Pushing the docs to dev/ for branch: master, commit 384c8ad3d33d41e6f76d09cd0db45e85e443e9bb

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -0,0 +1,54 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# SVM Tie Breaking Example\n\nTie breaking is costly if ``decision_function_shape='ovr'``, and therefore it\nis not enabled by default. This example illustrates the effect of the\n``break_ties`` parameter for a multiclass classification problem and\n``decision_function_shape='ovr'``.\n\nThe two plots differ only in the area in the middle where the classes are\ntied. If ``break_ties=False``, all input in that area would be classified as\none class, whereas if ``break_ties=True``, the tie-breaking mechanism will\ncreate a non-convex decision boundary in that area.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(__doc__)\n\n\n# Code source: Andreas Mueller, Adrin Jalali\n# License: BSD 3 clause\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.svm import SVC\nfrom sklearn.datasets import make_blobs\n\nX, y = make_blobs(random_state=27)\n\nfig, sub = plt.subplots(2, 1, figsize=(5, 8))\ntitles = (\"break_ties = False\",\n          \"break_ties = True\")\n\nfor break_ties, title, ax in zip((False, True), titles, sub.flatten()):\n\n    svm = SVC(kernel=\"linear\", C=1, break_ties=break_ties,\n              decision_function_shape='ovr').fit(X, y)\n\n    xlim = [X[:, 0].min(), X[:, 0].max()]\n    ylim = [X[:, 1].min(), X[:, 1].max()]\n\n    xs = np.linspace(xlim[0], xlim[1], 1000)\n    ys = np.linspace(ylim[0], ylim[1], 1000)\n    xx, yy = np.meshgrid(xs, ys)\n\n    pred = svm.predict(np.c_[xx.ravel(), yy.ravel()])\n\n    colors = [plt.cm.Accent(i) for i in [0, 4, 7]]\n\n    points = ax.scatter(X[:, 0], X[:, 1], c=y, cmap=\"Accent\")\n    classes = [(0, 1), (0, 2), (1, 2)]\n    line = np.linspace(X[:, 1].min() - 5, X[:, 1].max() + 5)\n    ax.imshow(-pred.reshape(xx.shape), cmap=\"Accent\", alpha=.2,\n              extent=(xlim[0], xlim[1], ylim[1], ylim[0]))\n\n    for coef, intercept, col in zip(svm.coef_, svm.intercept_, classes):\n        line2 = -(line * coef[1] + intercept) / coef[0]\n        ax.plot(line2, line, \"-\", c=colors[col[0]])\n        ax.plot(line2, line, \"--\", c=colors[col[1]])\n    ax.set_xlim(xlim)\n    ax.set_ylim(ylim)\n    ax.set_title(title)\n    ax.set_aspect(\"equal\")\n\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": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

dev/_downloads/auto_examples_python.zip

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+"""
+=========================================================
+SVM Tie Breaking Example
+=========================================================
+Tie breaking is costly if ``decision_function_shape='ovr'``, and therefore it
+is not enabled by default. This example illustrates the effect of the
+``break_ties`` parameter for a multiclass classification problem and
+``decision_function_shape='ovr'``.
+
+The two plots differ only in the area in the middle where the classes are
+tied. If ``break_ties=False``, all input in that area would be classified as
+one class, whereas if ``break_ties=True``, the tie-breaking mechanism will
+create a non-convex decision boundary in that area.
+"""
+print(__doc__)
+
+
+# Code source: Andreas Mueller, Adrin Jalali
+# License: BSD 3 clause
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.svm import SVC
+from sklearn.datasets import make_blobs
+
+X, y = make_blobs(random_state=27)
+
+fig, sub = plt.subplots(2, 1, figsize=(5, 8))
+titles = ("break_ties = False",
+          "break_ties = True")
+
+for break_ties, title, ax in zip((False, True), titles, sub.flatten()):
+
+    svm = SVC(kernel="linear", C=1, break_ties=break_ties,
+              decision_function_shape='ovr').fit(X, y)
+
+    xlim = [X[:, 0].min(), X[:, 0].max()]
+    ylim = [X[:, 1].min(), X[:, 1].max()]
+
+    xs = np.linspace(xlim[0], xlim[1], 1000)
+    ys = np.linspace(ylim[0], ylim[1], 1000)
+    xx, yy = np.meshgrid(xs, ys)
+
+    pred = svm.predict(np.c_[xx.ravel(), yy.ravel()])
+
+    colors = [plt.cm.Accent(i) for i in [0, 4, 7]]
+
+    points = ax.scatter(X[:, 0], X[:, 1], c=y, cmap="Accent")
+    classes = [(0, 1), (0, 2), (1, 2)]
+    line = np.linspace(X[:, 1].min() - 5, X[:, 1].max() + 5)
+    ax.imshow(-pred.reshape(xx.shape), cmap="Accent", alpha=.2,
+              extent=(xlim[0], xlim[1], ylim[1], ylim[0]))
+
+    for coef, intercept, col in zip(svm.coef_, svm.intercept_, classes):
+        line2 = -(line * coef[1] + intercept) / coef[0]
+        ax.plot(line2, line, "-", c=colors[col[0]])
+        ax.plot(line2, line, "--", c=colors[col[1]])
+    ax.set_xlim(xlim)
+    ax.set_ylim(ylim)
+    ax.set_title(title)
+    ax.set_aspect("equal")
+
+plt.show()