也谈贝叶斯分类（C#）版本

   

代码下载

 最近在做一个大作业。搭建一个信息检索平台。用到了贝叶斯分类参考了洞庭散人大哥的技术博客

http://www.cnblogs.com/phinecos/archive/2008/10/21/1316044.html

但是，他的算法运行起来很慢，原因是IO操作过于频繁，而且有些IO操作是可以避免的。下面开始介绍我的贝叶斯分类算法实现。

采用分词器为河北理工大学吕震宇老师的SHARPICTCLAS 该分词器没有Lucene接口，自己实现Analyzer 和Tokenizer 类如下

using System;
using System.Collections.Generic;
using System.Text;
using System.IO;
using Lucene.Net.Analysis;
using Lucene.Net.Analysis.Standard;

namespace Bayes
{
    class ICTCLASAnalyzer:Analyzer
    {
        public static readonly System.String[] CHINESE_ENGLISH_STOP_WORDS = new string[400];
        public string NoisePath = Environment.CurrentDirectory + "\\data\\stopwords.txt";
        public ICTCLASAnalyzer()
        {
           StreamReader reader = new StreamReader(NoisePath, System.Text.Encoding.Default);
            string noise = reader.ReadLine();
            int i = 0;
           
            while (!string.IsNullOrEmpty(noise)&&i<400)
            {
                CHINESE_ENGLISH_STOP_WORDS[i] = noise;
               noise = reader.ReadLine();
               i++;
             }
            
      }

               /**//**//**//// Constructs a {@link StandardTokenizer} filtered by a {@link
       /// StandardFilter}, a {@link LowerCaseFilter} and a {@link StopFilter}. 
       /// 
        public override TokenStream TokenStream(System.String fieldName, System.IO.TextReader reader)
        {
           TokenStream result = new ICTCLASTokenizer(reader);
            result = new StandardFilter(result);
            result = new LowerCaseFilter(result);
            result = new StopFilter(result, CHINESE_ENGLISH_STOP_WORDS);
           return result;
        }


    }
}

using System;
using System.Collections.Generic;
using System.Text;
using Lucene.Net.Analysis;
using Lucene.Net.Documents;
using Lucene.Net.Analysis.Standard;
using System.IO;
using SharpICTCLAS;


namespace Bayes
{
    class ICTCLASTokenizer:Tokenizer
    {
         int nKind = 1;
         List<WordResult[]> result;
         int startIndex = 0;
         int endIndex = 0;
         int i = 1;
         /**//**/
         /**//// 
        /// 待分词的句子
        /// 
        private string sentence;
         /**//**/
        /**//// Constructs a tokenizer for this Reader. 
        public ICTCLASTokenizer(System.IO.TextReader reader)
        {
             this.input = reader;
             sentence = input.ReadToEnd();
             sentence = sentence.Replace("\r\n", "");
             string DictPath = Path.Combine(Environment.CurrentDirectory, "Data") + Path.DirectorySeparatorChar;
            //Console.WriteLine("正在初始化字典库，请稍候");
            WordSegment wordSegment = new WordSegment();
             wordSegment.InitWordSegment(DictPath);
             result = wordSegment.Segment(sentence, nKind);
         }
 
         /**//**/
         /**//// 进行切词，返回数据流中下一个token或者数据流为空时返回null
         /// 
         public override Token Next()
         {
             Token token = null;
            while (i < result[0].Length - 1)
             {
                 string word = result[0][i].sWord;
                 endIndex = startIndex + word.Length - 1;
                 token = new Token(word, startIndex, endIndex);
                startIndex = endIndex + 1;

                 i++;
                 return token;

            }
            return null;
         }

    }
}

 下面开始介绍我的实现：分为五个类： ChineseSpliter用于分词，ClassifyResult用于储存结果。MemoryTrainingDataManager,用于管理IO操作 FastNaiveBayesClassification 用于实现贝叶斯算法。和洞庭散人不同之处在于我的各个计算前向概率，条件概率，联合概率的函数写在了一个类里，而不是多个类，这样做的目的在于避免不必要的IO操作。

using System;
using System.Collections.Generic;
using System.Text;

namespace Bayes
{
    class ClassifyResult
    {
        public string className;
        public float score;
        public ClassifyResult()
        {
            className = "";
            score = 0;
        }
    
    
    }
}

using System;
using System.Collections.Generic;
using System.Text;
using System.IO;
using Lucene.Net.Analysis;


namespace Bayes
{
    class ChineseSpliter
    {    public string Split(string text,string splitToken)
        {
          StringBuilder sb = new StringBuilder();

            Analyzer an = new ICTCLASAnalyzer();

            //TokenStream ts = an.ReusableTokenStream("", new StringReader(text));

           TokenStream ts = an.TokenStream("", new StringReader(text));

             Lucene.Net.Analysis.Token token;
              while ((token = ts.Next()) != null)
              {
                   sb.Append(splitToken + token.TermText());
               }
 
             return sb.ToString().Substring(1);
         }
        public string[] GetTerms(string result, string spliter)
        {
            string[] terms = result.Split(new string[] { spliter }, StringSplitOptions.RemoveEmptyEntries);
            return terms;

        }

    }
}

  

using System;
using System.Collections.Generic;
using System.Text;
using System.IO;



namespace Bayes
{
    class MemoryTrainingDataManager
    {   //调用 函数GetClassifications()获取类别子目录在磁盘中的储存位置，为公有成员变量 txtClassification赋值
        //调用 GetTtotalFileCount() 获取总共的样本集文章数目，为公有成员变量 totalFileCount赋值
        public String[] txtClassifications;//训练语料分类集合
        private static String defaultPath = "F:\\TrainingSet";
        public int totalFileCount;
        public void   GetClassifications()
        {
            this.txtClassifications = Directory.GetDirectories(defaultPath);
           
        }

        public int GetSubClassFileCount(string subclass)
        {
            string[] paths = Directory.GetFiles(subclass);
            return paths.Length;
        }
        public void  GetTotalFileCount()
        {
            int count = 0;
            for (int i = 0; i < txtClassifications.Length; i++)
            {
                count += GetSubClassFileCount(txtClassifications[i]);
            }
            totalFileCount = count;
        }
       
        public string GetText(string filePath)
        {
            StreamReader sr = new StreamReader(filePath, Encoding.Default);
            string text = sr.ReadToEnd();
            sr.Close();
            return text;
        }
        public void  SetMainMemoryStructure(ref StoreClass sc ,string subclass)
        {
           
               string []paths=Directory.GetFiles(subclass);
                sc.classificationName = subclass;
               sc.classificationCount = paths.Length;
               sc.strFileContentList = new string[sc.classificationCount];
                for (int k = 0; k < paths.Length; k++)
                {
                    sc.strFileContentList[k]=GetText(paths[k]);
                }
           }

        public int GetKeyCountOfSubClass(string key, ref StoreClass sc)
        {
            int count = 0;
            for (int i = 0;  i < sc.classificationCount; i++)
            {
                if (sc.strFileContentList[i].Contains(key))
                {
                    count++;
                }
            }
                return count;


        }
         
        




    }
}

using System;
using System.Collections.Generic;
using System.Text;

namespace Bayes
{
    class FastNaiveBayesClassification
    {
       // public  StoreClass memorystore=new StoreClass();
        public MemoryTrainingDataManager mtdm=new MemoryTrainingDataManager();
        private ChineseSpliter spliter = new ChineseSpliter();
        private static float ZoomFactor = 10;
       
        public FastNaiveBayesClassification()
        {
            mtdm.GetClassifications();
            mtdm.GetTotalFileCount();
        }
        /// <summary>
        /// Nc 表示属于c类的文本数，N表示总文件数
        /// </summary>
        /// <param name="Nc"></param>
        /// <param name="N"></param>
        /// <returns></returns>
        public float CalculatePriorProbability(float Nc,float N)
        {
            float ret = 0F;
            ret = Nc / N;
            return ret;
        }
        /// <summary>
        /// 
        /// </summary>
        /// <param name="NxC">某一类别中某一词频出现的文件数</param>
        /// <param name="Nc">该类别文件总数</param>
        /// <returns></returns>
        public float CalculateConditionalProbability(float NxC, float Nc)
        {
            float M = 0F;
            float ret = 0F;
            ret = (NxC + 1) / (Nc + M + mtdm.txtClassifications.Length);
            return ret;
        }
        public float CalculateJointProbability(float []NxC, float Nc, float  N)
        {
            float ret = 1;
            for (int i = 0; i < NxC.Length; i++)
            {
                ret *= CalculateConditionalProbability(NxC[i], Nc) * ZoomFactor;
            }
            ret = ret * CalculatePriorProbability(Nc, N) ;
            return ret;

        }
        public string[] SplitTerms(string text)
        {
            //string result = tokenizer.TextSplit(text, "@@@");
            // string[] terms = tokenizer.GetTerms(result, "@@@");
            string result = spliter.Split(text, "@@@");
            string[] terms = spliter.GetTerms(result, "@@@");
            return terms;
        }

        public ClassifyResult Classify(string text)
        {   int end=mtdm.txtClassifications.Length;
            ClassifyResult[] results = new ClassifyResult[end];
            for (int i = 0; i < end; i++)
            {
                results[i] = new ClassifyResult();
            }
            string[] terms = SplitTerms(text);
            float N = mtdm.totalFileCount;
            for (int i = 0; i < end; i++)
            {
                StoreClass sc = new StoreClass();
                mtdm.SetMainMemoryStructure(ref sc,  mtdm.txtClassifications[i]);
                float  Nc = sc.classificationCount;
                float[] Nxc = new float[terms.Length];
               
                for(int k=0;k<terms.Length;k++)
                {
                  Nxc[k]=mtdm.GetKeyCountOfSubClass(terms[k],ref sc);
                 // Console.WriteLine("含有的关键词数量{0}",Nxc[k]);
                }
                 results[i].score= CalculateJointProbability(Nxc, Nc, N);  
                 results[i].className = sc.classificationName;
                 Console.WriteLine("类别{0},分数{1}", results[i].className, results[i].score);
            
            }
            //选择法排序
            for (int m = 0; m < results.Length - 1; m++)
            {
                int k = m;
                for (int n = m + 1; n < results.Length; n++)
                {
                    if (results[n].score > results[k].score)
                    {
                        k = n;
                    }
                }
                if (k != m)
                {
                    ClassifyResult temp = new ClassifyResult();
                    temp.score = results[k].score;
                    temp.className = results[k].className;
                    results[k].className = results[m].className;
                    results[k].score = results[m].score;
                    results[m].score = temp.score;
                    results[m].className = temp.className;
                }
            }
            return results[0];

        }
    }
}