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cvi/proximity/cosdist.m

@@ -0,0 +1,3 @@
+function D = cosdist(A,B)
+% D = bsxfun(@plus,0.5*dot(B,B,1),0.5*dot(A,A,1)') - mtimesx(A,'T',B);
+D = bsxfun(@plus,0.5*dot(B,B,1),0.5*dot(A,A,1)') - A'*B;

cvi/proximity/lapdist.m

@@ -0,0 +1,4 @@
+function D = lapdist(A,B)
+De = bsxfun(@plus,dot(B,B,1),dot(A,A,1)') - 2*(A'*B);%mtimesx(A,'T',B);
+cosAB = 1 - 0.5*De;
+D = (1-cosAB)./(1+cosAB);

cvi/proximity/medist.m

@@ -0,0 +1,21 @@
+function GXX = medist(DXX)
+
+% Construccion del grafo no dirigido (triangulo inferior de DXX)
+LXX = tril(DXX);
+UG = sparse(LXX);
+% Encontrar el arbol de recubrimiento minimo
+[MST,~] = graphminspantree(UG);
+
+nVertex = length(MST);
+MED = zeros(nVertex);
+for i = 1:nVertex;
+    for j = i+1:nVertex
+        [~,path,~] = graphshortestpath(MST,i,j,'directed',false);
+        v_i = path(1:end-1);    % Vertex i
+        v_j = path(2:end);      % Vertex j
+        subMST = MST(v_i,v_j) + MST(v_j,v_i);   % Subgraph of MST
+        [~,~,distances] = find(subMST);   
+        MED(i,j) = max(distances);  % MED distance from subgraph of MST
+    end
+end
+GXX = MED + MED';

cvi/proximity/neucdist.m

@@ -0,0 +1,7 @@
+function D = neucdist(A, B)
+A = bsxfun(@minus,A,mean(A,2));
+B = bsxfun(@minus,B,mean(B,2));
+A = bsxfun(@rdivide,A,std(A,0,2));
+B = bsxfun(@rdivide,B,std(B,0,2));
+% D = sqrt(bsxfun(@plus,dot(B,B,1),dot(A,A,1)')- 2*mtimesx(A,'T',B));
+D = sqrt(bsxfun(@plus,dot(B,B,1),dot(A,A,1)')- 2*(A'*B));

cvi/proximity/pcorr.m

@@ -0,0 +1,5 @@
+function D = pcorr(A,B)
+A = bsxfun(@minus,A,mean(A,2));
+B = bsxfun(@minus,B,mean(B,2));
+% D = bsxfun(@plus,0.5*dot(B,B,1),0.5*dot(A,A,1)') - mtimesx(A,'T',B);
+D = bsxfun(@plus,0.5*dot(B,B,1),0.5*dot(A,A,1)') - A'*B;

cvi/proximity/proxconfig.m

@@ -0,0 +1,22 @@
+function fun = proxconfig(opt)
+if strcmpi(opt,'euc')
+    % Euclidean distance
+    fun = @eucdist;
+elseif strcmpi(opt,'neuc')
+    % Normalized Euclidean distance
+    fun = @neucdist; 
+elseif strcmpi(opt,'cos')
+    % Cosine similarity
+    fun = @cosdist;
+elseif strcmpi(opt,'pcorr')
+    % Pearson's correlation coefficient
+    fun = @pcorr;
+elseif strcmpi(opt,'scorr')
+    % Spearman's correlation coefficient
+    fun = @scorr; 
+elseif strcmpi(opt,'lap')
+    % Laplacian distance
+    fun = @lapdist; 
+else
+    error('Unknown proximity measure');
+end

cvi/proximity/scorr.m

@@ -0,0 +1,8 @@
+function D = scorr(~,B)
+global Xrank;
+A = Xrank; % A = ranking(A);
+B = ranking(B);
+A = bsxfun(@minus,A,mean(A,2));
+B = bsxfun(@minus,B,mean(B,2));
+% D = bsxfun(@plus,0.5*dot(B,B,1),0.5*dot(A,A,1)') - mtimesx(A,'T',B);
+D = bsxfun(@plus,0.5*dot(B,B,1),0.5*dot(A,A,1)') - A'*B;