mne_rsa.compute_dsm(data, metric='correlation', **kwargs)[source]

Compute a dissimilarity matrix (DSM).

datandarray, shape (n_items, …)

For each item, all the features. The first dimension are the items and all other dimensions will be flattened and treated as features.

metricstr | function

The distance metric to use to compute the DSM. 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’.

**kwargsdict, optional

Extra arguments for the distance metric. Refer to scipy.spatial.distance for a list of all other metrics and their arguments.

dsmndarray, shape (n_classes * n_classes-1,)

The DSM, in condensed form. See scipy.spatial.distance.squareform().

See also