Error while calculating Cosine distance

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Balaji M. Sontakke
Balaji M. Sontakke el 25 de Dic. de 2018
Editada: madhan ravi el 25 de Dic. de 2018
Following is the error while calculating Cosine distance....
Subscripted assignment dimension mismatch.
Error in CosineDistance (line 28)
Distance(i,j) = - (ClientMean(i,:)'*EvalSet(j,:))/(norm_x*norm_y);
function Distance = CosineDistance(ClientMean, EvalSet)
% Calculate Cosine Distance
%
% Inputs:
% ClientSet ---- c* dim matrix
% EvalSet ---- n*dim matrix
% Outputs:
% Distance ---- c*n matrix
if ~exist('ClientMean','var')
error('Input arguments error.');
end
if ~exist('EvalSet','var')
error('Input arguments error.');
end
[c, d] = size(ClientMean);
[n, dim] = size(EvalSet);
if (d ~= dim)
error('Dimensionality disagreement.');
end
for i = 1 : c
for j = 1 : n
norm_x = norm(ClientMean);
norm_y = norm(EvalSet);
Distance(i,j) = - (ClientMean(i,:)'*EvalSet(j,:))/(norm_x*norm_y);
end
end
  1 comentario
Walter Roberson
Walter Roberson el 25 de Dic. de 2018
Are you sure about that code? It would make more sense if it was
ClientMean(i,:)*EvalSet(j,:)'
as that would be 1 x d * d x 1, giving 1 x 1.

Iniciar sesión para comentar.

Respuesta aceptada

madhan ravi
madhan ravi el 25 de Dic. de 2018
Editada: madhan ravi el 25 de Dic. de 2018
To view answer:
ClientMean=....your matrix;
EvalSet=....your matrix;
Distance = CosineDistance(ClientMean, EvalSet) % function call
celldisp(Distance) % after the calling of the function
Just change yours to below:
function Distance = CosineDistance(ClientMean, EvalSet) % function definition
% Calculate Cosine Distance
%
% Inputs:
% ClientSet ---- c* dim matrix
% EvalSet ---- n*dim matrix
% Outputs:
% Distance ---- c*n matrix
if ~exist('ClientMean','var')
error('Input arguments error.');
end
if ~exist('EvalSet','var')
error('Input arguments error.');
end
[c, d] = size(ClientMean);
[n, dim] = size(EvalSet);
if (d ~= dim)
error('Dimensionality disagreement.');
end
Distance=cell(c,n); % pre-allocate
ctr=1;
for i = 1 : c
for j = 1 : n
norm_x = norm(ClientMean);
norm_y = norm(EvalSet);
Distance{ctr} = - (ClientMean(i,:)'*EvalSet(j,:))/(norm_x*norm_y);
ctr=ctr+1;
end
end
end

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