Description Usage Arguments Value See Also
View source: R/feature_extraction.R
chooseK_seq2seq
chooses the number of features to be extracted
by crossvalidation.
1 2 3 4 
seqs 
an object of class 
ae_type 
a string specifies the type of autoencoder. The autoencoder can be an action sequence autoencoder ("action"), a time sequence autoencoder ("time"), or an actiontime sequence autoencoder ("both"). 
K_cand 
the candidates of the number of features. 
rnn_type 
the type of recurrent unit to be used for modeling
response processes. 
n_epoch 
the number of training epochs for the autoencoder. 
method 
the method for computing features from the output of an
recurrent neural network in the encoder. Available options are

step_size 
the learning rate of optimizer. 
optimizer_name 
a character string specifying the optimizer to be used
for training. Availabel options are 
n_fold 
the number of folds for crossvalidation. 
cumulative 
logical. If TRUE, the sequence of cumulative time up to each event is used as input to the neural network. If FALSE, the sequence of interarrival time (gap time between an event and the previous event) will be used as input to the neural network. Default is FALSE. 
log 
logical. If TRUE, for the timestamp sequences, input of the neural net is the base10 log of the original sequence of times plus 1 (i.e., log10(t+1)). If FALSE, the original sequence of times is used. 
weights 
a vector of 2 elements for the weight of the loss of action sequences (categorical_crossentropy) and time sequences (mean squared error), respectively. The total loss is calculated as the weighted sum of the two losses. 
valid_prop 
the proportion of validation samples in each fold. 
verbose 
logical. If TRUE, training progress is printed. 
chooseK_seq2seq
returns a list containing
K 
the candidate in 
K_cand 
the candidates of number of features. 
cv_loss 
the crossvalidation loss for each candidate in 
seq2feature_seq2seq
for feature extraction given the number of features.
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