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October 6, 2021 13:38
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mat pca uts.r
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library(ggplot2) | |
cr = function(d){ | |
print(c( | |
nrow(d), ncol(d) | |
)) | |
} | |
ple = function(d){ | |
print(length(d)) | |
} | |
printx = function(d){ | |
print(d) | |
quit() | |
} | |
hr = function(d){ | |
print(paste("================",d,"================")) | |
} | |
data = matrix( | |
c( | |
2.5, 2.4, | |
0.5, 0.7, | |
2.2, 2.9, | |
1.9, 2.2, | |
3.1, 3, | |
2.3, 2.7, | |
2, 1.6, | |
1, 1.1, | |
1.5, 1.6, | |
1.1, 0.9 | |
), | |
ncol=2, | |
byrow=T | |
) | |
pca = function(data=NULL){ | |
obj = list( | |
data=data, | |
x=NULL, | |
adjustData=NULL, | |
e=NULL, | |
rowFeatVector = NULL, | |
rowZeroMeanData = NULL, | |
finalData = NULL, | |
rowOriginalData = NULL | |
) | |
getcovars = function(m){ | |
n = ncol(m) | |
covars = c() | |
for (i in 1: n){ | |
for ( j in 1:n) { | |
# print(cov(m[,i], m[,j])) | |
covars = append(covars, cov(m[,i], m[,j])) | |
} | |
} | |
covars | |
} | |
adjustData = function(m){ | |
n = ncol(m) | |
means = c() | |
for (i in 1:n){ | |
me = mean(m[,i]) | |
means = append(means, me) | |
m[,i] = m[,i] - me | |
} | |
list( | |
m=m, | |
means=means | |
) | |
} | |
covars = getcovars(data) | |
x = matrix( | |
covars, | |
# c( | |
# 0.616555556, 0.615444444, | |
# 0.615444444, 0.716555556 | |
# ), | |
nrow=ncol(data), | |
byrow=T | |
) | |
obj$x = x | |
e = eigen(x, T) | |
obj$e = e | |
hr("vector") | |
print(e$vector) | |
print(t(e$vector)) | |
objad = adjustData(data) | |
adjData = objad$m | |
# hr("adjdata") | |
# print(adjData) | |
# print(t(adjData)) | |
obj$adjustData = adjData | |
obj$rowFeatVector = t(adjData) | |
obj$rowZeroMeanData = t(e$vector) | |
# finalData = rowFeatVector %*% rowZeroMeanData | |
obj$finalData = obj$rowZeroMeanData %*% obj$rowFeatVector | |
print(obj$finalData[,1]) | |
obj$rowOriginalData = t(obj$rowFeatVector) %*% obj$finalData + objad$means | |
objad$means | |
# finalData = t(finalData) | |
print(x) | |
print(e) | |
print(data) | |
plot(obj$finalData[1,], obj$finalData[1,]) | |
print(t(obj$finalData)) | |
print(obj$rowOriginalData) | |
obj | |
} | |
# pca1 = pca(data) | |
# pca1 | |
fname = "/media/data1/_S2/grad-notes/1.3-mtk-stat/dummy/phising.csv" | |
# fname = "./dummy/phising.csv" | |
df = read.csv(fname) | |
x = as.matrix( subset(df, select=having_IP_Address:Statistical_report) ) | |
x = x[1:30,] | |
head(x) | |
doPCA1 = function(x){ | |
# https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/prcomp | |
pcaresult = prcomp(x) | |
plot(pcaresult) | |
summary(pcaresult) | |
} | |
doPCA1(x) | |
# pca(x) |
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