Cluster thresholding algorithm for a group-level searchlight analysis

Inheritance diagram of mvpa2.algorithms.group_clusterthr


Doi(\*args, \*\*kwargs) Perform no good and no bad
get_cluster_pvals(sizes, null_sizes) Get p-value per each cluster size given cluster sizes for null-distribution
get_cluster_sizes(ds[, cluster_counter]) Compute cluster sizes from all samples in a boolean dataset.
get_thresholding_map(data[, p]) Return array of thresholds corresponding to a probability of such value in the input
mean_sample([attrfx]) Returns a mapper that computes the mean sample of a dataset.
repeat_cluster_vals(cluster_counts[, vals]) Repeat vals for each count of a cluster size as given in cluster_counts


Counter(\*args, \*\*kwds) Dict subclass for counting hashable items.
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
EnsureFloat() Ensure that an input (or several inputs) are of a data type ‘float’.
EnsureInt() Ensure that an input (or several inputs) are of a data type ‘int’.
EnsureRange([min, max]) Ensure an input is within a particular range
GroupClusterThreshold(\*\*kwargs) Statistical evaluation of group-level average accuracy maps
IdentityMapper(\*\*kwargs) A mapper that performs an identity transformation (i.e.
Learner([auto_train, force_train]) Common trainable processing object.
Parameter(default[, constraints, ro, index, ...]) This class shall serve as a representation of a parameter.
dok_matrix(arg1[, shape, dtype, copy]) Dictionary Of Keys based sparse matrix.