timecorr.reduce

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timecorr.reduce#

timecorr.reduce(corrs, rfun=None)[source]#
Parameters:

  • corrs – a matrix of vectorized correlation matrices (output of mat2vec), or a list of such matrices

  • rfun –

    function to use for dimensionality reduction. All hypertools and scikit-learn functions are supported: PCA, IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA, FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning, TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, MDS, and UMAP.

    Can be passed as a string, but for finer control of the model parameters, pass as a dictionary, e.g. reduction={‘model’ : ‘PCA’, ‘params’ : {‘whiten’ : True}}.

    See scikit-learn specific model docs for details on parameters supported for each model.

    Another option is to use graph theoretic measures computed for each node. The following measures are supported: eigenvector_centrality, pagerank_centrality, and strength. (Each of these must be specified as a string; dictionaries not supported.)

    Default: None (no dimensionality reduction)

Returns:

dimensionality-reduced (or original) correlation matrices

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