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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* 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 .
*/
/*
* UnivariateNominalMultiwaySplit.java
* Copyright (C) 2013 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.trees.ht;
import java.io.Serializable;
import weka.core.Attribute;
import weka.core.Instance;
/**
* A multiway split based on a single nominal attribute
*
* @author Richard Kirkby ([email protected])
* @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
* @version $Revision: 9705 $
*/
public class UnivariateNominalMultiwaySplit extends Split implements
Serializable {
/**
* For serialization
*/
private static final long serialVersionUID = -9094590488097956665L;
/**
* Constructor
*
* @param attName the name of the attribute to split on
*/
public UnivariateNominalMultiwaySplit(String attName) {
m_splitAttNames.add(attName);
}
@Override
public String branchForInstance(Instance inst) {
Attribute att = inst.dataset().attribute(m_splitAttNames.get(0));
if (att == null || inst.isMissing(att)) {
return null;
}
return att.value((int) inst.value(att));
}
@Override
public String conditionForBranch(String branch) {
return m_splitAttNames.get(0) + " = " + branch;
}
}
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