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LoadData.R
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# Load Data Original, with minor changes to accomodate R
library(R.matlab)
# library(Matrix)
library(parallel); options(mc.cores = 4)
library(itertools)
load_data <- function(N) {
# % This method loads the training, validation and test set.
# % It also divides the training set into mini-batches.
# % Inputs:
# % N: Mini-batch size.
# % Outputs:
# % train_input: An array of size D X N X M, where
# % D: number of input dimensions (in this case, 3).
# % N: size of each mini-batch (in this case, 100).
# % M: number of minibatches.
# % train_target: An array of size 1 X N X M.
# % valid_input: An array of size D X number of points in the validation set.
# % test: An array of size D X number of points in the test set.
# % vocab: Vocabulary containing index to word mapping.
data.mat <- readMat("Neural Net Language Model/data.mat")
data <- list(testData = (data.mat$data[1,1,1][[1]]),
trainData = (data.mat$data[2,1,1][[1]]),
validData = (data.mat$data[3,1,1][[1]]),
vocab = unlist(data.mat$data[4,1,1])
)
numdims = size(data$trainData, 1)
D = numdims - 1 # subtract 1 because 1:D is the number of input words and D is the predicted word
M = floor(size(data$trainData, 2) / N)
# shift to an list of M minibatches, each with D*N
# looks like we threw out the remainder training data
start <- seq.int(1, N*M, by=1000)
end <- seq.int(1000, N*M, by=1000)
train_input <- mapply(function(x, start, end) x[,start:end],
start=seq.int(1, N*M, by=1000),
end=seq.int(1000, N*M, by=1000),
MoreArgs=list(x=data$trainData[1:D, 1:(N*M)]), SIMPLIFY=F)
train_target <- mapply(function(x, start, end) x[,start:end],
start=seq.int(1, N*M, by=1000),
end=seq.int(1000, N*M, by=1000),
MoreArgs=list(x=data$trainData[D + 1, 1:(N*M), drop=F]), SIMPLIFY=F)
valid_input <- data$validData[1:D,, drop=F]
valid_target <- data$validData[D + 1, , drop=F]
test_input <- data$validData[1:D, , drop=F]
test_target <- data$testData[D + 1, , drop=F]
vocab <- data$vocab
return(list(train_input=train_input,
train_target=train_target,
valid_input=valid_input,
valid_target=valid_target,
test_input=test_input,
test_target=test_target,
vocab=vocab))
}
# faster to access lists than indices in an array
# but slower to create a set of lists than to reshape the training data into an array
# train_input3 <- vector(M, "list")
# fn <- function() {
# it <- isplitCols(data$trainData[1:D, 1:(N*M)], chunks = M)
# replicate(M, nextElem(it), simplify=F)
# }
# #
# fn2 <- function() {
# as.list(enumerate(isplitCols(data$trainData[1:D, 1:(N*M)], chunks = M)))
# }
#
myReshape <- function(A, ...) {
if (!is.array(A)) {
stop(sprintf("argument %s must be matrix or array", sQuote("A")))
}
nargs <- length(dots <- list(...))
dims <- as.integer(if (nargs == 1 && matlab:::is.size_t(dots[[1]])) {
dots[[1]]
} else {
unlist(dots)
})
if (!(length(dims) > 1)) {
stop("dimensions must be of length greater than 1")
}
else if (!(all(dims > 0))) {
stop("dimensions must be a positive quantity")
}
else if (prod(dims) != prod(dim(A))) {
stop("number of elements must not change")
}
dim(A) <- dims
return(A)
}
# fn3 <- function() {
# res <- split(x=data$trainData[1:D, 1:(N*M), drop=F],
# f=as.factor((seq_len(D*N*M)-1) %% M))
# lapply(res, function(x) { dim(x) <- c(3, 1000); return(x) })
# }
# #
# #
# #
# #
# benchmark(
# train_input1 <- reshape(data$trainData[1:D, 1:(N*M), drop=F], D, N, M), # 1.5x slower
# train_input2 <- lapply(1:M, splitMatrixIntoBatch, N=N, dat=data$trainData[1:D,], byCol=TRUE), # 1.5 x slower
# train_input3 <- fn(), # 3.8x slower
# train_input4 <- myReshape(data$trainData[1:D, 1:(N*M), drop=F], D, N, M), # fastest
# train_input5 <- fn2(), # 5x slower
# train_input6 <- mapply(function(x, start, end) x[,start:end],
# start=seq.int(1, N*M, by=1000),
# end=seq.int(1000, N*M, by=1000),
# MoreArgs=list(x=data$trainData[1:D, 1:(N*M)]), SIMPLIFY=F), # slightly better than lapply or reshape; slower if using parallel
# #train_input7 <- fn3(), # 100x slower
# replications=100
# )
# #
# #
# #
# library(microbenchmark)
#
# mfn1 <- function() reshape(data$trainData[1:D, 1:(N*M), drop=F], D, N, M)
# mfn2 <- function() lapply(1:M, splitMatrixIntoBatch, N=N, dat=data$trainData[1:D,], byCol=TRUE)
# mfn3 <- function() {
# it <- isplitCols(data$trainData[1:D, 1:(N*M)], chunks = M)
# replicate(M, nextElem(it), simplify=F)
# }
# mfn4 <- function() myReshape(data$trainData[1:D, 1:(N*M), drop=F], D, N, M)
# mfn5 <- function() as.list(enumerate(isplitCols(data$trainData[1:D, 1:(N*M)], chunks = M)))
# mfn6 <- function() mapply(function(x, start, end) x[,start:end],
# start=seq.int(1, N*M, by=1000),
# end=seq.int(1000, N*M, by=1000),
# MoreArgs=list(x=data$trainData[1:D, 1:(N*M)]), SIMPLIFY=F)
#
# m <- microbenchmark(
# mfn1(), # second best
# mfn2(), # second best
# mfn3(), # worst
# mfn4(), # best
# mfn5(), # worst
# mfn6(), # second best
# times=1000)
#
# print(m)
# boxplot(m)
#
# res <- split(x=data$trainData[1:D, 1:(N*M), drop=F],
# f=as.factor((seq_len(D*N*M)-1) %% M))
#
# fn1 <- function(x) matrix(x, nrow=3, ncol=1000)
# fn2 <- function(x) { dim(x) <- c(3, 1000); return(x) }
#
# m <- microbenchmark(
# lapply(res, fn1),
# lapply(res, fn2),
# times=1000
# )
#
# print(m)
# boxplot(m)
#
#
#
# start <- seq.int(1, N*M, by=1000)
# end <- seq.int(1000, N*M, by=1000)
#
# res <- mapply(function(x, start, end) x[,start:end], start=start, end=end, MoreArgs=list(x=data$trainData[1:D, 1:(N*M)]), SIMPLIFY=F)
# benchmark(
# data1 <- load_data_original(batchsize),
# data2 <- load_data(batchsize),
#
# replications <- 10
# )
#
# benchmark(
# for(m in 1:372) tmp1 <- data1$train_input[,,m],
# for(m in 1:372) tmp2 <- data2$train_input[[m]],
# replications <- 2
# )