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