Pushing the docs to dev/ for branch: master, commit c298cb771dfcc607d44c8219710f7cefaead39ea

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n# Authors: Alexandre Gramfort, Gael Varoquaux\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.decomposition import PCA, FastICA\n\n# #############################################################################\n# Generate sample data\nrng = np.random.RandomState(42)\nS = rng.standard_t(1.5, size=(20000, 2))\nS[:, 0] *= 2.\n\n# Mix data\nA = np.array([[1, 1], [0, 2]])  # Mixing matrix\n\nX = np.dot(S, A.T)  # Generate observations\n\npca = PCA()\nS_pca_ = pca.fit(X).transform(X)\n\nica = FastICA(random_state=rng)\nS_ica_ = ica.fit(X).transform(X)  # Estimate the sources\n\nS_ica_ /= S_ica_.std(axis=0)\n\n\n# #############################################################################\n# Plot results\n\ndef plot_samples(S, axis_list=None):\n    plt.scatter(S[:, 0], S[:, 1], s=2, marker='o', zorder=10,\n                color='steelblue', alpha=0.5)\n    if axis_list is not None:\n        colors = ['orange', 'red']\n        for color, axis in zip(colors, axis_list):\n            axis /= axis.std()\n            x_axis, y_axis = axis\n            # Trick to get legend to work\n            plt.plot(0.1 * x_axis, 0.1 * y_axis, linewidth=2, color=color)\n            plt.quiver(0, 0, x_axis, y_axis, zorder=11, width=0.01, scale=6,\n                       color=color)\n\n    plt.hlines(0, -3, 3)\n    plt.vlines(0, -3, 3)\n    plt.xlim(-3, 3)\n    plt.ylim(-3, 3)\n    plt.xlabel('x')\n    plt.ylabel('y')\n\nplt.figure()\nplt.subplot(2, 2, 1)\nplot_samples(S / S.std())\nplt.title('True Independent Sources')\n\naxis_list = [pca.components_.T, ica.mixing_]\nplt.subplot(2, 2, 2)\nplot_samples(X / np.std(X), axis_list=axis_list)\nlegend = plt.legend(['PCA', 'ICA'], loc='upper right')\nlegend.set_zorder(100)\n\nplt.title('Observations')\n\nplt.subplot(2, 2, 3)\nplot_samples(S_pca_ / np.std(S_pca_, axis=0))\nplt.title('PCA recovered signals')\n\nplt.subplot(2, 2, 4)\nplot_samples(S_ica_ / np.std(S_ica_))\nplt.title('ICA recovered signals')\n\nplt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.36)\nplt.show()"
+        "print(__doc__)\n\n# Authors: Alexandre Gramfort, Gael Varoquaux\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.decomposition import PCA, FastICA\n\n# #############################################################################\n# Generate sample data\nrng = np.random.RandomState(42)\nS = rng.standard_t(1.5, size=(20000, 2))\nS[:, 0] *= 2.\n\n# Mix data\nA = np.array([[1, 1], [0, 2]])  # Mixing matrix\n\nX = np.dot(S, A.T)  # Generate observations\n\npca = PCA()\nS_pca_ = pca.fit(X).transform(X)\n\nica = FastICA(random_state=rng)\nS_ica_ = ica.fit(X).transform(X)  # Estimate the sources\n\nS_ica_ /= S_ica_.std(axis=0)\n\n\n# #############################################################################\n# Plot results\n\ndef plot_samples(S, axis_list=None):\n    plt.scatter(S[:, 0], S[:, 1], s=2, marker='o', zorder=10,\n                color='steelblue', alpha=0.5)\n    if axis_list is not None:\n        colors = ['orange', 'red']\n        for color, axis in zip(colors, axis_list):\n            axis /= axis.std()\n            x_axis, y_axis = axis\n            # Trick to get legend to work\n            plt.plot(0.1 * x_axis, 0.1 * y_axis, linewidth=2, color=color)\n            plt.quiver((0, 0), (0, 0), x_axis, y_axis, zorder=11, width=0.01,\n                       scale=6, color=color)\n\n    plt.hlines(0, -3, 3)\n    plt.vlines(0, -3, 3)\n    plt.xlim(-3, 3)\n    plt.ylim(-3, 3)\n    plt.xlabel('x')\n    plt.ylabel('y')\n\nplt.figure()\nplt.subplot(2, 2, 1)\nplot_samples(S / S.std())\nplt.title('True Independent Sources')\n\naxis_list = [pca.components_.T, ica.mixing_]\nplt.subplot(2, 2, 2)\nplot_samples(X / np.std(X), axis_list=axis_list)\nlegend = plt.legend(['PCA', 'ICA'], loc='upper right')\nlegend.set_zorder(100)\n\nplt.title('Observations')\n\nplt.subplot(2, 2, 3)\nplot_samples(S_pca_ / np.std(S_pca_, axis=0))\nplt.title('PCA recovered signals')\n\nplt.subplot(2, 2, 4)\nplot_samples(S_ica_ / np.std(S_ica_))\nplt.title('ICA recovered signals')\n\nplt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.36)\nplt.show()"
       ]
     }
   ],

dev/_downloads/4ee88a807e060ca374ab95e0d8d819ed/plot_ica_vs_pca.ipynb

@@ -70,8 +70,8 @@ def plot_samples(S, axis_list=None):
             x_axis, y_axis = axis
             # Trick to get legend to work
             plt.plot(0.1 * x_axis, 0.1 * y_axis, linewidth=2, color=color)
-            plt.quiver(0, 0, x_axis, y_axis, zorder=11, width=0.01, scale=6,
-                       color=color)
+            plt.quiver((0, 0), (0, 0), x_axis, y_axis, zorder=11, width=0.01,
+                       scale=6, color=color)
 
     plt.hlines(0, -3, 3)
     plt.vlines(0, -3, 3)