simplewordsdatasource.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.bayesian;

import java.io.Serializable;
import java.util.Collection;
import java.util.HashMap;
import java.util.Map;

/**
 *
 * @author Nick Lothian
 * @author Peter Leschev
 *  
 */
public class SimpleWordsDataSource implements IWordsDataSource, Serializable {

    private Map words = new HashMap();

    public void setWordProbability(WordProbability wp) {
        words.put(wp.getWord(), wp);
    }

    /**
     * @see net.sf.classifier4J.bayesian.IWordsDataSource#getWordProbability(java.lang.String)
     */
    public WordProbability getWordProbability(String word) {
        if (words.containsKey(word)) {
            return (WordProbability) words.get(word);
        } else {
            return null;
        }
    }

    public Collection getAll() {
        return words.values();
    }

    /**
     * @see net.sf.classifier4J.bayesian.IWordsDataSource#addMatch(java.lang.String)
     */
    public void addMatch(String word) {
        WordProbability wp = (WordProbability) words.get(word);
        if (wp == null) {
            wp = new WordProbability(word, 1, 0);
        } else {
            wp.setMatchingCount(wp.getMatchingCount() + 1);
        }
        setWordProbability(wp);
    }

    /**
     * @see net.sf.classifier4J.bayesian.IWordsDataSource#addNonMatch(java.lang.String)
     */
    public void addNonMatch(String word) {
        WordProbability wp = (WordProbability) words.get(word);
        if (wp == null) {
            wp = new WordProbability(word, 0, 1);
        } else {
            wp.setNonMatchingCount(wp.getNonMatchingCount() + 1);
        }
        setWordProbability(wp);
    }

}