initial sd-vbs

initial sd-vbs

add sd-vbs

sd-vbs

1 files changed, 31 insertions, 0 deletions

diff --git a/SD-VBS/common/toolbox/MultiNcut/gaussian.m b/SD-VBS/common/toolbox/MultiNcut/gaussian.m
new file mode 100755
index 0000000..509b129
--- /dev/null
+++ b/SD-VBS/common/toolbox/MultiNcut/gaussian.m
@@ -0,0 +1,31 @@
+function p=gaussian(x,m,C);
+% p=gaussian(x,m,C);
+%
+% Evaluate the multi-variate density with mean vector m and covariance
+% matrix C for the input vector x.
+% 
+% p=gaussian(X,m,C);
+% 
+% Vectorized version: Here X is a matrix of column vectors, and p is 
+% a vector of probabilities for each vector.
+
+d=length(m);
+
+if size(x,1)~=d
+   x=x';
+end
+N=size(x,2);
+
+detC = det(C);
+if rcond(C)<eps
+%   fprintf(1,'Covariance matrix close to singular. (gaussian.m)\n');
+   p = zeros(N,1);
+else
+   m=m(:);
+   M=m*ones(1,N);
+   denom=(2*pi)^(d/2)*sqrt(abs(detC));
+   mahal=sum(((x-M)'*inv(C)).*(x-M)',2);   % Chris Bregler's trick
+   numer=exp(-0.5*mahal);
+   p=numer/denom;
+end
+