tensor_embed_mat {BayesFluxR} | R Documentation |
This is used when working with recurrent networks, especially in the case of seq-to-one modelling. Creates overlapping subsequences of the data with length 'len_seq'. Returned dimensions are seq_len x num_vars x num_subsequences.
tensor_embed_mat(mat, len_seq)
mat |
Matrix of time series |
len_seq |
subsequence length |
A tensor of dimension: len_seq x num_vars x num_subsequences
## Not run:
## Needs previous call to `BayesFluxR_setup` which is time
## consuming and requires Julia and BayesFlux.jl
BayesFluxR_setup(installJulia=TRUE, seed=123)
net <- Chain(RNN(5, 1))
like <- likelihood.seqtoone_normal(net, Gamma(2.0, 0.5))
prior <- prior.gaussian(net, 0.5)
init <- initialise.allsame(Normal(0, 0.5), like, prior)
data <- matrix(rnorm(5*1000), ncol = 5)
# Choosing sequences of length 10 and predicting one period ahead
tensor <- tensor_embed_mat(data, 10+1)
x <- tensor[1:10, , , drop = FALSE]
# Last value in each sequence is the target value
y <- tensor[11,1,]
bnn <- BNN(x, y, like, prior, init)
BNN.totparams(bnn)
## End(Not run)