/*
* AddNoiseFilter.java
* Copyright (C) 2007 University of Waikato, Hamilton, New Zealand
* @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
package tr.gov.ulakbim.jDenetX.streams.filters;
import tr.gov.ulakbim.jDenetX.core.AutoExpandVector;
import tr.gov.ulakbim.jDenetX.core.DoubleVector;
import tr.gov.ulakbim.jDenetX.core.GaussianEstimator;
import tr.gov.ulakbim.jDenetX.core.InstancesHeader;
import tr.gov.ulakbim.jDenetX.options.FloatOption;
import tr.gov.ulakbim.jDenetX.options.IntOption;
import weka.core.Instance;
import java.util.Random;
public class AddNoiseFilter extends AbstractStreamFilter {
@Override
public String getPurposeString() {
return "Adds random noise to examples in a stream.";
}
private static final long serialVersionUID = 1L;
public IntOption randomSeedOption = new IntOption("randomSeed", 'r',
"Seed for random noise.", 1);
public FloatOption attNoiseFractionOption = new FloatOption("attNoise",
'a', "The fraction of attribute values to disturb.", 0.1, 0.0, 1.0);
public FloatOption classNoiseFractionOption = new FloatOption("classNoise",
'c', "The fraction of class labels to disturb.", 0.1, 0.0, 1.0);
protected Random random;
protected AutoExpandVector<Object> attValObservers;
@Override
protected void restartImpl() {
this.random = new Random(this.randomSeedOption.getValue());
this.attValObservers = new AutoExpandVector<Object>();
}
public InstancesHeader getHeader() {
return this.inputStream.getHeader();
}
public Instance nextInstance() {
Instance inst = (Instance) this.inputStream.nextInstance().copy();
for (int i = 0; i < inst.numAttributes(); i++) {
double noiseFrac = i == inst.classIndex() ? this.classNoiseFractionOption
.getValue()
: this.attNoiseFractionOption.getValue();
if (inst.attribute(i).isNominal()) {
DoubleVector obs = (DoubleVector) this.attValObservers.get(i);
if (obs == null) {
obs = new DoubleVector();
this.attValObservers.set(i, obs);
}
int originalVal = (int) inst.value(i);
if (!inst.isMissing(i)) {
obs.addToValue(originalVal, inst.weight());
}
if ((this.random.nextDouble() < noiseFrac)
&& (obs.numNonZeroEntries() > 1)) {
do {
inst.setValue(i, this.random.nextInt(obs.numValues()));
} while (((int) inst.value(i) == originalVal)
|| (obs.getValue((int) inst.value(i)) == 0.0));
}
} else {
GaussianEstimator obs = (GaussianEstimator) this.attValObservers
.get(i);
if (obs == null) {
obs = new GaussianEstimator();
this.attValObservers.set(i, obs);
}
obs.addObservation(inst.value(i), inst.weight());
inst.setValue(i, inst.value(i) + this.random.nextGaussian()
* obs.getStdDev() * noiseFrac);
}
}
return inst;
}
public void getDescription(StringBuilder sb, int indent) {
// TODO Auto-generated method stub
}
}