DEM (version 0.0.0.2)

DEM2: The DEM2 algorithm is a one-step average algorithm in distributed manner, which is used to solve the parameter estimation of multivariate Gaussian mixture model.

Description

The DEM2 algorithm is a one-step average algorithm in distributed manner, which is used to solve the parameter estimation of multivariate Gaussian mixture model.

Usage

DEM2(y, M, seed, alpha0, mu0, sigma0, i, epsilon)

Arguments

y

is a data matrix

M

is the number of subsets

seed

is the recommended way to specify seeds

alpha0

is the initial value of the mixing weight

mu0

is the initial value of the mean

sigma0

is the initial value of the covariance

i

is the number of iterations

epsilon

is the threshold value

Value

DEM2alpha,DEM2mu,DEM2sigma,DEM2time

Examples

# NOT RUN {
library(mvtnorm)
alpha1= c(rep(1/4,4)) 
mu1=matrix(0,nrow=4,ncol=4) 
for (k in 1:4){
mu1[4,]=c(runif(4,(k-1)*3,k*3)) 
}
sigma1=list()
for (k in 1:4){
sigma1[[k]]= diag(4)*0.1
}
y= matrix(0,nrow=200,ncol=4) 
for(k in 1:4){
y[c(((k-1)*200/4+1):(k*200/4)),] = rmvnorm(200/4,mu1[k,],sigma1[[k]]) 
}
M=5
seed=123
alpha0= alpha1
mu0=mu1
sigma0=sigma1
i=10
epsilon=0.005
DEM2(y,M,seed,alpha0,mu0,sigma0,i,epsilon)
# }