDXtcOZE4e+Y01MGj/NschUEp1lqNvPb3PzJn+eYy48yeOY0APx+++epLDAYDCxcu5PWXnub7n8u/9aizLsafeUn828bdV+Z5bxt332X5my7J39fPD183/AV3Ns7L458kMd/K/6lGGFCsVVm8++r4Mov1iXZjafEnSPrA+R7/nIOHUOn5/FvH2z9vFq+vWsv3t97k0uepbXyNxnLv5y0ck835tUjp8enLJxJTzp2hZYHt6dIArZU/5y+kOIyVknyWTp2uKlmO1qlTJ84nn3Nbro7iOzzuYv6edu7MKdppv0vyMReaHbbPbtzZNtl41rndkim5ubS0+lz6efPyq5y3qLukWNcCprAobhoTjlLK4URit559WOFnJlkXYtGaWcYsuna7coy1pH2P3vwwdy6nTp3CYrHwyaRP6dq9l9tydhTf4XEX8/e0/oOGs0hnluTzHelERTleE5wW1IK4t95yOn7XuFhWGHIufl7S6RIra46FYzIMUsM5uyOx74Ah3PnHv/HoO29gtVrp2OYqJpaz06/vgCHcfe9jjBg5EovVSo+effjvh+57DnLfAUO49Y57GTpsGFpr2rbvyKdTZhMeEVnmecMjIvnDX//BY2+9itVqpVuHHkwoJ39Pe/iJJ9m1dRMPLVmABiJDwvhq5vxy35MW1KKkd13RZGPfhAR+3/UqHtuyFavWdIuJ4b8D+7rvA4g6RyYYazBXNrrk5+dx37jr8fMxEh/fkBUrV/LOh1/S65ry74VssVgoLDTj7x/gxswhPT2V0YN70KhRIomJiSxbtow/PTmeex/6Y7nn9VQ+lWU2m8nNzSY83Ln7f0TkHObc0087PdlosVoptFrxN0m/ScgEY61UPJHo7Prp2TO+JjI8lMmfTkIpxZIlS/j3a88xe8Gact9nNBoxGt1fGF8d/yTt27djypdfluTzj2eeKSnWjs7rqXwqy9fXF19f52/UlBbUgsQBHZ3e2Wg0GDDKfZ2FE+SnpAaqzEaXC+dT6NChPco+YdWuXTsunPfeBF1KSjJdunS5JJ+CggKv5VOdku94lYQBHWVno3ArKdY1iDMTiY707N2X2bPnkJSUREFBAe9/8CE9e3tv08GQ4dcyffr0knwmTJhAbJzju9zVNX6du6AtFinYwm1kGKQaFZrzOLBrHgX52TRu2ZfouNYlr1X11qa9rxnIQ088xdhbbiU/P49+A4by5jufAJCXl8uCn2aTk51F736DaNW6nbs+kkP3PfQndu/YynWjR2OxWIiNjeOrmb94/LzuVJXrVnpnoxDuID3ralJozuXbT25ly5rZHNizk+8m3c7RAysA20TimP4WfE2VK9TFbr/rftbvPMbWfWf56LNvCQ0LJzc3h9/fNorFP8/i+OHfuPeO0axe4Z5HdJUnNzeHY0cP07t3H2677XcUmAs4knTI4+d1F3dcN7/OXeRGT8JtpGddTXZvngGGaNp0fQWlFBExfVg273XmLr0RVyYSK6KUKrlDHdgmHhPjGzDxo49QSjF0yGDefP15+g0c6pbzOXL5eUeOGF4t53UXd1y3yuxsFMIR6VlXk7ycVPyDmpRMuAWGNMNaaLt5oSfvmJeWeoFWrVqVnLdVq1akp3v+IT/eOq+7uCt/V3c2CuGIFOtq0rhlX86fWkh2xkGKCrM5deh/dO890OO3Nu3TdyCzvv+ePXv2kJWVxdv/fYc+fQd69JzePK+7uCt/V3c2CuGIFOtqktisFwOufZpD28ezdemtNG/uz3/++47Hz9ujV1/+8vcXuf+BB+ndpw8FRfDCa/+ts+cFSD53hqf+eB8P/eEW5s2dWWH7nOwspn/1PyZPnMCe3TsA9+ZfemejEJUlOxirkbsfvSWudPbMKW4Y0I32Vl8a4sMiSzr3/vkp/vTk+DLbZ2dlctetI2netDGJiYn88MNcXvvPhwwaOsrtuZkfH+vSbVRF/SM7GGsAV3ckisp54+VnuMrqy3jVEICehkD+PfE9h8V6zsxptGrRnA/efw+AgQMH8uprL3mkWCcO6MhJWconKkmGQapBTX30Vl2UnpZKI+1T8n288qGwnMduZWSk07Rpk5LvmzVtSmamZ55al3zHqyTKzkZRSVKsPagqOxJF5Yy+8VZ+tmawV+eRpov4lPMkJDq+A17fAUOYOXMWW7duJSUlhTfe/JdHlxfKzkZRWTIM4iEyPu0dt991P4cP7uOlqZ9TZLWQkNCIqXMWO2zftXsvnn3xTf721N/Jyspi8NCR/PPl/3gsP1l7LSpLJhg9QAq1KE/JMxvB5YfsirpNJhirSfHYtEwkivJI71pUhoxZu8HlY9NSqEVFZGejcJUU6ypy102YRP0iOxuFq2QYpApqy9h06oXzzJj2BdnZmQwYPIKr+/T3dkoC157ZKIT0rCvppjHhtI23jU3X5EKdnpbKuJuHcvbkQSKCffjHXx/kZye2YIvq4TtxttxGVThFetYuqupDAqrbnFnT6NG9G2++8QYAV/fqxbPPjWf0jbd5OTNRrHhnoyXltGxFFw5Jz9oFta1QA+Tl5hIbG1vyfVxsLLm5OV7MSFyueGejEOWRYu2k2jqROGjoSGbOnMWyZcs4ePAgL7z4EsNGXu/ttMRlZGejqIgMgzihNt+EqX3HLrz5ziTe/e//kZWVyaCho/jbMy97Oy1xGVl7LSoixboCdeEmTP0HDaP/oGHeTkM4QVaHCEdkGMQBuQmTqG4n2o2VtdfCISnWZaiNE4mi7pCdjaIsUqwvU1snEkXdIDsbhSMyZl1KbZ5IFHWH7GwUZZGe7F7awAAABchJREFUtV1dmEgUdYfvxNkkyFNlRCn1vljLRKKoyWTttShWr4t18fi0TCSKmkh2NorS6m2xLn3HPCnUoqYq2dkoq0PqvXpZrGvLHfOEsO1svNvbaYgaoN4Va5lIFLWRrL0W9aZYy0SiqK1kZ6OAerLOurY80UWI8sja6/qtzvespVCLuiAtqAW+E2d7Ow3hRXW6Zy07EkVdo4GisycwSe+63qmzPWuZSBR1ke/E2STKzsZ6qc4Va5lIFPWB7Gysf+pUsZY75on6IPmOV2XtdT1UZ8asi8enZSJR1BfFOxtldUj9UCd61qXHp6VQi/pAdjbWP7W6WMv4tKjPsht3lp2N9UitLdby6C1R38lTZeqXWjlmLRtdhLhIdjbWD7WuZy13zBPiItnZWH/UqmItG12EKFvCgI4UnT0ha6/rsFpRrGUiUYjyyVNl6r4aX6xlIlEI58nOxrqrRhdr2ZEohPNkZ2PdVmNXg8gd84SoHNnZWDfVyJ61TCQKUTmys7HuqlHFWiYShag62dlYN9WYYi3j00K4h+xsrJtqRLGWHYlCuFdaUAvpXdcxXi/WsiNRCM/wnTgb7e0khNt4tVjLRKIQnpUoOxvrDK8Ua5lIFKJ6yM7GuqPai7VMJApRvfw6d5GdjXVAtRZrmUgUovqVrL22WLydiqiCatvBKDsShfAuue917VYtPWuZSBTCu2RnY+3n0WItE4lC1Byys7F281ixlolEIWoW2dlYu3mkWEeEGWUiUQgh3MhjPWvZkShEzZMW1IKEAR1lKKQW8kixNhk9EVUI4Q7Jd7xqG7uWdde1itfvDSKEqH6JAzrKRplaRoq1EPWQbEOvfZTW7r8vl1IqBTjm9sBCCFG3NdFax5T1gkeKtRBCCPeSYRAhhKgFpFgLIUQtIMVaCCFqASnWotoppcYrpX5TSu1USm1XSvVyc/xBSql5zh5343nDlVKPV9f5RP1SbbdIFQJAKdUHGAN001oXKKWiAV8vp+Uu4cDjwERvJyLqHulZi+rWEDivtS4A0Fqf11qfBlBKdVdKrVBKbVFKLVRKNbQfX66Ues/eC9+tlLrafvxqpdQ6pdQ2pdRapVSbyiSklBphj7NVKTVTKRVsP35UKfWK/fgupVRb+/EYpdRi+28H/1NKHbP/o/MvoIU9z+I7JgUrpWYppfYppaYppeT2k6JSpFiL6rYIaKSUOqCUmqiUGgiglPIBPgBu1Vp3Bz4HXi/1vkCtdRdsPdfP7cf2Af211l2BF4E3XE3GXmSfB4ZprbsBm4EnSzU5bz/+MfB3+7GXgKVa6w7ALKCx/fizwGGtdRet9dP2Y12BvwLtgeZAX1dzFAJkGERUM611tlKqO9AfGAx8p5R6FluR7Agstnc+jcCZUm+dbn//SqVUqFIqHAgBpiilWmF7EIpPJVLqja2QrrGf1xdYV+r12fb/bgHG2v/cj/9v535WKYqiOI5/f0oGlAxMZSCUiak/M2Wu/MmIkjfwAGQgXoA3UJ5AGbgDIin/lflVJpJMkNsyOFvdgb83Lie/T93uOfvsvVqTu9p37TowlPJZl3TzTvy9iCgCSDoEWoGtCvK0f87F2qouIkpAAShIOgEmyIrhWUT0vLXslft5YDMihiS1pphfJWAjIsbfeP6QvktU9nt5KLuuNIaZ2yBWXZI60k74RTfZqwkugOZ0AImkWkldZfPG0ng/cBsRt0AjcJmeT1aY0i7QJ6ktxa+X1P7Bmm1gNM0fBJrS+B3Zbt/s27lYW7U1kLUuziUdk7UgZiPiERgGFiUdAYdAb9m6e0kHwAowlcaWgIU0/tkd64Ck4ssHaCMr9Kspnx2g84MYc8CgpFNgBLgC7iLimqydclp2wGj2LfxuEPvzJBWAmYjY/+1cACTVAaWIeEr/BJbT4afZj3H/zOzrWoA1STXAIzD9y/nYP+CdtZlZDrhnbWaWAy7WZmY54GJtZpYDLtZmZjngYm1mlgPP0qLmnBklBuUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7LnolRgGNhid"
      },
      "source": [
        "##kernel=\"linear\" and C=100"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBixo_KWM1vZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "c1b23876-4f2e-447a-a2fd-d0549769f206"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "# Select 2 features / variable for the 2D plot that we are going to create.\n",
        "X = iris.data[:, :2]  # we only take the first two features.\n",
        "y = iris.target\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 = clf.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",
        "model = svm.SVC(kernel='linear',C=100)\n",
        "clf = model.fit(X, y)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "# title for the plots\n",
        "title = ('Decision surface of linear SVC, C=100')\n",
        "# Set-up grid for plotting.\n",
        "X0, X1 = X[:, 0], X[:, 1]\n",
        "xx, yy = make_meshgrid(X0, X1)\n",
        "\n",
        "plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\n",
        "ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')\n",
        "ax.set_xlabel('Sepal Length')\n",
        "ax.set_ylabel('Sepal Width')\n",
        "ax.set_xticks(())\n",
        "ax.set_yticks(())\n",
        "ax.set_title(title)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RWR+ippJiLS7jHxjJnY/NZ2v8F+Rkn2HQLa/RtPWgKs3p2KG1rFn8LuacDJq0HkDPIU9jNHq4FENmfYiaToq1uEpAUDR9hr9Q1WkAcO7UHn765nGatH0aL996HN7/KVbL2/Qb+bLTMWxLyEcBUqhFzSV91qJaS9i7lMi4YYRF98I/qDlN2j7NgV2/OL2/LCEXtYVcWYtqzcPThzzzxcLvzeYUTCZvp/Z9reMKEp+bIYOJolaQYi2qtTadbmXr2q9I3P0eXj71OHN0Hr1v/IfT+0uhFrWFFGtRbut+m8TWtV9hteQTHN6Y2x/8Bi/vALfE9vUP467HfmD7+q/JzUmj8+3v0rB5r1L3Gxc/gcR46foQtYcUa1Eue7fNZ/Pq/9Gy8yt4+UaTuPs9vptyO/f8xfl+5dL4BUTQY/BTTrUtmPUBUqhF7SIDjKJcdm+eTb2GNxMS2RVf/wY0a/8MqeePVEkuo0YES6EWtZYUa1Eunl7+5GafLfzenJNUJbdClVkforaTbhBRLn2Gv8j092/kwLa38PaL5VTiHNp1ua1Scyic9WE0YoyIqdRjC1FZpFjXYT9Mf4Bjh9ahtRUPT3/uefIn/IPquRQjOKwhf35yIct/fIXcrO30Gvp3Ot0wrkz5HNy9iNWL3iHPnEHjlv0ZcPOrmDxKn6Zn3rndNutDCnW1tvTwEd7buJnM/Hz61I/j+R7d8DY5LkGutq/tlNba7UGj4trrux6rXXdqq23WLnmHLfHTaH3dW3j7RpOwayLZGQk88uKmKsnn9PFtzJ/2AM07voS3bzRH9nxEVEwsg299u8T9xsVPAKTro7rbeS6Jx39dyjM6iig8mKrOE9Mwktf69HRL+9qi3RdfbdFadynuNemzrqP2bf+Reg1vISisI14+UTRp9zfMuRml71hBjuxfRUTssMJ8GrZ+jMR9yx22b9U5Tgp1DRJ/4iQDrQFco3yJVB48oMNZdfyE29rXBVKs6ygPT19yMo8Xfp+bdRqlqu7Hwcs7AHPOmcvy8fTyL7atPOGl5vH39OCswVL4/RmdR4CH45txudq+Lqi7HUB13NDb32PW5FvZt/llvP1iOXN0AY1aVN1HzDbXjmH7hm84uP0NPL3rkXTyVwaPfrPEfaRQ1xyjmjdn9h/7eCf3DJFWE8sM6bzctYfb2tcFUqzrqMjo1nQf8ATrfpuE1lZCI5oydMzEEvfZu/0H1iz6D2b7AOCgW97E08vP4XZXePsEctfj8/ljy/fk5qTRa9D/qFe/Y3neoqhGAr08mXXLSH44eIh0s5mP42K5JiLCbe3rglIHGJVSo4F/A5HYbrWgAK21DnS0jwwwVn8nj25mwfRHaNn5Dbx9ozm85wPCIkIY9qd3XWrfvusdLsVxh4JuELnvh6htyjvA+B/gJq11kNY6UGsdUFKhFjXD0YNriIwbSkBIazy8gmnY6iGOHlztcntX45RXq85xtIrJIPzf71TYMYSojpwp1me11nsrPBNRqbx9gsnJujS6np1xHC/vIJfbuxqnPEaNCKZVTAYRgYq0gKbE9m6H5czx0ncUohZw2Gdt7/4A2KyU+g74AcgteF1rPa+Cc6tztNYopSosTtHt7bqMYdfv37F/y0t4+tTjwqnfGHb7fx3GdNQ+ttF1LsUpq1EjggGICLz0vs7d8Tp69WgsSadkQUwZuOvnTVQOh33WSqkvS9hPa63vc/Si9Fm7Zvem71j963/IM2fQqEU/ho55p0y3GHUUx9F2c24m+3YswJyTTsPmvYiIblNifEftXY3jClNQGCN6WVBKEV7MKam/dx4JH8pSc1fM23+ASb9vITM/n14xMbzZrxf+nrXvCfY1UUl91s4MMPbQWq8tbVtRUqyddyJxAz998xdadfkX3r71OPzHBwQGezJi7PtuidOx61i3xK8KBYXa06QI8nXcLvLblzm5ercMNjph0+kz/GPpCl7R0UThwRSVhGdsMP8Z0LeqUxOUf4DxQye3iTI4lrCO8Ngh+AU2wWjypX6L+ziRuN5tcdwVvyo0a+qDUiUXagCvDh3RgCXpVKXkVZNtPH2a/lZ/GikvfJSBu3Qov58+U/qOosqV1GfdHbgBiFBKFb3zeyBQ+ffArKV8/cPIydhe2H+YlX4Yb58Qt8VxV/zKVjDrw8NYep/q8dajafoEJH44oxIyq9lCvb3ZYcgr/Hk4qs0Ee3pVdVrCCSUtivEE/O1tivYWpgGVew/MWqzttX9i16a57Nv8LF4+9bhwZjUj7nT9g4ujODENrmXXprns+f0ZvHwiSTm37rL4VquV/PwcPD0vv3zVWmO15mM0lm+Jb1ni2AYTMy4bTCxNRoMOaGZgOXNcukNKcEuL5vyw7yCvZp4mUnuwzpDBuz36VnVawgnO9Fk31FofdSWo9Fm7Jj8vhwO7f8Gck079pj0Ii2zm1jg/zniExL2/obUVb98w7nz0e4JC6/PTN4+R8McStLbi4x/B2IfnEBRan23rviJ+8TtYLLnUb9KD4WPfx9vH9el4ZYlT3KwPZ9XfO4/ED2dIsS5FTn4+S44cJcNspmtMNE2Dg6s6JWFXpgFGpdRPgMNKrrW+ydFrUqyrj02rPmXjiilc0+MjvHyiSNg1kay03XTsdmex2wff+haLvvsHra9/By+fKA7/8QF+fhZuuvtjl4579FC8S3FKm/XhLPOjo2Vlo6ixyjrA+C7wX+AwkA1MtX9lAAnuTlJUjMP7lxPVYCQ+fnEYDB40aDGerIwkh9tPHv6dsJhBhdvjmv2Zk0d+d/m4rsQpOuujPIUaIOqddxxfYQhRgznss9ZarwJQSv33ikr/k1Jqc4VnJtzCLyCKs6f3FA4oZVw8iNHoVcL2CLLTt1y23dc/vAzHdT6ObdZHZqmzPpyR4teUuN7tZCqfqHWc6bPeCwzXWifav28M/KK1bu1oH+kGcZ3Vko/FYsbD07mKlW/OwWzOKLWQ5mSl8fm7ffH0isTLN4aUc+vpPfQZWne6jc/f7YuHZwRePvVIPf87vYc+wzXX38XsqWPJybHi5R3BxfNbuOnuycQ16eZSPvl5ucyeOpbcXI23Tz1Szm0sNk7BrI/S5lK7KuvhUeQpAwHRzhXsfKsVs8WC7xX3THa0XYiKUFI3iDO3SP0bsFIplYjtjnsNgYfcmF+dt3n1VNb9NhGtNfXiOnLT3ZPx8Qt12H7+Vw9w9MAKADy9Q7jj4W8JjWhabFtv30DqxXbgWMIqMi7ux8MzkIYt+uDtG8i1PcezcfmHZGUkEhbZklYdR2Hy8KJpm0FsWPYe6XoPEdHtCItqUWL+jvL504OzSNy7DHNuOnGNnyM4rOFl+5Vl1oczvvzsQz5asR6r1UqHiDD+27cPId6On+X45c7dfLR1G1pD+7AwJg3uT4i3t8PtQlSFUhfFaK1/BZoDfwGeBFpqrRdXdGJ1xZEDq9gS/xWd+kyn29BfwBjH4u+fd9h+4/IPOXlkG9f2n0X3G5cQEtmTuZ87fkDtxuUfcvrELroM+I4bhi8jLLo/cz8fx5EDq9ix4Ts695tJ92GL8fRtxeLvn+fIgVVsXzeTzn1n0G3Yrxi9mpQ5H5PJixbX3Ei7LrdfVqhNQWGMGhGMUsrthXrNyt/4dsZUlixdyq7du2ndbwCv/b7FYfv4Eyf5ZvtuPlUNmWNoSkxKPq+sWutwuxBVxWGxVkr1t/93NDAcaGr/Gl7kJk+inE4d3UJo9AC8fCJRykhM49s5fWyrw/ZHD60hqv6Nhe3jmt1JduYFl9s7Om5F5+OuWR+ObN/6OzffdBMxMTEYjUYeePhhtp9xvEJv+7lz9LH6EaE8MCrFaILZcS7J4XYhqkpJV9Z97P8dWczXiArOq87wD6xHVto+tLYCkJ6yB7+ASMftg2JIS9l5WXujyfEKNEftHR23IvNx56wPRyKjotmxYydWqy2f7du3E+njuOsi0teXgwYzVvvYzT6dQ4SPt8PtQlSVkuZZdwR26NJGIIshA4zOy8/P5fvPx5GZkY6XTxQXL2xn1Lj/EdOgc7Htc3PS+fydfhhNgXj51OPiha30G/kK7a8fW2J7g9EfT+8I0pJ30v+mV2nTeTTffz6OjPR0vH2iuJhsO25kTNsKy6dV5zhax2ZWWKEGMOfm8tC9t5KdlU5sTCy/b9zAxJaN6RgSVOzsELPFwoO/LCYjNZMI5cEuaxYfDR1Im7AwHvxlMempmYRj4g+dzUdDB9Ix0vEfrrJIzckhJSeXhoEBGAzy/Oq6rqyLYjYDTYAtwDpgLbBea51e2gGlWLvGYsnj6ME1mHPSiW10HQHBJd/q02zOYvPqKeRkptC60y1EN+hUYvu1SyayefWnKIOJwJD6jLl/Bn4BEaxdOonNqz5BKQPB4U249b6v8AuIqJB8yrMy0VV5eXmsW7Oc9PQ0rr2uO9df/L3ElY15VivrTp4iw2ymc1QU0f6250dO+GUxm86cQQG+RhNfjbyRFqHuu6/K/T//yqazZzGi8DEa+dLN8UXNU+ZbpCqlfIHrsd3Q6QbgOuAMsFZr/aij/aRYVx+J+5bx2/zXadN1Eh5eoRzbNwWj4Rwdu99Z7PbR4z93ew6VWagdcfU2qh9u2cb3O/cyyVCfYIx8zgU2eObw29gxbsnnwy3b+H7HHiYZG9jiW8+zwSOb3+6+3S3xRc1U5lukaq2ztNYrgfeBScDHgB8w1N1Jiopx5vgOQuv1w9M7DKUU9RqN5uzJHQ63u1NFzvpwVd7IcS7dRnXzmTMMVAGEKBNKKUYRREp2jtvy2XT6DANV4KX4hmBSzLml7yjqrJJmg9yplPpIKRUP/AgMAnYBPbXWTSorQVE+AcExZFzcjbbmA5CWvJOAoBiH292lomd9uKpgZSMWi1PtY/z92UEOFvsnz906Gx+T++4MHBvgzw6yLovvbZA7DwvHSloUMwXYD3wKrNZaH6iclGqHnOw08nIz8A+sh3Ji4MhR+4spJ8hKTyIq5hoMJmfWMF2uTedbObBrETvXPoSXTwSZFw9wy/gviYhuzYFdi9i17iG8fKLISN3HLeNLepKb85x9wos7pF1MJTMzg6h6MaUO0LnyzMaXenRnxMl5PJx7lAijJ/utmbzc84bC10+mZ3A+O5u24WGYihw3LddMZl4eUX6+GEp4vuFLPboz/PhcHjIfIVKZ2K9zeLn3DaXGcXW7I662F1WvpN/+YKADtr7qV5VSLYHTwHpsA43LKyG/GkdrTfzid9i+/iuMJh/8AyIZPf4L/IPqudTeNyCS76bcztmTOzAYPDEYjIy5/2ui4tq7lI/BYCIiujUnDm8kJ/ssAQHR+AdEYjR6cMu9X3Di8EbMOelEN+iMX0CEO06BW+/14YjWmvffeYMZX03Bz9eP8IhIJn8xm6h6JRfhpk/cTcKHM0ot2L4mE7/+aTTf7N1Hck4OrzRpQsuwUKxWK/f+sphd55LwRGEwGph64xDahofx/qYtzNyzDx9lINzPl0+GDSLKz89h/MV33HZVfK11sXEifX0dbv9g+05m7t2Hn7c3YV5efNy3l8PjOorvqL2oPkq9N0hhQ6WigDHAX4HGWmuHn9nq8gDjoT1LWPHTv2jTdRImjyCOH/wSLEe4bcJXLrWPimvLrt/n06HXp5g8gji6/3OSTy/l4Rc2VGg+5VVZg4m/LV7IB+++xqyZMwkJCeG9999nx+59TJk2t9R9y/PMxkmbt7Bw1wEmGuoTiIGvSWaVRxb/7NGNiavW85aOJRADM0nmeLgHU4YPcSn+sqPHio3zp3atHW7/8FAis+bMsZ2HiRPZsfhXPunb26X4ruYpKkaZBhiVUu2VUg8rpaYrpQ4Bm4Ce2J6/2LViUq35zp3cTUhkbzw8bQNrUfVHknT6D5fbnz6ymYi4wYXboxveTG7OxQrPpzwqc9bH3t07GDpkCKGhoSilGHvHHezd7dwAqauDjUVtP3uOfvgTpIwopbiRQFJzctlz/gI3WHwLtw8lkL3JyS7HdxTH0fa9Fy4wdOTIS+fhrrvYm3Te5fii+iupk28a0AZYBPTXWjfQWt+htX5fay23SHUgKLQ+6ak7sFrzAEg9v5nA4DiX2weHNyI16fdL25M2Y/LwqfB8yqIqZn3E1m/Ixo2/YzabAYhfu5bY+kJl7bEAACAASURBVA1L2csmxa8pTZ+42+nBxqIaBAawVWWTZ/9Euo0sfEwm4gL82W3MLdy+XWcR6+/vcnxHcRxtj/X3Z2N8/KXzEB9PbGCgy/FF9ed0N4gr6nI3iNWSz48zH+Xcqf14+USQnXGMW++bRkR0mxLbnz25Dy/vcHKyTnDrfdMICWvCFxMHkGfOw9M7nKz0wwwb8w4t2ttW+memJ2HOTScwpP5lzzdMOr2XzIxzxDS8Dk9PX5fzcVVF3eK0NPn5+Tz12DgSDuylXr16HEpIYMq0ubRqc43TMcryVJmc/HxumvMDOdm5BBlMnLTm8lbfXgxs1JCnli7nwNkLhBs8OIGZT4cNplWY47snAmw4eYpTGRkMbNyIQE9P8q3WYuM0CwnmqaXL2XX6HP7KSKqyMHX4UJqFBPP3+HUkZOcQFRVJwqEEPunXx+FxHcUvLU9ROcq8KKas6nKxBtBWK6dPbMeck05UXHt8fB2vStNas+Kn19m9eTYmkzdePoGMuf9rAkPisObns3vrHLIzztOi/QhCwhujtWblwjf4Y8tcPDwD8PTy4db7puEfFMM3H4/iwtkDGD180dY8Ro//kthGXVzKxxWVOeujOFarlZ3bNpOensY1HToTHOJawam/dx4JH87A5EKx1lrzr3UbmX/gIL7KiI+3J5+PGEqMvz9WrdmZlESGOY924WEEl3A7VYvFwuA580jJysZXGcnBytt9ezGocaNi41gsFgZ/O5eU3Fx8Mdja9+vtsH1JXG0vKo8U62rswK5fWL1okn0AMIATh2Zgyd3LmAdmuNQ+MqYVe7YvpkPPTzB5BHD84HTOHV/IIy9urLDcK+NeHxXN1cHGJYeP8NGa33lLx+CvjMzWyRwIM/C/EcNcOu5jS5Zx8OQ5Jhrq46+MfKuTWcBF1o+702H7AyfOMsnYwNbecoEFpLJ+/N0uHVdUb2VewSgq3vkz+wiO6IHJw1bxImKHcP7sPpfbnz2xk/CY/oXbI+OGYc5Nq7C8R40IplVMRo0u1OD6YOP+5BS6WnzwV7bJUP0J4EBKqsvHPZCSQm/8C+MMJACzJd9x++Rk+qiAS+0NgZjtdzoUdUNJs0F+Ukr96OirMpOszYLDGpKWvBWrxbbUOOXcBoJCHQ+UOWofEtmUlLPrC7cnn1uPh0fF9E1Uh3t9uIurg40NAgPZYcotLJSbyaR+gOt/seIC/NmksgrjbCILjxJWMMYFBvC7zrzU3pqJSa616pSSFsW8W2lZ1GAXk49jzk0nJLwpJg+vUrdfqVXHUSTuW8n21ffi5ROOOecct06YXmr7LSvvwtMjEIslg9vu/5rgsEYcPTiIzctux8MrlJysUwwf+77L+ZSkpCXkJ44fISM9ncZNmuPlRB/opo1rOZ90jh69+hEYFFxpcYprf7z1aDQzsJw5Xmp3yIimjVlz9BiPnDpOmMHEeWXhsz6DC19flJjI6YxMRjZrRoTvpdk7J9LTyTDn0TgoCC+TkckD+zPou3mMy0skQJk4r/N4umuXEtsPnDWH8ZbDBGPkDHk81bXYT8uXuTJOadvdpaqOW5uV+nRzUTytNb/Nf5EDuxfh6RWMUhZumzCdoNAGxW6/8vmDBQwGI8PHfkDS6b2Yc9KIiGmLl3dJV2qKC2cPYsnLIg8D+XkZZGWcJyyyOfWb3MD+nT+Sn5eKj18o4VEtHebpKB9HHM360Frz+otPsXTRj4SGhZJnzmfK9Hk0aNi42DhWq5Xbhvfi+LEjBAUF8UpaGh989g1du/dyOc7oAV05fjiBAIOJDGXlo6/n0bV7L177++MsWTifIA8vrH4+TJ39M3H1GxbbvtsNtsUjzq5sNBoMvDOgL/uTU0g3m2kdFoq/pycWi4X+M2eTlZ+HP0Ymb97GK71vYETTpryxaTO/HTtBaHAQ5sxMPu3Xhzh/fyJ8vTlxMY88NEZloFlIsO18rlnH0sNHCTKYsHoYmHLjYBoEBrL67jv4cvcezmZmMrZ1K5qEBDvM01Gc+gEBDuO7Q1Udty4o9WYTSqnmwNvY5lwXXqLU9Zs5Hdi5kGMJW+jUdyYmky8nE79j8ffP0bHrncVuv/3BWQ5jKaWIjHFuKt2qn18nOzuHLgPnYjL5ciLhW36e9Vf6j3yJE4e3FW4vTz5XMgWFOZyet+ineezevokVy5fh7+/P1P/9j1eff5Ivvvmp2Fhvv/YceeYc1q1da2s/dSrP/fUB/vHiWy7HyT56jK+MTfBVBr7XKfzjwbt57q1JbFv0M5/lx+BrMTAvJ5WXn3yA5h07F9t+9e5jgO3qOq73dk6u3l3q+VBKXTXV7ZGly/HJ13xibGqLb0nmzTUbMBqM/GHOY8Xq1YXv97VvZ9HU15ec9JzL8nl2+Rr+ccP1bD9yks9oiK82MC8nhVdWxvPlTTdiNBq5v4NzUxMXHT5SbJwx7Vo7jO8OVXXcusCZTq8vgU+AfKAfMB0ofqpCHXLh3EGCwrthMtmqV3h0f5LPHXS43V2STu0hLLpPYfyImAGYc9MrNB/bvT6Kn56XcHAf/fr1xd++sGLkiBEcOuB4gHTfnl0MGzbsUvuRI0lPTytDnJ300H74KtuPcB/8Sc/M4NDBvXTJMRZu7639OJRwwGH7osqzsvFwaio9VcCl+IYA8rSVxNRU+g0ectn7TUhOYX9y8tX5mM0kpKTSxeJzKX/lT8JF11euOorjrvjV7bh1gTPF2kdrvQzbNL+jWutXsT1At04LjWjKxQu/Y7HY7nF84cwaQsKbONzuLuH1WpF8Zs2l+KdX4eHpV2H5lDbro3GzFqxevYbs7GwAFi9ZQuOmzR3Ga9GqLUuWLCls/+uvv+LvH1CGOO1Yr7LIsQ+4rdOZ+Pv60aRZS7Z6Wwu3r1dZNG7U1GH7osqzsrFhUBDrdcal+NYMPFA0Dgpi9W+/XXpfv/5K45BgmoeEsF5lXp6PhwdNgoPYasq5lL/OoHEZugocxXFX/Op23LrAmXtu5iqlDMBBpdTjwEmgzq9Pbdl+JEcOxrNt5d14eYditWRy2/3TCQ5txJED8WxbeRceXsFoazZj7v/abcftO/xljh4axqbfbsPDM4i83GRu+vMnNGzay3E+xWwvTcFgIpQ86+PGkbeyYe1K+g8YQFh4OGlp6UydPt9h++df+Rejh/Xghh49CAkJISnpPO99Mp0bevVjffwKevfpQ2BgILnmPD6fsaDEOLesWsa9xw8TqEykkM/7k2dzQ69+rPttMQ8u+YUgowc5Pp58/sFU4ho04paVyxh34jBBRdqDrZ814dB+MtPTyWo1BJN9sNEQFUdi6kUy8vJoHhKMr4dHYfsrt08ePICBM75jnCWRQIwkk88LPboxrEljNpxLom+vXoSGhJCRksJnA/oSFxDA6FNnuDctkUCDiRSdz3v9+tM9Nob1x0/y0NGjhBg8yDRppvZ1/SZLw5o0LjZOg8BAh/GLe1+VcVzhHGeK9V8AX+BJ4A2gPzCuIpOqCZTBwJDb/kPqhcPk5qQTFtkCD08ftNWK1ZqP1hptzUdrK+5ceGQwmRj3t8UcS1hDZloSjVv2x9ff1n9aXD4lbXfElQcHGAwG3vj3Rxw9nEB6ehrNWrTCx8fxlEGDwUCbdh05f/4ceXn5BAQGEhvXALANGppMHvj4+GI2p5V43kwmEwtWbmHdmhUkJZ2lT//BhIaGY7Va0R4mVIAvKjAInZ2D1hqDwUDbLtdzISMVfHwJtFqJjWuA1WrlxWceY/3aFUSER5CckspPrz6MmvcbL/66hPVJyYQaTKQbNJ8NH0LDwEBeWv87G5LOEREaRnJSElMG9KVxUBAr7r6dBYcSOJWZya0tmhNtX9VosWo0mvz8fKxaowGTwcAPo29i/anTJGVn06d+HCH2WStv9OnJ0bQ00s15NAsJxqcM9zE3KOUwTnHbrVrzwoo1rD9x8rL32zgoqEKPK5xX6tnSWm8CsF9dP+nMA3PrCqXUVV0K+3b+yJkTB+nc7xuMRm9OH5nP4rnPMfaROW47rsFgoFHzPk7lU9L24pTlXh9KKRo1aeZU259/nMvRxP3Er16Nj48P07/+mleef5Ixd47nyKF9LF/222Xbp89e5DCWwWCgZ58BV8U/krCPVStWXBXf0XEPH9rLsqVLC7c/NHUhExoHcPDcRT6lAd5WAwvzU3llZTx/ateaIwYDy1ausrWfPp3XvvySaYP6YzQaGd2yxWX5/JJ4mKNGI6vWxF/V3mAw0CMutvjz6WKRLI6jOMVt/yXxMAdPnuVTffn7nX6z6z2erhxXOK/UPmulVBel1C5gJ7BLKbVDKXVtxadWM6UkJRIY1gWj0XaVFBrVk9QLh6s4K+cUzPqICKy4e30cSThIr1498fGxXd0PHjSII4mHHG6v6PiOtu8Prk8nqw/e9gGxbsqPI2lpHLl4kV79+19qP3gwR1Idr2B0tX1VOZx6kU753le9X1F9ODPA+AXwqNa6kda6EfAYthkiohhhUS1IPb+e/LxMAM6fWkZYZItS9qoeCro+KlKzFq1ZvnwF6em2D2g//vQTzVq2dri9ouM72l6/XQ82e5nJ0rZ++1Vk0Cw4mKbBwSxfvPhS+wULaBbq+AZSrravKs1CgtlkzL7q/Yrqw5lOI4vWek3BN1rreKWU45sY1HEtrhnO8cSNbFt5Jx5ewRiNlLgisbooWEJe0ff6GDriFjZvXEvP3r3x9w/Aw+TB/2YuIK5+QzZtjKdnr174+fnh4enJ5zNtc6y11uzZvYOM9DRat21/2UrF4uJv+X0dffv3JyQkFKtV89n0ebb4G+Lp3qMH3t4++Pr48sWsn4ir39BhPr+vXsH4uTPxVUa8vDyY2mcocQH+bE5Konv3bnh7euGrFP8bNOBSnheSycwz0yo0jEAvT4Y2bsTWCxfo27sXIYFBWHNy+LTf1V1YRRUXp6INbdyIzSdP80CibcBWeRqZ0ufSwKM78qmK91WbOFOsVymlpgCzAA3cDqxUSnUG0FpvrcD8ahylFANHvUHXvo+Qm5NOcHgjTKayLe+uDJX9FHKr1Ury6VP452tC03JIMmWTk51FXl4e69Ysx8fHh9DQUI4cOcr+vbuIia3P3x+8m13r1xJq9OSc0crU7xbSvGXxi4iUUrz4xrtMeOSvZKSn0bBRUzy9vDCbzayLX46/nx8REREcOXykMH5x+VitVlKTzxPo5UNIvpWk/HxyLPnkWSzEJxzFOyefkBw4hZn9KcnE+Pvx7Nr1/JGeQWR4OCfWbeTT/n1oHhLCC9d14b7WrcnIM9MwMBBPo+Nl1har1WGciqSU4qVeN3B/pw6X5WmxWnlm2Up2nT5HmMGDsyqfz24c7HI+7opTlzlTrDvY//vKFds7YSve/d2aUS0REBxDdb8hXWUXaoCf5n/HifUb+MRcDw9lYHFOGi//5SGad+hEgL8fv/7yM15eXnz77be89s+/kZGezon1G/g4N9LWXtvaz/p1TYnHiY65/Gk4//fS30uMf2U+t9//CCfWb2ByXrRtu+Uir66Mp1lEGN45Fj4yNsZDGfjVksprK9eSecP1JHn7sPiHBZfif/oJMwYPtOXj7weU/lDahQmJJcapaFfmuTAhkZOnk5ms6+NhNbBY287DzFtGuhTXXXHqslL7rLXW/Ur4kkJdQ7XqHFf44IDKvM3psaOJtM8x4GEfyLoWH46fOMrhxIP069sXLy/bp5C+ffuSlZXpsL2rXI1/1Xbly/H0dA6nXuQ65Ve4vYvBjyxLPsfT0+neu/dl8U9cdH2Azl1x3OVYWjrtrV5Fzo8vxzMyStmr4uLUZc7MBolSSn2ulFpk/76NUmpCxacmKkplzPpwpFWba9jgnU+GtthuMmXIpGXLNrTveC0/LVzIxYsX0Vrz3ezZBAYFO2zvKlfjX7VdpdPC34/2kRGs0umF25dYLxJo8qBlaChLf/mlMP6c2bNpERbmcp7uiuMurcJC2WDILny/S0mjRRm6LtwVpy5zphtkGrbZHy/Yvz8AfAd8XkE5iQpW2qwPq9XKti0bychIp32HawkJdV+xGDT0JratX8t930zD12giIDycqR9MpV50LFs3b6D7DTfg6+uLxWLhky/n0rHzdWxeu5p7Zn6JpzIQGBzG9A+mlniM4vJ/+vnXHcbfvmEdE2ZNw9/kiX9YOFM+mEp0TNxVec7/8wD09sNsOXmaP6ck4oMBq4IpQwbTPjKCHReS6d2zJ4F+fvgZFJP79nb5/Axs2MAtcdxlYMMGbG9+lgn7D+BvMOHv7cWnfftWWZy6rNTHeimlNmmtr1NKbdNad7Jv26617uhoH3msV/VV2oMD8vPz+cvDd3PsyCHqRUVz4MB+PvlyDm3adSi2vasK4h9O2E9oSBjHjh3l02lzC+MnHNpP0rmzdOx8Pd7e3qSmpjKoU1N8tSIME4fJZfQ9E3jp9eJvt15a/lfGL5B84TwZGenExNbHZDKRn5/Pk+PGcGjbZoKUB6dVPj++/Te8f17F05t3sT8lnQBl5Dz5fDZ8CK3tV7/J2Tlk5JmJ8ffHZCj7wwHcFcddauv7qm5KeqyXM1fWmUqpMGyDiSilugFyu6waxtnBxAXzZpGZlsLPP/2Eh4cH8+bP5/UXn+LbH5a5JY+C+L/+8kux8Zs2a0nTZi0L298xqh+x2oP/GOtjUopllotMmf6Fw2JdWv5Xxi8QGhZOaFj4ZXGSt23no9x6tuPqNB7+YA4PtI8mOSWbT2mICcUyaxqvrVrLt6NvssXx8SbUp/wPoHVXHHepre+rJnHmT9tTwI9AU6XUWmy3SH2iQrMSbuXKYOKp48e4/vrr8LDfxKd7t26cPnncbbm4Gj856TydlS8me7dNB4MvFhx/GnRX/iePH6VtjuHScfHh9JmTHA5vRDur96XtyofTmVkuxxfCVc7MBtkK9AFuAB4C2mqtd1Z0YsI9XL3XR9v2nVj0669cuHABrTUzv/mGNte4pwukLPGbtWrNcp3GRW27OdZCayom5fjH1l35t2vfmbXe+YXH/cWQQZs27WnWZQDrvXIvbecircOq34pEUfs47AZRSl0HHNdan9Fa59vvB3IrcFQp9arWOrnSshRlUnTWh7P6D7qRP3ZupW+/fnh7+xAdG8fH//u2TMe3WCxsXLea9PQ0OnfpSkRkvRLjF9f+m++X0KNtfcZlJuKBAhSvvTelTPkXF7+kOLvvGc/9n0/Gy2AiOq4+n3zwGZFR0ey+9wHun/IBXspAdIA/H/ftWabzU9dYrFY2nj5DhtlMp6hIInwreSpSDedwgFEptRUYqLVOVkr1Br7F1v3REWittb7NUVAZYKweRo0ILvOCl4z0NLKyMgmPiMJQhoEgs9nMYxNuJ/nCWWKiY9i+fTsff/4d7TteW2x8R+1btbmGxybczumTxwgNDSEhIZFPvpxTGMfZ/M1mM4/ceTPn9u4j0uDBPms2n8z8weU4BUxnd3Dk+X8S4euDR+TVd84Tl8uzWHh00VLOpaQRoTzYr3OYPHQg10REVHVq1UpZBxiNRa6ebwc+01p/D3yvlNru7iSFe5X3Xh/+AYH4B5T9SR4Lvp+FtppZMH8+RqORnxYu5P9efprZP64sNr6j9mPGjkdbzfy66Jdi4zib/4LvZ5GzZz8Tc8MxKsUqq4HXn36Uucs2luk85Ed1oPPga516ZqOABYcSyEnOZKKOs5//NN5YvY7Zt95c1anVGCVdMhmVUgXFfACwvMhrctfwasoUFFZ4Re1K94e7nTl1gs6dOmG03wejy7XXcva042cbOmrvahxHTp86TqschdE+MNhW+XD23BmX4xR17o7Xy/zMxrrmdEYmrayel5//rOwqzqpmKalYz8J2E6cFQDawBkAp1QyZulctVdUS8uK073wdCxf+zNmzZ7FarXw5bVqJXQ6O2rsax5EOna9njXceF7TtaS0LjBm0b9+5PG8RgKZP3I0uwzMb65oOkRGsNmReOv9c5JqIqluZWRM5vELWWr+plFoGRANL9KXObQMyda/aadU5jmYRKWxYtRSdl06Xrj2Iia1f4j55eXmsXrGEzAzn2ruiT7/B7N29g379+2MwGmnWrCWTv5jt8Lh9+g1mz67t9OnbF2Uw0KJFaz75cg5h4RHs37OL/gMGYDSZaNmqLe9/OqNM+ex76HEe/PAdjErRolkrPnz/sxL3ycrKYspH73AxNYWbb72TTtdef1Wb461HE9d7OydX78ZYz7Xzl2e1sub4CTLy8riuXhTR/rX30aa968ex75rWPLhjJ0YUzYOD+aCPDMy6otQVjGUhA4yVyxQUxuCuWTx333CyEg4TqUzssGbx0fS5dO7Srdh9zLm5PHDPLeTlZhMbG8v6Dev5YMpMh+1dVRA/NzuTevWi2LxlCx9MmUm7azoVe9x213RiwpgbyUo4TARGdursy/I35+aSnZNNUAn3snY2L2fiZKSnMbRbW4Ky84gyeLLNksGzb7zDHXdffVuc+nvnkfDhDJTRiDEixrk8LBYe+HkxWReziMTEDp3FB0MG0jkqskzvq6YwWyxk5+cT5FV9bxtclcq7glFUcyN6Wfhxzjeog0f4tzkMg1Kssxp54++PM3/l5mL3mTdnJj5eHnzz9TQMBgOLFy/mzVee4fufS771qLMuxZ9zWfwxY8cXe9wxY8dfkb/psvw9vbzwdMMvuLNxXn3hKeJyrPyfqo8BxTqVznuvv1BssT7eejRNn4DED52/4p9/8BAqNYd/6xj7+03nzTXr+P62US69n5rG02gs8X7ewjFZnF+DFQwmAmSlnqZZru3p0gAtlDfnLyQ53Dfp3Bnat7+mcDpa+/btOX/urNtycxTf4fazruVf0c6ePklr7XVZPuY8s8P2GQ062AYbzzi3WjIpK4tmVo/L3292TrnzFrWXFOsaqui9PiICFZ2v684qLzPndB4WrZlrTKdT56v7WAt07tKNHxYs4OTJk1gsFj6d8hmdru3qtvwcxXe43cX8K1qvvoNYotMK8/mOVMLCHM8JTvFrStQ77zgdv1NUJKsMmZfeL6l0jJQ5x8Ix6QapgYpbQt6jd3/ufPxvPDzxLaxWK+1aXsPkElb69ejdn7vvfYTBQ4ZgsVrpcl13/vuR+56D3KN3f267414GDByI1ppWbdrx2VfzCA4JLfa4wSGh3PPXf/DIO69jtVrp3LYLk0rIv6I9+NhT7Nq6iQeWLUIDoQFBfD3nlxL3SfFrWnh1XdpgY4/YWP7c6Roe2bIVq9Z0jojgv316uO8NiFpHBhhrGEe3OM3JyWb82JF4eRiJiYlm1erVTPxoGl1vKPleyBaLhbw8M97ePm7NMzU1meH9ulC/fhxxcXGsWLGCJ556gXsfeLzE41ZUPmVlNpvJysogONi5+3+EZCZw9plnnB5stFit5FmteJvkuknIAGOtYQoKAyzFLnaZN3sGocGBTP1sCkopli1bxr/feJ55i9aWGNNoNGI0ur8wvv7CU7Rp05qvpk0rzOcfzz5bWKwdHbei8ikrT09PPD2dv1FTil9T4nq3c3plo9FgwCj3dRZOkJ+SGqSkJ7xcOJ9E27ZtCl9v3bo1F85X3QBdUtI5OnbseFk+ubm5VZZPZTp3x+vE9m4nKxuFW0mxrgGKLiF3tDLxum49mDdvPomJieTm5vLBhx9xXbeqW3TQf9AwZs2aVZjPpEmTiIxyfJe72sarQ0e0xSIFW7iNdINUojxzNgd2LSQ3J4MGzXoQHtWi1H2cfcJLtxv68MBjTzP61tvIycmmZ+8BvD3xUwCys7NY9NM8MjPS6dazL81btHbXW3Jo/ANPsHvHVm4cPhyLxUJkZBRfz/m1wo/rTuU5b0VXNgrhDnJlXUnyzFl8++ltbFk7jwN7dvLdlNs5cmBVifu4eq+P2++6jw07j7J13xk+/vxbAoOCycrK5M9jhrL057kcS/iDe+8YTvwq9zyiqyRZWZkcPZJAt27dGTPmT+SaczmceKjCj+su7jhvXh06yo2ehNvIlXUl2b15NhjCadnpNZRShER0Z8XCNxn/VJ9i29tmfbj24AAApVThHerANvAYF1OPyR9/jFKKAf378fabL9Kzz4DyvJ1SXXncIYMHVcpx3cUd560sKxuFcESurCtJdmYy3n4NCwfcfAMak5udWmxb26wPx08gd0VK8gWaN29eeNzmzZuTmlrxD/mpquO6i7vyd3VloxCOSLGuJA2a9eD8ycVkXDxIfl4Gx/b/jwZNi18EUdKsD1d179GHud9/z549e0hPT+fd/06ke4/ir+bdqaqO6y7uyt/VlY1COCLFupLENe5K72HPsH/L82xedhsBgSYG3vLGZW2cmfXhqi5de/CXv7/MfRPup1v37uTmw0tv/Nc9wavhcQHOnT3N04+P54F7bmXhgjmlts/MSGfW1/9j6uRJ7Nm9A3Bv/kVXNgpRVrKCsZpw9SnkonhnTp/kpt6daWP1JBoPllhSuffJp3niqReKbZ+RnsZdtw2hSaMGxMXF8cMPC3jjPx/Rd8BQt+dmfnS0S7dRFXVPSSsY5cq6GpBC7T5vvfos11g9eVlF84AK5wVDDDMmv++w/fw5M2netAmTP/6Yfz7/PJMmTWTiv16pkNziereTp8qIMpNiXcVGjQimVYxt1ocU6vJLTUmmvvYo/D5GeZBXQoG8eDGVRo0aFn7fuFEj0tIq5ql15+54nThZ2SjKSIp1FXLnrA9hM/zm2/jZepG9OpsUnc9nnCc2zvEd8Hr07s+cOXPZunUrSUlJvPX2vyp0eqGsbBRlJcW6Crlz1oewuf2u+7jl3gm8ok8zwXKYs/VCmDZ/qcP2na7tynMvv83fnv47Q4bdiJdvIP989T8Vlp9t7vXdIN0hwkUywFgFnF1CLmqnwmc2gssP2RW1mwwwViOuLiEXtU/h1bUQLpBiXYlk1ocoICsbhaukWFcSmfUhipKVjcJVciOnSlDSE14qQ/KF88ye+SUZAToa7AAABwBJREFUGWn07jeY67v3qpI8xOVceWajEHJlXcGKDiZWhdSUZMbeMoAzJw4S4u/BP/56Pz87sQRbVA7PyfPkNqrCKXJlXYGqw6yP+XNn0uXazrz91lsAXN+1K889/wLDbx5TNQmJq8T1bseJ1buxJJ2SpejCIbmyriDVZdZHdlYWkZGRhd9HRUaSlZVZdQmJqxSsbBSiJFKsK0B1GkzsO2AIc+bMZcWKFRw8eJCXXn6FgUNGVm1S4iqyslGURrpB3Mz2hJfqs4S8TbuOvD1xCu/99/9IT0+j74Ch/O3ZV6s6LXEFeaqMKI0UazcyBYWhlLXaLXbp1XcgvfoOrOo0hBNkdohwRLpB3KRgMFGIsjreerTMvRYOSbF2g+ow60PUHrKyURRHinU5VZdZH6J2kJWNwhHpsy4H22BiRrUZTBS1g6xsFMWRK+syqm6zPkTt4jl5HrHyVBlRhBTrMrDN+lBSqEWFk7nXooAUaxfJrA9RWWRloyhKirULZDBRVLbClY0yO6TOk2LtJHlwgKgK8lQZUUBmgzhBZn2IqiazQ4RcWZdCZn2IqiYrGwVIsS6RzPoQ1YmsbKzbpFg7ILM+RHWS4tcUz8nzqjoNUYWkz7oYMpgoqisN5J85jkn6ruscubK+ghRqUZ15Tp5HnKxsrJPkyroImfUhaoqClY3yzMa6Q66s7WTWh6gpzt3xusy9roOkWCOzPkTNJCsb65Y6X6xl1oeoiWRlY91Tp4u13OtD1GQZDTrI3Os6pM4W61EjgmkVYxtMlFkfoiaSp8rULXWyWMtgoqhN5Oq6bqhTxdoUFMaoEcEymChqDVnZWHfUqWINyBPIRa0U27sd+WeOy2KZWqzOFGuZ9SFqM3mqTO1XJ1YwyhJyUVfIysbaq9ZfWcusD1FXyMrG2q1WF2uZ9SHqIlnZWDvVymItsz5EXSUrG2uvWlmsQWZ9iLpLVjbWTrWuWBcsIfcwVnUmQlQNWdlYO9WqYi2zPoSwSfFrKlfXtUytKdYy60OIy3lOnoeu6iSE29SKYi2zPoQoXpysbKw1anSxllkfQpRMVjbWHjW6WIPM+hCiNF4dOhaubBQ1V40t1jLrQwjnFM69tsi9cWqyGlmsZdaHEK6T2SE1W40r1jLrQwjXycrGmq9GFWuZ9SFE2cnKxpqtRhRrmfUhRPnJysaarUYUa5BZH0K4g6xsrLmqfbGWWR9CuFfBykaZylezVOtiLYOJQlSMuN7tZO51DVNti7UMJgpRcWRlY81TLYu1DCYKUfFkZWPNorR2/325lFJJwFG3BxZCiNqtodY6orgXKqRYCyGEcK9q2Q0ihBDiclKshRCiBpBiLYQQNYAUa1HplFIvKKX+UErtVEptV0p1dXP8vkqphc5ud+Nxg5VSj1bW8UTdYqrqBETdopTqDowAOmutc5VS4YBnFaflLsHAo8Dkqk5E1D5yZS0qWzRwXmudC6C1Pq+1PgWglLpWKbVKKbVFKbVYKRVt375SKfW+/Sp8t1Lq+v9v735CogjDOI5/f4UFZf8OHhMpS8kOmhCVSkTh3bCiU0F0jzDwEJVEf69BdgihQ0glnSOhJDIjLC0tLAgKjDoUIXZISZ4O8whzyGoHW1t8PrDs7DPzvvtc9tl3nmFnPb5ZUp+kAUmPJFVkSUhSo8/zTNItScUefyepzeNDkio9XiKp288Orkp6718654G1nuf0HZOKJXVJGpF0XVL8eCBkEsU65NtdYLWkN5IuS9oOIKkIuAQ0m1kt0AGcSY1bYmbVJCvXDo+NAA1mVgOcAM7mmowX2ePALjPbBPQDR1OHfPZ4O9DisZPAPTOrArqAUo+3Am/NrNrMjnmsBjgCbADWAHW55hgCRBsk5JmZfZNUCzQAO4AbklpJiuRGoNsXnwuBj6mhnT7+gaTlklYCy4BrktaR/BFKUYaUtpAU0l5/30VAX2r/bX9+Cuz27XqgyfO5I+nrb+Z/YmajAJIGgTLgYYY8wzwXxTrknZlNAT1Aj6Qh4ABJMXxpZltnGvaL16eB+2bWJKnM58yVgG4z2z/D/gl/niLb52UitZ11jhCiDRLyS1KFr4SnVZPcmuA1UOIXIJFUJKkqddw+j9cDY2Y2BqwAPvj+gxlTegzUSSr3+ZdKWv+HMb3AXj++EVjl8XGS1X4Isy6Kdci3YpLWxStJL0haEKfMbBJoBi5Ieg4MAttS475LGgCuAIc8dhE45/G/XbHulDQ6/QDKSQp9p+fTB1T+YY42oFHSMLAH+ASMm9kXknbKcOoCYwizIu4NEv57knqAFjPrn+tcACQtBqbM7IefCbT7xc8Q/pnon4WQu1LgpqQFwCRweI7zCfNArKxDCKEARM86hBAKQBTrEEIoAFGsQwihAESxDiGEAhDFOoQQCsBP7fV0jHRvByIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xgM7RffGNiDF"
      },
      "source": [
        "##kernel=\"linear\" and C=1000"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s4-nLQNWNVa5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "54e37df6-ee91-42d1-d187-666f047cb384"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "# Select 2 features / variable for the 2D plot that we are going to create.\n",
        "X = iris.data[:, :2]  # we only take the first two features.\n",
        "y = iris.target\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 = clf.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",
        "model = svm.SVC(kernel='linear',C=1000)\n",
        "clf = model.fit(X, y)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "# title for the plots\n",
        "title = ('Decision surface of linear SVC, C=1000')\n",
        "# Set-up grid for plotting.\n",
        "X0, X1 = X[:, 0], X[:, 1]\n",
        "xx, yy = make_meshgrid(X0, X1)\n",
        "\n",
        "plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\n",
        "ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')\n",
        "ax.set_xlabel('Sepal Length')\n",
        "ax.set_ylabel('Sepal Width')\n",
        "ax.set_xticks(())\n",
        "ax.set_yticks(())\n",
        "ax.set_title(title)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VJMe2Yb7NxsZ"
      },
      "source": [
        "##kernel=\"rbf\", C=1, gamma=1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IFQpM1GnNVdG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "93819f35-f24b-4d10-e7f7-4b598a888a6f"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "# Select 2 features / variable for the 2D plot that we are going to create.\n",
        "X = iris.data[:, :2]  # we only take the first two features.\n",
        "y = iris.target\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 = clf.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",
        "model = svm.SVC(kernel='rbf')\n",
        "clf = model.fit(X, y)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "# title for the plots\n",
        "title = ('Decision surface of rbf SVC, C=1,gamma=1')\n",
        "# Set-up grid for plotting.\n",
        "X0, X1 = X[:, 0], X[:, 1]\n",
        "xx, yy = make_meshgrid(X0, X1)\n",
        "\n",
        "plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\n",
        "ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')\n",
        "ax.set_xlabel('Sepal Length')\n",
        "ax.set_ylabel('Sepal Width')\n",
        "ax.set_xticks(())\n",
        "ax.set_yticks(())\n",
        "ax.set_title(title)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uz-uhlcJOQZF"
      },
      "source": [
        "##kernel=\"rbf\", C=100, gamma=1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_HbmFizNVe9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "3e712242-7341-4639-e2d5-19ebb7211e9f"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "# Select 2 features / variable for the 2D plot that we are going to create.\n",
        "X = iris.data[:, :2]  # we only take the first two features.\n",
        "y = iris.target\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 = clf.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",
        "model = svm.SVC(kernel='rbf',C=100)\n",
        "clf = model.fit(X, y)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "# title for the plots\n",
        "title = ('Decision surface of rbf SVC, C=100,gamma=1')\n",
        "# Set-up grid for plotting.\n",
        "X0, X1 = X[:, 0], X[:, 1]\n",
        "xx, yy = make_meshgrid(X0, X1)\n",
        "\n",
        "plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\n",
        "ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')\n",
        "ax.set_xlabel('Sepal Length')\n",
        "ax.set_ylabel('Sepal Width')\n",
        "ax.set_xticks(())\n",
        "ax.set_yticks(())\n",
        "ax.set_title(title)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pfrxwqLBOr6d"
      },
      "source": [
        "##kernel=\"rbf\", C=1000, gamma=1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SafMeIsfOr6k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "8dd1058f-e73d-476a-fcad-0e14267c2189"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "# Select 2 features / variable for the 2D plot that we are going to create.\n",
        "X = iris.data[:, :2]  # we only take the first two features.\n",
        "y = iris.target\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 = clf.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",
        "model = svm.SVC(kernel='rbf',C=1000)\n",
        "clf = model.fit(X, y)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "# title for the plots\n",
        "title = ('Decision surface of rbf SVC, C=1000,gamma=1')\n",
        "# Set-up grid for plotting.\n",
        "X0, X1 = X[:, 0], X[:, 1]\n",
        "xx, yy = make_meshgrid(X0, X1)\n",
        "\n",
        "plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\n",
        "ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')\n",
        "ax.set_xlabel('Sepal Length')\n",
        "ax.set_ylabel('Sepal Width')\n",
        "ax.set_xticks(())\n",
        "ax.set_yticks(())\n",
        "ax.set_title(title)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_1g9ZeGzPOVK"
      },
      "source": [
        "##kernel=\"rbf\", C=1, gamma=100"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "97HRuk50POVU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "34603d32-d5ae-4f1e-83f1-55835a30278a"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "# Select 2 features / variable for the 2D plot that we are going to create.\n",
        "X = iris.data[:, :2]  # we only take the first two features.\n",
        "y = iris.target\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 = clf.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",
        "model = svm.SVC(kernel='rbf',gamma=100)\n",
        "clf = model.fit(X, y)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "# title for the plots\n",
        "title = ('Decision surface of rbf SVC, C=1,gamma=100')\n",
        "# Set-up grid for plotting.\n",
        "X0, X1 = X[:, 0], X[:, 1]\n",
        "xx, yy = make_meshgrid(X0, X1)\n",
        "\n",
        "plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\n",
        "ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')\n",
        "ax.set_xlabel('Sepal Length')\n",
        "ax.set_ylabel('Sepal Width')\n",
        "ax.set_xticks(())\n",
        "ax.set_yticks(())\n",
        "ax.set_title(title)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZB8XEMkbPdUK"
      },
      "source": [
        "##kernel=\"rbf\", C=1, gamma=1000"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w6rdPUKHPdUM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "6cc6b3d9-60fd-4ce9-e0de-c5b558a316eb"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm, datasets\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "# Select 2 features / variable for the 2D plot that we are going to create.\n",
        "X = iris.data[:, :2]  # we only take the first two features.\n",
        "y = iris.target\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 = clf.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",
        "model = svm.SVC(kernel='rbf',gamma=1000)\n",
        "clf = model.fit(X, y)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "# title for the plots\n",
        "title = ('Decision surface of rbf SVC, C=1,gamma=1000')\n",
        "# Set-up grid for plotting.\n",
        "X0, X1 = X[:, 0], X[:, 1]\n",
        "xx, yy = make_meshgrid(X0, X1)\n",
        "\n",
        "plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\n",
        "ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')\n",
        "ax.set_xlabel('Sepal Length')\n",
        "ax.set_ylabel('Sepal Width')\n",
        "ax.set_xticks(())\n",
        "ax.set_yticks(())\n",
        "ax.set_title(title)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3eTab6ZRPxBe"
      },
      "source": [
        "#4.Importance of Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XlG90AwBbodq"
      },
      "source": [
        "## Before Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fxFAGqk8euKz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec35ad0a-9041-4792-df6a-43a7bed002d7"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "cancer = load_breast_cancer()\n",
        "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target)\n",
        "svm = SVC(C=1)\n",
        "svm.fit(X_train, y_train)\n",
        "print(\"Test set accuracy: {:.2f}\".format(svm.score(X_test, y_test)))\n",
        "print(\"Train set accuracy: {:.2f}\".format(svm.score(X_train, y_train)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set accuracy: 0.90\n",
            "Train set accuracy: 0.91\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p5NvWZe3bxgC"
      },
      "source": [
        "## Result after Scaling with MinMaxScaler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OjO_nzdueuPK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77d32e8b-084e-454e-c689-b89d34c5d0ef"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(X_train)\n",
        "X_train_scaled = scaler.transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "# learning an SVM on the scaled training data\n",
        "svm.fit(X_train_scaled, y_train)\n",
        "# scoring on the scaled test set\n",
        "print(\"Scaled train set accuracy: {:.2f}\".format(svm.score(X_train_scaled, y_train)))\n",
        "print(\"Scaled test set accuracy: {:.2f}\".format(svm.score(X_test_scaled, y_test)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scaled train set accuracy: 0.98\n",
            "Scaled test set accuracy: 0.98\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lux9I8jbb9i8"
      },
      "source": [
        "## Result after Scaling with StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcP4wNFfeuUl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfcff6f5-6687-4e61-89b0-94607701aca3"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train)\n",
        "X_train_scaled = scaler.transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "# learning an SVM on the scaled training data\n",
        "svm.fit(X_train_scaled, y_train)\n",
        "# scoring on the scaled test set\n",
        "print(\"Scaled train set accuracy: {:.2f}\".format(svm.score(X_train_scaled, y_train)))\n",
        "print(\"Scaled test set accuracy: {:.2f}\".format(svm.score(X_test_scaled, y_test)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scaled train set accuracy: 0.99\n",
            "Scaled test set accuracy: 0.98\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YqAWve2AcBKu"
      },
      "source": [
        "## Result after Scaling with RobustScaler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ANoxzAsAeBZe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9cbceb4-0965-462d-c75e-4802cb78b081"
      },
      "source": [
        "from sklearn.preprocessing import RobustScaler\n",
        "scaler = RobustScaler()\n",
        "scaler.fit(X_train)\n",
        "X_train_scaled = scaler.transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "# learning an SVM on the scaled training data\n",
        "svm.fit(X_train_scaled, y_train)\n",
        "# scoring on the scaled test set\n",
        "print(\"Scaled train set accuracy: {:.2f}\".format(svm.score(X_train_scaled, y_train)))\n",
        "print(\"Scaled test set accuracy: {:.2f}\".format(svm.score(X_test_scaled, y_test)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scaled train set accuracy: 0.99\n",
            "Scaled test set accuracy: 0.98\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ohaPdYfcLrh"
      },
      "source": [
        "Comparison of different preprocessing techniques\n",
        "\n",
        "https://stackoverflow.com/questions/30918781/right-function-for-normalizing-input-of-sklearn-svm\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Wxt_S6jc_Fn"
      },
      "source": [
        "#5. Advantages and Disadvantages of SVM\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqseSpngfcuW"
      },
      "source": [
        "**Advantages**\n",
        "\n",
        "1. Effective in high dimensional spaces.\n",
        "\n",
        "2. Still effective in cases where number of dimensions is greater than the number of samples.\n",
        "\n",
        "3. Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.\n",
        "\n",
        "4. Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.\n",
        "\n",
        "**Disadvantages**\n",
        "\n",
        "1. If the number of features is much greater than the number of samples, avoid over-fitting in choosing Kernel functions and regularization term is crucial.\n",
        "\n",
        "2. SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x_t2dz-EdY1L"
      },
      "source": [
        "#6. Comparsion of different Classifiers\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C23M_LuYdzb0"
      },
      "source": [
        "**Nearest neighbors**\n",
        "\n",
        "For small datasets, good as a baseline, easy to explain.\n",
        "\n",
        "**Linear models**\n",
        "\n",
        "Go-to as a first algorithm to try, good for very large datasets, good for very highdimensional data.\n",
        "\n",
        "**Naive Bayes**\n",
        "\n",
        "Only for classification. Even faster than linear models, good for very large datasets and high-dimensional data. Often less accurate than linear models.\n",
        "\n",
        "**Decision trees**\n",
        "\n",
        "Very fast, don’t need scaling of the data, can be visualized and easily explained.\n",
        "\n",
        "**Random forests**\n",
        "\n",
        "Nearly always perform better than a single decision tree, very robust and powerful. Don’t need scaling of data. Not good for very high-dimensional sparse data.\n",
        "\n",
        "**Gradient boosted decision trees**\n",
        "\n",
        "Often slightly more accurate than random forests. Slower to train but faster to predict than random forests, and smaller in memory. Need more parameter tuning than random forests.\n",
        "\n",
        "**Support vector machines**\n",
        "\n",
        "Powerful for medium-sized datasets of features with similar meaning. Require\n",
        "scaling of data, sensitive to parameters.\n",
        "\n",
        "**Neural networks**\n",
        "\n",
        "Can build very complex models, particularly for large datasets. Sensitive to scaling of the data and to the choice of parameters. Large models need a long time to\n",
        "train."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D9uSzNY283D7"
      },
      "source": [
        "# Reflections\n",
        "\n",
        "1.   Support Vector Machine Classification (Implementation Only)\n",
        "2.   Parameter Tuning\n",
        "3. Visualization\n",
        "4. Importance of Preprocessing\n",
        "5. Advantages and Disadvantages of SVM\n",
        "6. Comparison of Different Classifiers\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "zlfSj5pyrGjD"
      },
      "source": [
        "#@title  { run: \"auto\", vertical-output: true }\n",
        "#@title  { run: \"auto\", vertical-output: true }\n",
        "name = \"Abhijith A Thampi\" #@param {type:\"string\"}\n",
        "roll_number = \"AM.EN.U4AIE20102\" #@param {type:\"string\"}\n",
        "self_assessment = 10 #@param {type:\"slider\", min:1, max:10, step:1}\n",
        "print(\"Name : \",name)\n",
        "print(\"Roll Number : \",roll_number)\n",
        "print(\"Self Assessment Marks : \",self_assessment,\"/10\")"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}