tensor_embed_mat {BayesFluxR}

Embed a matrix of timeseries into a tensor

Description

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.

Usage

tensor_embed_mat(mat, len_seq)

Arguments

mat

Matrix of time series

len_seq

subsequence length

Value

A tensor of dimension: len_seq x num_vars x num_subsequences

Examples

## 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)