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‎sklearn/decomposition/truncated_svd.py

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@@ -96,7 +96,7 @@ class TruncatedSVD(BaseEstimator, TransformerMixin):
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>>> print(svd.singular_values_) # doctest: +ELLIPSIS
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[2.5841... 2.5245... 2.3201... 2.1753... 2.0443...]
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>>> # to test LOBPCG:
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>>> # to test LOBPCG instead of randomized:
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>>> svd = TruncatedSVD(algorithm='lobpcg', n_components=5, n_iter=7, random_state=42)
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>>> svd.fit(X)
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