Daniel@0: function display_mahalanobis_metric(A, labels)
Daniel@0: % display a mala matrix and its stats
Daniel@0: 
Daniel@0: if nargin < 2
Daniel@0:     labels = num2cell(1:size(A,1));
Daniel@0:     
Daniel@0: elseif ~iscell(labels)
Daniel@0:     
Daniel@0:     features = labels;
Daniel@0:     labels = features.labels;
Daniel@0: end
Daniel@0: 
Daniel@0: 
Daniel@0: 
Daniel@0: figure;
Daniel@0: 
Daniel@0: % plot matrix
Daniel@0: imagesc(A);
Daniel@0: axis xy;
Daniel@0: 
Daniel@0: % set labels
Daniel@0: set(gca,'YTick', 1:numel(labels), ...
Daniel@0:     'YTickLabel', labels);
Daniel@0: set(gca,'XTick',1:numel(labels), ...
Daniel@0:     'XTickLabel', labels);
Daniel@0: 
Daniel@0: % ---
Daniel@0: % approximate parameter weights: 
Daniel@0: %   diagonal and sum(abs(row))
Daniel@0: % TODO: make nshow dependend on percentile
Daniel@0: % ---
Daniel@0: 
Daniel@0: nshow = min(numel(labels), 50);
Daniel@0: figure;
Daniel@0: 
Daniel@0: % get diagonal values of the Matrix
Daniel@0: diagw = abs(diag(A));
Daniel@0: 
Daniel@0: % ---
Daniel@0: % weight with feature values if possible
Daniel@0: % ---
Daniel@0: if exist('features','var')
Daniel@0:     
Daniel@0:     diagw = diagw.* mean(features.vector(),2);
Daniel@0: end
Daniel@0:     
Daniel@0: 
Daniel@0: [diagw, idx] = sort(diagw, 'descend');
Daniel@0: 
Daniel@0: % normalise
Daniel@0: alld = sum(diagw);
Daniel@0: 
Daniel@0: % plot
Daniel@0: bar(diagw(1:nshow)./ alld);
Daniel@0: set(gca,'XTick',1:nshow, ...
Daniel@0:     'XTickLabel', labels(idx(1:nshow)));
Daniel@0: 
Daniel@0: ylabel ('relevance factor');
Daniel@0: 
Daniel@0: if exist('features','var')
Daniel@0:     xlabel 'normalised weight'
Daniel@0: else
Daniel@0:     xlabel 'matrix factors'
Daniel@0: end
Daniel@0: 
Daniel@0: end