/** \param[in] model The model to score.
\param[in] model_data The corresponding ModelData.
\param[in] float_indices Indices of optimizable variables.
\param[in] x Current value of optimizable variables.
\param[out] dscore First derivatives for current state.
\return The model score.
*/
ConjugateGradients::NT ConjugateGradients::get_score(
Vector<FloatIndex> float_indices, Vector<NT> &x,
Vector<NT> &dscore) {
int i, opt_var_cnt = float_indices.size();
/* set model state */
for (i = 0; i < opt_var_cnt; i++) {
IMP_CHECK_VALUE(x[i]);
#ifdef IMP_CG_SCALE
double v = get_scaled_value(float_indices[i]); // scaled
#else
double v = get_value(float_indices[i]); // scaled
#endif
if (std::abs(x[i] - v) > max_change_) {
if (x[i] < v) {
x[i] = v - max_change_;
} else {
x[i] = v + max_change_;
}
}
#ifdef IMP_CG_SCALE
set_scaled_value(float_indices[i], x[i]);
#else
set_value(float_indices[i], x[i]);
#endif
}
NT score;
/* get score */
try {
score = get_scoring_function()->evaluate(true);
}
catch (ModelException) {
// if we took a bad step, just return a bad score
return std::numeric_limits<NT>::infinity();
}
/* get derivatives */
for (i = 0; i < opt_var_cnt; i++) {
#ifdef IMP_CG_SCALE
dscore[i] = get_scaled_derivative(float_indices[i]); // scaled
#else
dscore[i] = get_derivative(float_indices[i]); // scaled
#endif
IMP_USAGE_CHECK(is_good_value(dscore[i]), "Bad input to CG");
}
return score;
}
double GSLOptimizer::evaluate_derivative(const gsl_vector *v,
gsl_vector *df) {
/* set model state */
write_state(v);
/* get score */
double score= get_scoring_function()->evaluate(true);
best_score_=std::min(score, best_score_);
if (score < stop_score_) {
throw AllDone();
}
/* get derivatives */
{
for (unsigned int i=0; i< fis_.size(); ++i) {
double d= get_scaled_derivative(fis_[i]);
gsl_vector_set(df, i, d);
}
}
return score;
}
