AbstractModel::SufficientStatisticsVector
MultivariateModel
::GetSufficientStatistics(const Realizations &R, const Observations& Obs)
{
/// Computation of the suffisient statistics of the model
/// Basically, it is a vector (named S1 to S9) of vectors
/// S1 <- y_ij * eta_ij & S2 <- eta_ij * eta_ij
VectorType S1(m_NbTotalOfObservations), S2(m_NbTotalOfObservations);
auto itS1 = S1.begin(), itS2 = S2.begin();
for(size_t i = 0; i < m_NumberOfSubjects; ++i)
{
for(size_t j = 0; j < Obs.GetNumberOfTimePoints(i); ++j)
{
VectorType PC = ComputeParallelCurve(i, j);
*itS1 = dot_product(PC, Obs.GetSubjectCognitiveScore(i, j));
*itS2 = PC.squared_magnitude();
++itS1, ++itS2;
}
}
/// S3 <- Ksi_i & S4 <- Ksi_i * Ksi_i
VectorType S3 = R.at("Ksi");
VectorType S4 = R.at("Ksi") % R.at("Ksi");
/// S5 <- Tau_i & S6 <- Tau_i * Tau_i
VectorType S5 = R.at("Tau");
VectorType S6 = R.at("Tau") % R.at("Tau");
/// S7 <- G
VectorType S7(1, R.at("G", 0));
/// S8 <- beta_k
VectorType S8((m_ManifoldDimension-1) * m_NbIndependentSources);
ScalarType * itS8 = S8.memptr();
for(size_t i = 0; i < S8.size(); ++i)
itS8[i] = R.at("Beta#" + std::to_string(i), 0);
/// S8 <- delta_k
VectorType S9(m_ManifoldDimension - 1);
ScalarType * itS9 = S9.memptr();
for(size_t i = 1; i < S9.size(); ++i)
itS9[i] = R.at("Delta#" + std::to_string(i), 0);
return {S1, S2, S3, S4, S5, S6, S7, S8, S9};
}
