mvpa2.algorithms.group_clusterthrΒΆ
Cluster thresholding algorithm for a group-level searchlight analysis
Functions
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 |
Classes
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. |
