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# File name: dist_iris.r
# Run: mpiexec -np 4 Rscript dist_iris.r
rm(list = ls()) # Clean environment
library(pbdMPI, quietly = TRUE) # Load library
library(ape, quietly = TRUE) # Load library
if(comm.size() != 4)
comm.stop("4 processors are required.")
### Load data
X <- as.matrix(iris[, -5]) # Dimension 150 by 4
X.cid <- iris[, 5] # True id
### Distribute data
jid <- get.jid(nrow(X))
X.gbd <- X[jid,] # GBD row-major format
### Compute distance
X.dist.common <- comm.dist(X.gbd)
X.dist.gbd <- comm.dist(X.gbd, return.type = "gbd")
### Verify
X.dist.gbd <- unlist(allgather(X.dist.gbd[, 3]))
n.diff <- sum(X.dist.common != X.dist.gbd)
### Hierarchical clustering
X.hc <- hclust(X.dist.common, method = "average")
if(comm.rank() == 0){
X.hc$labels <- as.character(X.cid)
X.phylo <- as.phylo(X.hc)
pdf("dist_iris.pdf", width = 10, height = 4)
plot(X.phylo, tip.color = as.numeric(X.cid) + 1,
direction = "downwards", no.margin = TRUE,
label.offset = 0.01, y.lim = c(-0.3, 2.1), cex = 0.5)
dev.off()
cat("Difference of common and gbd: ", n.diff, "\n", sep = "")
}
### Finish
finalize()
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