matlab code

Author: wangsiwei2010

ClusteringEvaluation/ClusteringEvaluation/MutualInfo.m

@@ -0,0 +1,52 @@
+function MIhat = MutualInfo(L1,L2)
+%   mutual information
+%===========    
+L1 = L1(:);
+L2 = L2(:);
+if size(L1) ~= size(L2)
+    error('size(L1) must == size(L2)');
+end
+
+Label = unique(L1);
+nClass = length(Label);
+
+Label2 = unique(L2);
+nClass2 = length(Label2);
+if nClass2 < nClass
+     % smooth
+     L1 = [L1; Label];
+     L2 = [L2; Label];
+elseif nClass2 > nClass
+     % smooth
+     L1 = [L1; Label2];
+     L2 = [L2; Label2];
+end
+
+
+G = zeros(nClass);
+for i=1:nClass
+    for j=1:nClass
+        G(i,j) = sum(L1 == Label(i) & L2 == Label(j));
+    end
+end
+sumG = sum(G(:));
+
+P1 = sum(G,2);  P1 = P1/sumG;
+P2 = sum(G,1);  P2 = P2/sumG;
+if sum(P1==0) > 0 || sum(P2==0) > 0
+    % smooth
+    error('Smooth fail!');
+else
+    H1 = sum(-P1.*log2(P1));
+    H2 = sum(-P2.*log2(P2));
+    P12 = G/sumG;
+    PPP = P12./repmat(P2,nClass,1)./repmat(P1,1,nClass);
+    PPP(abs(PPP) < 1e-12) = 1;
+    MI = sum(P12(:) .* log2(PPP(:)));
+    MIhat = MI / max(H1,H2);
+    %%%%%%%%%%%%%   why complex ?       %%%%%%%%
+    MIhat = real(MIhat);
+end
+
+
+

ClusteringEvaluation/ClusteringEvaluation/bestMap.m

@@ -0,0 +1,50 @@
+function [newL2] = bestMap(L1,L2)
+%bestmap: permute labels of L2 match L1 as good as possible
+%   [newL2] = bestMap(L1,L2);
+
+%===========    
+L1 = L1(:);
+L2 = L2(:);
+if size(L1) ~= size(L2)
+    error('size(L1) must == size(L2)');
+end
+
+Label1 = unique(L1);
+nClass1 = length(Label1);
+Label2 = unique(L2);
+nClass2 = length(Label2);
+
+nClass = max(nClass1,nClass2);
+G = zeros(nClass);
+for i=1:nClass1
+    for j=1:nClass2
+        G(i,j) = length(find(L1 == Label1(i) & L2 == Label2(j)));
+    end
+end
+[c,t] = hungarian(-G);
+newL2 = zeros(size(L2));
+for i=1:nClass2
+    newL2(L2 == Label2(i)) = Label1(c(i));
+end
+
+
+return;
+
+%=======backup old===========
+
+L1 = L1 - min(L1) + 1;      %   min (L1) <- 1;
+L2 = L2 - min(L2) + 1;      %   min (L2) <- 1;
+%===========    make bipartition graph  ============
+nClass = max(max(L1), max(L2));
+G = zeros(nClass);
+for i=1:nClass
+    for j=1:nClass
+        G(i,j) = length(find(L1 == i & L2 == j));
+    end
+end
+%===========    assign with hungarian method    ======
+[c,t] = hungarian(-G);
+newL2 = zeros(nClass,1);
+for i=1:nClass
+    newL2(L2 == i) = c(i);
+end