Non-negative neural network
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
)A matrix of predictors for the training dataset of shape (nsamples, nfeatures)
A vector of output values for the training data with a length similar to the number of rows of x
A vector of the data to adjust the analysis for such as calendar time (training data) with the same number of rows as x.
A matrix of predictors for the test dataset of shape (nsamples, nfeatures)
A vector of output values for the test data with a length similar to the number of rows of x
A vector the data to adjust the analysis for such as calendar time (training data) with the same number of rows as x.
Input-hidden layer weights of shape (nfeatuers, hidden)
Biases for the hidden layer of shape (1, hidden)
Hidden-output layer weights of shape (hidden, 1)
Bias for the output layer (the baseline risk) af shape (1, 1)
Bias for the data to adjust the analysis for
a vector of weights per observation to allow for inverse probability of censoring weighting to correct for selection bias
Initial learning rate
The maximum number of epochs
Regularisation decreasing parameter value at each iteration for the input parameters
To drop connections if their weights reaches zero.
To fix the baseline risk at a value.
A list of class "SCL" giving the estimated matrices and performance indicators