/// <summary>
/// Use this model to evaluate a context and return an array of the
/// likelihood of each outcome given that context.
/// </summary>
/// <param name="context">
/// The names of the predicates which have been observed at
/// the present decision point.
/// </param>
/// <param name="outcomeSums">
/// This is where the distribution is stored.
/// </param>
/// <returns>
/// The normalized probabilities for the outcomes given the
/// context. The indexes of the double[] are the outcome
/// ids, and the actual string representation of the
/// outcomes can be obtained from the method
/// GetOutcome(int outcomeIndex).
/// </returns>
public double[] Evaluate(string[] context, double[] outcomeSums)
{
for (int outcomeIndex = 0; outcomeIndex < _outcomeCount; outcomeIndex++)
{
outcomeSums[outcomeIndex] = _initialProbability;
_featureCounts[outcomeIndex] = 0;
}
foreach (string con in context)
{
_reader.GetPredicateData(con, _featureCounts, outcomeSums);
}
double normal = 0.0;
for (int outcomeIndex = 0; outcomeIndex < _outcomeCount; outcomeIndex++)
{
outcomeSums[outcomeIndex] = Math.Exp((outcomeSums[outcomeIndex] * _correctionConstantInverse) + ((1.0 - (_featureCounts[outcomeIndex] / CorrectionConstant)) * CorrectionParameter));
normal += outcomeSums[outcomeIndex];
}
for (int outcomeIndex = 0; outcomeIndex < _outcomeCount; outcomeIndex++)
{
outcomeSums[outcomeIndex] /= normal;
}
return(outcomeSums);
}
/// <summary>
/// Use this model to evaluate a context and return an array of the
/// likelihood of each outcome given that context.
/// </summary>
/// <param name="context">
/// The names of the predicates which have been observed at
/// the present decision point.
/// </param>
/// <param name="outcomeSums">
/// This is where the distribution is stored.
/// </param>
/// <returns>
/// The normalized probabilities for the outcomes given the
/// context. The indexes of the double[] are the outcome
/// ids, and the actual string representation of the
/// outcomes can be obtained from the method
/// GetOutcome(int outcomeIndex).
/// </returns>
public double[] Evaluate(string[] context, double[] outcomeSums)
{
for (int outcomeIndex = 0; outcomeIndex < mOutcomeCount; outcomeIndex++)
{
outcomeSums[outcomeIndex] = mInitialProbability;
mFeatureCounts[outcomeIndex] = 0;
}
for (int currentContext = 0; currentContext < context.Length; currentContext++)
{
mReader.GetPredicateData(context[currentContext], mFeatureCounts, outcomeSums);
}
double normal = 0.0;
for (int outcomeIndex = 0; outcomeIndex < mOutcomeCount; outcomeIndex++)
{
outcomeSums[outcomeIndex] = System.Math.Exp((outcomeSums[outcomeIndex] * mCorrectionConstantInverse) + ((1.0 - (mFeatureCounts[outcomeIndex] / mCorrectionConstant)) * mCorrectionParameter));
normal += outcomeSums[outcomeIndex];
}
for (int outcomeIndex = 0; outcomeIndex < mOutcomeCount; outcomeIndex++)
{
outcomeSums[outcomeIndex] /= normal;
}
return(outcomeSums);
}
