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fixed bad flag statements for saving new samples, plus fixed small er…
…rors to sample_morphs. adding CE's MDS script
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,191 @@ | ||
| clear all | ||
| close all | ||
| clc | ||
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| addpath(genpath('./helpers')) | ||
| addpath(genpath('./hmaxMatlab')) | ||
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| corrTypes= {'correlation', 'euclidean'}; %{'correlation', 'euclidean'}; | ||
| input = 'V1features'; | ||
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| [~, user_name] = system('whoami'); | ||
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| if strfind(user_name, 'rhee') % ON DIXIE | ||
| base_root = '/nas/volume1/behavior/stimuli/pnas_morphs/samples/'; %,... | ||
| feature_base_root = '/nas/volume1/behavior/stimuli/pnas_morphs/V1features/'; | ||
| else | ||
| base_root = '/media/nas/volume1/behavior/stimuli/pnas_morphs/samples/'; | ||
| feature_base_root = '/media/nas/volume1/behavior/stimuli/pnas_morphs/V1features/'; | ||
| end | ||
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| sample_dirs = dir(base_root); | ||
| sample_dirs = sample_dirs([sample_dirs.isdir]); | ||
| sample_dirs = sample_dirs(arrayfun(@(x) x.name(1), sample_dirs) ~= '.'); | ||
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| D = struct(); | ||
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| for CORR=1:length(corrTypes) | ||
| corrType = corrTypes{CORR}; | ||
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| for root=1:length(sample_dirs) | ||
| clear dist_mat D M | ||
| source_root = [base_root, sample_dirs(root).name, '/']; | ||
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| parts = strsplit(source_root,'/'); | ||
| stimset = parts{end-1}; | ||
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| cond_info = strsplit(stimset, '_'); | ||
| D.source_root = source_root; | ||
| D.stimset = stimset; | ||
| D.sampled_feature = cond_info{1}; | ||
| D.sampled_distance = cond_info{2}; | ||
| D.sampled_comparison = cond_info{3}; | ||
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| if strfind(stimset, 'v1_') | ||
| nstims = 2002; | ||
| feature_root = [, ]; | ||
| else | ||
| nstims = 22; | ||
| % Samples generated with python (i.e,. not using | ||
| % V1-features) do not have associated sample_idxs... | ||
| % Instead, use V1 features created for specific stimsets: | ||
| feature_root = [feature_base_root, sprintf('%s_%s20/', D.sampled_distance, D.sampled_comparison)] | ||
| end | ||
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| D.nstims = nstims | ||
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| % out_root=fullfile(parts{1:end-2}); | ||
| out_root = [strjoin(parts(1:end-3), '/'), '/figures/']; | ||
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| if ~isdir(out_root) | ||
| mkdir(out_root) | ||
| sprintf('Created output dir: %s', out_root) | ||
| end | ||
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| sprintf('SOURCE: %s\nSTIMSET: %s\nCORR: %s | INPUT: %s\n', source_root, stimset, corrType, input) | ||
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| % Get the stimuli for pdist matrix: | ||
| % if strfind(input, 'pixels') % Compare images pixel-wise | ||
| % iminfo = dir([source_root,'*.png']); | ||
| % | ||
| % elseif strfind(input, 'V1features') | ||
| % iminfo = dir([base_root,'*.mat']); % Compare V1-feature responses to image | ||
| % end | ||
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| iminfo = dir([source_root,'/*.png']); | ||
| imnames = cell(1, length(iminfo)); | ||
| for i=1:length(iminfo) | ||
| imnames{i} = iminfo(i).name; | ||
| end | ||
| imnames = sort_nat(imnames); | ||
| sprintf('N sampled image: %i', length(imnames)) | ||
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| % Load main .mat for V1 feature vector to get the sample_idxs (need | ||
| % this to grab the correct V1-feature-vector from source bank. | ||
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| % NO .MAT for any _fixedref, since sampling looked terrible | ||
| main_mfiles = dir([base_root,'*.mat']); | ||
| mfiles = cell(1,length(main_mfiles)); | ||
| for m=1:length(main_mfiles) | ||
| %curr_mfile = main_mfiles(m).name; | ||
| mfiles{m} = main_mfiles(m).name; | ||
| end | ||
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| curr_mfile_idx = ~cellfun('isempty', strfind(mfiles, sprintf('_%s_%s_%i', D.sampled_distance, D.sampled_comparison, D.nstims))) | ||
| curr_mfile = mfiles(curr_mfile_idx); | ||
| curr_mfile = curr_mfile{1} | ||
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| if strfind(curr_mfile, '_fixedref') | ||
| sprintf('Skipping MDS for bad-sampling of %s stimset...', curr_mfile) | ||
| continue; | ||
| end | ||
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| save_new = 0; | ||
| if isempty(curr_mfile) | ||
| M = struct(); | ||
| M.sample_idxs = linspace(1, length(imnames), length(imnames)); | ||
| curr_mfile = sprintf('pdistmat_%s_%s_%i.mat', D.sampled_distance, D.sampled_comparison, D.nstims); | ||
| save_new = 1; | ||
| else | ||
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| M = load([base_root, curr_mfile]); | ||
| if isfield(M, 'M') | ||
| M = M.M; | ||
| end | ||
| end | ||
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| if isfield(M, 'pdist') && isfield(M.pdist, corrType) | ||
| dist_mat = M.pdist.(corrType); | ||
| nsamples = length(M.sample_idxs); | ||
| else | ||
| if ~isfield(M, 'pdist') | ||
| M.pdist = struct(); | ||
| end | ||
| sampled_feature_vects = []; | ||
| for idx=1:length(M.sample_idxs) | ||
| curr_feat = load([feature_root, sprintf('V1_features_morph%i.mat', (M.sample_idxs(idx)-1))]); | ||
| sampled_feature_vects = [sampled_feature_vects; curr_feat.featureVector]; % F = [F curr_feat.featureVector']; doesn't work.. too big | ||
| end | ||
| nsamples = length(M.sample_idxs); | ||
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| dist_mat = pdist(sampled_feature_vects, corrType); | ||
| dist_mat=squareform(dist_mat); | ||
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| %get rid of float-point artifacts that make matrix unsymmetric | ||
| dist_mat=round(dist_mat*10000)/10000; | ||
| M.pdist.(corrType) = dist_mat; | ||
| if save_new==1 | ||
| save([base_root, curr_mfile], 'M') | ||
| else | ||
| save([base_root, curr_mfile], 'M', '-append') | ||
| end | ||
| end | ||
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| % opts = statset('Display','iter', 'MaxIter', 1500); | ||
| opts = statset('MaxIter', 5000); | ||
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| [distMatrixMap,distMatrixStress]=mdscale(dist_mat, 2, 'Options', opts); | ||
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| %plot w/ color scatter plot | ||
| colorList={'r','b'}; | ||
| sz=10; | ||
| hF=figure; | ||
| hold all | ||
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| scatter(distMatrixMap(1:nsamples,1),distMatrixMap(1:nsamples,2),sz,colorList{1},'o') | ||
| scatter(distMatrixMap(1,1),distMatrixMap(1,2),sz,'b','o') | ||
| title(sprintf('_%s_MDS_%s_scatter.png', input, stimset)) | ||
| saveas(hF,[out_root,corrType,sprintf('_%s_MDS_%s_scatter.png', input, stimset)]) | ||
| outstring = [out_root,corrType,sprintf('_%s_MDS_%s_scatter.png', input, stimset)]; | ||
| sprintf('Saved SCATTER to:\n%s', outstring) | ||
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| %plot w/ images | ||
| % im_source_root='/media/nas/volume1/behavior/stimuli/pnas_morphs/pov20_gray_resize/'; | ||
| % im_source_root = source_root; | ||
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| sz=.03; | ||
| hF=figure; | ||
| hold all | ||
| for i=1:length(imnames) | ||
| imName=[source_root,imnames{i}]; | ||
| im0=double(imread(imName)); | ||
| centerX=distMatrixMap(i,1); | ||
| centerY=distMatrixMap(i,2); | ||
| X1=centerX-(sz/2)*2; | ||
| X2=centerX+(sz/2)*2; | ||
| Y1=centerY-(sz/2); | ||
| Y2=centerY+(sz/2); | ||
| image([X2, X1],[Y2, Y1],(im0/255)*64) | ||
| end | ||
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| colormap('gray') | ||
| title('MDS map') | ||
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| saveas(hF,[out_root,corrType,sprintf('_%s_MDS_%s.png', input, stimset)]) | ||
| outstring = [out_root,corrType,sprintf('_%s_MDS_%s.png', input, stimset)]; | ||
| sprintf('Saved IMAGES to:\n%s', outstring) | ||
|
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| end | ||
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| end |
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