ответ

0
%%// Hierarchical clustering 
T = linkage(data,'average','spearman'); 
D = pdist(data, 'spearman'); 
leafOrder = optimalleaforder(T, D); 
th = 0.726; 
H = dendrogram(T, 0,'ReOrder', leafOrder, 'Orientation', 'left', 'ColorThreshold', th); 
h = gca; 
set(h, 'YTickLabel', labels(leafOrder)); 

%// Changing the colours 
lineColours = cell2mat(get(H,'Color')); 
colourList = unique(lineColours, 'rows'); 

myColours = [230,186,0; 
      127,127,127; 
      10,35,140; 
      176,0,0; 
      158,182,72; 
      79,129,189; 
      209,99,9; 
      4,122,146]/255; 

%// Replace each colour (colour by colour). Start from 2 because the first colour are the "unclustered" black lines    
for colour = 2:size(colourList,1) 
    %// Find which lines match this colour 
    idx = ismember(lineColours, colourList(colour,:), 'rows'); 
    %// Replace the colour for those lines 
    lineColours(idx, :) = repmat(myColours(colour-1,:),sum(idx),1); 
end 
%// Apply the new colours to the chart's line objects (line by line) 
for line = 1:size(H,1) 
    set(H(line), 'Color', lineColours(line,:)); 
end 

 Смежные вопросы

  • Нет связанных вопросов^_^