Mercurial > hg > smallbox
view util/SMALL_learn.m @ 112:5b34af39bf9a sup_163_ssim_IMG_T_dependeny
new branch for removing ssim_index dependecy on Image Procesing toolbox
author | Ivan Damnjanovic lnx <ivan.damnjanovic@eecs.qmul.ac.uk> |
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date | Tue, 24 May 2011 14:34:44 +0100 |
parents | f6cc633fd94b |
children | 5ded5e2e7d07 |
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function DL = SMALL_learn(Problem,DL) %%% SMALL Dictionary Learning % % Centre for Digital Music, Queen Mary, University of London. % This file copyright 2009 Ivan Damnjanovic. % % This program is free software; you can redistribute it and/or % modify it under the terms of the GNU General Public License as % published by the Free Software Foundation; either version 2 of the % License, or (at your option) any later version. See the file % COPYING included with this distribution for more information. % % Function gets as input Problem and Dictionary Learning (DL) structures % In Problem structure field b with the training set needs to be defined % In DL fields with name of the toolbox and solver, and parameters file % for particular dictionary learning technique needs to be present. % % Outputs are Learned dictionary and time spent as a part of DL structure %% fprintf('\nStarting Dictionary Learning %s... \n', DL.name); start=cputime; tStart=tic; if strcmpi(DL.toolbox,'KSVD') param=DL.param; param.data=Problem.b; D = eval([DL.name,'(param)']);%, ''t'', 5);']); elseif strcmpi(DL.toolbox,'KSVDS') param=DL.param; param.data=Problem.b; D = eval([DL.name,'(param, ''t'', 5);']); elseif strcmpi(DL.toolbox,'SPAMS') X = Problem.b; param=DL.param; D = eval([DL.name,'(X, param);']); % As some versions of SPAMS does not produce unit norm column % dictionaries, we need to make sure that columns are normalised to % unit lenght. for i = 1: size(D,2) D(:,i)=D(:,i)/norm(D(:,i)); end elseif strcmpi(DL.toolbox,'SMALL') X = Problem.b; param=DL.param; D = eval([DL.name,'(X, param);']); % we need to make sure that columns are normalised to % unit lenght. for i = 1: size(D,2) D(:,i)=D(:,i)/norm(D(:,i)); end elseif strcmpi(DL.toolbox,'mpv2') X = Problem.b(:,1:1:40000); jD0 = mpv2.SimpleMatrix(DL.param.D); jDicLea = mpv2.DictionaryLearning(jD0, 1,1); % !!!!! MAYBE lambda is not needed for ILS - NEED TO CHECK!!!! jDicLea.setLambda('L', DL.param.lambda, DL.param.lambda, 1000); jDicLea.setVerbose(2); jDicLea.setORMP(16, 1e-6, DL.param.abs); eval(['jDicLea.',DL.name,'( X(:), DL.param.iternum );']); jD = jDicLea.getDictionary(); D = reshape(jD.getAll(), size(X,1), DL.param.K); % To introduce new dictionary learning technique put the files in % your Matlab path. Next, unique name <TolboxID> for your toolbox needs % to be defined and also prefferd API for toolbox functions <Preffered_API> % % elseif strcmpi(DL.toolbox,'<ToolboxID>') % % This is an example of API that can be used: % % - get training set from Problem part of structure % % - assign parameters defined in the main program % % X = Problem.b; % param=DL.param; % % % - Evaluate the function (DL.name - defined in the main) with % % parameters given above % % D = eval([DL.name,'(<Preffered_API>);']); else printf('\nToolbox has not been registered. Please change SMALL_learn file.\n'); return end %% % Dictionary Learning time tElapsed=toc(tStart); DL.time = cputime - start; fprintf('\n%s finished task in %2f seconds (cpu time). \n', DL.name, DL.time); fprintf('\n%s finished task in %2f seconds (tic-toc time). \n', DL.name, tElapsed); DL.time=tElapsed; % If dictionary is given as a sparse matrix change it to full DL.D = full(D); end