％ 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
