tweak kmeans

chapter06/knKmeans.m

@@ -1,5 +1,5 @@
-function [label, energy, model] = knKmeans(X, init, kn)
-% Perform kernel k-means clustering.
+function [label, model, energy] = knKmeans(X, init, kn)
+% Perform kernel kmeans clustering.
 % Input:
 %   K: n x n kernel matrix
 %   init: either number of clusters (k) or initial label (1xn)
@@ -21,15 +21,13 @@
     kn = @knGauss;
 end
 K = kn(X,X);
-last = 0;
+last = zeros(1,n);
 while any(label ~= last)
-    [u,~,label(:)] = unique(label);   % remove empty clusters
-    k = numel(u);
-    E = sparse(label,1:n,1,k,n,n);
-    E = spdiags(1./sum(E,2),0,k,k)*E;
+    [~,~,last(:)] = unique(label);   % remove empty clusters
+    E = sparse(last,1:n,1);
+    E = E./sum(E,2);
     T = E*K;
-    last = label;
-    [val, label] = max(bsxfun(@minus,T,diag(T*E')/2),[],1);
+    [val, label] = max(T-diag(T*E')/2,[],1);
 end
 energy = trace(K)-2*sum(val); 
 if nargout == 3

chapter09/kmeans.m

@@ -1,5 +1,5 @@
-function [label, energy, model] = kmeans(X, init)
-% Perform k-means clustering.
+function [label, m, energy] = kmeans(X, init)
+% Perform kmeans clustering.
 % Input:
 %   X: d x n data matrix
 %   init: k number of clusters or label (1 x n vector)
@@ -9,20 +9,18 @@
 %   model: trained model structure
 % Written by Mo Chen ([email protected]).
 n = size(X,2);
+idx = 1:n;
+last = zeros(1,n);
 if numel(init)==1
     k = init;
     label = ceil(k*rand(1,n));
 elseif numel(init)==n
     label = init;
 end
-last = 0;
 while any(label ~= last)
-    [u,~,label(:)] = unique(label);   % remove empty clusters
-    k = numel(u);
-    E = sparse(1:n,label,1,n,k,n);  % transform label into indicator matrix
-    m = X*(E*spdiags(1./sum(E,1)',0,k,k));    % compute centers 
-    last = label;
-    [val,label] = max(bsxfun(@minus,m'*X,dot(m,m,1)'/2),[],1); % assign labels
+    [~,~,last(:)] = unique(label);   % remove empty clusters
+    E = sparse(idx,last,1);  % transform label into indicator matrix
+    m = X*(E./sum(E,1));    % compute centers 
+    [val,label] = min(dot(m,m,1)'/2-m'*X,[],1); % assign labels
 end
-energy = dot(X(:),X(:))-2*sum(val); 
-model.means = m;
+energy = dot(X(:),X(:),1)+2*sum(val);

chapter09/kmeansPred.m

@@ -1,11 +1,11 @@
-function [label, energy] = kmeansPred(model, Xt)
+function [label, energy] = kmeansPred(m, X)
 % Prediction for kmeans clusterng
 % Input:
-%   model: trained model structure
-%   Xt: d x n testing data
+%   model: dx k cluster center matrix
+%   X: d x n testing data
 % Output:
 %   label: 1 x n cluster label
 %   energy: optimization target value
 % Written by Mo Chen ([email protected]).
-[val,label] = min(sqdist(model.means, Xt));
+[val,label] = min(dot(X,X,1)+dot(m,m,1)'-2*m'*X,[],1); % assign labels
 energy = sum(val);

demo/ch06/knLin_demo.m

@@ -25,8 +25,8 @@
 n = 500;
 [X,y] = kmeansRnd(d,k,n);
 init = ceil(k*rand(1,n));
-[y_kn,en_kn,model_kn] = knKmeans(X,init,@knLin);
-[y_lin,en_lin,model_lin] = kmeans(X,init);
+[y_kn,model_kn,en_kn] = knKmeans(X,init,@knLin);
+[y_lin,model_lin,en_lin] = kmeans(X,init);
 
 idx = 1:2:n;
 Xt = X(:,idx);

demo/ch09/kmeans_demo.m

@@ -3,7 +3,7 @@
 k = 3;
 n = 5000;
 [X,label] = kmeansRnd(d,k,n);
-y = kmeans(X,k);
+y = litekmeans(X,k);
 plotClass(X,label);
 figure;
 plotClass(X,y);