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- library(rnn)
- df <- read.csv("power_dataset.csv")
- train <- df[1:2016,] # train set from 5-16 June
- test <- df[145:dim(df)[1],] # test set from 6-18 June
- # prepare data to train a model
- trainX <- train[1:1872,]$power # using only power column now
- trainY <- train[1873:dim(train)[1],]$power
- # data formatting acc. to rnn as [samples, timesteps, features]
- tx <- array(trainX,dim=c(NROW(trainX),144,1))
- ty <- array(trainY,dim=c(NROW(trainY),144,1))
- model <- trainr(X=tx,Y=ty,learningrate = 0.04, hidden_dim = 10, numepochs = 100)
- The sample dimension of X is different from the sample dimension of Y.
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