Inheritance diagram of PDist

class mvpa2.measures.rsa.PDist(**kwargs)

Compute dissimiliarity matrix for samples in a dataset

This Measure returns the upper triangle of the n x n disimilarity matrix defined as the pairwise distances between samples in the dataset, and where n is the number of samples.


Available conditional attributes:

  • calling_time+: None
  • null_prob+: None
  • null_t: None
  • raw_results: None
  • trained_dataset: None
  • trained_nsamples+: None
  • trained_targets+: None
  • training_time+: None

(Conditional attributes enabled by default suffixed with +)



pairwise_metric : str, optional

Distance metric to use for calculating pairwise vector distances for dissimilarity matrix (DSM). See scipy.spatial.distance.pdist for all possible metrics. Constraints: value must be a string. [Default: ‘correlation’]

center_data : bool, optional

If True then center each column of the data matrix by subtracing the column mean from each element. This is recommended especially when using pairwise_metric=’correlation’. Constraints: value must be convertible to type bool. [Default: False]

square : bool, optional

If True return the square distance matrix, if False, returns the flattened upper triangle. Constraints: value must be convertible to type bool. [Default: False]

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

null_dist : instance of distribution estimator

The estimated distribution is used to assign a probability for a certain value of the computed measure.

auto_train : bool

Flag whether the learner will automatically train itself on the input dataset when called untrained.

force_train : bool

Flag whether the learner will enforce training on the input dataset upon every call.

space : str, optional

Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

pass_attr : str, list of str|tuple, optional

Additional attributes to pass on to an output dataset. Attributes can be taken from all three attribute collections of an input dataset (sa, fa, a – see Dataset.get_attr()), or from the collection of conditional attributes (ca) of a node instance. Corresponding collection name prefixes should be used to identify attributes, e.g. ‘ca.null_prob’ for the conditional attribute ‘null_prob’, or ‘fa.stats’ for the feature attribute stats. In addition to a plain attribute identifier it is possible to use a tuple to trigger more complex operations. The first tuple element is the attribute identifier, as described before. The second element is the name of the target attribute collection (sa, fa, or a). The third element is the axis number of a multidimensional array that shall be swapped with the current first axis. The fourth element is a new name that shall be used for an attribute in the output dataset. Example: (‘ca.null_prob’, ‘fa’, 1, ‘pvalues’) will take the conditional attribute ‘null_prob’ and store it as a feature attribute ‘pvalues’, while swapping the first and second axes. Simplified instructions can be given by leaving out consecutive tuple elements starting from the end.

postproc : Node instance, optional

Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.

descr : str

Description of the instance


Dataset :

If square is False, contains a column vector of length = n(n-1)/2 of pairwise distances between all samples. A sample attribute pairs identifies the indices of input samples for each individual pair. If square is True, the dataset contains a square dissimilarty matrix and the entire sample attributes collection of the input dataset.


is_trained = True