void CrossHypotheses::InitializeDerivedQualities() {
InitializeResponsibility(); // depends on hypotheses
InitializeTmpVariables();
// in theory don't need to compute any but test flows
ComputeBasicResiduals(); // predicted and measured
InitializeSigma(); // depends on predicted
ComputeBasicLikelihoods(); // depends on residuals and sigma
// compute log-likelihoods
ComputeLogLikelihoods(); // depends on test flow(s)
}
void CrossHypotheses::InitializeDerivedQualities() {
InitializeResponsibility(); // depends on hypotheses
// in theory don't need to compute any but test flows
SetModPredictions(); // make sure that mod-predictions=predictions
ComputeBasicResiduals(); // predicted and measured
InitializeSigma(); // depends on predicted
my_t.SetV(heavy_tailed);
ComputeBasicLikelihoods(); // depends on residuals and sigma
// compute log-likelihoods
ComputeLogLikelihoods(); // depends on test flow(s)
}
// responsibility depends on the relative global probability of the hypotheses and the likelihoods of the observations under each hypothesis
// divide the global probabilities into "typical" data points and outliers
// divide the variant probabilities into each hypothesis (summing to 1)
// treat the 2 hypothesis case to start with
void CrossHypotheses::UpdateResponsibility(const vector<float > &hyp_prob, float typical_prob) {
if (!success) {
//cout << "alert: fail to splice still called" << endl;
InitializeResponsibility();
} else {
// vector<double> tmp_prob(3);
tmp_prob_d[0] = (1.0f-typical_prob)*scaled_likelihood[0]; // i'm an outlier
for (unsigned int i_hyp=1; i_hyp<scaled_likelihood.size(); i_hyp++)
tmp_prob_d[i_hyp] = typical_prob * hyp_prob[i_hyp-1] * scaled_likelihood[i_hyp];
double ll_denom = 0.0;
for (unsigned int i_hyp=0; i_hyp<scaled_likelihood.size(); i_hyp++) {
ll_denom += tmp_prob_d[i_hyp];
}
for (unsigned int i_hyp=0; i_hyp<responsibility.size(); i_hyp++)
responsibility[i_hyp] = tmp_prob_d[i_hyp]/ll_denom;
}
}
