CreMA is a open-source java toolbox that provides multiple learning and inference algorithms for credal models.
An example of exact inference in a credal network is given below.
import ch.idsia.crema.factor.credal.vertex.VertexFactor;
import ch.idsia.crema.inference.ve.CredalVariableElimination;
import ch.idsia.crema.model.ObservationBuilder;
import ch.idsia.crema.model.Strides;
import ch.idsia.crema.model.graphical.SparseModel;
public class Starting {
public static void main(String[] args) {
double p = 0.2;
double eps = 0.0001;
/* CN defined with vertex Factor */
// Define the model (with vertex factors)
SparseModel<VertexFactor> model = new SparseModel<>();
int A = model.addVariable(3);
int B = model.addVariable(2);
model.addParent(B,A);
// Define a credal set of the partent node
VertexFactor fu = new VertexFactor(model.getDomain(A), Strides.empty());
fu.addVertex(new double[]{0., 1-p, p});
fu.addVertex(new double[]{1-p, 0., p});
model.setFactor(A,fu);
// Define the credal set of the child
VertexFactor fx = new VertexFactor(model.getDomain(B), model.getDomain(A));
fx.addVertex(new double[]{1., 0.,}, 0);
fx.addVertex(new double[]{1., 0.,}, 1);
fx.addVertex(new double[]{0., 1.,}, 2);
model.setFactor(B,fx);
// Run exact inference
CredalVariableElimination<VertexFactor> inf = new CredalVariableElimination<>(model);
inf.query(A, ObservationBuilder.observe(B,0));
}
}
Add the following code in the pom.xml of your project:
<repositories>
<repository>
<id>cremaRepo</id>
<url>https://raw.github.com/idsia/crema/mvn-repo/</url>
</repository>
</repositories>
<dependencies>
<dependency>
<groupId>ch.idsia</groupId>
<artifactId>crema</artifactId>
<version>0.1.4</version>
<scope>compile</scope>
</dependency>
</dependencies>
If you write a scientific paper describing research that made use of the CREMA library, please cite the following paper:
Huber, D., Cabañas, R., Antonucci, A., Zaffalon, M. (2020).
CREMA: a Java library for credal network inference.
In Jaeger, M., Nielsen, T.D. (Eds),
Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020),
Proceedings of Machine Learning Research, PMLR, Aalborg, Denmark.
In BiBTeX format (for your convenience):
@INPROCEEDINGS{huber2020a,
title = {{CREMA}: a {J}ava library for credal network inference},
editor = {Jaeger, M. and Nielsen, T.D.},
publisher = {PMLR},
address = {Aalborg, Denmark},
series = {Proceedings of Machine Learning Research},
booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},
author = {Huber, D. and Caba\~nas, R. and Antonucci, A. and Zaffalon, M.},
year = {2020},
url = {https://pgm2020.cs.aau.dk}
}