Representational (dis)similarity analysis

Inheritance diagram of mvpa2.measures.rsa


cdist(XA, XB[, metric, p, V, VI, w]) Computes distance between each pair of the two collections of inputs.
mean_group_sample(attrs[, attrfx]) Returns a mapper that computes the mean samples of unique sample groups.
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 non-correlation.
rankdata(a[, method]) 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.


CDist(\*\*kwargs) Compute cross-validated dissimiliarity matrix for samples in a dataset
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.
Regression(predictors[, keep_pairs]) Given a dataset, compute regularized regression (Ridge or Lasso) on the computed neural dissimilarity matrix using an arbitrary number of predictors (model dissimilarity matrices).
combinations combinations(iterable, r) –> combinations object
product product(*iterables) –> product object