Pushing the docs to dev/ for branch: master, commit df7942af77fbe98c5436035cb088893964a74026

Author: sklearn-ci

dev/_downloads/31fce550d708a7ea90cb460cdc208cf2/plot_linearsvc_support_vectors.py

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+"""
+=====================================
+Plot the support vectors in LinearSVC
+=====================================
+
+Unlike SVC (based on LIBSVM), LinearSVC (based on LIBLINEAR) does not provide
+the support vectors. This example demonstrates how to obtain the support
+vectors in LinearSVC.
+
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_blobs
+from sklearn.svm import LinearSVC
+
+X, y = make_blobs(n_samples=40, centers=2, random_state=0)
+
+plt.figure(figsize=(10, 5))
+for i, C in enumerate([1, 100]):
+    # "hinge" is the standard SVM loss
+    clf = LinearSVC(C=C, loss="hinge", random_state=42).fit(X, y)
+    # obtain the support vectors through the decision function
+    decision_function = clf.decision_function(X)
+    # we can also calculate the decision function manually
+    # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]
+    support_vector_indices = np.where((2 * y - 1) * decision_function <= 1)[0]
+    support_vectors = X[support_vector_indices]
+
+    plt.subplot(1, 2, i + 1)
+    plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)
+    ax = plt.gca()
+    xlim = ax.get_xlim()
+    ylim = ax.get_ylim()
+    xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 50),
+                         np.linspace(ylim[0], ylim[1], 50))
+    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+    plt.contour(xx, yy, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,
+                linestyles=['--', '-', '--'])
+    plt.scatter(support_vectors[:, 0], support_vectors[:, 1], s=100,
+                linewidth=1, facecolors='none', edgecolors='k')
+    plt.title("C=" + str(C))
+plt.tight_layout()
+plt.show()

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Plot the support vectors in LinearSVC\n\n\nUnlike SVC (based on LIBSVM), LinearSVC (based on LIBLINEAR) does not provide\nthe support vectors. This example demonstrates how to obtain the support\nvectors in LinearSVC.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import make_blobs\nfrom sklearn.svm import LinearSVC\n\nX, y = make_blobs(n_samples=40, centers=2, random_state=0)\n\nplt.figure(figsize=(10, 5))\nfor i, C in enumerate([1, 100]):\n    # \"hinge\" is the standard SVM loss\n    clf = LinearSVC(C=C, loss=\"hinge\", random_state=42).fit(X, y)\n    # obtain the support vectors through the decision function\n    decision_function = clf.decision_function(X)\n    # we can also calculate the decision function manually\n    # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]\n    support_vector_indices = np.where((2 * y - 1) * decision_function <= 1)[0]\n    support_vectors = X[support_vector_indices]\n\n    plt.subplot(1, 2, i + 1)\n    plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n    ax = plt.gca()\n    xlim = ax.get_xlim()\n    ylim = ax.get_ylim()\n    xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 50),\n                         np.linspace(ylim[0], ylim[1], 50))\n    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n    Z = Z.reshape(xx.shape)\n    plt.contour(xx, yy, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,\n                linestyles=['--', '-', '--'])\n    plt.scatter(support_vectors[:, 0], support_vectors[:, 1], s=100,\n                linewidth=1, facecolors='none', edgecolors='k')\n    plt.title(\"C=\" + str(C))\nplt.tight_layout()\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}