protected override double Calculate_Prob_f_c(int c_i, int f_i)
{
// The 2 below represents 2 distributions:
// 1. Probability of getting word f_i.
// 2. Probability of NOT getting word f_i.
return(SmoothingHelper.GetAddDeltaProbability(count_f_c[f_i, c_i], count_c[c_i], ConditionalProbabilityDelta, 2));
}
// Methods
protected override void Train()
{
base.Train();
logNegativeVocabulary = new double[NoOfClasses];
logProbOOV_c = new double[NoOfClasses];
logNegProb_f_c = new double[NoOfFeatures, NoOfClasses];
for (int c_i = 0; c_i < NoOfClasses; c_i++)
{
for (int f_i = 0; f_i < NoOfFeatures; f_i++)
{
logNegProb_f_c[f_i, c_i] = Math.Log10(1 - prob_f_c[f_i, c_i]);
logNegativeVocabulary[c_i] += logNegProb_f_c[f_i, c_i];
}
logProbOOV_c[c_i] = Math.Log10(SmoothingHelper.GetAddDeltaProbability(0, count_c[c_i], ConditionalProbabilityDelta, 2));
}
}
// Methods
protected override void Train()
{
countW_c = new double[NoOfClasses];
logProbOOV_c = new double[NoOfClasses];
for (int v_i = 0; v_i < TrainingVectors.Count; v_i++)
{
for (int f_i = 0; f_i < TrainingVectors[v_i].Features.Length; f_i++)
{
countW_c[TrainingVectors[v_i].Headers[Gold_i]] += TrainingVectors[v_i].Features[f_i];
countW += countW_c[TrainingVectors[v_i].Headers[Gold_i]];
}
}
base.Train();
for (int c_i = 0; c_i < NoOfClasses; c_i++)
{
logProbOOV_c[c_i] = Math.Log10(SmoothingHelper.GetAddDeltaProbability(0, countW_c[c_i], ConditionalProbabilityDelta, NoOfFeatures));
}
}
protected override double Calculate_Prob_c(int c_i)
{
return(SmoothingHelper.GetAddDeltaProbability(countW_c[c_i], countW, ClassPriorDelta, NoOfClasses));
}
protected override double Calculate_Prob_f_c(int c_i, int f_i)
{
return(SmoothingHelper.GetAddDeltaProbability(count_f_c[f_i, c_i], countW_c[c_i], ConditionalProbabilityDelta, NoOfFeatures));
}
