private LazyPredictor()
{
dictionary = LazyTrainer.ReadDictionary(
HttpContext.Current.Server.MapPath("~/App_Data/dictionary.txt"));
model = LazyTrainer.LoadModel(
HttpContext.Current.Server.MapPath("~/App_Data/segments.va"),
HttpContext.Current.Server.MapPath("~/App_Data/words.va"));
}
public string Predict(string query)
{
List <string> prediction = new List <string>();
string[] words = query.Split(new char[] { ' ' }, StringSplitOptions.RemoveEmptyEntries);
for (short iw = 0; iw < words.Length; iw++)
{
string w = words[iw];
if (!dictionary.ContainsKey(w))
{
prediction.Add(w);
}
else
{
int wKey = dictionary[w];
if (model.ContainsKey(wKey))
{
var locations = model[wKey];
short maxMatchCount = -1;
List <string> mostLikelySequence = new List <string>();
string mostLikelyWord = String.Empty;
ConcurrentBag <Tuple <short, List <string>, string> > results = new ConcurrentBag <Tuple <short, List <string>, string> >();
Parallel.For(0, locations.Count, new ParallelOptions()
{
MaxDegreeOfParallelism = 8
}, (int i) =>
{
short iWord = locations[i].Item2;
// The accented word list
List <string> actual = locations[i].Item1.Split(new char[0], StringSplitOptions.RemoveEmptyEntries).ToList();
// The converted raw word list
List <string> rawActual = new List <string>();
foreach (string aw in actual)
{
rawActual.Add(unaccentor.Convert(aw));
}
short matchCount = 0;
LazyTrainer.MatchSequence(words.ToList(), iw, rawActual, iWord, out matchCount);
results.Add(new Tuple <short, List <string>, string>(matchCount, actual, actual[iWord]));
});
foreach (var item in results)
{
if (item.Item1 >= maxMatchCount)
{
maxMatchCount = item.Item1;
mostLikelySequence = item.Item2;
mostLikelyWord = item.Item3;
}
}
prediction.Add(mostLikelyWord);
}
}
}
return(String.Join(" ", prediction));
}
