mvpa2.measures.rsaΒΆ
Representational (dis)similarity analysis
Functions
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. |
Classes
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 |
