Pushing the docs to dev/ for branch: master, commit 8ebb9a90776a2eb6707beebc7eb42af3cc627ce9

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n# Author: Alexandre Gramfort <[email protected]>\n#         Fabian Pedregosa <[email protected]>\n#\n# License: BSD 3 clause (C) INRIA\n\n\n# #############################################################################\n# Generate sample data\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import neighbors\n\nnp.random.seed(0)\nX = np.sort(5 * np.random.rand(40, 1), axis=0)\nT = np.linspace(0, 5, 500)[:, np.newaxis]\ny = np.sin(X).ravel()\n\n# Add noise to targets\ny[::5] += 1 * (0.5 - np.random.rand(8))\n\n# #############################################################################\n# Fit regression model\nn_neighbors = 5\n\nfor i, weights in enumerate(['uniform', 'distance']):\n    knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)\n    y_ = knn.fit(X, y).predict(T)\n\n    plt.subplot(2, 1, i + 1)\n    plt.scatter(X, y, c='k', label='data')\n    plt.plot(T, y_, c='g', label='prediction')\n    plt.axis('tight')\n    plt.legend()\n    plt.title(\"KNeighborsRegressor (k = %i, weights = '%s')\" % (n_neighbors,\n                                                                weights))\n\nplt.show()"
+        "print(__doc__)\n\n# Author: Alexandre Gramfort <[email protected]>\n#         Fabian Pedregosa <[email protected]>\n#\n# License: BSD 3 clause (C) INRIA\n\n\n# #############################################################################\n# Generate sample data\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import neighbors\n\nnp.random.seed(0)\nX = np.sort(5 * np.random.rand(40, 1), axis=0)\nT = np.linspace(0, 5, 500)[:, np.newaxis]\ny = np.sin(X).ravel()\n\n# Add noise to targets\ny[::5] += 1 * (0.5 - np.random.rand(8))\n\n# #############################################################################\n# Fit regression model\nn_neighbors = 5\n\nfor i, weights in enumerate(['uniform', 'distance']):\n    knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)\n    y_ = knn.fit(X, y).predict(T)\n\n    plt.subplot(2, 1, i + 1)\n    plt.scatter(X, y, c='k', label='data')\n    plt.plot(T, y_, c='g', label='prediction')\n    plt.axis('tight')\n    plt.legend()\n    plt.title(\"KNeighborsRegressor (k = %i, weights = '%s')\" % (n_neighbors,\n                                                                weights))\n\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -46,4 +46,5 @@
     plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors,
                                                                 weights))
 
+plt.tight_layout()
 plt.show()