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na_grnn_cli.R
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na_grnn_cli.R
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# 22.3.17 IVA
# http://stackoverflow.com/questions/18695335/replacing-all-nas-with-smoothing-spline
# install.packages('grnn', dependencies=TRUE, repos='http://cran.rstudio.com/')
# install.packages('grt', dependencies=TRUE, repos='http://cran.rstudio.com/')
# install.packages('neuralnet', dependencies=TRUE, repos='http://cran.rstudio.com/')
# install.packages('caret', dependencies=TRUE, repos='http://cran.rstudio.com/')
require(zoo)
require(grnn)
require(grt)
require(foreach)
is.leapyear=function(year){
#http://en.wikipedia.org/wiki/Leap_year
#https://www.r-bloggers.com/leap-years/
#https://github.com/hadley/lubridate/blob/master/R/leap-years.r
return(((year %% 4 == 0) & (year %% 100 != 0)) | (year %% 400 == 0))
}
setZeroNegativeNumber <- function(n){
if(n < 0) return(0)
else return(n)
}
createCliDF <- function(fileName){
cli_dataset <-read.table(fileName, header=FALSE, sep="")
names(cli_dataset) <- c("day", "month", "year", "prec", "temp");
cli_dataset[cli_dataset$prec == -9999, 4] <- NA ; prec_vector <- cli_dataset[, 4]
cli_dataset[cli_dataset$temp == -9999, 5] <- NA ; temp_vector <- cli_dataset[, 5]
days <- c(1:dim(cli_dataset)[1])
return(data.frame(days=days, prec=prec_vector, temp=temp_vector))
}
# Replacement numbers-9999 to missing value NA
replacementNumbersMinus9999.NA <- function(cli_dataset){
for(i in 1:nrow(cli_dataset)){
if(!is.na(cli_dataset[i,2])){
if(cli_dataset[i,2]==-9999) cli_dataset[i,2]<-NA
}
if(!is.na(cli_dataset[i,3])) {
if(cli_dataset[i,3]==-9999) cli_dataset[i,3]<-NA
}
}
return(cli_dataset)
}
plotTempFilled <- function(naDF, filledDF, fileName){
plot(filledDF[,3], main=fileName, ylab="Temp(c)*10", xlab="Days", col="red", pch=19);
points(naDF[,3], col="blue", pch=19)
grid (10,10, lty = 6, col = "cornsilk2")
points(filledDF[,3], col="red", pch=19);
points(naDF[,3], col="blue", pch=19)
# Add a legend
legend("topleft", legend=c("Real CLI", "Guess CLI"), col=c("red", "blue"), pch=c(19,19), cex=0.8)
}
plotPrecFilled <- function(naDF, filledDF, fileName){
plot(filledDF[,2], main=fileName, ylab="Prec(mm)*10", xlab="Days", col="red", pch=19);
points(naDF[,2], col="blue", pch=19)
grid (10,10, lty = 6, col = "cornsilk2")
points(filledDF[,2], col="red", pch=19);
points(naDF[,2], col="blue", pch=19)
# Add a legend
legend("topleft", legend=c("Real CLI", "Guess CLI"), col=c("red", "blue"), pch=c(19,19), cex=0.8)
}
na.grnn <- function(vec.cli, setTrain, setSigma, searchSigma, trace){
# PRE-PROCESSING DATA
vec.na<-vec.cli; vec.na.scale <- grt::scale(vec.na);
vec.na.scale.min<-min(vec.na.scale, na.rm = T); vec.na.scale.max<-max(vec.na.scale, na.rm = T);
vec.na.index <- which(is.na(vec.na)) #
vec.na.scale.index <- which(is.na(vec.na.scale)) #
vec.na.scale.na.omit <- na.omit(vec.na.scale);
days <- 1:length(vec.na); days.scale <- grt::scale(days) # äëÿ ïðîãíîçà
days.scale.na.omit <- days.scale[-vec.na.scale.index]; # base::scale(days)
XY <- data.frame(days.scale.na.omit, vec.na.scale.na.omit)
if(searchSigma == TRUE){
## SPLIT DATA SAMPLES
#seed.init<-2017; set.seed(seed.init)
num.test<-setTrain;
rows <- sample(1:nrow(XY), nrow(XY) - num.test)
trainXY <- XY[rows, ]; testXY <- XY[-rows, ]
s.seq <- seq(0.01, 0.99, 0.05)
cv <- foreach(s = s.seq) %do% {
L <- grnn::learn(trainXY, variable.column = ncol(trainXY))
grnn <- grnn::smooth(L, sigma = s)
guessY <- testXY[, 2]
for(i in 1:length(testXY[, 1])){
guessY[i] <- grnn::guess(grnn, testXY[i, 1])
}
test.sse <- round(sum((guessY-testXY[, 2]))^2, 6)
if(trace == TRUE){
cat("Sigma= ", round(s,3), "SSE= ", test.sse, "\n")
}
data.frame(s, sse = test.sse)
# http://r-train.ru/r-%D0%BA%D0%B2%D0%B0%D0%B4%D1%80%D0%B0%D1%82-rse-%D0%B8-rmse/
}
idx<-1; val<-cv[[1]][1,2]
for(i in 2: length(s.seq)){
if(cv[[i]][1,2] < val){
idx <- i
val <- cv[[i]][1,2]
}
}
idx; val;
## predicting missing values with the calculated optimum Sigma GRNN
s<-cv[[idx]][1,1]
} else s <- setSigma
L <- grnn::learn(XY, variable.column = ncol(XY))
grnn <- grnn::smooth(L, sigma = s)
for(i in vec.na.index){
#todo ïîñòàâèòü ïðîâåðêó âûõîäà çà min max
vec.na.scale[i] <- grnn::guess(grnn, days.scale[i, 1])
#cat(i, vec.na.scale[i],"\n")
}
vec.na.unscale <- grt::unscale(vec.na.scale)
#plot(vec.na.unscale, col="red"); points(vec.na, col="blue");
}
na.grnn.cli <- function(cli_dataset, setTrain, setSigma, searchSigma, trace){
cli_dataset<-replacementNumbersMinus9999.NA(cli_dataset)
prec_vector <- cli_dataset[, 2]
temp_vector <- cli_dataset[, 3]
#days <- length(cli_dataset[,1])
days <- c(1:dim(cli_dataset)[1])
if(sum(is.na(prec_vector)) != 0){
prec_filled <- sapply(as.integer(na.grnn(prec_vector, setTrain, setSigma, searchSigma, trace)), setZeroNegativeNumber)
} else prec_filled <- prec_vector
if(sum(is.na(temp_vector)) != 0){
#temp_filled <- as.integer(na.spline(temp_vector))
temp_filled <- as.integer(na.grnn(temp_vector, setTrain, setSigma, searchSigma, trace))
} else temp_filled <- temp_vector
new_cli <- data.frame(day=cli_dataset[,1], prec=prec_filled, temp=temp_filled)
return(new_cli)
}
na.grnn.cli.test <- function(fileName, setTrain, setSigma, searchSigma, trace){
filled_path <- "C:/Users/IVA/Dropbox/Apps/na_grnn"
#filled_path <- "/home/larisa/Dropbox/Apps/na_grnn"
setwd(filled_path); getwd(); file_name <- fileName
cli_dataset <- createCliDF(file_name)
summary(cli_dataset)
#system.time(na.na<-na.grnn(cli_dataset[, 3]))
#user system elapsed Atom home
#23.52 2.03 32.03
#ïîëüçîâàòåëü ñèñòåìà ïðîøëî
#3.43 0.00 3.81
cli_dataset_filled <- na.grnn.cli(cli_dataset, setTrain, setSigma, searchSigma, trace)
plotTempFilled(cli_dataset, cli_dataset_filled, file_name)
#plotPrecFilled(cli_dataset, cli_dataset_filled, file_name)
}
### TEST SCRIPT
if(FALSE){
# https://gist.githubusercontent.com/anonymous/75b0aa2a79091549247e6596f989712e/raw/def3b6967dc8c1a527a0472012cf57a44b1b75ab/1967.cli
na.grnn.cli.test("1967na.cli", 67, 0.01, TRUE, TRUE)
na.grnn.cli.test("1967na.cli", 67, 0.01, TRUE, FALSE)
}