######## Intro ########

### 2 vektory
area <- c(1:12)

rate <- c(1.2, 1.5, 1.02, 1.63, 1.14, 1.86, 2.1, 1.43, 1.76, 2.08, 1.32, 1.6)

### vypsat promenne
ls()

### zobrazit
area

### matice
mat <- cbind(area, rate)

### zobrazit 2 radek matice
mat[2, ]

### vykreslit matici
plot(mat)

### dalsi vektor
area <- c(1:12)

### vytvoreni sekvenci
rate <- seq(length=12, from=1, by=.1)

mat2 <- cbind(area,rate)

plot(mat)
### okno se nepremaze
par(new=TRUE)
plot(mat2)

### rozlisit a mit stejnou stupnici

plot(mat, main="pokusne matice", ylim=c(1, 2.2));par(new=TRUE);plot(mat2, ylim=c(1, 2.2), pch=2)
### pridame popisky
legend(x="topleft", legend=c("mat", "mat2"), pch=c(1,2), bg="white", inset=0.01)
### pridame "ohraniceni"
abline(v=c(6, 8), lty=4)
### text
text(7,1.2,"spatna oblast",srt=90)

######### tm ##########
### Pavel Brazdil, LIAAD, FEP Universidade do Porto ###
library(tm)

### data volne dostupna na internetu - 20newsgroups
### http://people.csail.mit.edu/jrennie/20Newsgroups/
electr.train <- Corpus(DirSource("20news-bydate-train/sci.electronics"), readerControl=list(reader=readPlain, language="en_US"))

print(electr.train)
summary(electr.train)
inspect(electr.train[1:3])

### dalsi 3 korpusy
religion.train <- Corpus(DirSource("20news-bydate-train/talk.religion.misc"), readerControl=list(reader=readPlain, language="en_US"))

electr.test <- Corpus(DirSource("20news-bydate-test/sci.electronics"), readerControl=list(reader=readPlain, language="en_US"))

religion.test <- Corpus(DirSource("20news-bydate-test/talk.religion.misc"), readerControl=list(reader=readPlain, language="en_US"))

# indexy jednotlivych dokumentu
#   	electr.train 	– documents 1 .. 591 
#   	religion.train 	– documents 592 .. 968  (377 docs)
#   	electr.test 	– documents 969 .. 1361 (393 docs)
#   	religion.test 	– documents 1362 .. 1612  (251 docs)

all <- c(electr.train, religion.train, electr.test, religion.test)

### predzpracovani
 all <- tm_map(all, PlainTextDocument)
 all <- tm_map(all, removeWords, stopwords(language="english")) 
 all <- tm_map(all, stripWhitespace)
 all <- tm_map(all, tolower)
 all <- tm_map(all, removePunctuation)
 all <- tm_map(all, removeNumbers)
### stemming - velmi dlouhe...
### library(rJava); library(rWeka); library(Snowball)
### all <- tm_map(all, stemDocument)

DocumentTermMatrix(all)
dtm.all <- DocumentTermMatrix( all, control=list(minWordLength=3, minDocFreq=5, weighting=weightTfIdf))

findFreqTerms(dtm.all, 40)

dtm.all.frame <- as.data.frame(inspect( dtm.all))
class <- c(rep("sci",591), rep("rel",377), rep("sci",393), rep("rel",251))
dtm.all.frame <- cbind(dtm.all.frame, class)

## pripravime trenovaci data
train <- dtm.all.frame[1:968, 1:ncol( dtm.all.frame )]

### testovaci
test <- dtm.all.frame[969:1612, 1:ncol( dtm.all.frame ) - 1]
testclass <- dtm.all.frame[969:1612,ncol(dtm.all.frame)]


### vyber termu pro klasifikaci
clas.formula<-class ~ cable + circuit + ground + neutral + outlets + subject + wire + wiring + judas + christ + elohim + father + god + gods + jehovah + jesus + lord + mcconkie + son + unto

######## rozhodovaci strom
library(rpart)
dt <- rpart(clas.formula, train)

### predikce
dt.predictions <- predict(dt, test, type="class")
conf.mx.dt<-table(testclass, dt.predictions)
conf.mx.dt
error.rate.dt <- (sum(conf.mx.dt)-sum(diag(conf.mx.dt))) / sum(conf.mx.dt)
error.rate.dt

######## Neural net
library(nnet)
nnet.classifier <- nnet(clas.formula, data=train, size=2, rang=0.1, 
   decay=5e-4, maxit=200)

preds.nn <- predict(nnet.classifier, test, type="class")

conf.mx.nn <- table(testclass, preds.nn)
conf.mx.nn
error.rate.nn <- (sum(conf.mx.nn)-sum(diag(conf.mx.nn))) / sum(conf.mx.nn)
error.rate.nn 


######## SVM
library(e1071)
 svm.classifier <- svm(clas.formula, train, scale=F, kernel="linear")
 preds.svm <- predict(svm.classifier, test)
 conf.mx.svm <- table(testclass, preds.svm)
 conf.mx.svm
 error.rate.svm <- (sum(conf.mx.svm)-sum(diag(conf.mx.svm))) / sum(conf.mx.svm)
 error.rate.svm


######## NaiveBayes - Weka
 library(RWeka)
 NB<-make_Weka_classifier("weka/classifiers/bayes/NaiveBayes")
 nb.classifier<-NB(clas.formula, train)
 preds.nb<-predict(nb.classifier, test)
              
 conf.mx.nb<-table(testclass, preds.nb)
 conf.mx.nb
 error.rate.nb <- (sum(conf.mx.nb)-sum(diag(conf.mx.nb))) / sum(conf.mx.nb)
 error.rate.nb



####### Prejmenovani termu pro as.formula (termy jako if, then, else, for, break koliduji s prikazy R)

rename.terms.in.dtm <- function(dtm) {
	for (i in 1:length(dtm)) {
		cat("replaced to ", paste(colnames(dtm)[i],".t", sep=""), "\n")
		colnames(dtm)[i] <- paste(colnames(dtm)[i],".t", sep="")
	} #end for i
	return(dtm)
}
train<-rename.terms.in.dtm(train)
test<-rename.terms.in.dtm(test)

####### Vytvoreni klasifikacni formule ze vsech termu
clas.formula<-as.formula(paste("class.t ~",paste(colnames(train)[vybrane], collapse="+")))