demo/iris_gbdr.r

In snoweye/pmclust: Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model

### Setup environment.
suppressMessages(library(pmclust, quietly = TRUE))

### Load data
X <- as.matrix(iris[, -5])

### Distribute data
jid <- get.jid(nrow(X))
X.gbd <- X[jid,]

### Standardized
N <- allreduce(nrow(X.gbd))
p <- ncol(X.gbd)
mu <- allreduce(colSums(X.gbd / N))
X.std <- sweep(X.gbd, 2, mu, FUN = "-")
std <- sqrt(allreduce(colSums(X.std^2 / (N - 1))))
X.std <- sweep(X.std, 2, std, FUN = "/")

### Clustering
suppressMessages(library(pmclust, quietly = TRUE))
comm.set.seed(123, diff = TRUE)

ret.mb1 <- pmclust(X.std, K = 3)
comm.print(ret.mb1)

ret.kms <- pkmeans(X.std, K = 3)
comm.print(ret.kms)

### Finish
finalize()