.NET下文本相似度算法余弦定理和SimHash浅析及应用实例分析

    public class TFIDFMeasure
    {
        private string[] _docs;
        private string[][] _ngramDoc;
        private int _numDocs=0;
        private int _numTerms=0;
        private ArrayList _terms;
        private int[][] _termFreq;
        private float[][] _termWeight;
        private int[] _maxTermFreq;
        private int[] _docFreq;
 
        public class TermVector
        {        
            public static float ComputeCosineSimilarity(float[] vector1, float[] vector2)
            {
                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)
            {
            
                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)
            {            
                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() ;
 
        public TFIDFMeasure(string[] documents)
        {
            _docs=documents;
            _numDocs=documents.Length ;
            MyInit();
        }
 
        private void GeneratNgramText()
        {
            
        }
 
        private ArrayList GenerateTerms(string[] docs)
        {
            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)
        {
            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()
        {
            _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()
        {
            for(int i=0; i < _numDocs  ; i++)
            {                                
                string curDoc=_docs[i];
                IDictionary freq=GetWordFrequency(curDoc);
                IDictionaryEnumerator enums=freq.GetEnumerator() ;
                _maxTermFreq[i]=int.MinValue ;
                while (enums.MoveNext())
                {
                    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()
        {            
            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)
        {                
            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;
        }                
}