cosmo naive bayes classifier searchlight hdrΒΆ

function result=cosmo_naive_bayes_classifier_searchlight(ds, nbrhood, varargin)
% Run (fast) Naive Bayes classifier searchlight with crossvalidation
%
% result=cosmo_naive_bayes_classifier_searchlight(ds, nbrhood, ...)
%
% Inputs:
%   ds                   dataset struct
%   nbrhood              Neighborhood structure with fields:
%         .a               struct with dataset attributes
%         .fa              struct with feature attributes. Each field
%                            should have NF values in the second dimension
%         .neighbors       cell with NF mappings from center_ids in output
%                        dataset to feature ids in input dataset.
%                        Suitable neighborhood structs can be generated
%                        using:
%                        - cosmo_spherical_neighborhood (fmri volume)
%                        - cosmo_surficial_neighborhood (fmri surface)
%                        - cosmo_meeg_chan_neigborhood (MEEG channels)
%                        - cosmo_interval_neighborhood (MEEG time, freq)
%                        - cosmo_cross_neighborhood (to cross neighborhoods
%                                                    from the neighborhood
%                                                    functions above)
%   'partitions', par    Partition scheme to use. Typically this is the
%                        output from cosmo_nfold_partitioner or
%                        cosmo_oddeven_partitioner. Partitions schemes
%                        with more than one prediction for samples in the
%                        test set (such as the output from
%                        cosmo_nchoosek_partitioner(N) with N>1) are not
%                        supported
%   'output', out        One of:
%                        'accuracy'      return classification accuracy
%                        'predictions'   return prediction for each sample
%                                        in a test set in partitions
%   'progress', p        Show progress every p folds (default: 1)
%
% Output:
%   results_map          a dataset struct where the samples
%                        contain classification accuracies or class
%                        predictions
%
% Example:
%     % generate tiny dataset (6 voxels) and define a tiny spherical
%     % neighborhood with a radius of 1 voxel.
%     ds=cosmo_synthetic_dataset('nchunks',10,'ntargets',5);
%     nh=cosmo_spherical_neighborhood(ds,'radius',1,'progress',false);
%     %
%     % set options
%     opt=struct();
%     opt.progress=false;
%     % define take-one-chunk-out crossvalidation scheme (10 folds)
%     opt.partitions=cosmo_nfold_partitioner(ds);
%     %
%     % run searchlight
%     result=cosmo_naive_bayes_classifier_searchlight(ds,nh,opt);
%     %
%     % show result
%     cosmo_disp(result);
%     %|| .a
%     %||   .fdim
%     %||     .labels
%     %||       { 'i'  'j'  'k' }
%     %||     .values
%     %||       { [ 1         2         3 ]  [ 1         2 ]  [ 1 ] }
%     %||   .vol
%     %||     .mat
%     %||       [ 2         0         0        -3
%     %||         0         2         0        -3
%     %||         0         0         2        -3
%     %||         0         0         0         1 ]
%     %||     .dim
%     %||       [ 3         2         1 ]
%     %||     .xform
%     %||       'scanner_anat'
%     %|| .fa
%     %||   .nvoxels
%     %||     [ 3         4         3         3         4         3 ]
%     %||   .radius
%     %||     [ 1         1         1         1         1         1 ]
%     %||   .center_ids
%     %||     [ 1         2         3         4         5         6 ]
%     %||   .i
%     %||     [ 1         2         3         1         2         3 ]
%     %||   .j
%     %||     [ 1         1         1         2         2         2 ]
%     %||   .k
%     %||     [ 1         1         1         1         1         1 ]
%     %|| .samples
%     %||   [ 0.6      0.74      0.44       0.6       0.7       0.4 ]
%     %|| .sa
%     %||   .labels
%     %||     { 'accuracy' }
%
%
% Notes:
%   - this function runs considerably faster than using a searchlight with
%     a classifier function and a crossvalidation scheme, because model
%     parameters during training are estimated only once for each feature.
%     Thus, speedups are most significant if elements in the neighborhood
%     have many overlapping features
%   - for other classifiers or other measures, use the more flexible
%     cosmo_searchlight function
%
% See also: cosmo_searchlight
%
% #   For CoSMoMVPA's copyright information and license terms,   #
% #   see the COPYING file distributed with CoSMoMVPA.           #