Pushing the docs to dev/ for branch: master, commit e63bf0ca3940706da42f955947bd1449c7e41102

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\nfrom sklearn import neighbors, datasets\n\nn_neighbors = 15\n\n# import some data to play with\niris = datasets.load_iris()\n\n# we only take the first two features. We could avoid this ugly\n# slicing by using a two-dim dataset\nX = iris.data[:, :2]\ny = iris.target\n\nh = .02  # step size in the mesh\n\n# Create color maps\ncmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])\ncmap_bold = ListedColormap(['darkorange', 'c', 'darkblue'])\n\nfor weights in ['uniform', 'distance']:\n    # we create an instance of Neighbours Classifier and fit the data.\n    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)\n    clf.fit(X, y)\n\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    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n                         np.arange(y_min, y_max, h))\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.figure()\n    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n\n    # Plot also the training points\n    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,\n                edgecolor='k', s=20)\n    plt.xlim(xx.min(), xx.max())\n    plt.ylim(yy.min(), yy.max())\n    plt.title(\"3-Class classification (k = %i, weights = '%s')\"\n              % (n_neighbors, weights))\n\nplt.show()"
+        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\nfrom sklearn import neighbors, datasets\n\nn_neighbors = 15\n\n# import some data to play with\niris = datasets.load_iris()\n\n# we only take the first two features. We could avoid this ugly\n# slicing by using a two-dim dataset\nX = iris.data[:, :2]\ny = iris.target\n\nh = .02  # step size in the mesh\n\n# Create color maps\ncmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])\ncmap_bold = ListedColormap(['darkorange', 'c', 'darkblue'])\n\nfor weights in ['uniform', 'distance']:\n    # we create an instance of Neighbours Classifier and fit the data.\n    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)\n    clf.fit(X, y)\n\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    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n                         np.arange(y_min, y_max, h))\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.figure()\n    plt.contourf(xx, yy, Z, cmap=cmap_light)\n\n    # Plot also the training points\n    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,\n                edgecolor='k', s=20)\n    plt.xlim(xx.min(), xx.max())\n    plt.ylim(yy.min(), yy.max())\n    plt.title(\"3-Class classification (k = %i, weights = '%s')\"\n              % (n_neighbors, weights))\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/47f024d726d245e034c7690b4664721f/plot_classification.ipynb

@@ -45,7 +45,7 @@
     # Put the result into a color plot
     Z = Z.reshape(xx.shape)
     plt.figure()
-    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
+    plt.contourf(xx, yy, Z, cmap=cmap_light)
 
     # Plot also the training points
     plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,