public static List <GroupRepresentationResult> QueryListToGroupRepresentationList(List <QueryResult> QRList, List <Tuple <string, string> > GroupNames)
{
ConcurrentBag <GroupRepresentationResult> GRBag = new ConcurrentBag <GroupRepresentationResult>();
DenseVector SummedVector = new DenseVector(QRList[0].ReturnAccessVector().Count);
Parallel.ForEach(QRList, QR => {
SummedVector = SummedVector + (DenseVector)QR.ReturnAccessVector();
});
Parallel.For(0, GroupNames.Count, i =>
{
GroupRepresentationResult CurrentGR = new GroupRepresentationResult(GroupNames[i].Item1, GroupNames[i].Item2, (double)Decimal.Divide((decimal)SummedVector[i], QRList.Count));
GRBag.Add(CurrentGR);
});
return((from GR in GRBag
where Convert.ToDouble(GR.Percent.Substring(0, GR.Percent.Length - 1)) > 0
select GR).Distinct().OrderBy(o => o.Name).ToList());
}
public static string ReturnRecommendationString(List <QueryResult> AllInputs, List <Tuple <string, string> > AllGroupNamesAndDescriptions, double Threshold)
{
Vector CountVector = new DenseVector(AllInputs[0].ReturnAccessVector().Count);
//bool TFIDFAttempted = false;
/*if (ByTFIDF)
* {
* TFIDFAttempted = true;
* try
* {
* GroupingQueryResult GQR = (GroupingQueryResult)AllInputs[0];
* Vector TestVector = GQR.ReturnTF_IDFVector();
* }
* catch
* {
* ByTFIDF = false;
* }
* if (ByTFIDF)
* {
* Parallel.ForEach<QueryResult>(AllInputs, QR => {
* GroupingQueryResult GQR = (GroupingQueryResult)QR;
* CountVector = (DenseVector)(CountVector + GQR.ReturnTF_IDFVector());
* });
* CountVector = (DenseVector)(CountVector / AllInputs.Count);
* Parallel.For(0, CountVector.Count, i => {
* if (CountVector[i] < Threshold)
* {
* CountVector[i] = 0;
* }
* });
*
* string Titlestring = $"Template Recommended By TF-IDF, with Threshold of {Threshold.ToString()}";
* return RecommendationString(CountVector, AllGroupNamesAndDescriptions, Titlestring);
* }
* }*/
//the following executes if TFIDF is not being used
if (AllInputs[0].GetType() == typeof(RBACS.UserQueryResult))
{
Parallel.ForEach <QueryResult>(AllInputs, QR =>
{
CountVector = (DenseVector)(CountVector + QR.ReturnAccessVector());
});
}
else if (AllInputs[0].GetType() == typeof(GroupingQueryResult))
{
Parallel.ForEach(AllInputs, QR => {
GroupingQueryResult GQR = (GroupingQueryResult)QR;
CountVector = (DenseVector)(CountVector + GQR.ReturnAccessVector());
});
}
else if (AllInputs[0].GetType() == typeof(UserClusteringResult))
{
Parallel.ForEach(AllInputs, QR => {
UserClusteringResult UCR = (UserClusteringResult)QR;
CountVector = (DenseVector)(CountVector + UCR.ReturnAccessVector());
});
}
else if (AllInputs[0].GetType() == typeof(GroupingClusteringResult))
{
Parallel.ForEach(AllInputs, QR => {
GroupingClusteringResult GCR = (GroupingClusteringResult)QR;
CountVector = (DenseVector)(CountVector + GCR.ReturnAccessVector());
});
}
CountVector = (DenseVector)(CountVector / AllInputs.Count);
Parallel.For(0, CountVector.Count, i => {
if (CountVector[i] < Threshold)
{
CountVector[i] = 0;
}
});
string TitleString;
TitleString = $"Template Recommended By Relative Count with Threshold {Threshold.ToString()}";
return(RecommendationString(CountVector, AllGroupNamesAndDescriptions, TitleString));
}
