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# File name: iris_dmat.r
# Run: mpiexec -np 4 Rscript iris_dmat.r
rm(list = ls()) # Clean environment
library(pbdDMAT, quietly = TRUE) # Load library
init.grid()
#if(comm.size() != 4)
# comm.stop("4 processors are required.")
dmat_opts$BLDIM <- 5
### Load data
X <- as.matrix(iris[, -5]) # Dimension 150 by 4
X.cid <- as.numeric(iris[, 5]) # True id
### Convert to ddmatrix
X.dmat <- as.ddmatrix(X)
### Standardized
X.std <- scale(X.dmat)
#mu <- as.matrix(colMeans(X.std))
#cov <- as.matrix(cov(X.std))
#comm.print(mu)
#comm.print(cov)
### SVD
#X.svd <- svd(X.std)
### Project on column space of singular vectors
#A <- X.svd$u %*% diag(X.svd$d, type="ddmatrix")
#B <- X.std %*% X.svd$v # A ~ B
#X.prj <- as.matrix(A[, 1:2]) # Only useful for plot
### Clustering
library(pmclust, quiet = TRUE)
comm.set.seed(123, diff = TRUE)
X.dmat <- X.std
PARAM.org <- set.global.dmat(K = 3) # Preset storage
.pmclustEnv$CONTROL$debug <- 0 # Disable debug messages
PARAM.org <- initial.center.dmat(PARAM.org)
PARAM.kms <- kmeans.step.dmat(PARAM.org) # K-means
X.kms.cid <- as.vector(.pmclustEnv$CLASS)
### Validation
X.kms.adjR <- EMCluster::RRand(X.cid, X.kms.cid)$adjRand
comm.print(X.kms.adjR)
### Swap classification id
tmp <- X.kms.cid
X.kms.cid[tmp == 1] <- 3
X.kms.cid[tmp == 2] <- 1
X.kms.cid[tmp == 3] <- 2
### Display on first 2 components
#if(comm.rank() == 0){
# pdf("dmat_plot.pdf")
#
# par(mfrow = c(2, 2))
# plot(X.prj, col = X.cid + 1, pch = X.cid,
# main = "iris (true)", xlab = "PC1", ylab = "PC2")
# plot(X.prj, col = X.kms.cid + 1, pch = X.kms.cid,
# main = paste("iris (kmeans)", sprintf("%.4f", X.kms.adjR)),
# xlab = "PC1", ylab = "PC2")
# dev.off()
#}
### Finish
finalize()
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