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        "\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"
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      "source": [
        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import datasets, svm\nfrom sklearn.inspection import DecisionBoundaryDisplay\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\nax = plt.gca()\nDecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    cmap=plt.cm.Paired,\n    ax=ax,\n    response_method=\"predict\",\n    plot_method=\"pcolormesh\",\n    shading=\"auto\",\n)\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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