Parameters: | dataset : string
The name of the example dataset. Can be a .geo file, or one of a
number of example datasets listed below. weights is an fmri dataset
comprised of 36 subjects. For each subject, the rows are fMRI
measurements and the columns are parameters of a model fit to the fMRI data. weights_sample is a sample of 3 subjects from that dataset. weights_avg is the dataset split in half and averaged into two groups. spiral is 3D spiral to
highlight the procrustes function. mushrooms is an example dataset
comprised of features (columns) of a collection of mushroomm samples (rows).
normalize : str or False or None
If set to ‘across’, the columns of the input data will be z-scored
across lists (default). That is, the z-scores will be computed with
with repect to column n across all arrays passed in the list. If set
to ‘within’, the columns will be z-scored within each list that is
passed. If set to ‘row’, each row of the input data will be z-scored.
If set to False, the input data will be returned with no z-scoring.
reduce : str or dict
Decomposition/manifold learning model to use. Models supported: PCA,
IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA,
FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning,
TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, and MDS. Can be
passed as a string, but for finer control of the model parameters, pass
as a dictionary, e.g. reduce={‘model’ : ‘PCA’, ‘params’ : {‘whiten’ : True}}.
See scikit-learn specific model docs for details on parameters supported
for each model.
ndims : int
Number of dimensions to reduce
align : str or dict
If str, either ‘hyper’ or ‘SRM’. If ‘hyper’, alignment algorithm will be
hyperalignment. If ‘SRM’, alignment algorithm will be shared response
model. You can also pass a dictionary for finer control, where the ‘model’
key is a string that specifies the model and the params key is a dictionary
of parameter values (default : ‘hyper’).
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