贝叶斯基本分类

【实例简介】

基本的贝叶斯分类方法

【实例截图】

【核心代码】

/* ====================================================================
 * Copyright (c) 2006 Erich Guenther (erich_guenther@hotmail.com)
 * 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. This code must not be used in commercial products
 *
 * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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. 
 */
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Text;
using System.Windows.Forms;

namespace BayesClassifierDemo
{
	public partial class Form1 : Form
	{
		BayesClassifier.Classifier m_Classifier = new BayesClassifier.Classifier();
		public Form1()
		{
			InitializeComponent();
		}

		private void buttonTest_Click(object sender, EventArgs e)
		{
			string file = @"..\..\Test.txt";
			// Cat1 is a match with the file itself
			m_Classifier.TeachCategory("Cat1", new System.IO.StreamReader(file));
			// Cat2 is a match with totally different Data
			m_Classifier.TeachPhrases("Cat2", new string[] { "Hi", "Ho Ho" });
			// Cat3 is again a perfect match and should give the same results as cat1
			m_Classifier.TeachCategory("Cat3", new System.IO.StreamReader(file));
			// Cat4 is a match with double the original data
			m_Classifier.TeachCategory("Cat4", new System.IO.StreamReader(file));
			m_Classifier.TeachCategory("Cat4", new System.IO.StreamReader(file));
			// Cat5 does not match (but trained with less Data then Cat2
			m_Classifier.TeachPhrases("Cat5", new string[] { "B" });
			this.listBox1.Items.Clear();
			Dictionary<string, double> score = m_Classifier.Classify(new System.IO.StreamReader(file));
			foreach (string c in score.Keys)
			{
				this.listBox1.Items.Add(c   ":"   score[c].ToString());
			}
		}
	}
}