adding comments and fix the bugs according to the commend especially …

…the dimensionality requirements for the input of these two files.

svm/util/find_labels_dnc.m

@@ -1,18 +1,25 @@
 %this function labels data with kmeans result in batches/blocks, useful for removing memory requirements
 
 function labels = find_labels_dnc(center,X)
+% center dimension should be fixed as n x d;
+% X      dimsnsion should be n x d
+% However since X might be larget X might be n x d or d x n. 
 
 blksize = 1e5;
-nr_desc = size(X, 1);
 
 % X might be huge matrix. However, center is always relative small matrix
 % if center dimension should be equal to X dimenstion. 
-isIdentical = isequal(size(center, 1), size(X, 1));
+% since size(center, 2) is feature dimension
+isIdentical = isequal(size(center, 2), size(X, 2));
 
 if isIdentical
-    labels = zeros(size(X, 2), 1);    
+    % X: n x d
+    nr_desc = size(X, 1);
+    labels = zeros(size(X, 1), 1);    
 else
-    labels = zeros(size(X, 1),1);
+    % X: d x n
+    nr_desc = size(X, 2);
+    labels = zeros(size(X, 2),1);
 end
 
 nr_calculated = 0;
@@ -21,11 +28,16 @@
     head = nr_calculated+1;
     tail = nr_calculated+nr_tocalc;
     if isIdentical
+        % X: n x d
+        X_blk = X(head:tail, :);   
+    else
+        % X: d x n
         X_blk = X(:, head:tail);
         X_blk = X_blk';
-    else
-        X_blk = X(head:tail,:);
     end
+    % findvword input should be 
+    % center: n x d
+    % X_blk:  n x d
     labels(head:tail) = findvword(center, X_blk);
     nr_calculated  = nr_calculated + nr_tocalc;
 end

svm/util/findvword.m

@@ -1,6 +1,9 @@
 %quick wrapper for min Euclidean distance for kmeans labeling
 
 function vword = findvword(vocab, X)
+% Input: 
+% vocad:    n x d
+% X:        n x d
 %   tic
    center = vocab';
    X = X';