Pushing the docs to dev/ for branch: master, commit 8611b9a9af10b46cbf68bb7fc71a44a49ed6ec1f

Author: sklearn-ci

dev/_downloads/036b9372e2e7802453cbb994da7a6786/plot_linearsvc_support_vectors.py

@@ -24,7 +24,10 @@
     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]
+    # The support vectors are the samples that lie within the margin
+    # boundaries, whose size is conventionally constrained to 1
+    support_vector_indices = np.where(
+        np.abs(decision_function) <= 1 + 1e-15)[0]
     support_vectors = X[support_vector_indices]
 
     plt.subplot(1, 2, i + 1)

dev/_downloads/12a392e818ac5fa47dd91461855f3f77/plot_linearsvc_support_vectors.ipynb

@@ -26,7 +26,7 @@
       },
       "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()"
+        "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    # The support vectors are the samples that lie within the margin\n    # boundaries, whose size is conventionally constrained to 1\n    support_vector_indices = np.where(\n        np.abs(decision_function) <= 1 + 1e-15)[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()"
       ]
     }
   ],