cosmo dim generalization measure hdrΒΆ

function result=cosmo_dim_generalization_measure(ds,varargin)
% measure generalization across pairwise combinations over time (or any other dimension)
%
% result=cosmo_dim_generalization_measure(ds,varargin)
%
% Inputs:
%   ds                  dataset struct with d being a sample dimension, and
%                       with ds.sa.chunks==1 for samples to use for
%                       training and ds.sa.chunks==2 for those to use for
%                       testing. Other values for chunks are not allowed.
%   'measure',m         function handle to apply to combinations of samples
%                       in the input dataset ds, such as
%                       - @cosmo_correlation_measure
%                       - @cosmo_crossvalidation_measure
%                       - @cosmo_target_dsm_corr_measure
%   'dimension',d       dimension along which to generalize. Typically this
%                       will be 'time' for MEEG data
%   'radius',r          radius used for the d dimension. For example, when
%                       set to r=4 with d='time', then 4*2+1 time points
%                       are used to asses generalization, (except on the
%                       edges). Note that when using a radius>0, it is
%                       assumed that splits of the dataset by dimension d
%                       have corresponding elements in the same order
%                       (such as provided by cosmo_dim_transpose).
%   'nproc', np         Use np parallel threads. (Multiple threads may
%                       speed up computations). If parallel processing is
%                       not available, or if this option is not provided,
%                       then a single thread is used.
%   K,V                 any other key-value pairs necessary for the measure
%                       m, for example 'classifier' if
%                       m=@cosmo_crossvalidation_measure.
%
% Output:
%    result             dataset with ['train_' d] and ['test_' d] as sample
%                       dimensions, i.e. these are in ds.a.sdim.labels
%                       result.samples is Nx1, where N=K*J is the number of
%                       combinations of (1) the K points in ds with
%                       chunks==1 and different values in dimension d, and
%                       (2) the J points in ds with chunks==2 and different
%                       values in dimension d.
%
% Examples:
%     % Generalization over time
%     sz='big';
%     train_ds=cosmo_synthetic_dataset('type','timelock','size',sz,...
%                                              'nchunks',2,'seed',1);
%     test_ds=cosmo_synthetic_dataset('type','timelock','size',sz,...
%                                              'nchunks',3,'seed',2);
%     % set chunks
%     train_ds.sa.chunks(:)=1;
%     test_ds.sa.chunks(:)=2;
%     %
%     % construct the dataset
%     ds=cosmo_stack({train_ds, test_ds});
%     %
%     % make time a sample dimension
%     dim_label='time';
%     ds_time=cosmo_dim_transpose(ds,dim_label,1);
%     %
%     % set measure and its arguments
%     measure_args=struct();
%     %
%     % use correlation measure
%     measure_args.measure=@cosmo_correlation_measure;
%     % dimension of interest is 'time'
%     measure_args.dimension=dim_label;
%     %
%     % run time-by-time generalization analysis
%     dgm_ds=cosmo_dim_generalization_measure(ds_time,measure_args,...
%                                               'progress',false);
%     %
%     % the output has train_time and test_time as sample dimensions
%     cosmo_disp(dgm_ds.a)
%     %|| .sdim
%     %||   .labels
%     %||     { 'train_time'  'test_time' }
%     %||   .values
%     %||     { [  -0.2        [  -0.2
%     %||         -0.15          -0.15
%     %||          -0.1           -0.1
%     %||           :              :
%     %||             0              0
%     %||          0.05           0.05
%     %||           0.1 ]@7x1      0.1 ]@7x1 }
%
%
%     % Searchlight example
%     % (This example requires FieldTrip)
%     cosmo_skip_test_if_no_external('fieldtrip');
%     %
%     sz='big';
%     train_ds=cosmo_synthetic_dataset('type','timelock','size',sz,...
%                                              'nchunks',2,'seed',1);
%     test_ds=cosmo_synthetic_dataset('type','timelock','size',sz,...
%                                              'nchunks',3,'seed',2);
%     % set chunks
%     train_ds.sa.chunks(:)=1;
%     test_ds.sa.chunks(:)=2;
%     %
%     % construct the dataset
%     ds=cosmo_stack({train_ds, test_ds});
%     %
%     % make time a sample dimension
%     dim_label='time';
%     ds_time=cosmo_dim_transpose(ds,dim_label,1);
%     %
%     % set measure and its arguments
%     measure_args=struct();
%     %
%     % use correlation measure
%     measure_args.measure=@cosmo_correlation_measure;
%     % dimension of interest is 'time'
%     measure_args.dimension=dim_label;
%     %
%     % only to make this example run fast, most channels are eliminated
%     % (there is no other reason to do this step)
%     ds_time=cosmo_slice(ds_time,ds_time.fa.chan<=20,2);
%     ds_time=cosmo_dim_prune(ds_time);
%     %
%     % define neighborhood for channels
%     nbrhood=cosmo_meeg_chan_neighborhood(ds_time,...
%                                 'chantype','meg_combined_from_planar',...
%                                 'count',5,'label','dataset');
%     %
%     % run searchlight with generalization measure
%     measure=@cosmo_dim_generalization_measure;
%     dgm_sl_ds=cosmo_searchlight(ds_time,nbrhood,measure,measure_args,...
%                                                 'progress',false);
%     %
%     % the output has train_time and test_time as sample dimensions,
%     % and chan as feature dimension
%     cosmo_disp(dgm_sl_ds.a,'edgeitems',1)
%     %|| .fdim
%     %||   .labels
%     %||     { 'chan' }
%     %||   .values
%     %||     { { 'MEG0112+0113' ... 'MEG0712+0713'   }@1x7 }
%     %|| .meeg
%     %||   .samples_type
%     %||     'timelock'
%     %||   .samples_field
%     %||     'trial'
%     %||   .samples_label
%     %||     'rpt'
%     %|| .sdim
%     %||   .labels
%     %||     { 'train_time'  'test_time' }
%     %||   .values
%     %||     { [ -0.2        [ -0.2
%     %||           :             :
%     %||          0.1 ]@7x1     0.1 ]@7x1 }
%
%
% Notes:
%   - this function can be used together with searchlight
%   - to make a dimension d a sample dimension from a feature dimension
%     (usually necessary before running this function), or the other way
%     around (usually necessary after running this function), use
%     cosmo_dim_transpose.
%   - a 'partition' argument should not be provided, because this function
%     generates them itself. The partitions are generated so that there
%     is a single fold; samples with chunks==1 are always used for training
%     and those with chunks==2 are used for testing (e.g. when using
%     m=@cosmo_crossvalidation_measure). In the case of using
%     m=@cosmo_correlation_measure, this amounts to split-half
%     correlations.
%
% See also: cosmo_correlation_measure, cosmo_crossvalidation_measure
%           cosmo_target_dsm_corr_measure, cosmo_searchlight,
%           cosmo_dim_transpose
%
% #   For CoSMoMVPA's copyright information and license terms,   #
% #   see the COPYING file distributed with CoSMoMVPA.           #