Pushing the docs to dev/ for branch: master, commit d0ce40e8b27235a550dc7c23d2fdfed929be555b

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Author: Jake Vanderplas -- <[email protected]>\n\nprint(__doc__)\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import manifold, datasets\n\n# Next line to silence pyflakes. This import is needed.\nAxes3D\n\nn_points = 1000\nX, color = datasets.samples_generator.make_s_curve(n_points, random_state=0)\nn_neighbors = 10\nn_components = 2\n\nfig = plt.figure(figsize=(15, 8))\nplt.suptitle(\"Manifold Learning with %i points, %i neighbors\"\n             % (1000, n_neighbors), fontsize=14)\n\ntry:\n    # compatibility matplotlib < 1.0\n    ax = fig.add_subplot(251, projection='3d')\n    ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)\n    ax.view_init(4, -72)\nexcept:\n    ax = fig.add_subplot(251, projection='3d')\n    plt.scatter(X[:, 0], X[:, 2], c=color, cmap=plt.cm.Spectral)\n\nmethods = ['standard', 'ltsa', 'hessian', 'modified']\nlabels = ['LLE', 'LTSA', 'Hessian LLE', 'Modified LLE']\n\nfor i, method in enumerate(methods):\n    t0 = time()\n    Y = manifold.LocallyLinearEmbedding(n_neighbors, n_components,\n                                        eigen_solver='auto',\n                                        method=method).fit_transform(X)\n    t1 = time()\n    print(\"%s: %.2g sec\" % (methods[i], t1 - t0))\n\n    ax = fig.add_subplot(252 + i)\n    plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\n    plt.title(\"%s (%.2g sec)\" % (labels[i], t1 - t0))\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    plt.axis('tight')\n\nt0 = time()\nY = manifold.Isomap(n_neighbors, n_components).fit_transform(X)\nt1 = time()\nprint(\"Isomap: %.2g sec\" % (t1 - t0))\nax = fig.add_subplot(257)\nplt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\nplt.title(\"Isomap (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\n\nt0 = time()\nmds = manifold.MDS(n_components, max_iter=100, n_init=1)\nY = mds.fit_transform(X)\nt1 = time()\nprint(\"MDS: %.2g sec\" % (t1 - t0))\nax = fig.add_subplot(258)\nplt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\nplt.title(\"MDS (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\n\nt0 = time()\nse = manifold.SpectralEmbedding(n_components=n_components,\n                                n_neighbors=n_neighbors)\nY = se.fit_transform(X)\nt1 = time()\nprint(\"SpectralEmbedding: %.2g sec\" % (t1 - t0))\nax = fig.add_subplot(259)\nplt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\nplt.title(\"SpectralEmbedding (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\nt0 = time()\ntsne = manifold.TSNE(n_components=n_components, init='pca', random_state=0)\nY = tsne.fit_transform(X)\nt1 = time()\nprint(\"t-SNE: %.2g sec\" % (t1 - t0))\nax = fig.add_subplot(2, 5, 10)\nplt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\nplt.title(\"t-SNE (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\nplt.show()"
+        "# Author: Jake Vanderplas -- <[email protected]>\n\nprint(__doc__)\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import manifold, datasets\n\n# Next line to silence pyflakes. This import is needed.\nAxes3D\n\nn_points = 1000\nX, color = datasets.samples_generator.make_s_curve(n_points, random_state=0)\nn_neighbors = 10\nn_components = 2\n\nfig = plt.figure(figsize=(15, 8))\nplt.suptitle(\"Manifold Learning with %i points, %i neighbors\"\n             % (1000, n_neighbors), fontsize=14)\n\n\nax = fig.add_subplot(251, projection='3d')\nax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)\nax.view_init(4, -72)\n\nmethods = ['standard', 'ltsa', 'hessian', 'modified']\nlabels = ['LLE', 'LTSA', 'Hessian LLE', 'Modified LLE']\n\nfor i, method in enumerate(methods):\n    t0 = time()\n    Y = manifold.LocallyLinearEmbedding(n_neighbors, n_components,\n                                        eigen_solver='auto',\n                                        method=method).fit_transform(X)\n    t1 = time()\n    print(\"%s: %.2g sec\" % (methods[i], t1 - t0))\n\n    ax = fig.add_subplot(252 + i)\n    plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\n    plt.title(\"%s (%.2g sec)\" % (labels[i], t1 - t0))\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    plt.axis('tight')\n\nt0 = time()\nY = manifold.Isomap(n_neighbors, n_components).fit_transform(X)\nt1 = time()\nprint(\"Isomap: %.2g sec\" % (t1 - t0))\nax = fig.add_subplot(257)\nplt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\nplt.title(\"Isomap (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\n\nt0 = time()\nmds = manifold.MDS(n_components, max_iter=100, n_init=1)\nY = mds.fit_transform(X)\nt1 = time()\nprint(\"MDS: %.2g sec\" % (t1 - t0))\nax = fig.add_subplot(258)\nplt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\nplt.title(\"MDS (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\n\nt0 = time()\nse = manifold.SpectralEmbedding(n_components=n_components,\n                                n_neighbors=n_neighbors)\nY = se.fit_transform(X)\nt1 = time()\nprint(\"SpectralEmbedding: %.2g sec\" % (t1 - t0))\nax = fig.add_subplot(259)\nplt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\nplt.title(\"SpectralEmbedding (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\nt0 = time()\ntsne = manifold.TSNE(n_components=n_components, init='pca', random_state=0)\nY = tsne.fit_transform(X)\nt1 = time()\nprint(\"t-SNE: %.2g sec\" % (t1 - t0))\nax = fig.add_subplot(2, 5, 10)\nplt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)\nplt.title(\"t-SNE (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/auto_examples_python.zip

@@ -43,14 +43,10 @@
 plt.suptitle("Manifold Learning with %i points, %i neighbors"
              % (1000, n_neighbors), fontsize=14)
 
-try:
-    # compatibility matplotlib < 1.0
-    ax = fig.add_subplot(251, projection='3d')
-    ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)
-    ax.view_init(4, -72)
-except:
-    ax = fig.add_subplot(251, projection='3d')
-    plt.scatter(X[:, 0], X[:, 2], c=color, cmap=plt.cm.Spectral)
+
+ax = fig.add_subplot(251, projection='3d')
+ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)
+ax.view_init(4, -72)
 
 methods = ['standard', 'ltsa', 'hessian', 'modified']
 labels = ['LLE', 'LTSA', 'Hessian LLE', 'Modified LLE']

dev/_downloads/plot_compare_methods.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Author: Jaques Grobler <[email protected]>\n# License: BSD 3 clause\n\nprint(__doc__)\n\nfrom time import time\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import manifold\nfrom sklearn.utils import check_random_state\n\n# Next line to silence pyflakes.\nAxes3D\n\n# Variables for manifold learning.\nn_neighbors = 10\nn_samples = 1000\n\n# Create our sphere.\nrandom_state = check_random_state(0)\np = random_state.rand(n_samples) * (2 * np.pi - 0.55)\nt = random_state.rand(n_samples) * np.pi\n\n# Sever the poles from the sphere.\nindices = ((t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8))))\ncolors = p[indices]\nx, y, z = np.sin(t[indices]) * np.cos(p[indices]), \\\n    np.sin(t[indices]) * np.sin(p[indices]), \\\n    np.cos(t[indices])\n\n# Plot our dataset.\nfig = plt.figure(figsize=(15, 8))\nplt.suptitle(\"Manifold Learning with %i points, %i neighbors\"\n             % (1000, n_neighbors), fontsize=14)\n\nax = fig.add_subplot(251, projection='3d')\nax.scatter(x, y, z, c=p[indices], cmap=plt.cm.rainbow)\ntry:\n    # compatibility matplotlib < 1.0\n    ax.view_init(40, -10)\nexcept:\n    pass\n\nsphere_data = np.array([x, y, z]).T\n\n# Perform Locally Linear Embedding Manifold learning\nmethods = ['standard', 'ltsa', 'hessian', 'modified']\nlabels = ['LLE', 'LTSA', 'Hessian LLE', 'Modified LLE']\n\nfor i, method in enumerate(methods):\n    t0 = time()\n    trans_data = manifold\\\n        .LocallyLinearEmbedding(n_neighbors, 2,\n                                method=method).fit_transform(sphere_data).T\n    t1 = time()\n    print(\"%s: %.2g sec\" % (methods[i], t1 - t0))\n\n    ax = fig.add_subplot(252 + i)\n    plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\n    plt.title(\"%s (%.2g sec)\" % (labels[i], t1 - t0))\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    plt.axis('tight')\n\n# Perform Isomap Manifold learning.\nt0 = time()\ntrans_data = manifold.Isomap(n_neighbors, n_components=2)\\\n    .fit_transform(sphere_data).T\nt1 = time()\nprint(\"%s: %.2g sec\" % ('ISO', t1 - t0))\n\nax = fig.add_subplot(257)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"%s (%.2g sec)\" % ('Isomap', t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\n# Perform Multi-dimensional scaling.\nt0 = time()\nmds = manifold.MDS(2, max_iter=100, n_init=1)\ntrans_data = mds.fit_transform(sphere_data).T\nt1 = time()\nprint(\"MDS: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(258)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"MDS (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\n# Perform Spectral Embedding.\nt0 = time()\nse = manifold.SpectralEmbedding(n_components=2,\n                                n_neighbors=n_neighbors)\ntrans_data = se.fit_transform(sphere_data).T\nt1 = time()\nprint(\"Spectral Embedding: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(259)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"Spectral Embedding (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\n# Perform t-distributed stochastic neighbor embedding.\nt0 = time()\ntsne = manifold.TSNE(n_components=2, init='pca', random_state=0)\ntrans_data = tsne.fit_transform(sphere_data).T\nt1 = time()\nprint(\"t-SNE: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(2, 5, 10)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"t-SNE (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\nplt.show()"
+        "# Author: Jaques Grobler <[email protected]>\n# License: BSD 3 clause\n\nprint(__doc__)\n\nfrom time import time\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib.ticker import NullFormatter\n\nfrom sklearn import manifold\nfrom sklearn.utils import check_random_state\n\n# Next line to silence pyflakes.\nAxes3D\n\n# Variables for manifold learning.\nn_neighbors = 10\nn_samples = 1000\n\n# Create our sphere.\nrandom_state = check_random_state(0)\np = random_state.rand(n_samples) * (2 * np.pi - 0.55)\nt = random_state.rand(n_samples) * np.pi\n\n# Sever the poles from the sphere.\nindices = ((t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8))))\ncolors = p[indices]\nx, y, z = np.sin(t[indices]) * np.cos(p[indices]), \\\n    np.sin(t[indices]) * np.sin(p[indices]), \\\n    np.cos(t[indices])\n\n# Plot our dataset.\nfig = plt.figure(figsize=(15, 8))\nplt.suptitle(\"Manifold Learning with %i points, %i neighbors\"\n             % (1000, n_neighbors), fontsize=14)\n\nax = fig.add_subplot(251, projection='3d')\nax.scatter(x, y, z, c=p[indices], cmap=plt.cm.rainbow)\nax.view_init(40, -10)\n\nsphere_data = np.array([x, y, z]).T\n\n# Perform Locally Linear Embedding Manifold learning\nmethods = ['standard', 'ltsa', 'hessian', 'modified']\nlabels = ['LLE', 'LTSA', 'Hessian LLE', 'Modified LLE']\n\nfor i, method in enumerate(methods):\n    t0 = time()\n    trans_data = manifold\\\n        .LocallyLinearEmbedding(n_neighbors, 2,\n                                method=method).fit_transform(sphere_data).T\n    t1 = time()\n    print(\"%s: %.2g sec\" % (methods[i], t1 - t0))\n\n    ax = fig.add_subplot(252 + i)\n    plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\n    plt.title(\"%s (%.2g sec)\" % (labels[i], t1 - t0))\n    ax.xaxis.set_major_formatter(NullFormatter())\n    ax.yaxis.set_major_formatter(NullFormatter())\n    plt.axis('tight')\n\n# Perform Isomap Manifold learning.\nt0 = time()\ntrans_data = manifold.Isomap(n_neighbors, n_components=2)\\\n    .fit_transform(sphere_data).T\nt1 = time()\nprint(\"%s: %.2g sec\" % ('ISO', t1 - t0))\n\nax = fig.add_subplot(257)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"%s (%.2g sec)\" % ('Isomap', t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\n# Perform Multi-dimensional scaling.\nt0 = time()\nmds = manifold.MDS(2, max_iter=100, n_init=1)\ntrans_data = mds.fit_transform(sphere_data).T\nt1 = time()\nprint(\"MDS: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(258)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"MDS (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\n# Perform Spectral Embedding.\nt0 = time()\nse = manifold.SpectralEmbedding(n_components=2,\n                                n_neighbors=n_neighbors)\ntrans_data = se.fit_transform(sphere_data).T\nt1 = time()\nprint(\"Spectral Embedding: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(259)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"Spectral Embedding (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\n# Perform t-distributed stochastic neighbor embedding.\nt0 = time()\ntsne = manifold.TSNE(n_components=2, init='pca', random_state=0)\ntrans_data = tsne.fit_transform(sphere_data).T\nt1 = time()\nprint(\"t-SNE: %.2g sec\" % (t1 - t0))\n\nax = fig.add_subplot(2, 5, 10)\nplt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)\nplt.title(\"t-SNE (%.2g sec)\" % (t1 - t0))\nax.xaxis.set_major_formatter(NullFormatter())\nax.yaxis.set_major_formatter(NullFormatter())\nplt.axis('tight')\n\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/plot_compare_methods.py

@@ -68,11 +68,7 @@
 
 ax = fig.add_subplot(251, projection='3d')
 ax.scatter(x, y, z, c=p[indices], cmap=plt.cm.rainbow)
-try:
-    # compatibility matplotlib < 1.0
-    ax.view_init(40, -10)
-except:
-    pass
+ax.view_init(40, -10)
 
 sphere_data = np.array([x, y, z]).T
 

dev/_downloads/plot_manifold_sphere.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Author: Fabian Pedregosa -- <[email protected]>\n# License: BSD 3 clause (C) INRIA 2011\n\nprint(__doc__)\n\nimport matplotlib.pyplot as plt\n\n# This import is needed to modify the way figure behaves\nfrom mpl_toolkits.mplot3d import Axes3D\nAxes3D\n\n#----------------------------------------------------------------------\n# Locally linear embedding of the swiss roll\n\nfrom sklearn import manifold, datasets\nX, color = datasets.samples_generator.make_swiss_roll(n_samples=1500)\n\nprint(\"Computing LLE embedding\")\nX_r, err = manifold.locally_linear_embedding(X, n_neighbors=12,\n                                             n_components=2)\nprint(\"Done. Reconstruction error: %g\" % err)\n\n#----------------------------------------------------------------------\n# Plot result\n\nfig = plt.figure()\ntry:\n    # compatibility matplotlib < 1.0\n    ax = fig.add_subplot(211, projection='3d')\n    ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)\nexcept:\n    ax = fig.add_subplot(211)\n    ax.scatter(X[:, 0], X[:, 2], c=color, cmap=plt.cm.Spectral)\n\nax.set_title(\"Original data\")\nax = fig.add_subplot(212)\nax.scatter(X_r[:, 0], X_r[:, 1], c=color, cmap=plt.cm.Spectral)\nplt.axis('tight')\nplt.xticks([]), plt.yticks([])\nplt.title('Projected data')\nplt.show()"
+        "# Author: Fabian Pedregosa -- <[email protected]>\n# License: BSD 3 clause (C) INRIA 2011\n\nprint(__doc__)\n\nimport matplotlib.pyplot as plt\n\n# This import is needed to modify the way figure behaves\nfrom mpl_toolkits.mplot3d import Axes3D\nAxes3D\n\n#----------------------------------------------------------------------\n# Locally linear embedding of the swiss roll\n\nfrom sklearn import manifold, datasets\nX, color = datasets.samples_generator.make_swiss_roll(n_samples=1500)\n\nprint(\"Computing LLE embedding\")\nX_r, err = manifold.locally_linear_embedding(X, n_neighbors=12,\n                                             n_components=2)\nprint(\"Done. Reconstruction error: %g\" % err)\n\n#----------------------------------------------------------------------\n# Plot result\n\nfig = plt.figure()\n\nax = fig.add_subplot(211, projection='3d')\nax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)\n\nax.set_title(\"Original data\")\nax = fig.add_subplot(212)\nax.scatter(X_r[:, 0], X_r[:, 1], c=color, cmap=plt.cm.Spectral)\nplt.axis('tight')\nplt.xticks([]), plt.yticks([])\nplt.title('Projected data')\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/plot_manifold_sphere.py

@@ -33,13 +33,9 @@
 # Plot result
 
 fig = plt.figure()
-try:
-    # compatibility matplotlib < 1.0
-    ax = fig.add_subplot(211, projection='3d')
-    ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)
-except:
-    ax = fig.add_subplot(211)
-    ax.scatter(X[:, 0], X[:, 2], c=color, cmap=plt.cm.Spectral)
+
+ax = fig.add_subplot(211, projection='3d')
+ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)
 
 ax.set_title("Original data")
 ax = fig.add_subplot(212)