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)

numberOfRNNLayer | The number of RNN layer, only 1, 2, or 3 layers available now. eg. 1, c(1,2), c(1,2,3) |
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units | The number of units of RNN layer - as a list of vectors |

recurrentDropout | The reccurrent dropout rate (regularisation) |

layerDropout | The layer dropout rate (regularisation) |

lr | Learning rate |

decay | Learning rate decay over each update. |

outcomeWeight | The weight of the outcome class in the loss function |

batchSize | The number of data points to use per training batch |

epochs | Number of times to iterate over dataset |

earlyStoppingMinDelta | 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. |

earlyStoppingPatience | Number of epochs with no improvement after which training will be stopped. |

useVae | logical (either TRUE or FALSE) value for using Variational AutoEncoder before RNN |

vaeDataSamplingProportion | Data sampling proportion for VAE |

vaeValidationSplit | Validation split proportion for VAE |

vaeBatchSize | batch size for VAE |

vaeLatentDim | Number of latent dimesion for VAE |

vaeIntermediateDim | Number of intermediate dimesion for VAE |

vaeEpoch | Number of times to interate over dataset for VAE |

vaeEpislonStd | Epsilon |

seed | Random seed used by deep learning model |

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