/**
* Conditional maximum likelihood estimate of the variance scaler:
* sigma = [{sum_y (w_y' V^{-1} w_y)/ W_y^2} / sum_y (B_y-1) ]^0.5
*
* Conditional on the covariance matrix V and the weights W_y
**/
void logistic_normal::compute_mle_sigma(const dvar3_array& dCor)
{
int i;
dvariable SS = 0.0;
double wt2;
for( i = m_y1; i <= m_y2; i++ )
{
wt2 = m_dWy(i) * m_dWy(i);
SS += ( m_w(i) * inv(dCor(i)) * m_w(i) ) / wt2;
}
m_sig2 = SS/sum(m_nB2-m_b1);
m_sig = pow(m_sig2,0.5);
}
void Booster::eval(Sample& sample, Result& result) {
for (int nBase = 0; nBase < m_hp->numBases; nBase++) {
Result baseResult(*m_numClasses);
m_bases[nBase]->eval(sample, baseResult);
baseResult.confidence *= m_w(nBase);
result.confidence += baseResult.confidence;
}
result.confidence.maxCoeff(&result.prediction);
}
/**
* Compute negative loglikelihood:
* nll = t1 + t2 + t3 + t4 + t5 + t6; where:
* bym1 = sum_y(B_y-1);
* t1 = 0.5*log(2pi) * bym1;
* t2 = sum_{by} log(O_{by});
* t3 = log(sigma) * bym1;
* t4 = 0.5*sum_y log(det(V_y))
* t5 = sum_y (By-1)*log(W_y)
* t6 = 0.5*sigma^{-2}* sum_y (w'_y V_y^{-1} w_y)/W_y^2
**/
void logistic_normal::compute_negative_loglikelihood()
{
m_nll.initialize();
double bym1 = sum( dvector(m_nB2-m_b1) - 1.);
double t1 = 0.5 * log(2. * PI) * bym1;
double t2 = sum( log(m_Op) );
dvariable t3 = log(m_sig) * bym1;
dvariable t4 = 0;
dvariable t5 = 0;
dvariable t6 = 0;
for(int i = m_y1; i <= m_y2; i++ )
{
dvar_matrix Vinv = inv(m_V(i));
t4 += 0.5*log( det(m_V(i)) );
t5 += (m_nB2(i)-m_b1-1) * log(m_dWy(i));
t6 += (0.5/(m_dWy(i)*m_dWy(i))) * m_w(i) * Vinv * m_w(i);
}
m_nll = t1 + t2 + t3 + t4 + t5 + t6;
}
/**
* Compute residuals between observed and expected proportions for likleihoods & residuals
**/
void logistic_normal::compute_residual_arrays()
{
m_w.allocate(m_y1,m_y2,m_b1,m_nB2-1);
// m_s.allocate(m_y1,m_y2,1,m_nB2);
m_w.initialize();
// m_s.initialize();
int i;
for( i = m_y1; i <= m_y2; i++ )
{
// matrix of log_residuals for likelihood.
int nB = m_nB2(i);
m_w(i) = ( log(m_Op(i)(m_b1,nB-1)) - log(m_Op(i,nB)) )
-( log(m_Ep(i)(m_b1,nB-1)) - log(m_Ep(i,nB)) );
}
}
