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#include "sample_selection.h"
#include <hugin.hpp>
#include "powerlist.h"
#include "random.h"
#include "settings.h"
#include <algorithm>
namespace
{
std::size_t const MIN_SAMPLE_SET_SIZE = 26u;
float pow2(float f)
{
return f*f;
}
}
SampleSelection::SampleSelection(Settings& settings, Random& rand, const PowerList& powerlist)
: settings(settings), rand(rand), powerlist(powerlist), alg(SelectionAlg::Objective)
{
}
void SampleSelection::setSelectionAlg(SelectionAlg alg)
{
this->alg = alg;
}
void SampleSelection::finalise()
{
last.assign(powerlist.getPowerListItems().size(), 0);
}
const Sample* SampleSelection::get(level_t level, std::size_t pos)
{
switch (alg)
{
case SelectionAlg::Objective:
return getObjective(level, pos);
break;
case SelectionAlg::Old:
default:
return getOld(level, pos);
}
}
const Sample* SampleSelection::getOld(level_t level, std::size_t pos)
{
auto velocity_stddev = settings.velocity_stddev.load();
const auto& samples = powerlist.getPowerListItems();
if(!samples.size())
{
return nullptr;
}
int retry = settings.sample_selection_retry_count.load();
Sample* sample{nullptr};
auto power_max = powerlist.getMaxPower();
auto power_min = powerlist.getMinPower();
float power_span = power_max - power_min;
float width = std::max(samples.size(), MIN_SAMPLE_SET_SIZE);
float mean_stepwidth = power_span / width;
float mean = level * (power_span - mean_stepwidth) + (mean_stepwidth / 2.0);
float stddev = velocity_stddev * mean_stepwidth;
std::size_t index{0};
float power{0.f};
do
{
--retry;
float lvl = rand.normalDistribution(mean, stddev);
lvl += power_min;
DEBUG(rand,
"level: %f, lvl: %f (mean: %.2f, stddev: %.2f, mean_stepwidth: %f, power_min: %f, power_max: %f)\n",
level, lvl, mean, stddev, mean_stepwidth, power_min, power_max);
for (std::size_t i = 0; i < samples.size(); ++i)
{
auto const& item = samples[i];
if (sample == nullptr || std::fabs(item.power - lvl) < std::fabs(power - lvl))
{
sample = item.sample;
index = i;
power = item.power;
}
}
} while (lastsample == sample && retry >= 0);
DEBUG(rand, "Chose sample with index: %d, power %f", (int)index, power);
lastsample = sample;
return sample;
}
const Sample* SampleSelection::getObjective(level_t level, std::size_t pos)
{
const auto& samples = powerlist.getPowerListItems();
if(!samples.size())
{
return nullptr;
}
auto power_max = powerlist.getMaxPower();
auto power_min = powerlist.getMinPower();
float power_span = power_max - power_min;
float mean = level - .5f/127.f;
float stddev = settings.enable_velocity_modifier.load() ?
settings.velocity_stddev.load()/127.0f : 0.;
float lvl = power_min + rand.normalDistribution(mean, stddev)*power_span;
std::size_t index_opt = 0;
float power_opt{0.f};
float value_opt{std::numeric_limits<float>::max()};
float random_opt = 0.;
float close_opt = 0.;
float diverse_opt = 0.;
DEBUG(rand, "level: %f, lvl: %f (mean: %.2f, stddev: %.2f,"
"power_min: %f, power_max: %f)\n", level, lvl, mean, stddev, power_min, power_max);
const float f_close = settings.sample_selection_f_close.load();
const float f_diverse = settings.sample_selection_f_diverse.load();
const float f_random = settings.sample_selection_f_random.load();
auto closest_it = std::lower_bound(samples.begin(), samples.end(), lvl);
std::size_t up_index = std::distance(samples.begin(), closest_it);
std::size_t down_index = (up_index == 0 ? 0 : up_index - 1);
float up_value_lb = (up_index < samples.size() ? f_close*pow2(samples[up_index].power-lvl) : std::numeric_limits<float>::max());
float down_value_lb = (up_index != 0 ? f_close*pow2(samples[down_index].power-lvl) : std::numeric_limits<float>::max());
std::size_t count = 0;
do
{
std::size_t current_index;
if (up_value_lb < down_value_lb)
{
current_index = up_index;
if (up_index != samples.size()-1)
{
++up_index;
up_value_lb = f_close*pow2(samples[up_index].power-lvl);
}
else
{
up_value_lb = std::numeric_limits<float>::max();
}
}
else
{
current_index = down_index;
if (down_index != 0)
{
--down_index;
down_value_lb = f_close*pow2(samples[down_index].power-lvl);
}
else
{
down_value_lb = std::numeric_limits<float>::max();
}
}
auto random = rand.floatInRange(0.,1.);
auto close = samples[current_index].power - lvl;
auto diverse = (float)settings.samplerate/std::max<std::size_t>(pos - last[current_index], 1);
auto value = f_close*pow2(close) + f_diverse*pow2(diverse) + f_random*random;
if (value < value_opt)
{
index_opt = current_index;
power_opt = samples[current_index].power;
value_opt = value;
random_opt = random;
close_opt = close;
diverse_opt = diverse;
}
++count;
}
while (up_value_lb <= value_opt || down_value_lb <= value_opt);
DEBUG(rand, "Chose sample with index: %d, value: %f, power %f, random: %f, close: %f, diverse: %f, count: %d", (int)index_opt, value_opt, power_opt, random_opt, close_opt, diverse_opt, (int)count);
last[index_opt] = pos;
return samples[index_opt].sample;
}
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