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/*
* 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 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see .
*/
package weka.classifiers.neural.common.training;
import weka.classifiers.neural.common.NeuralModel;
import weka.classifiers.neural.common.RandomWrapper;
import weka.core.Instance;
import weka.core.Instances;
import java.util.Enumeration;
/**
* Title: Weka Neural Implementation
* Description: ...
* Copyright: Copyright (c) 2003
* Company: N/A
*
* @author Jason Brownlee
* @version 1.0
*/
public class OnlineTrainer extends NeuralTrainer {
public OnlineTrainer(RandomWrapper aRand) {
super(aRand);
}
public void trainModel(NeuralModel aModel,
Instances aInstances,
int numIterations) {
Instances epochInstances = new Instances(aInstances);
// train until we can stop
for (int iteration = 0; iteration < numIterations; iteration++) {
// prepare the model for an epoch
aModel.startingEpoch();
// get the learning rate
double learingRate = aModel.getLearningRate(iteration);
// randomize the dataset
epochInstances.randomize(rand.getRand());
// perform a single epoch
Enumeration e = epochInstances.enumerateInstances();
while (e.hasMoreElements()) {
// get an instance
Instance instance = (Instance) e.nextElement();
// update the model for a given instance
aModel.updateModel(instance, learingRate);
}
// finished epoch
aModel.finishedEpoch(epochInstances, learingRate);
}
}
}
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