DataBaser.Net: 병렬처리

1 병렬처리 #

library(dplyr)
df <- tbl_df(df)
last_dt_df <- group_by(df, seq) %>% summarise(last_dt = last(std_dt))
last_dt_df <- filter(last_dt_df, last_dt == edt)
df <- inner_join(df, last_dt_df, by="seq")
df <- arrange(df, seq, std_dt)


library("foreach")
library(doParallel)
cl<-makeCluster(3)
registerDoParallel(cl)

start.time <- Sys.time()
#result <- foreach(i = unique(df$seq), .combine='rbind') %dopar%{
result <- foreach(i = 1:10000, .combine='rbind') %dopar%{
    require(dplyr)
    tmp <- dplyr::filter(df, seq == i) %>% dplyr::mutate(x=row_number()) %>% dplyr::select(std_dt, seq, x)
    
    if(nrow(tmp) >= 3){        
        reference <- dplyr::filter(tmp, std_dt < edt)
        target <- dplyr::filter(tmp, std_dt == edt)
        
        require(MASS)
        model <- rlm(statvalue ~ x, data=reference)
        pred <- predict(model, target, interval="prediction", level=0.68)    
        
        if(1==0){
            plot(tmp$x, tmp$statvalue, ylim=c(pred[2]*1.1, max(tmp$statvalue)))
            line <- data.frame(predict(model, reference,interval = "prediction", level=0.68), reference)
            line <- line[order(line$x),]
            lines(line$x, line$fit, col="black", lty=2)
            lines(line$x, line$upr, col="blue", lty=2)
            lines(line$x, line$lwr, col="red", lty=2)
            add.error.bars(target$x, pred[1], pred[1]-pred[2], 0.1)
            points(target$x, target$statvalue, col="blue", pch=19)
            points(target$x, pred[1], col="red", pch=19)
        }
        
        if(target$statvalue > pred[3] & target$statvalue < pred[2]){
            #print("경고")
            data.frame(dplyr::filter(tmp, std_dt == edt), fit=pred[1], lwr=pred[2], upr=pred[3])
        }          
    }
    #print(paste0(seq))
}
stopCluster(cl)
end.time <- Sys.time()
time.taken <- end.time - start.time
time.taken
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1 병렬처리 #

2 참고자료 #
