【文件属性】：
文件名称：文本相似度计算（TF-IDF）C#
文件大小：29KB
文件格式：RAR
更新时间：2017-04-24 14:53:16
文本相似度 TF-IDF C#
namespace ServiceRanking
{
///
/// Summary description for TF_IDFLib.
///
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;
}
}
}
【文件预览】：
tfidf---c#
----TF_IDFWeighting.csproj(4KB)
----TF_IDFWeighting.sln(1KB)
----Tokeniser.cs(1KB)
----PorterStemmer.cs(8KB)
----bin()
--------Debug()
----NGram.cs(3KB)
----obj()
--------TF_IDFWeighting.csproj.FileList.txt(166B)
--------Debug()
----_UpgradeReport_Files()
--------UpgradeReport.xslt(12KB)
--------UpgradeReport_Plus.gif(71B)
--------UpgradeReport.css(3KB)
--------UpgradeReport_Minus.gif(69B)
----TFIDFMeasure.cs(6KB)
----backup()
--------Backup()
----Class1.cs(844B)
----StopWordsHandler.cs(13KB)
----UpgradeLog.XML(2KB)
----AssemblyInfo.cs(2KB)
----TF_IDFWeighting.suo(17KB)
www.pudn.com.txt
