comparison core/tools/machine_learning/display_mahalanobis_metric.m @ 0:e9a9cd732c1e tip

first hg version after svn

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