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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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