|
|
function [K K_test]= kernel(X, Y, kernelName, wantK) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if (wantK || strcmp(kernelName,'echi2')==1 || strcmp(kernelName,'emd')==1 || strcmp(kernelName,'rbf')==1) |
|
|
switch kernelName |
|
|
case 'emd' |
|
|
K = emd_dist(X,X); |
|
|
mu= 1 ./ mean(K(:)) ; |
|
|
K = exp(- mu * K) ; |
|
|
case 'echi2' |
|
|
K = alldist2(X, 'chi2') ; |
|
|
mu= 1 ./ mean(K(:)) ; |
|
|
K = exp(- mu * K) ; |
|
|
case 'kl1' |
|
|
K = vl_alldist2(X, 'kl1'); |
|
|
case'kl2' |
|
|
K = X' * X ; |
|
|
case 'kchi2' |
|
|
K = vl_alldist2(X, 'kchi2') ; |
|
|
case 'gb' |
|
|
K = gbDistance(X, X); |
|
|
case 'rbf' |
|
|
X = X'; |
|
|
norm1 = sum(X.^2,2); |
|
|
norm2 = sum(X.^2,2); |
|
|
dist = (repmat(norm1 ,1,size(X,1)) + repmat(norm2',size(X,1),1) - 2*X*X'); |
|
|
mu=sqrt(mean(dist(:))/2); |
|
|
K = exp(-0.5/mu^2 * dist); |
|
|
end |
|
|
if ~isempty(find(isnan(K))) |
|
|
disp('something element is NaN in the K matrix'); |
|
|
K(find(isnan(K)))=10^20; |
|
|
end |
|
|
else |
|
|
K = []; |
|
|
end |
|
|
|
|
|
switch kernelName |
|
|
case 'emd' |
|
|
K_test = emd_dist(X, Y); |
|
|
K_test = exp(- mu * K_test) ; |
|
|
case 'echi2' |
|
|
K_test = vl_alldist(X, Y, 'chi2') ; |
|
|
K_test = exp(- mu * K_test) ; |
|
|
case 'kl1' |
|
|
K_test = vl_alldist2(X, Y, 'kl1'); |
|
|
case'kl2' |
|
|
K_test = X' * Y ; |
|
|
case 'kchi2' |
|
|
K_test = vl_alldist2(X, Y, 'kchi2') ; |
|
|
case 'gb' |
|
|
K_test = gbDistance(X, Y); |
|
|
case 'rbf' |
|
|
Y = Y'; |
|
|
norm1 = sum(X.^2,2); |
|
|
norm2 = sum(Y.^2,2); |
|
|
dist = (repmat(norm1 ,1,size(Y,1)) + repmat(norm2',size(X,1),1) - 2*X*Y'); |
|
|
|
|
|
K_test = exp(-0.5/mu^2 * dist); |
|
|
end |
|
|
|
|
|
if ~isempty(find(isnan(K_test))) |
|
|
disp('something element is NaN in the K_test matrix'); |
|
|
K_test(find(isnan(K_test)))=10^20; |
|
|
end |