0.22/notebooks/auto_examples/svm/plot_svm_nonlinear.ipynb

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      "source": [
        "\n# Non-linear SVM\n\n\nPerform binary classification using non-linear SVC\nwith RBF kernel. The target to predict is a XOR of the\ninputs.\n\nThe color map illustrates the decision function learned by the SVC.\n"
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        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm\n\nxx, yy = np.meshgrid(np.linspace(-3, 3, 500),\n                     np.linspace(-3, 3, 500))\nnp.random.seed(0)\nX = np.random.randn(300, 2)\nY = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)\n\n# fit the model\nclf = svm.NuSVC(gamma='auto')\nclf.fit(X, Y)\n\n# plot the decision function for each datapoint on the grid\nZ = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\nZ = Z.reshape(xx.shape)\n\nplt.imshow(Z, interpolation='nearest',\n           extent=(xx.min(), xx.max(), yy.min(), yy.max()), aspect='auto',\n           origin='lower', cmap=plt.cm.PuOr_r)\ncontours = plt.contour(xx, yy, Z, levels=[0], linewidths=2,\n                       linestyles='dashed')\nplt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired,\n            edgecolors='k')\nplt.xticks(())\nplt.yticks(())\nplt.axis([-3, 3, -3, 3])\nplt.show()"
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