/*
* 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.linear;
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.simple.EncogUtility;
public class TrainLinearRegression extends BasicTraining {
private final LinearRegression method;
private final MLDataSet training;
public TrainLinearRegression(LinearRegression theMethod, MLDataSet theTraining) {
super(theMethod.getInputCount()==1?TrainingImplementationType.OnePass:TrainingImplementationType.Iterative);
this.method = theMethod;
this.training = theTraining;
}
/**
* @return the training
*/
public MLDataSet getTraining() {
return training;
}
@Override
public void iteration() {
int m = (int)this.training.getRecordCount();
double sumX = 0;
double sumY = 0;
double sumXY = 0;
double sumX2 = 0;
for(MLDataPair pair: this.training) {
sumX+=pair.getInputArray()[0];
sumY+=pair.getIdealArray()[0];
sumX2+=Math.pow(pair.getInputArray()[0], 2);
sumXY+=pair.getInputArray()[0]*pair.getIdealArray()[0];
}
this.method.getWeights()[1] = ((m*sumXY)-(sumX*sumY))/((m*sumX2)-Math.pow(sumX, 2));
this.method.getWeights()[0] = ((1.0/m)*sumY)-( (this.method.getWeights()[1]/m) * sumX);
this.setError(EncogUtility.calculateRegressionError(this.method, this.training));
}
@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;
}
}