public Entry(string word, long frequency, Lexicon lexicon)
{
this.word = word;
this.frequency = frequency;
int key = -1;
for (int i = 0; i < word.Length; ++i)
{
key = word[i] - 'a';
pts.Add(lexicon.keyPos[key]);
}
locationSample[0] = PathCalc.TemporalSampling(pts.ToArray());
bool ins = false;
if (Vector2.Distance(pts[0], StartPoint) > eps)
{
ins = true;
}
if (ins)
{
pts.Insert(0, StartPoint);
}
locationSample[1] = PathCalc.TemporalSampling(pts.ToArray());
if (ins)
{
pts.RemoveAt(0);
}
for (int i = 0; i < (int)Parameter.Mode.End; ++i)
{
shapeSample[i] = PathCalc.Normalize(locationSample[i]);
}
}
public Candidate[] Recognize(Vector2[] rawStroke)
{
Vector2[] stroke = PathCalc.TemporalSampling(rawStroke);
for (int i = 0; i < CandidatesNum; ++i)
{
candsTmp[i] = new Candidate();
}
int h = history.Count;
string pre = (h == 0) ? "<s>" : history[h - 1].word.ToLower();
Debug.Log(pre);
foreach (Entry entry in dict)
{
if (rawStroke.Length == 1 && entry.word.Length != 1)
{
continue;
}
Candidate newCandidate = new Candidate
{
word = entry.word
};
if (Parameter.mode == Parameter.Mode.AnyStart)
{
if (Vector2.Distance(entry.locationSample[0][0], stroke[0]) > AnyStartThr * Parameter.keyWidth)
{
continue;
}
entry.pts.Insert(0, stroke[0]);
entry.locationSample[(int)Parameter.mode] = PathCalc.TemporalSampling(entry.pts.ToArray());
entry.shapeSample[(int)Parameter.mode] = PathCalc.Normalize(entry.locationSample[(int)Parameter.mode]);
entry.pts.RemoveAt(0);
}
if (Vector2.Distance(stroke[SampleSize - 1], entry.locationSample[(int)Parameter.mode][SampleSize - 1])
> Parameter.endOffset * Parameter.keyWidth &&
(h >= words.Length || entry.word != words[h]))
{
continue;
}
float biF = 0;
if (bigramMap.ContainsKey(pre + ' ' + entry.word))
{
biF = bigramMap[pre + ' ' + entry.word];
}
else
{
biF = katzAlpha[pre] * entry.frequency;
if (biF == 0)
{
continue;
}
}
newCandidate.language = Mathf.Log(biF);
newCandidate.location = PathCalc.Match(stroke, entry.locationSample[(int)Parameter.mode], Parameter.locationFormula,
(newCandidate.language - candsTmp[CandidatesNum - 1].confidence) / 100 * Parameter.keyboardWidth * SampleSize);
if (newCandidate.location == Parameter.inf)
{
continue;
}
newCandidate.confidence = newCandidate.language - 100 * newCandidate.location;
//if (newCandidate.confidence < candsTmp[CandidatesNum - 1].confidence)
//continue;
for (int i = 0; i < CandidatesNum; ++i)
{
if (newCandidate.confidence > candsTmp[i].confidence)
{
for (int j = CandidatesNum - 1; j > i; j--)
{
candsTmp[j] = candsTmp[j - 1];
}
candsTmp[i] = newCandidate;
break;
}
}
}
if (h < words.Length)
{
for (int i = 0; i < CandidatesNum; ++i)
{
if (candsTmp[i].word == words[h].ToLower())
{
candsTmp[i].word = words[h];
}
}
}
return(candsTmp);
}
