1.0/notebooks/auto_examples/exercises/plot_iris_exercise.ipynb

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        "\n# SVM Exercise\n\nA tutorial exercise for using different SVM kernels.\n\nThis exercise is used in the `using_kernels_tut` part of the\n`supervised_learning_tut` section of the `stat_learn_tut_index`.\n"
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      "source": [
        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets, svm\n\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\n\nX = X[y != 0, :2]\ny = y[y != 0]\n\nn_sample = len(X)\n\nnp.random.seed(0)\norder = np.random.permutation(n_sample)\nX = X[order]\ny = y[order].astype(float)\n\nX_train = X[: int(0.9 * n_sample)]\ny_train = y[: int(0.9 * n_sample)]\nX_test = X[int(0.9 * n_sample) :]\ny_test = y[int(0.9 * n_sample) :]\n\n# fit the model\nfor kernel in (\"linear\", \"rbf\", \"poly\"):\n    clf = svm.SVC(kernel=kernel, gamma=10)\n    clf.fit(X_train, y_train)\n\n    plt.figure()\n    plt.clf()\n    plt.scatter(\n        X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired, edgecolor=\"k\", s=20\n    )\n\n    # Circle out the test data\n    plt.scatter(\n        X_test[:, 0], X_test[:, 1], s=80, facecolors=\"none\", zorder=10, edgecolor=\"k\"\n    )\n\n    plt.axis(\"tight\")\n    x_min = X[:, 0].min()\n    x_max = X[:, 0].max()\n    y_min = X[:, 1].min()\n    y_max = X[:, 1].max()\n\n    XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]\n    Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])\n\n    # Put the result into a color plot\n    Z = Z.reshape(XX.shape)\n    plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired)\n    plt.contour(\n        XX,\n        YY,\n        Z,\n        colors=[\"k\", \"k\", \"k\"],\n        linestyles=[\"--\", \"-\", \"--\"],\n        levels=[-0.5, 0, 0.5],\n    )\n\n    plt.title(kernel)\nplt.show()"
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