/// <summary>
/// Constructor for a maximum entropy model trained using the
/// Generalized Iterative Scaling procedure.
/// </summary>
/// <param name="reader">
/// A reader providing the data for the model.
/// </param>
public GisModel(IO.IGisModelReader reader)
{
this._reader = reader;
_outcomeNames = reader.GetOutcomeLabels();
CorrectionConstant = reader.CorrectionConstant;
CorrectionParameter = reader.CorrectionParameter;
_outcomeCount = _outcomeNames.Length;
_initialProbability = Math.Log(1.0 / _outcomeCount);
_correctionConstantInverse = 1.0 / CorrectionConstant;
_featureCounts = new int[_outcomeCount];
}
/// <summary>
/// Constructor for a maximum entropy model trained using the
/// Generalized Iterative Scaling procedure.
/// </summary>
/// <param name="reader">
/// A reader providing the data for the model.
/// </param>
public GisModel(IO.IGisModelReader reader)
{
mReader = reader;
mOutcomeNames = reader.GetOutcomeLabels();
mCorrectionConstant = (double)reader.CorrectionConstant;
mCorrectionParameter = reader.CorrectionParameter;
mOutcomeCount = mOutcomeNames.Length;
mInitialProbability = System.Math.Log(1.0 / mOutcomeCount);
mCorrectionConstantInverse = 1.0 / mCorrectionConstant;
mFeatureCounts = new int[mOutcomeCount];
}
/// <summary>
/// Constructor for a maximum entropy model trained using the
/// Generalized Iterative Scaling procedure.
/// </summary>
/// <param name="reader">
/// A reader providing the data for the model.
/// </param>
public GisModel(IO.IGisModelReader reader)
{
mReader = reader;
mOutcomeNames = reader.GetOutcomeLabels();
// Quick & dirty solution for reading the latest OpenNLP sentence detection models
if (mOutcomeNames.Length == 2 && mOutcomeNames.Contains("n") && mOutcomeNames.Contains("s"))
{
mOutcomeNames[Array.IndexOf(mOutcomeNames, "n")] = "F";
mOutcomeNames[Array.IndexOf(mOutcomeNames, "s")] = "T";
}
mCorrectionConstant = (double)reader.CorrectionConstant;
mCorrectionParameter = reader.CorrectionParameter;
mOutcomeCount = mOutcomeNames.Length;
mInitialProbability = System.Math.Log(1.0 / mOutcomeCount);
mCorrectionConstantInverse = 1.0 / mCorrectionConstant;
mFeatureCounts = new int[mOutcomeCount];
}