ENH: Adding files to the initial empty repository these fiels work on…

… mac64 and LINUX64

compute/feComputeEvidence.m

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+function se = feComputeEvidence(rmse1,rmse2)
+% Computes a series of distance metrics between two RMSE distribtions.
+%
+% Compute summary statistics on to characterize the lesion,:
+% We compute:
+% - The strength of evidence, namely the effect size of the lesion
+% - The Kullback-Leibler Divergence
+% - Jeffrey's Divergence
+% - The Eath Mover's distance
+%
+% Copyright (2013-2014), Franco Pestilli, Stanford University, [email protected].
+
+% Prepare the distribution of errors and the histograms describing the
+% erros.
+se.nolesion.rmse.all  = rmse1;
+se.lesion.rmse.all    = rmse2;
+se.nolesion.rmse.mean = mean(se.nolesion.rmse.all);
+se.lesion.rmse.mean   = mean(se.lesion.rmse.all);
+
+% Histograms
+se.xrange(1) = ceil(min([se.lesion.rmse.all se.nolesion.rmse.all]));
+se.xrange(2) = floor(max([se.lesion.rmse.all se.nolesion.rmse.all]));
+se.nbins     = 60;
+se.bins      = linspace(se.xrange(1),se.xrange(2),se.nbins);
+[se.lesion.hist,   se.lesion.xhist]   = hist(se.lesion.rmse.all,  se.bins);
+[se.nolesion.hist, se.nolesion.xhist] = hist(se.nolesion.rmse.all,se.bins);
+se.lesion.hist     = se.lesion.hist ./   sum(se.lesion.hist);
+se.nolesion.hist   = se.nolesion.hist ./ sum(se.nolesion.hist);
+
+% Kullback-Leibler Divergence
+se.kl.name = sprintf('Kullback–Leibler divergence: http://en.wikipedia.org/wiki/Kullback-Leibler_divergence');
+tmp = se.nolesion.hist .* log2( (se.nolesion.hist) ./ (se.lesion.hist + eps) );
+se.kl.mean = nansum(tmp);clear tmp
+se.kl.std  = nan;
+
+% Jeffrey's divergence
+se.j.name = sprintf('Jeffrey''s divergence: http://en.wikipedia.org/wiki/Divergence_(statistics)');
+tmp = se.nolesion.hist .* log2( (se.nolesion.hist) ./ ((se.lesion.hist + se.lesion.hist + eps)./2)  ) + ...
+      se.lesion.hist .* log2( (se.lesion.hist) ./ ((se.lesion.hist + se.lesion.hist + eps)./2) );
+se.j.mean = nansum(tmp); clear tmp
+se.j.std   = nan;
+
+% Earth Mover's Distance:
+% Note: This can be very slow and my require large amounts of memory for more than 1000 voxels
+fprintf('[%s] Computing the Earth Mover''s distance... \n',mfilename)
+se.em.name = sprintf('Earth Mover''s distance: http://en.wikipedia.org/wiki/Earth_mover''s_distance');
+
+try
+%if (exist('emd_mex.m','file') == 2) % Using Rubinov c-code fastest
+    pairwiseDist = zeros(size(se.lesion.xhist,2));
+    for i=1:size(se.nolesion.xhist,2)
+        for j=1:size(se.lesion.xhist,2)
+            pairwiseDist(i,j) = abs(se.lesion.xhist(i)-se.nolesion.xhist(j));
+        end
+    end
+    tmp_em = emd_mex(se.nolesion.hist,se.lesion.hist,pairwiseDist);
+catch ME %else
+    fprintf('[%s] Cannot find compiled c-code for Earth Movers Distance.\nUsing the slower and less reliable MatLab implementation.',mfilename)
+    [~,tmp_em] = emd(se.nolesion.xhist',se.lesion.xhist',se.nolesion.hist',se.lesion.hist',@gdf);
+end
+se.em.mean = tmp_em;
+se.em.std  = nan;
+clear tmp_emp
+
+% Strenght of evidence (effect size)
+fprintf('[%s] Computing the Strength of Evidence... \n',mfilename)
+se.s.name = sprintf('strength of evidence, d-prime: http://en.wikipedia.org/wiki/Effect_size');
+se.s.nboots = 5000; 
+se.s.nmontecarlo = 5;
+se.s.nbins = 200;
+sizeunlesioned    = length(se.nolesion.rmse.all);
+nullDistributionW = nan(se.s.nboots,se.s.nmontecarlo);
+nullDistributionWO = nan(se.s.nboots,se.s.nmontecarlo);
+min_x = floor(mean([se.nolesion.rmse.all]) - mean([se.nolesion.rmse.all])*.05);
+max_x = ceil( mean([se.lesion.rmse.all])   + mean([se.lesion.rmse.all])*.05);
+
+for inm = 1:se.s.nmontecarlo
+    fprintf('.')
+    parfor ibt = 1:se.s.nboots
+        nullDistributionW(ibt,inm)  = mean(randsample(se.nolesion.rmse.all,   sizeunlesioned,true));      
+        nullDistributionWO(ibt,inm) = mean(randsample(se.lesion.rmse.all,sizeunlesioned,true));
+    end
+
+    % Distribution unlesioned
+    [y(:,inm),xhis] = hist(nullDistributionW(:,inm),linspace(min_x,max_x,se.s.nbins));
+    y(:,inm) = y(:,inm)./sum(y(:,inm));
+
+    % Distribution lesioned
+    [woy(:,inm),woxhis] = hist(nullDistributionWO(:,inm),linspace(min_x,max_x,se.s.nbins));
+    woy(:,inm) = woy(:,inm)./sum(woy(:,inm));
+end
+
+se.s.mean = mean(diff([mean(nullDistributionW,1); ...
+                          mean(nullDistributionWO,1)])./sqrt(sum([std(nullDistributionW,[],1);std(nullDistributionWO,[],1)].^2,1)));
+se.s.std  = std(diff([mean(nullDistributionW,1); ...
+                          mean(nullDistributionWO,1)])./sqrt(sum([std(nullDistributionW,[],1);std(nullDistributionWO,[],1)].^2,1)));
+disp(' done.')
+
+% Extract the mean and error of the botstrapped disributions of mean errors
+y_m   = mean(y,2);
+ywo_m = mean(woy,2);
+y_e   = [y_m, y_m] + 2*[-std(y,[],2),std(y,[],2)];
+ywo_e = [ywo_m, ywo_m] + 2*[-std(woy,[],2),std(woy,[],2)];
+se.s.lesioned_e   = ywo_e;
+se.s.lesioned_m   = ywo_m;
+se.s.unlesioned_e = y_e;
+se.s.unlesioned_m = y_m;
+se.s.lesioned.xbins   = woxhis;
+se.s.unlesioned.xbins = xhis;
+se.s.min_x   = min_x;
+se.s.max_x = max_x;
+
+end

compute/feFitModel.m

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+function [fit, w, R2] = feFitModel(M,dSig,fitMethod)
+% 
+% feFitModel() function in LiFE but restricted to the
+% 
+% BBNNLS algorithm and using the Factorization model.
+% M is the factorization model composed by:
+%
+%   M.DictSig    Dictionary
+%   
+%  Copyright (2015), Franco Pestilli (Indiana Univ.) - Cesar F. Caiafa (CONICET)
+%  email: [email protected] and [email protected]
+
+
+% Below are the old comments which are not longer correct in general
+% Fit the LiFE model.
+%
+% Finds the weights for each fiber to best predict the directional
+% diffusion signal (dSig)
+%
+%  fit = feFitModel(M,dSig,fitMethod)
+%
+% dSig:  The diffusion weighted signal measured at each
+%        voxel in each direction. These are extracted from 
+%        the dwi data at some white-matter coordinates.
+% M:     The LiFE difusion model matrix, constructed
+%        by feConnectomeBuildModel.m
+%
+% fitMethod: 
+%  - 'bbnnls' - DEFAULT and best, faster large-scale solver.
+%
+% See also: feCreate.m, feConnectomeBuildModel.m, feGet.m, feSet.m
+%
+% Example:
+%
+%  Copyright (2015), Franco Pestilli (Indiana Univ.) - Cesar F. Caiafa (CONICET)
+%  email: [email protected] and [email protected]
+%
+% Notes about the LiFE model:
+%
+% The rows of the M matrix are nVoxels*nBvecs. We are going to predict the
+% diffusion signal in each voxel for each direction.
+%
+% The columns of the M matrix are nFibers + nVoxels.  The diffusion signal
+% for each voxel is predicted as the weighted sum of predictions from each
+% fibers that passes through a voxel plus an isotropic (CSF) term.
+%
+% In addition to M, we typically return dSig, which is the signal measured
+% at each voxel in each direction.  These are extracted from the dwi data
+% and knowledge of the roiCoords.
+
+% fit the model, by selecting the proper toolbox.
+
+mycomputer = computer();
+release = version('-release');
+
+switch fitMethod
+   case {'bbnnls'}
+    [nFibers] = size(M.Phi,3); %feGet(fe,'nfibers');
+    %[nTheta]  = size(M.DictSig,1);
+    [nAtoms] = size(M.DictSig,2); %feGet(fe,'natoms');
+    [Nvoxels] = size(M.Phi,2); %feGet(fe,'nvoxels');
+
+    tic
+    fprintf('\nLiFE: Computing least-square minimization with BBNNLS...\n')
+    opt = solopt;
+    opt.maxit = 5000;
+    opt.use_tolo = 1;
+
+    switch strcat(mycomputer,'_',release)
+        case {'GLNXA64_2015a'}
+        out_data = bbnnls_GLNXA64(M,dSig,zeros(nFibers,1),opt);
+        case {'MACI64_2014b'}
+        out_data = bbnnls_MACI64(M,dSig,zeros(nFibers,1),opt);
+        otherwise
+        sprintf('WARNING: currently LiFE is optimized for an efficient usage of memory \n using the Sparse Tucker Decomposition aproach (Caiafa&Pestilli, 2015) \n ONLY for Linux (MatlabR2015a) and MacOS (MatlabR2014b). \n If you have a different system or version you can still \n use the old version of LiFE (memory intensive). \n\n')
+        sprintf('\n Starting using old version of LiFE...\n')
+        out_data = bbnnls_OLD(M.MmatrixM,dSig,zeros(nFibers,1),opt);
+    end
+    fprintf('BBNNLS status: %s\nReason: %s\n',out_data.status,out_data.termReason);
+    w = out_data.x;
+    fprintf(' ...fit process completed in %2.3fminutes\n',toc/60)
+    % Save the state of the random generator so that the stochasit cfit can be recomputed.
+    defaultStream = RandStream.getGlobalStream; %RandStream.getDefaultStream;
+    fit.randState = defaultStream.State;   
+
+    % Save out some results 
+    fit.results.R2        = [];
+    fit.results.nParams   = size(M,2);
+    fit.results.nMeasures = size(M,1);
+    R2=[];
+
+   otherwise
+     error('Cannot fit LiFE model using method: %s.\n',fitMethod);
+end
+
+% Save output structure.
+fit.weights             = w;
+fit.params.fitMethod    = fitMethod;
+
+end