Nothing
# create training numbers
X1 = sample(0:127, 7000, replace=TRUE)
X2 = sample(0:127, 7000, replace=TRUE)
# create training response numbers
Y <- X1 + X2
# function int2bin and bin2int
int2bin <- function(integer, length=8) {
t(sapply(integer, i2b, length=length))
}
i2b <- function(integer, length=8){
rev(as.numeric(intToBits(integer))[1:length])
}
bin2int <- function(binary){
# round
binary <- round(binary)
# determine length of binary representation
length <- dim(binary)[2]
# apply to full matrix
apply(binary, 1, b2i)
}
b2i <- function(binary)
packBits(as.raw(rev(c(rep(0, 32-length(binary) ), binary))), 'integer')
# convert to binary
X1 <- int2bin(X1)
X2 <- int2bin(X2)
Y <- int2bin(Y)
# Create 3d array: dim 1: samples; dim 2: time; dim 3: variables.
X <- array( c(X1,X2), dim=c(dim(X1),2) )
Y <- array( Y, dim=c(dim(Y),1) )
# train the model
model <- trainr(Y=Y[,dim(Y)[2]:1,,drop=F],
X=X[,dim(X)[2]:1,,drop=F],
learningrate = 0.1,
hidden_dim = c(10,10),
numepochs = 5,
batch_size = 100,
momentum =0,
use_bias = F,
learningrate_decay = 1)
# create test inputs
A1 = int2bin( sample(0:127, 7000, replace=TRUE) )
A2 = int2bin( sample(0:127, 7000, replace=TRUE) )
# create 3d array: dim 1: samples; dim 2: time; dim 3: variables
A <- array( c(A1,A2), dim=c(dim(A1),2) )
# predict
B <- predictr(model, A[,dim(A)[2]:1,,drop=F])[,dim(A)[2]:1]
# inspect the differences
# expect_equal(sum(bin2int(B)), 888626)
# print(sum(bin2int(B)))
# print(sum(bin2int(A1))+sum(bin2int(A2)))
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