#indhent og gem træningsdata som .rds:
library(cluster)
library(HSAUR)
library(fpc)
library(magrittr)
files <- dir("out/data_import")
files <- files[!grepl(".py",files)]
file <- files[[1]]
file2 <- files[[2]]
train <- read.csv2(paste0("out/data_import/",file), header=F, stringsAsFactors = F)
label <- read.table(paste0("out/data_import/",file2), header=F, stringsAsFactors = F)
contract <- cbind(train,label)
#rm(train, label, file, file2)
train <- lapply(train, as.numeric) %>% as.data.frame
# Kmeans clustre analysis
clus <- kmeans(train, centers=2)
# Fig 01
plotcluster(train, clus$cluster)
clusplot(train, clus$cluster, color=TRUE, shade=TRUE,
labels=2, lines=0)
#with(contract, pairs(train, col=c(1:5)[clus$cluster]))
# apply PCA - scale. = TRUE is highly
# advisable, but default is FALSE.
train.pca <- prcomp(train,
center = TRUE,
scale. = TRUE)
print(ir.pca)
plot(ir.pca, type = "l")
summary(ir.pca)
library(devtools)
install_github("ggbiplot", "vqv")
library(ggbiplot)
g <- ggbiplot(ir.pca, obs.scale = 1, var.scale = 1,
groups = ir.species, ellipse = TRUE,
circle = TRUE)
g <- g + scale_color_discrete(name = '')
g <- g + theme(legend.direction = 'horizontal',
legend.position = 'top')
print(g)
require(caret)
trans = preProcess(iris[,1:4],
method=c("BoxCox", "center",
"scale", "pca"))
PC = predict(trans, iris[,1:4])
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.