public void Update(VariationalInferenceParameter varInference, CorpusDocument document)
{
for (int i = 0; i < CovarianceSufficientStatistic.RowCount; i++)
{
MuSufficientStatistic[i] = MuSufficientStatistic[i] + varInference.Lambda[i];
for (int j = 0; j < CovarianceSufficientStatistic.ColumnCount; j++)
{
double tmp = varInference.Lambda[i] * varInference.Lambda[j];
if (i == j)
{
CovarianceSufficientStatistic[i, j] = CovarianceSufficientStatistic[i, j] + varInference.Nu[i] + tmp;
}
else
{
CovarianceSufficientStatistic[i, j] = CovarianceSufficientStatistic[i, j] + tmp;
}
}
}
for (int i = 0; i < document.NbTerms; i++)
{
for (int j = 0; j < BetaSufficientStatistic.RowCount; j++)
{
var word = document.GetWord(i);
var count = document.GetCount(i);
BetaSufficientStatistic[j, word] = BetaSufficientStatistic[j, word] + count * varInference.Phi[i, j];
}
}
NData++;
}
public void UpdateLikelihoodBound(CorpusDocument doc, LLNAModel model)
{
TopicScores.SetValue(0);
// p(η | μ, Σ) (distribution of topic proportions) && q(η | λ, ν)
// E[log p(\eta | \mu, \Sigma)] + H(q(\eta | \lambda, \nu)
double lHood = 0.5 * model.LogDeterminantInvCovariance + 0.5 * (model.K - 1);
for (int i = 0; i < (model.K - 1); i++)
{
double v = -0.5 * Nu[i] * model.InvCovariance[i, i];
for (int j = 0; j < model.K - 1; j++)
{
v -= 0.5 *
(Lambda[i] - model.Mu[i]) *
model.InvCovariance[i, j] *
(Lambda[j] - model.Mu[j]);
}
v += 0.5 * Math.Log(Nu[i]);
lHood += v;
}
// E[log p(z_n | \eta)] + E[log p(w_n | \beta)] + H(q(z_n | \phi_n))
lHood -= CalculateLikelihoodBound() * doc.Total;
for (int i = 0; i < doc.NbTerms; i++)
{
for (int j = 0; j < model.K; j++)
{
var phi = Phi[i, j];
var logPhi = LogPhi[i, j];
if (phi > 0)
{
TopicScores[j] += phi * doc.GetCount(i);
double a1 = Lambda[j];
int ww = doc.GetWord(i);
double a2 = model.LogBeta[j, doc.GetWord(i)];
var ss = doc.GetCount(i) * phi *
((Lambda[j] + model.LogBeta[j, doc.GetWord(i)]) - logPhi);
lHood += ss;
}
}
}
LHood = lHood;
}
