Package 'LDNN'

Title: Longitudinal Data Neural Network
Description: This is a Neural Network regression model implementation using 'Keras', consisting of 10 Long Short-Term Memory layers that are fully connected along with the rest of the inputs.
Authors: Vasileios Karapoulios
Maintainer: Vasileios Karapoulios <[email protected]>
License: GNU General Public License
Version: 1.10
Built: 2025-03-06 05:04:57 UTC
Source: https://github.com/vasileioskarapoulios/ldnn

Help Index

Pre-defined Neural Network for Longitudinal Data

Description

Create the Neural Network model (Keras).

Usage

create_model(
  rnn_inputs,
  recurrent_droppout,
  inputs,
  layer_dropout,
  n_nodes_hidden_layers,
  loss_function,
  opt,
  metric
)

Arguments

rnn_inputs

The number of inputs (integers) per each LSTM (vector of length 10).
recurrent_droppout

The dropout to be applied in the LSTMs (between 0 and 1).
inputs

The number of inputs (integer) to be concatenated with the output of the LSTMs.
layer_dropout

The dropout to be applied between the hidden layers (between 0 and 1).
n_nodes_hidden_layers

The number of nodes in the hidden layers (2 in total).
loss_function

The loss function to be used.
opt

The optimizer to be used.
metric

The metric to be used.

Value

The model object built in Keras.

Examples

inp = c(20,24,24,24,16,16,16,16,16,15)
rec_drop = rep(0.1,10)
l_drop = c(0.1,0.1)
## Not run: 
create_model(inp,rec_drop,232,c(0.1,0.1),l_drop,'mean_squared_error','adam','mean_absolute_error')

## End(Not run)
# The functions require to have python installed
# As well as tensorflow, keras and reticulate package.

Evaluate the pre-defined Neural Network for Longitudinal Data

Description

Evaluate the fitted Neural Network model (Keras).

Usage

evaluate_model(
  model,
  X1_test,
  X2_test,
  X3_test,
  X4_test,
  X5_test,
  X6_test,
  X7_test,
  X8_test,
  X9_test,
  X10_test,
  Xif_test,
  y_test,
  bsize
)

Arguments

model

The fitted model object produced by create_model().
X1_test

Features as inputs of 1st LSTM.
X2_test

Features as inputs of 2nd LSTM.
X3_test

Features as inputs of 3rd LSTM.
X4_test

Features as inputs of 4th LSTM.
X5_test

Features as inputs of 5th LSTM.
X6_test

Features as inputs of 6th LSTM.
X7_test

Features as inputs of 7th LSTM.
X8_test

Features as inputs of 8th LSTM.
X9_test

Features as inputs of 9th LSTM.
X10_test

Features as inputs of 10th LSTM.
Xif_test

The features to be concatenated with the outputs of the LSTMs.
y_test

The target variable.
bsize

The batch size.

Value

The evaluation results.

Examples

X1_test <- matrix(runif(500*20), nrow=500, ncol=20)
X2_test <- matrix(runif(500*24), nrow=500, ncol=24)
X3_test <- matrix(runif(500*24), nrow=500, ncol=24)
X4_test <- matrix(runif(500*24), nrow=500, ncol=24)
X5_test <- matrix(runif(500*16), nrow=500, ncol=16)
X6_test <- matrix(runif(500*16), nrow=500, ncol=16)
X7_test <- matrix(runif(500*16), nrow=500, ncol=16)
X8_test <- matrix(runif(500*16), nrow=500, ncol=16)
X9_test <- matrix(runif(500*16), nrow=500, ncol=16)
X10_test <- matrix(runif(500*15), nrow=500, ncol=15)
Xif_test <- matrix(runif(500*232), nrow=500, ncol=232)
y_test <- matrix(runif(500), nrow=500, ncol=1)
## Not run: 
evaluate_model(fitted_model,X1_test,X2_test,X3_test,X4_test,X5_test,X6_test,
X7_test,X8_test,X9_test,X10_test,Xif_test,y_test,32)

## End(Not run)
# The functions require to have python installed
# As well as tensorflow, keras and reticulate package.

Fit the pre-defined Neural Network for Longitudinal Data

Description

Fit the created Neural Network model (Keras).

Usage

fit_model(
  model,
  ver,
  n_epoch,
  bsize,
  X1,
  X2,
  X3,
  X4,
  X5,
  X6,
  X7,
  X8,
  X9,
  X10,
  Xif,
  y
)

Arguments

model

The model object produced by create_model().
ver

ver=0 to show nothing, ver=1 to show animated progress bar, ver=2 to just mention the number of epoch during training.
n_epoch

The number of epochs to train the model.
bsize

The batch size.
X1

Features as inputs of 1st LSTM.
X2

Features as inputs of 2nd LSTM.
X3

Features as inputs of 3rd LSTM.
X4

Features as inputs of 4th LSTM.
X5

Features as inputs of 5th LSTM.
X6

Features as inputs of 6th LSTM.
X7

Features as inputs of 7th LSTM.
X8

Features as inputs of 8th LSTM.
X9

Features as inputs of 9th LSTM.
X10

Features as inputs of 10th LSTM.
Xif

The features to be concatenated with the outputs of the LSTMs.
y

The target variable.

Value

The fitted model.

Examples

X1 <- matrix(runif(500*20), nrow=500, ncol=20)
X2 <- matrix(runif(500*24), nrow=500, ncol=24)
X3 <- matrix(runif(500*24), nrow=500, ncol=24)
X4 <- matrix(runif(500*24), nrow=500, ncol=24)
X5 <- matrix(runif(500*16), nrow=500, ncol=16)
X6 <- matrix(runif(500*16), nrow=500, ncol=16)
X7 <- matrix(runif(500*16), nrow=500, ncol=16)
X8 <- matrix(runif(500*16), nrow=500, ncol=16)
X9 <- matrix(runif(500*16), nrow=500, ncol=16)
X10 <- matrix(runif(500*15), nrow=500, ncol=15)
Xif <- matrix(runif(500*232), nrow=500, ncol=232)
y <- matrix(runif(500), nrow=500, ncol=1)
## Not run: 
fitted_model = fit_model(model,0,1,32,X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,Xif,y)

## End(Not run)
# The functions require to have python installed
# As well as tensorflow, keras and reticulate package.