/// <summary>
/// Returns the next object. Calling this method repeatedly until it returns,
/// null will return each object from the underlying source exactly once.
/// </summary>
/// <returns>
/// The next object or null to signal that the stream is exhausted.
/// </returns>
public Sequence Read()
{
var sample = psi.Read();
if (sample != null)
{
var events = new Event[sample.Sentence.Length];
for (int i = 0; i < sample.Sentence.Length; i++)
{
// it is safe to pass the tags as previous tags because
// the context generator does not look for non predicted tags
var tags = seqCodec.Encode(sample.Names, sample.Sentence.Length);
var context = pcg.GetContext(
i,
sample.Sentence,
useOutcomes ? tags : null,
null);
events[i] = new Event(tags[i], context);
}
return(new Sequence(events, sample));
}
return(null);
}
internal static List <Event> GenerateEvents(String[] sentence, String[] outcomes, INameContextGenerator cg)
{
var events = new List <Event>(outcomes.Length);
for (int i = 0; i < outcomes.Length; i++)
{
events.Add(new Event(outcomes[i], cg.GetContext(i, sentence, outcomes, null)));
}
cg.UpdateAdaptiveData(sentence, outcomes);
return(events);
}
/// <summary>
/// Adds name events for the specified sentence.
/// </summary>
/// <param name="sentence">
/// The sentence for which name events should be added.
/// </param>
private void AddEvents(string sentence)
{
string[] parts = sentence.Split(' ');
string outcome = MaximumEntropyNameFinder.Other;
List <string> tokens = new List <string>();
List <string> outcomesList = new List <string>();
for (int currentPart = 0, partCount = parts.Length; currentPart < partCount; currentPart++)
{
if (parts[currentPart] == "<START>")
{
outcome = MaximumEntropyNameFinder.Start;
}
else if (parts[currentPart] == "<END>")
{
outcome = MaximumEntropyNameFinder.Other;
}
else
{
//regular token
tokens.Add(parts[currentPart]);
outcomesList.Add(outcome);
if (outcome == MaximumEntropyNameFinder.Start)
{
outcome = MaximumEntropyNameFinder.Continue;
}
}
}
mEvents = new SharpEntropy.TrainingEvent[tokens.Count];
for (int currentToken = 0, tokenCount = tokens.Count; currentToken < tokenCount; currentToken++)
{
mEvents[currentToken] = new SharpEntropy.TrainingEvent(outcomesList[currentToken], mContextGenerator.GetContext(currentToken, tokens, outcomesList, mPreviousTags));
}
for (int currentToken = 0, tokenCount = tokens.Count; currentToken < tokenCount; currentToken++)
{
mPreviousTags[tokens[currentToken]] = outcomesList[currentToken];
}
}
