mvpa2.measures.rsaΒΆ

Representational (dis)similarity analysis

Inheritance diagram of mvpa2.measures.rsa

Functions

pdist(X[, metric, p, w, V, VI]) Pairwise distances between observations in n-dimensional space.
pearsonr(x, y) Calculates a Pearson correlation coefficient and the p-value for testing
rankdata(a) Assign ranks to data, dealing with ties appropriately.
squareform(X[, force, checks]) Converts a vector-form distance vector to a square-form distance matrix, and vice-versa.

Classes

Dataset(samples[, sa, fa, a]) Generic storage class for datasets with multiple attributes.
EnsureChoice(*values) Ensure an input is element of a set of possible values ..
Measure([null_dist]) A measure computed from a Dataset
PDist(**kwargs) Compute dissimiliarity matrix for samples in a dataset
PDistConsistency(**kwargs) Calculate the correlations of PDist measures across chunks
PDistTargetSimilarity(target_dsm, **kwargs) Calculate the correlations of PDist measures with a target
Parameter(default[, constraints, ro, index, ...]) This class shall serve as a representation of a parameter.
combinations combinations(iterable, r) –> combinations object

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mvpa2.measures.rsa.pdist

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