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      "cell_type": "code",
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      "source": [
        "%matplotlib inline"
      ]
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      "source": [
        "\n# SVM with custom kernel\n\nSimple usage of Support Vector Machines to classify a sample. It will\nplot the decision surface and the support vectors.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm, datasets\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data[:, :2]  # we only take the first two features. We could\n# avoid this ugly slicing by using a two-dim dataset\nY = iris.target\n\n\ndef my_kernel(X, Y):\n    \"\"\"\n    We create a custom kernel:\n\n                 (2  0)\n    k(X, Y) = X  (    ) Y.T\n                 (0  1)\n    \"\"\"\n    M = np.array([[2, 0], [0, 1.0]])\n    return np.dot(np.dot(X, M), Y.T)\n\n\nh = 0.02  # step size in the mesh\n\n# we create an instance of SVM and fit out data.\nclf = svm.SVC(kernel=my_kernel)\nclf.fit(X, Y)\n\n# Plot the decision boundary. For that, we will assign a color to each\n# point in the mesh [x_min, x_max]x[y_min, y_max].\nx_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\ny_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\nZ = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n\n# Put the result into a color plot\nZ = Z.reshape(xx.shape)\nplt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n\n# Plot also the training points\nplt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired, edgecolors=\"k\")\nplt.title(\"3-Class classification using Support Vector Machine with custom kernel\")\nplt.axis(\"tight\")\nplt.show()"
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