dataCorolla <- function(raw="false"){
data=read.csv("./data/ToyotaCorolla.csv")
#Zamiana danych na numeryczne a póżniej na ramke
data = as.data.frame(sapply(data, as.numeric))
#Podzial danych
bound <- floor((nrow(data)/4)*3)
s <- data[sample(nrow(data)), ]
train <- data[1:bound, ]
test <- data[(bound+1):nrow(data), ]
if(tolower(raw) == "true" ){
return (list("columns" = variable.names(data),
"train" = train,
"test"=test))
}
#
# Automatyczne dobieranie cech
#cat("\n------------------forward----------------------\n")
g1 <- lm(Price~1, data = train)
wprzod=step(g1, diretion = "forward",
scope = list(upper=.~.+Age+KM+FuelType+HP+MetColor+Automatic+CC+Doors+Weight), trace=0)
#Wybrany model: Price ~ Age + HP + Weight + KM + FuelType + CC + Doors
#lm(Price~., data=train[,kolumny])
#cat("\n------------------backward----------------------\n")
g2 <- lm(Price~Age+KM+FuelType+HP+MetColor+Automatic+CC+Doors+Weight, data = train)
wtyl = step(g2, direction = "backward",
scope = list(upper=.~.+Age+KM+FuelType+HP+MetColor+Automatic+CC+Doors+Weight), trace=0)
#Wybrnay model : Price ~ Age + KM + FuelType + HP + CC + Doors + Weight
#cat("\n------------------both----------------------\n")
# Model: Price ~ Age + HP + Weight + KM + FuelType + CC + Doors
g3 <- lm(Price~., data = train)
wdwie = step(g3, direction = "both",
scope = list(upper=.~.+Age+KM+FuelType+HP+MetColor+Automatic+CC+Doors+Weight), trace=0)
#cat("\n------------------Podsumowanie--------------------\n")
#cat("\nMultiple R-squared (forward): ", summary(wprzod)$r.squared) # Multiple R-squared: 0.8615953
#cat("\nMultiple R-squared (backward): ", summary(wtyl)$r.squared) # Multiple R-squared: 0.8615953
#cat("\nMultiple R-squared (both): ", summary(wdwie)$r.squared) #Multiple R-squared: 0.8615953
# Wszystkie podejścia dają taki samy błąd
kolumny <-variable.names(wprzod)
kolumny[1] <- "Price"
train = train[,c(kolumny)]
test = test[,c(kolumny)]
return (list("columns" = kolumny, "train" = train, "test"=test))
}
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