tfidf.java

tf-idf 是进行词频统计的程序,可对词频进行统计

	public class tfidf
	{
		private string[] _docs;//文档内容
		private string[][] _ngramDoc;
		private int _numDocs=0;
		private int _numTerms=0;
		private ArrayList _terms;//无重复
		private int[][] _termFreq;
		private float[][] _termWeight;
		private int[] _maxTermFreq;
		private int[] _docFreq;


		
	
			public static float ComputeCosineSimilarity(float[] vector1, float[] vector2)//相似度
			{
				if (vector1.Length != vector2.Length)				
					throw new Exception("DIFER LENGTH");
				

				float denom=(VectorLength(vector1) * VectorLength(vector2));
				if (denom == 0F)				
					return 0F;				
				else				
					return (InnerProduct(vector1, vector2) / denom);
				
			}

			public static float InnerProduct(float[] vector1, float[] vector2)//统一向量结构
			{
			
				if (vector1.Length != vector2.Length)
					throw new Exception("DIFFER LENGTH ARE NOT ALLOWED");
				
			
				float result=0F;
				for (int i=0; i < vector1.Length; i++)				
					result += vector1[i] * vector2[i];
				
				return result;
			}
		
			public static float VectorLength(float[] vector)//取向量长度
			{			
				float sum=0.0F;
				for (int i=0; i < vector.Length; i++)				
					sum=sum + (vector[i] * vector[i]);
						
				return (float)Math.Sqrt(sum);
			}

		

		private IDictionary _wordsIndex=new Hashtable() ;//索引项

		public tfidf(string[] documents)
		{
			_docs=documents;
			_numDocs=documents.Length ;
			MyInit();
		}

		private void GeneratNgramText()
		{
			
		}

		private ArrayList GenerateTerms(string[] docs)//建立索引项
		{
			ArrayList uniques=new ArrayList() ;
			_ngramDoc=new string[_numDocs][] ;
			for (int i=0; i < docs.Length ; i++)
			{
				Tokeniser tokenizer=new Tokeniser() ;
				string[] words=tokenizer.Partition(docs[i]);			

				for (int j=0; j < words.Length ; j++)
					if (!uniques.Contains(words[j]) )				
						uniques.Add(words[j]) ;
								
			}
			return uniques;
		}
		


		private static object AddElement(IDictionary collection, object key, object newValue)//完成哈希表标记
		{
			object element=collection[key];
			collection[key]=newValue;
			return element;
		}

		private int GetTermIndex(string term)
		{
			object index=_wordsIndex[term];
			if (index == null) return -1;
			return (int) index;
		}

		private void MyInit()//初始化
		{
			_terms=GenerateTerms (_docs );
			_numTerms=_terms.Count ;

			_maxTermFreq=new int[_numDocs] ;
			_docFreq=new int[_numTerms] ;
			_termFreq =new int[_numTerms][] ;
			_termWeight=new float[_numTerms][] ;

			for(int i=0; i < _terms.Count ; i++)			
			{
				_termWeight[i]=new float[_numDocs] ;
				_termFreq[i]=new int[_numDocs] ;

				AddElement(_wordsIndex, _terms[i], i);			
			}
			
			GenerateTermFrequency ();
			GenerateTermWeight();			
				
		}
		
		private float Log(float num)
		{
			return (float) Math.Log(num) ;//log2
		}

		//private void GenerateTermFrequency()//tf 不能用枚举
		//{
		//	for(int i=0; i < _numDocs  ; i++)
		//	{								
		//		string curDoc=_docs[i];
		//		IDictionary freq=GetWordFrequency(curDoc);
		//		IDictionaryEnumerator enums=freq.iterator() ;
		//		_maxTermFreq[i]=int.MinValue ;
		//		while (enums.MoveNext())//df
		//		{
		//			string word=(string)enums.Key;
		//			int wordFreq=(int)enums.Value ;
		//			int termIndex=GetTermIndex(word);

		//			_termFreq [termIndex][i]=wordFreq;
		//			_docFreq[termIndex] ++;

		//			if (wordFreq > _maxTermFreq[i]) _maxTermFreq[i]=wordFreq;					
		//		}
		//	}
		//}
		

		private void GenerateTermWeight()//tf*idf
		{			
			for(int i=0; i < _numTerms   ; i++)
			{
				for(int j=0; j < _numDocs ; j++)				
					_termWeight[i][j]=ComputeTermWeight (i, j);				
			}
		}

		private float GetTermFrequency(int term, int doc)
		{			
			int freq=_termFreq [term][doc];
			int maxfreq=_maxTermFreq[doc];			
			
			return ( (float) freq/(float)maxfreq );
		}

		private float GetInverseDocumentFrequency(int term)
		{
			int df=_docFreq[term];
			return Log((float) (_numDocs) / (float) df );
		}

		private float ComputeTermWeight(int term, int doc)
		{
			float tf=GetTermFrequency (term, doc);
			float idf=GetInverseDocumentFrequency(term);
			return tf * idf;
		}
		
		private  float[] GetTermVector(int doc)
		{
			float[] w=new float[_numTerms] ;
			for (int i=0; i < _numTerms; i++)											
				w[i]=_termWeight[i][doc];
			
				
			return w;
		}

		public float GetSimilarity(int doc_i, int doc_j)
		{
			float[] vector1=GetTermVector (doc_i);
			float[] vector2=GetTermVector (doc_j);

			return TermVector.ComputeCosineSimilarity(vector1, vector2) ;

		}
		
		private IDictionary GetWordFrequency(string input)
		{
			string convertedInput=input.ToLower() ;
					
			Tokeniser tokenizer=new Tokeniser() ;
			String[] words=tokenizer.Partition(convertedInput);			
			Array.Sort(words);
			
			String[] distinctWords=GetDistinctWords(words);
						
			IDictionary result=new Hashtable();
			for (int i=0; i < distinctWords.Length; i++)
			{
				object tmp;
				tmp=CountWords(distinctWords[i], words);
				result[distinctWords[i]]=tmp;
				
			}
			
			return result;
		}				
				
		private string[] GetDistinctWords(String[] input)//不是读文件，返回的tonkeniser
		{				
			if (input == null)			
				return new string[0];			
			else
			{
				ArrayList list=new ArrayList() ;
				
				for (int i=0; i < input.Length; i++)
					if (!list.Contains(input[i])) // N-GRAM SIMILARITY?				
						list.Add(input[i]);
				
				return Tokeniser.ArrayListToArray(list) ;
			}
		}
		

		
		private int CountWords(string word, string[] words)
		{
			int itemIdx=Array.BinarySearch(words, word);
			
			if (itemIdx > 0)			
				while (itemIdx > 0 && words[itemIdx].Equals(word))				
					itemIdx--;				
						
			int count=0;
			while (itemIdx < words.Length && itemIdx >= 0)
			{
				if (words[itemIdx].Equals(word)) count++;				
				
				itemIdx++;
				if (itemIdx < words.Length)				
					if (!words[itemIdx].Equals(word)) break;					
				
			}
			
			return count;
		}				
	}