cpp_train_network_relu {CoOL}

Function used as part of other functions

Description

Non-negative neural network

Usage

cpp_train_network_relu(
  x,
  y,
  c,
  testx,
  testy,
  testc,
  W1_input,
  B1_input,
  W2_input,
  B2_input,
  C2_input,
  ipw,
  lr = 0.01,
  maxepochs = 100,
  input_parameter_reg = 1e-06,
  drop_out = 0L,
  fix_baseline_risk = -1
)

Arguments

x

A matrix of predictors for the training dataset of shape (nsamples, nfeatures)

y

A vector of output values for the training data with a length similar to the number of rows of x

c

A vector of the data to adjust the analysis for such as calendar time (training data) with the same number of rows as x.

testx

A matrix of predictors for the test dataset of shape (nsamples, nfeatures)

testy

A vector of output values for the test data with a length similar to the number of rows of x

testc

A vector the data to adjust the analysis for such as calendar time (training data) with the same number of rows as x.

W1_input

Input-hidden layer weights of shape (nfeatuers, hidden)

B1_input

Biases for the hidden layer of shape (1, hidden)

W2_input

Hidden-output layer weights of shape (hidden, 1)

B2_input

Bias for the output layer (the baseline risk) af shape (1, 1)

C2_input

Bias for the data to adjust the analysis for

ipw

a vector of weights per observation to allow for inverse probability of censoring weighting to correct for selection bias

lr

Initial learning rate

maxepochs

The maximum number of epochs

input_parameter_reg

Regularisation decreasing parameter value at each iteration for the input parameters

drop_out

To drop connections if their weights reaches zero.

fix_baseline_risk

To fix the baseline risk at a value.

Value

A list of class "SCL" giving the estimated matrices and performance indicators

Author(s)

Andreas Rieckmann, Piotr Dworzynski, Leila Arras, Claus Ekstrøm