Pushing the docs to dev/ for branch: main, commit 70442b9ee3753a6d63a9b2bfd35e3e51cadce230

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.decomposition import FastICA, PCA\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"
+        "from sklearn.decomposition import FastICA, PCA\n\n# Compute ICA\nica = FastICA(n_components=3, whiten=\"arbitrary-variance\")\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"
       ]
     },
     {

dev/_downloads/3018e0d07b27e25beba9764116709763/plot_ica_blind_source_separation.ipynb

@@ -44,7 +44,7 @@
 from sklearn.decomposition import FastICA, PCA
 
 # Compute ICA
-ica = FastICA(n_components=3)
+ica = FastICA(n_components=3, whiten="arbitrary-variance")
 S_ = ica.fit_transform(X)  # Reconstruct signals
 A_ = ica.mixing_  # Get estimated mixing matrix