public SparseVectorList GetFeatureVector(Dictionary <string, int> vec)
{
SparseVectorList featurevector = new SparseVectorList();
int lexiconindexcount = Lexicon.Count;
foreach (var kvp in vec)
{
var word = kvp.Key;
int value = 0;
if (Lexicon == null || Lexicon.TryGetValue(word, out value) == false)
{
Lexicon.Add(word, lexiconindexcount);
value = lexiconindexcount;
lexiconindexcount++;
}
if (!featurevector.Increase(value, kvp.Value))
{
featurevector.Insert(value, kvp.Value);
}
}
featurevector.ListToArray();
featurevector.count = featurevector.keyarray.Length;
if (featurevector.count < 1)
{
return(null);
}
featurevector.InvalidateList();
featurevector.GetNorm();
return(featurevector);
}
public SparseVectorList GetFeatureVector(Document doc)
{
SparseVectorList featurevector = new SparseVectorList();
int lexiconindexcount = Lexicon.Count;
var content = LuceneOperations.GetDocumentContent(doc, _fieldWeightDict, _leadingSentencesCnt);
var words = NLPOperations.Tokenize(content, _tokenizeConfig);
foreach (var word in words)
{
int value = 0;
if (Lexicon == null || Lexicon.TryGetValue(word, out value) == false)
{
Lexicon.Add(word, lexiconindexcount);
value = lexiconindexcount;
lexiconindexcount++;
}
if (!featurevector.Increase(value, 1))
{
featurevector.Insert(value, 1);
}
}
featurevector.ListToArray();
featurevector.count = featurevector.keyarray.Length;
//featurevector.SumUpValueArray();
if (featurevector.count < 1)
{
return(null);
}
featurevector.InvalidateList();
featurevector.GetNorm();
return(featurevector);
}
private static int[] getMostFreqWordIndex(SparseVectorList featurevector, int k)
{
var sort = new HeapSortDouble(k);
for (int iword = 0; iword < featurevector.keyarray.Length; iword++)
{
sort.Insert(featurevector.keyarray[iword], featurevector.valuearray[iword]);
}
return(sort.GetTopIndices().ToArray <int>());
}
public static double DotProduct(SparseVectorList featurevector1, SparseVectorList featurevector2)
{
int pt1 = 0;
int pt2 = 0;
int length1 = featurevector1.count;
int length2 = featurevector2.count;
double ret = 0;
int[] keys1 = featurevector1.keyarray;
int[] values1 = featurevector1.valuearray;
int[] keys2 = featurevector2.keyarray;
int[] values2 = featurevector2.valuearray;
while (true)
{
while (pt1 < length1 && keys1[pt1] < keys2[pt2])
{
pt1++;
}
if (pt1 == length1)
{
break;
}
if (keys1[pt1] == keys2[pt2])
{
ret += (double)values1[pt1] * values2[pt2];
pt1++;
pt2++;
}
else
{
while (pt2 < length2 && keys2[pt2] < keys1[pt1])
{
pt2++;
}
if (pt2 == length2)
{
break;
}
if (keys2[pt2] == keys1[pt1])
{
ret += (double)values1[pt1] * values2[pt2];
pt1++;
pt2++;
}
}
if (pt1 == length1 || pt2 == length2)
{
break;
}
}
return(ret);
}
public static double Cosine(SparseVectorList featurevector1, SparseVectorList featurevector2)
{
double cosine;
if (featurevector1.count > featurevector2.count)
{
cosine = Cosine(featurevector2, featurevector1);
}
else
{
long t = DateTime.Now.Ticks;
cosine = DotProduct(featurevector1, featurevector2) / featurevector1.normvalue / featurevector2.normvalue;
}
return(cosine);
}
private List <int> RemoveSimilarDocumentsGranu(IndexReader reader, List <int> orgDocIDs,
int timeWindowSize, int wordWindowSize)
{
var newDocIDs = new List <int>();
var removeSimilarity = Configure.MinDistinctiveDocumentCosine;
Dictionary <int, Dictionary <int, List <SparseVectorList> > > uniqueDocHash = new Dictionary <int, Dictionary <int, List <SparseVectorList> > >();
int docNum = orgDocIDs.Count;
int removeDocNum = 0;
Dictionary <string, int> lexicon = new Dictionary <string, int>();
int timeslicesize = 1;
if (timeWindowSize >= 15)
{
int[] dividePieceNumbers = new int[] { 3, 4, 5, 7 };
foreach (int dividePieceNumber in dividePieceNumbers)
{
if (timeWindowSize % dividePieceNumber == 0)
{
timeslicesize = timeWindowSize / dividePieceNumber;
break;
}
}
if (timeslicesize == 1)
{
timeslicesize = (timeWindowSize + 2) / 3;
timeWindowSize = 3;
}
else
{
timeWindowSize /= timeslicesize;
}
Console.WriteLine("Reset window size! TimeSliceSize: {0}, WindowSize: {1}", timeslicesize, timeWindowSize);
}
int begintimedelta = -(timeWindowSize - 1) / 2;
int endtimedelta = timeWindowSize / 2;
var progress = new ProgramProgress(docNum);
StreamWriter debugSw = null;
if (Configure.IsDebug)
{
string fileName = Configure.OutputPath + "debug.txt";
FileOperations.EnsureFileFolderExist(fileName);
debugSw = new StreamWriter(fileName, true, Encoding.UTF8);
}
foreach (var iDoc in orgDocIDs)
{
var doc = reader.Document(iDoc);
SparseVectorList vector = GetFeatureVector(doc, lexicon);
if (vector == null)
{
removeDocNum++;
continue;
}
vector.documentid = iDoc;
int time = getDateTimeBingNews(doc) / timeslicesize;
int[] words = getMostFreqWordIndex(vector, wordWindowSize);
bool bunqiue = true;
for (int stime = time + begintimedelta; stime <= time + endtimedelta; stime++)
{
if (uniqueDocHash.ContainsKey(stime))
{
Dictionary <int, List <SparseVectorList> > wordHash = uniqueDocHash[stime];
foreach (int sword in words)
{
if (wordHash.ContainsKey(sword))
{
List <SparseVectorList> vectorList = wordHash[sword];
foreach (SparseVectorList svector in vectorList)
{
if (SparseVectorList.Cosine(svector, vector) >= removeSimilarity)
{
if (Configure.IsDebug && removeDocNum <= 10000)
{
double simi = SparseVectorList.Cosine(svector, vector);
if (simi <= Configure.MaxShowDebugCosine)
{
debugSw.WriteLine("---------------------------------------------------");
debugSw.WriteLine(reader.Document(svector.documentid).Get(BingNewsFields.NewsArticleHeadline)); //Get("NewsArticleDescription"));
debugSw.WriteLine(reader.Document(vector.documentid).Get(BingNewsFields.NewsArticleHeadline)); //Get("NewsArticleDescription"));
debugSw.WriteLine("");
string body1 = reader.Document(svector.documentid).Get(BingNewsFields.NewsArticleDescription);
string body2 = reader.Document(vector.documentid).Get(BingNewsFields.NewsArticleDescription);
if (body1.Length > 100)
{
body1 = body1.Substring(0, 100);
}
if (body2.Length > 100)
{
body2 = body2.Substring(0, 100);
}
debugSw.WriteLine(body1);
debugSw.WriteLine(body2);
debugSw.WriteLine(simi);
}
debugSw.Flush();
}
bunqiue = false;
break;
}
}
}
if (!bunqiue)
{
break;
}
}
}
if (!bunqiue)
{
break;
}
}
if (bunqiue)
{
int keytime = time;
int keyword = words[0];
if (!uniqueDocHash.ContainsKey(keytime))
{
uniqueDocHash.Add(keytime, new Dictionary <int, List <SparseVectorList> >());
}
Dictionary <int, List <SparseVectorList> > wordHash = uniqueDocHash[keytime];
if (!wordHash.ContainsKey(keyword))
{
wordHash.Add(keyword, new List <SparseVectorList>());
}
List <SparseVectorList> list = wordHash[keyword];
list.Add(vector);
newDocIDs.Add(iDoc);
}
else
{
removeDocNum++;
}
progress.PrintIncrementExperiment();
}
Console.WriteLine("Finished remove similar documents. Removed {0} out of {1}", removeDocNum, docNum);
int listLengthSum = 0, listCnt = 0;
foreach (Dictionary <int, List <SparseVectorList> > hash0 in uniqueDocHash.Values)
{
foreach (List <SparseVectorList> list in hash0.Values)
{
listLengthSum += list.Count;
listCnt++;
}
}
Console.WriteLine("AvgListLength: {0}, ListCnt: {1}", listLengthSum / listCnt, listCnt);
if (Configure.IsDebug)
{
debugSw.Flush();
debugSw.Close();
}
return(newDocIDs);
}
