/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 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.fitting.gaussian;
import org.encog.EncogError;
import org.encog.ml.MLMethod;
import org.encog.ml.TrainingImplementationType;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.train.BasicTraining;
import org.encog.neural.networks.training.propagation.TrainingContinuation;
import org.encog.util.EngineArray;
public class TrainGaussian extends BasicTraining {
private final GaussianFitting method;
private final MLDataSet training;
public TrainGaussian(GaussianFitting theMethod, MLDataSet theTraining) {
super(TrainingImplementationType.OnePass);
this.method = theMethod;
this.training = theTraining;
}
/**
* @return the training
*/
public MLDataSet getTraining() {
return training;
}
@Override
public void iteration() {
// calculate mu, which is the mean
double[] sum = new double[this.method.getInputCount()];
for(MLDataPair pair: this.training) {
for(int i=0;i<this.training.getInputSize();i++) {
sum[i]+=pair.getInput().getData(i);
}
}
double m = (double)this.training.getRecordCount();
for(int i=0;i<this.training.getInputSize();i++) {
this.method.getMu().set(0, i, sum[i]/m);
}
// calculate sigma
double[][] sigma = this.method.getSigma().getData();
EngineArray.fill(sigma, 0);
int inputCount = this.method.getInputCount();
for(MLDataPair pair: this.training) {
for(int i=0;i<inputCount;i++) {
for(int j=0;j<inputCount;j++) {
sigma[i][j] += (pair.getInput().getData(i) - this.method.getMu().get(0, i)) *
(pair.getInput().getData(j) - this.method.getMu().get(0, j));
}
}
}
for(int i=0;i<inputCount;i++) {
for(int j=0;j<inputCount;j++) {
sigma[i][j] /= m;
}
}
this.method.finalizeTraining();
}
@Override
public boolean canContinue() {
return false;
}
@Override
public TrainingContinuation pause() {
return null;
}
@Override
public void resume(TrainingContinuation state) {
throw new EncogError("Not supported");
}
@Override
public MLMethod getMethod() {
return this.method;
}
}