DEM1: The DEM1 algorithm is a divide and conquer algorithm, which...
In DEM: The Distributed EM Algorithms in Multivariate Gaussian Mixture Models

DEM1	R Documentation

The DEM1 algorithm is a divide and conquer algorithm, which is used to solve the parameter estimation of multivariate Gaussian mixture model.

Description

The DEM1 algorithm is a divide and conquer algorithm, which is used to solve the parameter estimation of multivariate Gaussian mixture model.

Usage

DEM1(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

DEM1alpha,DEM1mu,DEM1sigma,DEM1time

Examples

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
DEM1(y,M,seed,alpha0,mu0,sigma0,i,epsilon)

DEM documentation built on May 14, 2022, 9:05 a.m.