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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.lvq;
import weka.classifiers.neural.common.learning.LearningKernelFactory;
import weka.classifiers.neural.common.learning.LearningRateKernel;
import weka.classifiers.neural.lvq.algorithm.Lvq1Algorithm;
import weka.core.Instances;
import weka.core.Option;
import java.util.Collection;
/**
* Description: Implementation of the LVQ1 algorithm for use in WEKA
* Implements elements required for the common LVQ algorithm framework
* specific to the LVQ1 algorithm.
*
*
* Copyright (c) Jason Brownlee 2004
*
*
* @author Jason Brownlee
*/
public class Lvq1 extends LvqAlgorithmAncestor {
protected void trainModel(Instances instances) {
// construct the algorithm
LearningRateKernel learningKernel = LearningKernelFactory.factory(learningFunction, learningRate, trainingIterations);
Lvq1Algorithm algorithm = new Lvq1Algorithm(learningKernel, model, random);
// add event listeners
addEventListenersToAlgorithm(algorithm);
// train the algorithm
algorithm.trainModel(instances, trainingIterations);
}
/**
* Validate LVQ1 specific arguments
*
* @throws Exception
*/
protected void validateArguments()
throws Exception {
// do nothing
}
/**
* Provide list of LVQ1 specific options
*
* @return Collection
*/
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