mne_rsa.dsm_array¶

class mne_rsa.dsm_array(X, patches=None, dist_metric='correlation', dist_params={}, y=None, n_folds=1)[source]¶

Generate DSMs from an array of data, possibly in a searchlight pattern.

First use searchlight to compute the searchlight patches. Then you can use this function to compute DSMs for each searchlight patch.

Parameters:

Xndarray, shape (n_items, n_series, n_times): An array containing the data.
patchesgenerator of tuples | None: Searchlight patches as generated by searchlight. If None, no searchlight is used. Defaults to None.
dist_metricstr | function: The distance metric to use to compute the DSMs. Can be any metric supported by scipy.spatial.distance.pdist(). When a function is specified, it needs to take in two vectors and output a single number. See also the dist_params parameter to specify and additional parameter for the distance function. Defaults to ‘correlation’.
dist_paramsdict: Extra arguments for the distance metric used to compute the DSMs. Refer to scipy.spatial.distance for a list of all other metrics and their arguments. Defaults to an empty dictionary.
yndarray of int, shape (n_items,) | None: For each item, a number indicating the class to which the item belongs. When None, each item is assumed to belong to a different class. Defaults to None.
n_foldsint | sklearn.model_selection.BaseCrollValidator | None: Number of cross-validation folds to use when computing the distance metric. Folds are created based on the y parameter. Specify None to use the maximum number of folds possible, given the data. Alternatively, you can pass a Scikit-Learn cross validator object (e.g. sklearn.model_selection.KFold) to assert fine-grained control over how folds are created. Defaults to 1 (no cross-validation).

Yields:

dsmndarray, shape (n_patches, n_items * (n_items - 1)): A DSM (in condensed form) for each searchlight patch.