Pushing the docs to dev/ for branch: master, commit 5c98812eb0dce9923430fac51b638cfe99313937

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm, datasets\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data[:, :2]  # we only take the first two features. We could\n                      # avoid this ugly slicing by using a two-dim dataset\ny = iris.target\n\nh = .02  # step size in the mesh\n\n# we create an instance of SVM and fit out data. We do not scale our\n# data since we want to plot the support vectors\nC = 1.0  # SVM regularization parameter\nsvc = svm.SVC(kernel='linear', C=C).fit(X, y)\nrbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X, y)\npoly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X, y)\nlin_svc = svm.LinearSVC(C=C).fit(X, y)\n\n# create a mesh to plot in\nx_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\ny_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n                     np.arange(y_min, y_max, h))\n\n# title for the plots\ntitles = ['SVC with linear kernel',\n          'LinearSVC (linear kernel)',\n          'SVC with RBF kernel',\n          'SVC with polynomial (degree 3) kernel']\n\n\nfor i, clf in enumerate((svc, lin_svc, rbf_svc, poly_svc)):\n    # Plot the decision boundary. For that, we will assign a color to each\n    # point in the mesh [x_min, x_max]x[y_min, y_max].\n    plt.subplot(2, 2, i + 1)\n    plt.subplots_adjust(wspace=0.4, hspace=0.4)\n\n    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n\n    # Put the result into a color plot\n    Z = Z.reshape(xx.shape)\n    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)\n\n    # Plot also the training points\n    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)\n    plt.xlabel('Sepal length')\n    plt.ylabel('Sepal width')\n    plt.xlim(xx.min(), xx.max())\n    plt.ylim(yy.min(), yy.max())\n    plt.xticks(())\n    plt.yticks(())\n    plt.title(titles[i])\n\nplt.show()"
+        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm, datasets\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data[:, :2]  # we only take the first two features. We could\n                      # avoid this ugly slicing by using a two-dim dataset\ny = iris.target\n\nh = .02  # step size in the mesh\n\n# we create an instance of SVM and fit out data. We do not scale our\n# data since we want to plot the support vectors\nC = 1.0  # SVM regularization parameter\nsvc = svm.SVC(kernel='linear', C=C).fit(X, y)\nrbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X, y)\npoly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X, y)\nlin_svc = svm.LinearSVC(C=C).fit(X, y)\n\n# create a mesh to plot in\nx_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\ny_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n                     np.arange(y_min, y_max, h))\n\n# title for the plots\ntitles = ['SVC with linear kernel',\n          'LinearSVC (linear kernel)',\n          'SVC with RBF kernel',\n          'SVC with polynomial (degree 3) kernel']\n\n\nfor i, clf in enumerate((svc, lin_svc, rbf_svc, poly_svc)):\n    # Plot the decision boundary. For that, we will assign a color to each\n    # point in the mesh [x_min, x_max]x[y_min, y_max].\n    plt.subplot(2, 2, i + 1)\n    plt.subplots_adjust(wspace=0.4, hspace=0.4)\n\n    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n\n    # Put the result into a color plot\n    Z = Z.reshape(xx.shape)\n    plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)\n\n    # Plot also the training points\n    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm)\n    plt.xlabel('Sepal length')\n    plt.ylabel('Sepal width')\n    plt.xlim(xx.min(), xx.max())\n    plt.ylim(yy.min(), yy.max())\n    plt.xticks(())\n    plt.yticks(())\n    plt.title(titles[i])\n\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/auto_examples_python.zip

@@ -78,10 +78,10 @@
 
     # Put the result into a color plot
     Z = Z.reshape(xx.shape)
-    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
+    plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)
 
     # Plot also the training points
-    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
+    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm)
     plt.xlabel('Sepal length')
     plt.ylabel('Sepal width')
     plt.xlim(xx.min(), xx.max())