cpp_train_network_relu | R Documentation |
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 )
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. |
A list of class "SCL" giving the estimated matrices and performance indicators
Andreas Rieckmann, Piotr Dworzynski, Leila Arras, Claus Ekstrøm
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