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1 ##########
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2 args <- commandArgs(TRUE)
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3 nTrain <- read.csv(args[1],row.names= 1, header = T) # example nTrain.csv file of unknown activity
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4 #save(nTrain,file = "nTrain.RData")
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5 #load("nTrain.RData")
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6 load(args[2]) # model generated from previous programn
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7 newdata <- nTrain
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8 modelFit <- Fit
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9 ###########
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10 # input csv file must contaion the exact same column as used in model building #
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11 # Also do pre-proccessing by means of centering and scaling
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12
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13 library(caret)
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14 reqcol <- Fit$finalModel$xNames
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15 newdata <- newdata[,reqcol]
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16 newdata1 <- preProcess(newdata, method = c("center", "scale"))
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17 newdata11 <- predict(newdata1,newdata)
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18 ###########
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19 library(stats)
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20 testpredict <- predict(modelFit,newdata11)
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21 #table(testpredict)
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22 #save(testpredict, file = "predict.RData")
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23 #load("predict.RData")
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24 names <- as.data.frame(rownames(nTrain))
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25 colnames(names) <- "COMPOUND"
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26 activity <- as.data.frame(testpredict)
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27 colnames(activity) <- "ACTIVITY"
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28 dw <- cbind(names,activity)
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29 #write.table(dw,file=args[3])
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30 write.csv(dw,file=args[3],row.names=FALSE)
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31
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32
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33 ~
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