Pushing the docs to dev/ for branch: master, commit cee79b05b815eb370502cd9e36b944fa3efad06e

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import make_classification\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom matplotlib import cm\nfrom sklearn.utils.fixes import logsumexp\n\nprint(__doc__)\n\nn_neighbors = 1\nrandom_state = 0\n\n# Create a tiny data set of 9 samples from 3 classes\nX, y = make_classification(n_samples=9, n_features=2, n_informative=2,\n                           n_redundant=0, n_classes=3, n_clusters_per_class=1,\n                           class_sep=1.0, random_state=random_state)\n\n# Plot the points in the original space\nplt.figure()\nax = plt.gca()\n\n# Draw the graph nodes\nfor i in range(X.shape[0]):\n    ax.text(X[i, 0], X[i, 1], str(i), va='center', ha='center')\n    ax.scatter(X[i, 0], X[i, 1], s=300, c=cm.Set1(y[i]), alpha=0.4)\n\n\ndef p_i(X, i):\n    diff_embedded = X[i] - X\n    dist_embedded = np.einsum('ij,ij->i', diff_embedded,\n                              diff_embedded)\n    dist_embedded[i] = np.inf\n\n    # compute exponentiated distances (use the log-sum-exp trick to\n    # avoid numerical instabilities\n    exp_dist_embedded = np.exp(-dist_embedded -\n                               logsumexp(-dist_embedded))\n    return exp_dist_embedded\n\n\ndef relate_point(X, i, ax):\n    pt_i = X[i]\n    for j, pt_j in enumerate(X):\n        thickness = p_i(X, i)\n        if i != j:\n            line = ([pt_i[0], pt_j[0]], [pt_i[1], pt_j[1]])\n            ax.plot(*line, c=cm.Set1(y[j]),\n                    linewidth=5*thickness[j])\n\n\n# we consider only point 3\ni = 3\n\n# Plot bonds linked to sample i in the original space\nrelate_point(X, i, ax)\nax.set_title(\"Original points\")\nax.axes.get_xaxis().set_visible(False)\nax.axes.get_yaxis().set_visible(False)\nax.axis('equal')\n\n# Learn an embedding with NeighborhoodComponentsAnalysis\nnca = NeighborhoodComponentsAnalysis(max_iter=30, random_state=random_state)\nnca = nca.fit(X, y)\n\n# Plot the points after transformation with NeighborhoodComponentsAnalysis\nplt.figure()\nax2 = plt.gca()\n\n# Get the embedding and find the new nearest neighbors\nX_embedded = nca.transform(X)\n\nrelate_point(X_embedded, i, ax2)\n\nfor i in range(len(X)):\n    ax2.text(X_embedded[i, 0], X_embedded[i, 1], str(i),\n             va='center', ha='center')\n    ax2.scatter(X_embedded[i, 0], X_embedded[i, 1], s=300, c=cm.Set1(y[i]),\n                alpha=0.4)\n\n# Make axes equal so that boundaries are displayed correctly as circles\nax2.set_title(\"NCA embedding\")\nax2.axes.get_xaxis().set_visible(False)\nax2.axes.get_yaxis().set_visible(False)\nax2.axis('equal')\nplt.show()"
+        "# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import make_classification\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom matplotlib import cm\nfrom sklearn.utils.fixes import logsumexp\n\nprint(__doc__)\n\nn_neighbors = 1\nrandom_state = 0\n\n# Create a tiny data set of 9 samples from 3 classes\nX, y = make_classification(n_samples=9, n_features=2, n_informative=2,\n                           n_redundant=0, n_classes=3, n_clusters_per_class=1,\n                           class_sep=1.0, random_state=random_state)\n\n# Plot the points in the original space\nplt.figure()\nax = plt.gca()\n\n# Draw the graph nodes\nfor i in range(X.shape[0]):\n    ax.text(X[i, 0], X[i, 1], str(i), va='center', ha='center')\n    ax.scatter(X[i, 0], X[i, 1], s=300, c=cm.Set1(y[[i]]), alpha=0.4)\n\ndef p_i(X, i):\n    diff_embedded = X[i] - X\n    dist_embedded = np.einsum('ij,ij->i', diff_embedded,\n                              diff_embedded)\n    dist_embedded[i] = np.inf\n\n    # compute exponentiated distances (use the log-sum-exp trick to\n    # avoid numerical instabilities\n    exp_dist_embedded = np.exp(-dist_embedded -\n                               logsumexp(-dist_embedded))\n    return exp_dist_embedded\n\n\ndef relate_point(X, i, ax):\n    pt_i = X[i]\n    for j, pt_j in enumerate(X):\n        thickness = p_i(X, i)\n        if i != j:\n            line = ([pt_i[0], pt_j[0]], [pt_i[1], pt_j[1]])\n            ax.plot(*line, c=cm.Set1(y[j]),\n                    linewidth=5*thickness[j])\n\n\n# we consider only point 3\ni = 3\n\n# Plot bonds linked to sample i in the original space\nrelate_point(X, i, ax)\nax.set_title(\"Original points\")\nax.axes.get_xaxis().set_visible(False)\nax.axes.get_yaxis().set_visible(False)\nax.axis('equal')\n\n# Learn an embedding with NeighborhoodComponentsAnalysis\nnca = NeighborhoodComponentsAnalysis(max_iter=30, random_state=random_state)\nnca = nca.fit(X, y)\n\n# Plot the points after transformation with NeighborhoodComponentsAnalysis\nplt.figure()\nax2 = plt.gca()\n\n# Get the embedding and find the new nearest neighbors\nX_embedded = nca.transform(X)\n\nrelate_point(X_embedded, i, ax2)\n\nfor i in range(len(X)):\n    ax2.text(X_embedded[i, 0], X_embedded[i, 1], str(i),\n             va='center', ha='center')\n    ax2.scatter(X_embedded[i, 0], X_embedded[i, 1], s=300, c=cm.Set1(y[[i]]),\n                alpha=0.4)\n\n# Make axes equal so that boundaries are displayed correctly as circles\nax2.set_title(\"NCA embedding\")\nax2.axes.get_xaxis().set_visible(False)\nax2.axes.get_yaxis().set_visible(False)\nax2.axis('equal')\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -35,8 +35,7 @@
 # Draw the graph nodes
 for i in range(X.shape[0]):
     ax.text(X[i, 0], X[i, 1], str(i), va='center', ha='center')
-    ax.scatter(X[i, 0], X[i, 1], s=300, c=cm.Set1(y[i]), alpha=0.4)
-
+    ax.scatter(X[i, 0], X[i, 1], s=300, c=cm.Set1(y[[i]]), alpha=0.4)
 
 def p_i(X, i):
     diff_embedded = X[i] - X
@@ -87,7 +86,7 @@ def relate_point(X, i, ax):
 for i in range(len(X)):
     ax2.text(X_embedded[i, 0], X_embedded[i, 1], str(i),
              va='center', ha='center')
-    ax2.scatter(X_embedded[i, 0], X_embedded[i, 1], s=300, c=cm.Set1(y[i]),
+    ax2.scatter(X_embedded[i, 0], X_embedded[i, 1], s=300, c=cm.Set1(y[[i]]),
                 alpha=0.4)
 
 # Make axes equal so that boundaries are displayed correctly as circles