Disposable<Array> PenaltyFunction<Curve>::values(const Array& x) const {
Array::const_iterator guessIt = x.begin();
Size i = initialIndex_;
while (guessIt != x.end()) {
Traits::updateGuess(curve_->data_, *guessIt, i);
++guessIt;
++i;
}
curve_->interpolation_.update();
Array penalties(localisation_);
helper_iterator instIt = rateHelpersStart_;
Array::iterator penIt = penalties.begin();
while (instIt != rateHelpersEnd_) {
Real quoteError = (*instIt)->quoteError();
*penIt = std::fabs(quoteError);
++instIt;
++penIt;
}
return penalties;
}
GuidedLocalSearch::Candidate GuidedLocalSearch::search(const std::vector<std::pair<float, float>>& cities,
const int kIterLimit,
const int kNoImproveLimit,
const float kLambda)
{
srand(static_cast<unsigned>(time(nullptr)));
std::vector<std::vector<float>> penalties(cities.size(), std::vector<float>(cities.size(), 0.0f));
GuidedLocalSearch::Candidate current, best;
current.permutation = randomPermutation(cities);
for (int iter = 0; iter < kIterLimit; ++iter)
{
localSearch(current, cities, penalties, kNoImproveLimit, kLambda);
std::vector<float> utilities = calcualateFeaturesUtilities(cities, current.permutation, penalties);
updatePenalties(penalties, cities, current.permutation, utilities);
if (!iter || current.ordinaryCost < best.ordinaryCost)
{
best.permutation = current.permutation;
best.ordinaryCost = current.ordinaryCost;
best.augmentedCost = current.augmentedCost;
}
}
return best;
}
int calc_noise(
const lame_internal_flags * const gfc,
const int ix [576],
const gr_info * const cod_info,
const III_psy_xmin * const l3_xmin,
const III_scalefac_t * const scalefac,
III_psy_xmin * xfsf,
calc_noise_result * const res )
{
int sfb, l, i, over=0;
FLOAT8 over_noise_db = 0;
FLOAT8 tot_noise_db = 0; /* 0 dB relative to masking */
FLOAT8 max_noise = 1E-20; /* -200 dB relative to masking */
double klemm_noise = 1E-37;
int j = 0;
for (sfb = 0; sfb < cod_info->psy_lmax; sfb++) {
int s =
cod_info->global_gain
- ((scalefac->l[sfb] + (cod_info->preflag ? pretab[sfb] : 0))
<< (cod_info->scalefac_scale + 1));
FLOAT8 step;
FLOAT8 noise = 0.0;
if (s<0) {
step = pow(2.0, (double)(s - 210) * 0.25);
}else{
/* use table lookup. allegedly faster */
step = POW20(s);
}
l = gfc->scalefac_band.l[sfb+1] - gfc->scalefac_band.l[sfb];
do {
FLOAT8 temp = fabs(cod_info->xr[j]) - pow43[ix[j]] * step;
noise += temp * temp;
j++;
} while (--l > 0);
noise = xfsf->l[sfb] = noise / l3_xmin->l[sfb];
max_noise=Max(max_noise,noise);
klemm_noise += penalties (noise);
noise = FAST_LOG10(Max(noise,1E-20));
/* multiplying here is adding in dB, but can overflow */
//tot_noise *= Max(noise, 1E-20);
tot_noise_db += noise;
if (noise > 0.0) {
over++;
/* multiplying here is adding in dB -but can overflow */
//over_noise *= noise;
over_noise_db += noise;
}
}
for (sfb = cod_info->sfb_smin; sfb < cod_info->psy_smax; sfb++) {
int width = gfc->scalefac_band.s[sfb+1] - gfc->scalefac_band.s[sfb];
for ( i = 0; i < 3; i++ ) {
int s =
cod_info->global_gain
- (scalefac->s[sfb][i] << (cod_info->scalefac_scale + 1))
- cod_info->subblock_gain[i] * 8;
FLOAT8 step = POW20(s);
FLOAT8 noise = 0.0;
l = width;
do {
FLOAT8 temp;
temp = pow43[ix[j]] * step - fabs(cod_info->xr[j]);
noise += temp * temp;
j++;
} while (--l > 0);
noise = xfsf->s[sfb][i] = noise / l3_xmin->s[sfb][i];
max_noise = Max(max_noise,noise);
klemm_noise += penalties (noise);
noise = FAST_LOG10(Max(noise,1E-20));
tot_noise_db += noise;
if (noise > 0.0) {
over++;
over_noise_db += noise;
}
}
}
res->over_count = over;
res->tot_noise = 10.*tot_noise_db;
res->over_noise = 10.*over_noise_db;
res->max_noise = 10.*FAST_LOG10(max_noise);
res->klemm_noise = klemm_noise;
return over;
}
