Pushing the docs to dev/ for branch: master, commit 21eb82dea262f4354e50903c150316887c994101

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: Gael Varoquaux\n# License: BSD 3 clause (C) INRIA 2014\n\nprint(__doc__)\nfrom time import time\n\nimport numpy as np\nfrom scipy import ndimage\nfrom matplotlib import pyplot as plt\n\nfrom sklearn import manifold, datasets\n\ndigits = datasets.load_digits(n_class=10)\nX = digits.data\ny = digits.target\nn_samples, n_features = X.shape\n\nnp.random.seed(0)\n\ndef nudge_images(X, y):\n    # Having a larger dataset shows more clearly the behavior of the\n    # methods, but we multiply the size of the dataset only by 2, as the\n    # cost of the hierarchical clustering methods are strongly\n    # super-linear in n_samples\n    shift = lambda x: ndimage.shift(x.reshape((8, 8)),\n                                  .3 * np.random.normal(size=2),\n                                  mode='constant',\n                                  ).ravel()\n    X = np.concatenate([X, np.apply_along_axis(shift, 1, X)])\n    Y = np.concatenate([y, y], axis=0)\n    return X, Y\n\n\nX, y = nudge_images(X, y)\n\n\n#----------------------------------------------------------------------\n# Visualize the clustering\ndef plot_clustering(X_red, X, labels, title=None):\n    x_min, x_max = np.min(X_red, axis=0), np.max(X_red, axis=0)\n    X_red = (X_red - x_min) / (x_max - x_min)\n\n    plt.figure(figsize=(6, 4))\n    for i in range(X_red.shape[0]):\n        plt.text(X_red[i, 0], X_red[i, 1], str(y[i]),\n                 color=plt.cm.nipy_spectral(labels[i] / 10.),\n                 fontdict={'weight': 'bold', 'size': 9})\n\n    plt.xticks([])\n    plt.yticks([])\n    if title is not None:\n        plt.title(title, size=17)\n    plt.axis('off')\n    plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n\n#----------------------------------------------------------------------\n# 2D embedding of the digits dataset\nprint(\"Computing embedding\")\nX_red = manifold.SpectralEmbedding(n_components=2).fit_transform(X)\nprint(\"Done.\")\n\nfrom sklearn.cluster import AgglomerativeClustering\n\nfor linkage in ('ward', 'average', 'complete', 'single'):\n    clustering = AgglomerativeClustering(linkage=linkage, n_clusters=10)\n    t0 = time()\n    clustering.fit(X_red)\n    print(\"%s :\\t%.2fs\" % (linkage, time() - t0))\n\n    plot_clustering(X_red, X, clustering.labels_, \"%s linkage\" % linkage)\n\n\nplt.show()"
+        "# Authors: Gael Varoquaux\n# License: BSD 3 clause (C) INRIA 2014\n\nprint(__doc__)\nfrom time import time\n\nimport numpy as np\nfrom scipy import ndimage\nfrom matplotlib import pyplot as plt\n\nfrom sklearn import manifold, datasets\n\ndigits = datasets.load_digits(n_class=10)\nX = digits.data\ny = digits.target\nn_samples, n_features = X.shape\n\nnp.random.seed(0)\n\ndef nudge_images(X, y):\n    # Having a larger dataset shows more clearly the behavior of the\n    # methods, but we multiply the size of the dataset only by 2, as the\n    # cost of the hierarchical clustering methods are strongly\n    # super-linear in n_samples\n    shift = lambda x: ndimage.shift(x.reshape((8, 8)),\n                                  .3 * np.random.normal(size=2),\n                                  mode='constant',\n                                  ).ravel()\n    X = np.concatenate([X, np.apply_along_axis(shift, 1, X)])\n    Y = np.concatenate([y, y], axis=0)\n    return X, Y\n\n\nX, y = nudge_images(X, y)\n\n\n#----------------------------------------------------------------------\n# Visualize the clustering\ndef plot_clustering(X_red, labels, title=None):\n    x_min, x_max = np.min(X_red, axis=0), np.max(X_red, axis=0)\n    X_red = (X_red - x_min) / (x_max - x_min)\n\n    plt.figure(figsize=(6, 4))\n    for i in range(X_red.shape[0]):\n        plt.text(X_red[i, 0], X_red[i, 1], str(y[i]),\n                 color=plt.cm.nipy_spectral(labels[i] / 10.),\n                 fontdict={'weight': 'bold', 'size': 9})\n\n    plt.xticks([])\n    plt.yticks([])\n    if title is not None:\n        plt.title(title, size=17)\n    plt.axis('off')\n    plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n\n#----------------------------------------------------------------------\n# 2D embedding of the digits dataset\nprint(\"Computing embedding\")\nX_red = manifold.SpectralEmbedding(n_components=2).fit_transform(X)\nprint(\"Done.\")\n\nfrom sklearn.cluster import AgglomerativeClustering\n\nfor linkage in ('ward', 'average', 'complete', 'single'):\n    clustering = AgglomerativeClustering(linkage=linkage, n_clusters=10)\n    t0 = time()\n    clustering.fit(X_red)\n    print(\"%s :\\t%.2fs\" % (linkage, time() - t0))\n\n    plot_clustering(X_red, clustering.labels_, \"%s linkage\" % linkage)\n\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -56,7 +56,7 @@ def nudge_images(X, y):
 
 #----------------------------------------------------------------------
 # Visualize the clustering
-def plot_clustering(X_red, X, labels, title=None):
+def plot_clustering(X_red, labels, title=None):
     x_min, x_max = np.min(X_red, axis=0), np.max(X_red, axis=0)
     X_red = (X_red - x_min) / (x_max - x_min)
 
@@ -87,7 +87,7 @@ def plot_clustering(X_red, X, labels, title=None):
     clustering.fit(X_red)
     print("%s :\t%.2fs" % (linkage, time() - t0))
 
-    plot_clustering(X_red, X, clustering.labels_, "%s linkage" % linkage)
+    plot_clustering(X_red, clustering.labels_, "%s linkage" % linkage)
 
 
 plt.show()