abstractclassifier.java

Classifier4J是一个很好的基于java的分类器,里面有Native bayes和KNN等方法的文本分类.另外还 提供了分词和自动摘要等功能

/*
 * ====================================================================
 * 
 * The Apache Software License, Version 1.1
 *
 * Copyright (c) 2003 Nick Lothian. All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions
 * are met:
 *
 * 1. Redistributions of source code must retain the above copyright
 *    notice, this list of conditions and the following disclaimer. 
 *
 * 2. Redistributions in binary form must reproduce the above copyright
 *    notice, this list of conditions and the following disclaimer in
 *    the documentation and/or other materials provided with the
 *    distribution.
 *
 * 3. The end-user documentation included with the redistribution, if
 *    any, must include the following acknowlegement:  
 *       "This product includes software developed by the 
 *        developers of Classifier4J (http://classifier4j.sf.net/)."
 *    Alternately, this acknowlegement may appear in the software itself,
 *    if and wherever such third-party acknowlegements normally appear.
 *
 * 4. The name "Classifier4J" must not be used to endorse or promote 
 *    products derived from this software without prior written 
 *    permission. For written permission, please contact   
 *    http://sourceforge.net/users/nicklothian/.
 *
 * 5. Products derived from this software may not be called 
 *    "Classifier4J", nor may "Classifier4J" appear in their names 
 *    without prior written permission. For written permission, please 
 *    contact http://sourceforge.net/users/nicklothian/.
 *
 * THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESSED OR IMPLIED
 * WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
 * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED.  IN NO EVENT SHALL THE APACHE SOFTWARE FOUNDATION OR
 * ITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
 * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
 * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
 * USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
 * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
 * OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
 * SUCH DAMAGE.
 * ====================================================================
 */

package net.sf.classifier4J;

/**
 * 
 * Implementaion of the {@link net.sf.classifier4J.IClassifier#setMatchCutoff(double)} 
 * and {@link net.sf.classifier4J.IClassifier#isMatch(java.lang.String)} methods.
 * 
 * @author Nick Lothian
 *
 */
public abstract class AbstractClassifier implements IClassifier {

    protected double cutoff = IClassifier.DEFAULT_CUTOFF;

    /**
     * 
     * <p>This implementation throws an IllegalArgumentException if cutoff is 
     * greater than 1 or less than 0.</p>
     * 	 
     * 
     * @param cutoff Used to determine the mimimum probability that should be classified as a match  
     * @throws IllegalArgumentException if if cutoff is greater than 1 or less than 0 
     * 
     */
    public void setMatchCutoff(double cutoff) {
        if (cutoff > 1 || cutoff < 0) {
            throw new IllegalArgumentException("Cutoff must be equal or less than 1 and greater than or equal to 0");
        }

        this.cutoff = cutoff;
    }

    public double getMatchCutoff() {
        return this.cutoff;
    }

    /**
     * <p>Implementation of {@link net.sf.classifier4J.IClassifier#isMatch(java.lang.String)}
     * method.</p>
     * 
     * @see net.sf.classifier4J.IClassifier#isMatch(java.lang.String)
     */
    public boolean isMatch(String input) throws ClassifierException {
        double matchProbability = classify(input);

        return isMatch(matchProbability);
    }

    /**
     * @see net.sf.classifier4J.IClassifier#isMatch(double)
     */
    public boolean isMatch(double matchProbability) {
        return (matchProbability >= cutoff);
    }

}