public void NormalizeModel(Model Prev, bool laplas)
{
if (Prev == null)
{
foreach (var item in endmodel)
{
item.probability = ((float)item.finded) / endmodel.Sum(ngr => ngr.finded);
}
}
else
{
foreach (var item in endmodel)
{
// var prev = item.text.Skip(item.text.Length - 1).Take(1).ToArray();
NGramm prevngr = Prev.endmodel.Find(ngr => new ArraysComparer().Compare(item.text.Skip(item.text.Length - 2).Take(1).ToArray(), ngr.text) == 0);
if (prevngr != null)
{
if (laplas)
{
item.probability = ((float)item.finded + 1) / prevngr.finded + Prev.endmodel.Count;
}
else
{
item.probability = ((float)item.finded) / prevngr.finded;
}
}
}
}
}
//only unigramm
public Candidate ExistsWord(string word)
{
NGramm ngr = endmodel.First(n => n.textstr.Contains(word));
if (ngr != null)
{
return(new Candidate()
{
sts = ngr.textstr,
prob = ngr.probability
});
}
else
{
return(null);
}
}
public void AddToken(string[] token)
{
// bool Applied = false;
NGramm ngr = endmodel.FirstOrDefault(ng => new ArraysComparer().Compare(ng.text, token) == 0);
if (ngr != null)
{
// Applied = true;
ngr.finded++;
}
else
{
ngr = bufffer.FirstOrDefault(ng => new ArraysComparer().Compare(ng.text, token) == 0);
if (ngr != null)
{
ngr.finded++;
if (ngr.finded > Program.unknowncount)
{
endmodel.Add(ngr);
bufffer.Remove(ngr);
}
}
else
{
ngr = new NGramm(token);
if (Program.unknowncount == 0)
{
endmodel.Add(ngr);
}
else
{
bufffer.Add(ngr);
}
}
}
}
