/*
* 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.search.k2;
import java.util.ArrayList;
import java.util.List;
import org.encog.mathutil.EncogMath;
import org.encog.ml.bayesian.BayesianEvent;
import org.encog.ml.bayesian.BayesianNetwork;
import org.encog.ml.bayesian.query.enumerate.EnumerationQuery;
import org.encog.ml.bayesian.training.TrainBayesian;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
/**
* Search for optimal Bayes structure with K2.
*
*/
public class SearchK2 implements BayesSearch {
/**
* The data to use.
*/
private MLDataSet data;
/**
* The network to optimize.
*/
private BayesianNetwork network;
/**
* The trainer being used.
*/
private TrainBayesian train;
/**
* The last calculated value for p.
*/
private double lastCalculatedP;
/**
* The node ordering.
*/
private final List<BayesianEvent> nodeOrdering = new ArrayList<BayesianEvent>();
/**
* THe current index.
*/
private int index = -1;
/**
* {@inheritDoc}
*/
@Override
public void init(TrainBayesian theTrainer,BayesianNetwork theNetwork, MLDataSet theData) {
this.network = theNetwork;
this.data = theData;
this.train = theTrainer;
orderNodes();
this.index = -1;
}
/**
* Basically the goal here is to get the classification target, if it exists,
* to go first. This will greatly enhance K2's effectiveness.
*/
private void orderNodes() {
this.nodeOrdering.clear();
// is there a classification target?
if( this.network.getClassificationTarget()!=-1 ) {
this.nodeOrdering.add(this.network.getClassificationTargetEvent());
}
// now add the others
for(BayesianEvent event: this.network.getEvents()) {
if( !this.nodeOrdering.contains(event) ) {
this.nodeOrdering.add(event);
}
}
}
/**
* Find the value for z.
* @param event The event that we are clauclating for.
* @param n The value for n.
* @param old The old value.
* @return The new value for z.
*/
private BayesianEvent findZ(BayesianEvent event, int n, double old) {
BayesianEvent result = null;
double maxChildP = Double.NEGATIVE_INFINITY;
//System.out.println("Finding parent for: " + event.toString());
for(int i=0;i<n;i++) {
BayesianEvent trialParent = this.nodeOrdering.get(i);
List<BayesianEvent> parents = new ArrayList<BayesianEvent>();
parents.addAll(event.getParents());
parents.add(trialParent);
//System.out.println("Calculating adding " + trialParent.toString() + " to " + event.toString());
this.lastCalculatedP = this.calculateG(network, event, parents);
//System.out.println("lastP:" + this.lastCalculatedP);
//System.out.println("old:" + old);
if( this.lastCalculatedP>old && this.lastCalculatedP>maxChildP ) {
result = trialParent;
maxChildP = this.lastCalculatedP;
//System.out.println("Current best is: " + result.toString());
}
}
this.lastCalculatedP = maxChildP;
return result;
}
/**
* Calculate the value N, which is the number of cases, from the training data, where the
* desiredValue matches the training data. Only cases where the parents match the specifed
* parent instance are considered.
* @param network The network to calculate for.
* @param event The event we are calculating for. (variable i)
* @param parents The parents of the specified event we are considering.
* @param parentInstance The parent instance we are looking for.
* @param desiredValue The desired value.
* @return The value N.
*/
public int calculateN(BayesianNetwork network, BayesianEvent event,
List<BayesianEvent> parents, int[] parentInstance, int desiredValue) {
int result = 0;
int eventIndex = network.getEventIndex(event);
for (MLDataPair pair : this.data) {
int[] d = this.network.determineClasses(pair.getInput());
if ( d[eventIndex] == desiredValue) {
boolean reject = false;
for (int i = 0; i < parentInstance.length; i++) {
BayesianEvent parentEvent = parents.get(i);
int parentIndex = network.getEventIndex(parentEvent);
if (parentInstance[i] != d[parentIndex]) {
reject = true;
break;
}
}
if (!reject) {
result++;
}
}
}
return result;
}
/**
* Calculate the value N, which is the number of cases, from the training data, where the
* desiredValue matches the training data. Only cases where the parents match the specifed
* parent instance are considered.
* @param network The network to calculate for.
* @param event The event we are calculating for. (variable i)
* @param parents The parents of the specified event we are considering.
* @param parentInstance The parent instance we are looking for.
* @return The value N.
*/
public int calculateN(BayesianNetwork network, BayesianEvent event,
List<BayesianEvent> parents, int[] parentInstance) {
int result = 0;
for (MLDataPair pair : this.data) {
int[] d = this.network.determineClasses( pair.getInput());
boolean reject = false;
for (int i = 0; i < parentInstance.length; i++) {
BayesianEvent parentEvent = parents.get(i);
int parentIndex = network.getEventIndex(parentEvent);
if (parentInstance[i] != ((int) d[parentIndex])) {
reject = true;
break;
}
}
if (!reject) {
result++;
}
}
return result;
}
/**
* Calculate G.
* @param network The network to calculate for.
* @param event The event to calculate for.
* @param parents The parents.
* @return The value for G.
*/
public double calculateG(BayesianNetwork network,
BayesianEvent event, List<BayesianEvent> parents) {
double result = 1.0;
int r = event.getChoices().size();
int[] args = new int[parents.size()];
do {
double n = EncogMath.factorial(r - 1);
double d = EncogMath.factorial(calculateN(network, event,
parents, args) + r - 1);
double p1 = n/d;
double p2 = 1;
for(int k = 0; k<event.getChoices().size(); k++) {
p2 *= EncogMath.factorial(calculateN(network,event,parents,args,k));
}
result*=p1*p2;
} while(EnumerationQuery.roll(parents, args));
return result;
}
/**
* {@inheritDoc}
*/
@Override
public boolean iteration() {
if( index==-1 ) {
orderNodes();
} else {
BayesianEvent event = this.nodeOrdering.get(index);
double oldP = this.calculateG(network, event, event.getParents());
while( event.getParents().size()<this.train.getMaximumParents() ) {
BayesianEvent z = findZ(event,index,oldP);
if(z!=null) {
this.network.createDependency(z, event);
oldP = this.lastCalculatedP;
} else {
break;
}
}
}
index++;
return( index<this.data.getInputSize());
}
}