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 |
