% EM algorithm for k multidimensional Gaussian mixture estimation
%
% Inputs:
% X(n,d) - input data, n=number of observations, d=dimension of variable
% k - maximum number of Gaussian components allowed
% ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
% maxiter - maximum number of iteration allowed ([] for none)
% pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
% Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
%
% Ouputs:
% W(1,k) - estimated weights of GM
% M(d,k) - estimated mean vectors of GM
% V(d,d,k) - estimated covariance matrices of GM
% L - log likelihood of estimates
%
求標準偏差
> function c=myfunction(x)
> [m,n]=size(x)
> t=0
> for i=1:numel(x)
> t=t+x(i)*x(i)
> end
> c=sqrt(t/(m*n-1))
function c=myfunction(x)
[m,n]=size(x)
t=0
for i=1:m
for j=1:n
t=t+x(i,j)*x(i,j)
end
end
c=sqrt(t/(m*n-1
求標準偏差
> function c=myfunction(x)
> [m,n]=size(x)
> t=0
> for i=1:numel(x)
> t=t+x(i)*x(i)
> end
> c=sqrt(t/(m*n-1))
function c=myfunction(x)
[m,n]=size(x)
t=0
for i=1:m
for j=1:n
t=t+x(i,j)*x(i,j)
end
end
c=sqrt(t/(m*n-1
求標準偏差
> function c=myfunction(x)
> [m,n]=size(x)
> t=0
> for i=1:numel(x)
> t=t+x(i)*x(i)
> end
> c=sqrt(t/(m*n-1))
function c=myfunction(x)
[m,n]=size(x)
t=0
for i=1:m
for j=1:n
t=t+x(i,j)*x(i,j)
end
end
c=sqrt(t/(m*n-1
求標準偏差
> function c=myfunction(x)
> [m,n]=size(x)
> t=0
> for i=1:numel(x)
> t=t+x(i)*x(i)
> end
> c=sqrt(t/(m*n-1))
function c=myfunction(x)
[m,n]=size(x)
t=0
for i=1:m
for j=1:n
t=t+x(i,j)*x(i,j)
end
end
c=sqrt(t/(m*n-1