Create setting for CIReNN model

setCIReNN(numberOfRNNLayer = c(1), units = c(128, 64),
recurrentDropout = c(0.2), layerDropout = c(0.2), lr = c(1e-04),
decay = c(1e-05), outcomeWeight = c(1), batchSize = c(100),
epochs = c(100), earlyStoppingMinDelta = c(1e-04),
earlyStoppingPatience = c(10), useVae = T,
vaeDataSamplingProportion = 0.1, vaeValidationSplit = 0.2,
vaeBatchSize = 100L, vaeLatentDim = 10L, vaeIntermediateDim = 256L,
vaeEpoch = 100L, vaeEpislonStd = 1, seed = NULL)

## Arguments

numberOfRNNLayer The number of RNN layer, only 1, 2, or 3 layers available now. eg. 1, c(1,2), c(1,2,3) The number of units of RNN layer - as a list of vectors The reccurrent dropout rate (regularisation) The layer dropout rate (regularisation) Learning rate Learning rate decay over each update. The weight of the outcome class in the loss function The number of data points to use per training batch Number of times to iterate over dataset minimum change in the monitored quantity to qualify as an improvement for early stopping, i.e. an absolute change of less than min_delta in loss of validation data, will count as no improvement. Number of epochs with no improvement after which training will be stopped. logical (either TRUE or FALSE) value for using Variational AutoEncoder before RNN Data sampling proportion for VAE Validation split proportion for VAE batch size for VAE Number of latent dimesion for VAE Number of intermediate dimesion for VAE Number of times to interate over dataset for VAE Epsilon Random seed used by deep learning model

## Examples

# NOT RUN {
model.CIReNN <- setCIReNN()
# }