/**
* Copyright (C) 2001-2017 by RapidMiner and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapidminer.com
*
* This program is free software: you can redistribute it and/or modify it under the terms of the
* GNU Affero General Public License as published by the Free Software Foundation, either version 3
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without
* even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License along with this program.
* If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.learner.functions.kernel;
import java.util.Iterator;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.ExampleSetUtilities;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.OperatorProgress;
/**
* A model learned by the GPLearner.
*
* @author Piotr Kasprzak, Ingo Mierswa
*/
public class GPModel extends KernelModel {
private static final long serialVersionUID = 6094706651995436944L;
private static final int OPERATOR_PROGRESS_STEPS = 5000;
private com.rapidminer.operator.learner.functions.kernel.gaussianprocess.Model model = null;
public GPModel(ExampleSet exampleSet, com.rapidminer.operator.learner.functions.kernel.gaussianprocess.Model model) {
super(exampleSet, ExampleSetUtilities.SetsCompareOption.ALLOW_SUPERSET,
ExampleSetUtilities.TypesCompareOption.ALLOW_SAME_PARENTS);
this.model = model;
}
@Override
public boolean isClassificationModel() {
return getLabel().isNominal();
}
@Override
public double getBias() {
return 0;
}
@Override
public SupportVector getSupportVector(int index) {
return null;
}
@Override
public double getAlpha(int index) {
return Double.NaN;
}
@Override
public String getId(int index) {
return null;
}
@Override
public int getNumberOfSupportVectors() {
return this.model.getNumberOfBasisVectors();
}
@Override
public int getNumberOfAttributes() {
return this.model.getInputDim();
}
@Override
public double getAttributeValue(int exampleIndex, int attributeIndex) {
return this.model.getBasisVectorValue(exampleIndex, attributeIndex);
}
@Override
public String getClassificationLabel(int index) {
return "?";
}
@Override
public double getRegressionLabel(int index) {
return Double.NaN;
}
@Override
public double getFunctionValue(int index) {
return model.applyToVector(this.model.getBasisVector(index));
}
@Override
public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabel) throws OperatorException {
Iterator<Example> i = exampleSet.iterator();
OperatorProgress progress = null;
if (getShowProgress() && getOperator() != null && getOperator().getProgress() != null) {
progress = getOperator().getProgress();
progress.setTotal(exampleSet.size());
}
int progressCounter = 0;
while (i.hasNext()) {
Example e = i.next();
double functionValue = model.applyToVector(RVMModel.makeInputVector(e));
if (getLabel().isNominal()) {
if (functionValue > 0) {
e.setValue(predictedLabel, getLabel().getMapping().getPositiveIndex());
} else {
e.setValue(predictedLabel, getLabel().getMapping().getNegativeIndex());
}
// set confidence to numerical prediction, such that can be scaled later
e.setConfidence(predictedLabel.getMapping().getPositiveString(),
1.0d / (1.0d + java.lang.Math.exp(-functionValue)));
e.setConfidence(predictedLabel.getMapping().getNegativeString(),
1.0d / (1.0d + java.lang.Math.exp(functionValue)));
} else {
e.setValue(predictedLabel, functionValue);
}
if (progress != null && ++progressCounter % OPERATOR_PROGRESS_STEPS == 0) {
progress.setCompleted(progressCounter);
}
}
return exampleSet;
}
}