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51 lines
1.5 KiB
51 lines
1.5 KiB
"""Matrix decomposition algorithms.
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These include PCA, NMF, ICA, and more. Most of the algorithms of this module can be
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regarded as dimensionality reduction techniques.
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"""
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# Authors: The scikit-learn developers
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# SPDX-License-Identifier: BSD-3-Clause
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from sklearn.decomposition._dict_learning import (
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DictionaryLearning,
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MiniBatchDictionaryLearning,
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SparseCoder,
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dict_learning,
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dict_learning_online,
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sparse_encode,
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)
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from sklearn.decomposition._factor_analysis import FactorAnalysis
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from sklearn.decomposition._fastica import FastICA, fastica
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from sklearn.decomposition._incremental_pca import IncrementalPCA
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from sklearn.decomposition._kernel_pca import KernelPCA
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from sklearn.decomposition._lda import LatentDirichletAllocation
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from sklearn.decomposition._nmf import NMF, MiniBatchNMF, non_negative_factorization
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from sklearn.decomposition._pca import PCA
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from sklearn.decomposition._sparse_pca import MiniBatchSparsePCA, SparsePCA
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from sklearn.decomposition._truncated_svd import TruncatedSVD
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from sklearn.utils.extmath import randomized_svd
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__all__ = [
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"NMF",
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"PCA",
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"DictionaryLearning",
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"FactorAnalysis",
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"FastICA",
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"IncrementalPCA",
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"KernelPCA",
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"LatentDirichletAllocation",
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"MiniBatchDictionaryLearning",
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"MiniBatchNMF",
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"MiniBatchSparsePCA",
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"SparseCoder",
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"SparsePCA",
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"TruncatedSVD",
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"dict_learning",
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"dict_learning_online",
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"fastica",
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"non_negative_factorization",
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"randomized_svd",
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"sparse_encode",
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]
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