wolffd@0: % ---
wolffd@0: % class cvpartition_trunctrain
wolffd@0: % NOTE: this is a fake cvpartition double for 
wolffd@0: % using cvpartitions in truncated-training size experiments
wolffd@0: %
wolffd@0: % differently from cvpartition_trunctrain, we take all the training sizes 
wolffd@0: % at once and generate training partitions where the smaller ones are subsets 
wolffd@0: % of the bigger ones
wolffd@0: % ---
wolffd@0: classdef cvpartition_trunctrain_incsubsets
wolffd@0:   
wolffd@0: properties (Hidden)
wolffd@0: 
wolffd@0:     mtest;
wolffd@0:     mtraining;
wolffd@0: end
wolffd@0: properties
wolffd@0:     N;
wolffd@0:     NumTestSets;
wolffd@0:     TrainSize;
wolffd@0:     TestSize;
wolffd@0: end
wolffd@0:     
wolffd@0:         
wolffd@0: methods
wolffd@0:     
wolffd@0: % ---
wolffd@0: % constuctor: directly calculates the truncated testset
wolffd@0: % ---
wolffd@0: function P = cvpartition_trunctrain_incsubsets(Pin, perctrain)
wolffd@0:   
wolffd@0:     % ---
wolffd@0:     % NOTE: we use a different permutation for each cv-Buun (testset), 
wolffd@0:     % as otherwise the very small training sets will have about the same 
wolffd@0:     % data
wolffd@0:     % ---
wolffd@0:     if ~cvpartition_trunctrain_incsubsets.exists_permutation(Pin)
wolffd@0:         cvpartition_trunctrain_incsubsets.renew_permutation(Pin);
wolffd@0:     end
wolffd@0: 
wolffd@0:     P.N = Pin.N;
wolffd@0:     P.NumTestSets = Pin.NumTestSets;
wolffd@0:     
wolffd@0:     for i = 1:Pin.NumTestSets
wolffd@0: 
wolffd@0:         % copy testing data
wolffd@0:         P.TestSize(i) = Pin.TestSize(i);
wolffd@0:         P.mtest{i} = Pin.test(i);
wolffd@0: 
wolffd@0:         % calculate new training size
wolffd@0:         P.TrainSize(i) = ceil(perctrain * Pin.TrainSize(i));
wolffd@0:         
wolffd@0:         % get actual training indices
wolffd@0:         idx = find(Pin.training(i));
wolffd@0:         
wolffd@0:         % ---
wolffd@0:         % NOTE: the Test-Set-Specific permutation is applied
wolffd@0:         % we only extract as many indices as fit in Pin
wolffd@0:         % ---
wolffd@0:         permu = cvpartition_trunctrain_incsubsets.get_permutation(i,Pin.TrainSize(i));
wolffd@0:         
wolffd@0:         % truncate the indices
wolffd@0:         idx = idx(permu(1:P.TrainSize(i)));
wolffd@0:         
wolffd@0:         % build truncated training set
wolffd@0:         P.mtraining{i} = false(P.N, 1);
wolffd@0:         P.mtraining{i}(idx) = true;
wolffd@0:     end
wolffd@0: end   
wolffd@0: function out = test(P, i)
wolffd@0: 
wolffd@0:     out = P.mtest{i};
wolffd@0: end
wolffd@0: 
wolffd@0: function out = training(P, i)
wolffd@0: 
wolffd@0:     out = P.mtraining{i};
wolffd@0: end
wolffd@0: end
wolffd@0: 
wolffd@0: methods (Static)
wolffd@0:     
wolffd@0:     % ---
wolffd@0:     % TODO: save the permutation in a global variable,
wolffd@0:     % tomake the same smaller set available
wolffd@0:     % for all further experiments.
wolffd@0:     % moreover, it would be great if the smaller training sets
wolffd@0:     % are subsets of the bigger ones
wolffd@0:     % ---
wolffd@0:     function renew_permutation(P)
wolffd@0:         global globalvars;
wolffd@0:         
wolffd@0:         if isfield(globalvars.camir, ...
wolffd@0:                 'cvpartition_trunctrain_incsubsets');
wolffd@0:             warning 'renwewing permutations for train sets';
wolffd@0:         end
wolffd@0:         
wolffd@0:         for i = 1:P.NumTestSets
wolffd@0:                 globalvars.camir.cvpartition_trunctrain_incsubsets.permutation(i).data = ...
wolffd@0:                     randperm(P.N);
wolffd@0:         end
wolffd@0:     end
wolffd@0:     
wolffd@0:     function idx = get_permutation(testId, trainSize)
wolffd@0:         % returns the permutation for specific test set
wolffd@0:         global globalvars;
wolffd@0: 
wolffd@0:         idx = globalvars.camir.cvpartition_trunctrain_incsubsets.permutation(testId).data;
wolffd@0:         
wolffd@0:         % cut the permutation to contain no exxcess numbers
wolffd@0:         idx = idx(idx <= trainSize);
wolffd@0:     end
wolffd@0:     
wolffd@0:     function out = exists_permutation(P)
wolffd@0:         global globalvars;
wolffd@0:         if isfield(globalvars.camir, ...
wolffd@0:                 'cvpartition_trunctrain_incsubsets');
wolffd@0:             
wolffd@0:             out = (numel(globalvars.camir.cvpartition_trunctrain_incsubsets.permutation) == P.NumTestSets)  ...
wolffd@0:                     && (numel(globalvars.camir.cvpartition_trunctrain_incsubsets.permutation(1).data) == P.N);
wolffd@0: 
wolffd@0:   
wolffd@0:         else out = false;
wolffd@0:         end
wolffd@0:     end
wolffd@0:     
wolffd@0: end
wolffd@0: end