Pushing the docs to dev/ for branch: master, commit 48c1c41ef087f14bbd695b86ad548819c6986b3e

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n# Author: Gael Varoquaux [email protected]\n# License: BSD 3 clause\n\nfrom datetime import datetime\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib.collections import LineCollection\nfrom six.moves.urllib.request import urlopen\nfrom six.moves.urllib.parse import urlencode\nfrom sklearn import cluster, covariance, manifold"
+        "print(__doc__)\n\n# Author: Gael Varoquaux [email protected]\n# License: BSD 3 clause\n\nfrom datetime import datetime\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.collections import LineCollection\nfrom six.moves.urllib.request import urlopen\nfrom six.moves.urllib.parse import urlencode\nfrom sklearn import cluster, covariance, manifold"
       ]
     },
     {
@@ -116,7 +116,7 @@
       },
       "outputs": [],
       "source": [
-        "plt.figure(1, facecolor='w', figsize=(10, 8))\nplt.clf()\nax = plt.axes([0., 0., 1., 1.])\nplt.axis('off')\n\n# Display a graph of the partial correlations\npartial_correlations = edge_model.precision_.copy()\nd = 1 / np.sqrt(np.diag(partial_correlations))\npartial_correlations *= d\npartial_correlations *= d[:, np.newaxis]\nnon_zero = (np.abs(np.triu(partial_correlations, k=1)) > 0.02)\n\n# Plot the nodes using the coordinates of our embedding\nplt.scatter(embedding[0], embedding[1], s=100 * d ** 2, c=labels,\n            cmap=plt.cm.spectral)\n\n# Plot the edges\nstart_idx, end_idx = np.where(non_zero)\n#a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [[embedding[:, start], embedding[:, stop]]\n            for start, stop in zip(start_idx, end_idx)]\nvalues = np.abs(partial_correlations[non_zero])\nlc = LineCollection(segments,\n                    zorder=0, cmap=plt.cm.hot_r,\n                    norm=plt.Normalize(0, .7 * values.max()))\nlc.set_array(values)\nlc.set_linewidths(15 * values)\nax.add_collection(lc)\n\n# Add a label to each node. The challenge here is that we want to\n# position the labels to avoid overlap with other labels\nfor index, (name, label, (x, y)) in enumerate(\n        zip(names, labels, embedding.T)):\n\n    dx = x - embedding[0]\n    dx[index] = 1\n    dy = y - embedding[1]\n    dy[index] = 1\n    this_dx = dx[np.argmin(np.abs(dy))]\n    this_dy = dy[np.argmin(np.abs(dx))]\n    if this_dx > 0:\n        horizontalalignment = 'left'\n        x = x + .002\n    else:\n        horizontalalignment = 'right'\n        x = x - .002\n    if this_dy > 0:\n        verticalalignment = 'bottom'\n        y = y + .002\n    else:\n        verticalalignment = 'top'\n        y = y - .002\n    plt.text(x, y, name, size=10,\n             horizontalalignment=horizontalalignment,\n             verticalalignment=verticalalignment,\n             bbox=dict(facecolor='w',\n                       edgecolor=plt.cm.spectral(label / float(n_labels)),\n                       alpha=.6))\n\nplt.xlim(embedding[0].min() - .15 * embedding[0].ptp(),\n         embedding[0].max() + .10 * embedding[0].ptp(),)\nplt.ylim(embedding[1].min() - .03 * embedding[1].ptp(),\n         embedding[1].max() + .03 * embedding[1].ptp())\n\nplt.show()"
+        "plt.figure(1, facecolor='w', figsize=(10, 8))\nplt.clf()\nax = plt.axes([0., 0., 1., 1.])\nplt.axis('off')\n\n# Display a graph of the partial correlations\npartial_correlations = edge_model.precision_.copy()\nd = 1 / np.sqrt(np.diag(partial_correlations))\npartial_correlations *= d\npartial_correlations *= d[:, np.newaxis]\nnon_zero = (np.abs(np.triu(partial_correlations, k=1)) > 0.02)\n\n# Plot the nodes using the coordinates of our embedding\nplt.scatter(embedding[0], embedding[1], s=100 * d ** 2, c=labels,\n            cmap=plt.cm.spectral)\n\n# Plot the edges\nstart_idx, end_idx = np.where(non_zero)\n# a sequence of (*line0*, *line1*, *line2*), where::\n#            linen = (x0, y0), (x1, y1), ... (xm, ym)\nsegments = [[embedding[:, start], embedding[:, stop]]\n            for start, stop in zip(start_idx, end_idx)]\nvalues = np.abs(partial_correlations[non_zero])\nlc = LineCollection(segments,\n                    zorder=0, cmap=plt.cm.hot_r,\n                    norm=plt.Normalize(0, .7 * values.max()))\nlc.set_array(values)\nlc.set_linewidths(15 * values)\nax.add_collection(lc)\n\n# Add a label to each node. The challenge here is that we want to\n# position the labels to avoid overlap with other labels\nfor index, (name, label, (x, y)) in enumerate(\n        zip(names, labels, embedding.T)):\n\n    dx = x - embedding[0]\n    dx[index] = 1\n    dy = y - embedding[1]\n    dy[index] = 1\n    this_dx = dx[np.argmin(np.abs(dy))]\n    this_dy = dy[np.argmin(np.abs(dx))]\n    if this_dx > 0:\n        horizontalalignment = 'left'\n        x = x + .002\n    else:\n        horizontalalignment = 'right'\n        x = x - .002\n    if this_dy > 0:\n        verticalalignment = 'bottom'\n        y = y + .002\n    else:\n        verticalalignment = 'top'\n        y = y - .002\n    plt.text(x, y, name, size=10,\n             horizontalalignment=horizontalalignment,\n             verticalalignment=verticalalignment,\n             bbox=dict(facecolor='w',\n                       edgecolor=plt.cm.spectral(label / float(n_labels)),\n                       alpha=.6))\n\nplt.xlim(embedding[0].min() - .15 * embedding[0].ptp(),\n         embedding[0].max() + .10 * embedding[0].ptp(),)\nplt.ylim(embedding[1].min() - .03 * embedding[1].ptp(),\n         embedding[1].max() + .03 * embedding[1].ptp())\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -67,12 +67,13 @@
 from datetime import datetime
 
 import numpy as np
-from matplotlib import pyplot as plt
+import matplotlib.pyplot as plt
 from matplotlib.collections import LineCollection
 from six.moves.urllib.request import urlopen
 from six.moves.urllib.parse import urlencode
 from sklearn import cluster, covariance, manifold
 
+
 ###############################################################################
 # Retrieve the data from Internet
 
@@ -239,7 +240,7 @@ def quotes_historical_google(symbol, date1, date2):
 
 # Plot the edges
 start_idx, end_idx = np.where(non_zero)
-#a sequence of (*line0*, *line1*, *line2*), where::
+# a sequence of (*line0*, *line1*, *line2*), where::
 #            linen = (x0, y0), (x1, y1), ... (xm, ym)
 segments = [[embedding[:, start], embedding[:, stop]]
             for start, stop in zip(start_idx, end_idx)]