Pushing the docs to dev/ for branch: master, commit 3ab22f973fb1ff1643fb980075559316478f7bd4

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import signal\n\nfrom sklearn.decomposition import FastICA, PCA\n\n# #############################################################################\n# Generate sample data\nnp.random.seed(0)\nn_samples = 2000\ntime = np.linspace(0, 8, n_samples)\n\ns1 = np.sin(2 * time)  # Signal 1 : sinusoidal signal\ns2 = np.sign(np.sin(3 * time))  # Signal 2 : square signal\ns3 = signal.sawtooth(2 * np.pi * time)  # Signal 3: saw tooth signal\n\nS = np.c_[s1, s2, s3]\nS += 0.2 * np.random.normal(size=S.shape)  # Add noise\n\nS /= S.std(axis=0)  # Standardize data\n# Mix data\nA = np.array([[1, 1, 1], [0.5, 2, 1.0], [1.5, 1.0, 2.0]])  # Mixing matrix\nX = np.dot(S, A.T)  # Generate observations\n\n# Compute ICA\nica = FastICA(n_components=3)\nS_ = ica.fit_transform(X)  # Reconstruct signals\nA_ = ica.mixing_  # Get estimated mixing matrix\n\n# We can `prove` that the ICA model applies by reverting the unmixing.\nassert np.allclose(X, np.dot(S_, A_.T) + ica.mean_)\n\n# For comparison, compute PCA\npca = PCA(n_components=3)\nH = pca.fit_transform(X)  # Reconstruct signals based on orthogonal components\n\n# #############################################################################\n# Plot results\n\nplt.figure()\n\nmodels = [X, S, S_, H]\nnames = ['Observations (mixed signal)',\n         'True Sources',\n         'ICA recovered signals', \n         'PCA recovered signals']\ncolors = ['red', 'steelblue', 'orange']\n\nfor ii, (model, name) in enumerate(zip(models, names), 1):\n    plt.subplot(4, 1, ii)\n    plt.title(name)\n    for sig, color in zip(model.T, colors):\n        plt.plot(sig, color=color)\n\nplt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.46)\nplt.show()"
+        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import signal\n\nfrom sklearn.decomposition import FastICA, PCA\n\n# #############################################################################\n# Generate sample data\nnp.random.seed(0)\nn_samples = 2000\ntime = np.linspace(0, 8, n_samples)\n\ns1 = np.sin(2 * time)  # Signal 1 : sinusoidal signal\ns2 = np.sign(np.sin(3 * time))  # Signal 2 : square signal\ns3 = signal.sawtooth(2 * np.pi * time)  # Signal 3: saw tooth signal\n\nS = np.c_[s1, s2, s3]\nS += 0.2 * np.random.normal(size=S.shape)  # Add noise\n\nS /= S.std(axis=0)  # Standardize data\n# Mix data\nA = np.array([[1, 1, 1], [0.5, 2, 1.0], [1.5, 1.0, 2.0]])  # Mixing matrix\nX = np.dot(S, A.T)  # Generate observations\n\n# Compute ICA\nica = FastICA(n_components=3)\nS_ = ica.fit_transform(X)  # Reconstruct signals\nA_ = ica.mixing_  # Get estimated mixing matrix\n\n# We can `prove` that the ICA model applies by reverting the unmixing.\nassert np.allclose(X, np.dot(S_, A_.T) + ica.mean_)\n\n# For comparison, compute PCA\npca = PCA(n_components=3)\nH = pca.fit_transform(X)  # Reconstruct signals based on orthogonal components\n\n# #############################################################################\n# Plot results\n\nplt.figure()\n\nmodels = [X, S, S_, H]\nnames = ['Observations (mixed signal)',\n         'True Sources',\n         'ICA recovered signals', \n         'PCA recovered signals']\ncolors = ['red', 'steelblue', 'orange']\n\nfor ii, (model, name) in enumerate(zip(models, names), 1):\n    plt.subplot(4, 1, ii)\n    plt.title(name)\n    for sig, color in zip(model.T, colors):\n        plt.plot(sig, color=color)\n\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -69,5 +69,5 @@
     for sig, color in zip(model.T, colors):
         plt.plot(sig, color=color)
 
-plt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.46)
+plt.tight_layout()
 plt.show()