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      "source": [
        "\n# SVM: Maximum margin separating hyperplane\n\nPlot the maximum margin separating hyperplane within a two-class\nseparable dataset using a Support Vector Machine classifier with\nlinear kernel.\n"
      ]
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      "source": [
        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn import svm\nfrom sklearn.datasets import make_blobs\nfrom sklearn.inspection import DecisionBoundaryDisplay\n\n# we create 40 separable points\nX, y = make_blobs(n_samples=40, centers=2, random_state=6)\n\n# fit the model, don't regularize for illustration purposes\nclf = svm.SVC(kernel=\"linear\", C=1000)\nclf.fit(X, y)\n\nplt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n\n# plot the decision function\nax = plt.gca()\nDecisionBoundaryDisplay.from_estimator(\n    clf,\n    X,\n    plot_method=\"contour\",\n    colors=\"k\",\n    levels=[-1, 0, 1],\n    alpha=0.5,\n    linestyles=[\"--\", \"-\", \"--\"],\n    ax=ax,\n)\n# plot support vectors\nax.scatter(\n    clf.support_vectors_[:, 0],\n    clf.support_vectors_[:, 1],\n    s=100,\n    linewidth=1,\n    facecolors=\"none\",\n    edgecolors=\"k\",\n)\nplt.show()"
      ]
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