q_constraint: A custom kernel constraint function that restricts weights...
In ML2Pvae: Variational Autoencoder Models for IRT Parameter Estimation
View source: R/construction_functions.R
q_constraint R Documentation
A custom kernel constraint function that restricts weights between the learned distribution and output. Nonzero weights are determined by the Q matrix.
Description
A custom kernel constraint function that restricts weights between the learned distribution and output. Nonzero weights are determined by the Q matrix.
Usage
q_constraint(Q)
Arguments
Q
a binary matrix of size num_skills by num_items
Value
returns a function whose parameters match keras kernel constraint format
ML2Pvae documentation built on May 23, 2022, 9:05 a.m.
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View source: R/construction_functions.R
| q_constraint | R Documentation |
A custom kernel constraint function that restricts weights between the learned distribution and output. Nonzero weights are determined by the Q matrix.
Description
A custom kernel constraint function that restricts weights between the learned distribution and output. Nonzero weights are determined by the Q matrix.
Usage
q_constraint(Q)
Arguments
Q |
a binary matrix of size |
Value
returns a function whose parameters match keras kernel constraint format
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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