| Multivariate Pattern Analysis in Python

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This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# measures.ds

## Module: `measures.ds`

Inheritance diagram for `mvpa.measures.ds`:

Dissimilarity measure.

## `DSMDatasetMeasure`

class `mvpa.measures.ds.``DSMDatasetMeasure`(dsmatrix, dset_metric, output_metric='spearman')

  Bases: `mvpa.measures.base.DatasetMeasure`

  DSMDatasetMeasure creates a DatasetMeasure object where metric can be one of ‘euclidean’, ‘spearman’, ‘pearson’ or ‘confusion’

  Note

  Available state variables:

  - null_prob+: State variable
  - null_t: State variable
  - raw_results: Computed results before applying any transformation algorithm

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `DatasetMeasure`

  Initialize instance of DSMDatasetMeasure

  Parameters:

  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled

### Navigation

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- PyMVPA 0.4.x Home |
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