/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.ml.bayesian.training;
import org.encog.ml.MLMethod;
import org.encog.ml.TrainingImplementationType;
import org.encog.ml.bayesian.BayesianEvent;
import org.encog.ml.bayesian.BayesianNetwork;
import org.encog.ml.bayesian.training.estimator.BayesEstimator;
import org.encog.ml.bayesian.training.estimator.SimpleEstimator;
import org.encog.ml.bayesian.training.search.k2.BayesSearch;
import org.encog.ml.bayesian.training.search.k2.SearchK2;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.train.BasicTraining;
import org.encog.neural.networks.training.propagation.TrainingContinuation;
/**
* Train a Bayesian network.
*/
public class TrainBayesian extends BasicTraining {
/**
* What phase of training are we in?
*/
private enum Phase {
/**
* Init phase.
*/
Init,
/**
* Searching for a network structure.
*/
Search,
/**
* Search complete.
*/
SearchDone,
/**
* Finding probabilities.
*/
Probability,
/**
* Finished training.
*/
Finish,
/**
* Training terminated.
*/
Terminated
};
/**
* The phase that training is currently in.
*/
private Phase p = Phase.Init;
/**
* The data used for training.
*/
private final MLDataSet data;
/**
* The network to train.
*/
private final BayesianNetwork network;
/**
* The maximum parents a node should have.
*/
private final int maximumParents;
/**
* The method used to search for the best network structure.
*/
private final BayesSearch search;
/**
* The method used to estimate the probabilities.
*/
private final BayesEstimator estimator;
/**
* The method used to setup the initial Bayesian network.
*/
private BayesianInit initNetwork = BayesianInit.InitNaiveBayes;
/**
* Used to hold the query.
*/
private String holdQuery;
/**
* Construct a Bayesian trainer. Use K2 to search, and the SimpleEstimator
* to estimate probability. Init as Naive Bayes
*
* @param theNetwork
* The network to train.
* @param theData
* The data to train.
* @param theMaximumParents
* The max number of parents.
*/
public TrainBayesian(BayesianNetwork theNetwork, MLDataSet theData,
int theMaximumParents) {
this(theNetwork, theData, theMaximumParents,
BayesianInit.InitNaiveBayes, new SearchK2(),
new SimpleEstimator());
}
/**
* Construct a Bayesian trainer.
* @param theNetwork The network to train.
* @param theData The data to train with.
* @param theMaximumParents The maximum number of parents.
* @param theInit How to init the new Bayes network.
* @param theSearch The search method.
* @param theEstimator The estimation mehod.
*/
public TrainBayesian(BayesianNetwork theNetwork, MLDataSet theData,
int theMaximumParents, BayesianInit theInit, BayesSearch theSearch,
BayesEstimator theEstimator) {
super(TrainingImplementationType.Iterative);
this.network = theNetwork;
this.data = theData;
this.maximumParents = theMaximumParents;
this.search = theSearch;
this.search.init(this, theNetwork, theData);
this.estimator = theEstimator;
this.estimator.init(this, theNetwork, theData);
this.initNetwork = theInit;
setError(1.0);
}
/**
* Init to Naive Bayes.
*/
private void initNaiveBayes() {
// clear out anything from before
this.network.removeAllRelations();
// locate the classification target event
BayesianEvent classificationTarget = this.network
.getClassificationTargetEvent();
// now link everything to this event
for (BayesianEvent event : this.network.getEvents()) {
if (event != classificationTarget) {
network.createDependency(classificationTarget, event);
}
}
this.network.finalizeStructure();
}
/**
* Handle iterations for the Init phase.
*/
private void iterationInit() {
this.holdQuery = this.network.getClassificationStructure();
switch (this.initNetwork) {
case InitEmpty:
this.network.removeAllRelations();
this.network.finalizeStructure();
break;
case InitNoChange:
break;
case InitNaiveBayes:
initNaiveBayes();
break;
}
this.p = Phase.Search;
}
/**
* Handle iterations for the Search phase.
*/
private void iterationSearch() {
if (!this.search.iteration()) {
this.p = Phase.SearchDone;
}
}
/**
* Handle iterations for the Search Done phase.
*/
private void iterationSearchDone() {
this.network.finalizeStructure();
this.network.reset();
this.p = Phase.Probability;
}
/**
* Handle iterations for the Probability phase.
*/
private void iterationProbability() {
if (!this.estimator.iteration()) {
this.p = Phase.Finish;
}
}
/**
* Handle iterations for the Finish phase.
*/
private void iterationFinish() {
this.network.defineClassificationStructure(this.holdQuery);
setError(this.network.calculateError(this.data));
this.p = Phase.Terminated;
}
/**
* {@inheritDoc}
*/
@Override
public boolean isTrainingDone() {
if (super.isTrainingDone())
return true;
else
return this.p == Phase.Terminated;
}
/**
* {@inheritDoc}
*/
@Override
public void iteration() {
postIteration();
switch (p) {
case Init:
iterationInit();
break;
case Search:
iterationSearch();
break;
case SearchDone:
iterationSearchDone();
break;
case Probability:
iterationProbability();
break;
case Finish:
iterationFinish();
break;
}
preIteration();
}
/**
* {@inheritDoc}
*/
@Override
public boolean canContinue() {
return false;
}
/**
* {@inheritDoc}
*/
@Override
public TrainingContinuation pause() {
return null;
}
/**
* {@inheritDoc}
*/
@Override
public void resume(TrainingContinuation state) {
}
/**
* {@inheritDoc}
*/
@Override
public MLMethod getMethod() {
return this.network;
}
/**
* @return the network
*/
public BayesianNetwork getNetwork() {
return network;
}
/**
* @return the maximumParents
*/
public int getMaximumParents() {
return maximumParents;
}
/**
* @return The search method.
*/
public BayesSearch getSearch() {
return this.search;
}
/**
* @return The init method.
*/
public BayesianInit getInitNetwork() {
return initNetwork;
}
/**
* Set the network init method.
* @param initNetwork The init method.
*/
public void setInitNetwork(BayesianInit initNetwork) {
this.initNetwork = initNetwork;
}
}