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/* -*- Mode: c++ -*- */
/***************************************************************************
* sample_selection.h
*
* Mon Mar 4 23:58:12 CET 2019
* Copyright 2019 André Nusser
* andre.nusser@googlemail.com
****************************************************************************/
/*
* This file is part of DrumGizmo.
*
* DrumGizmo is free software; you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* DrumGizmo is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with DrumGizmo; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA.
*/
#include "sample_selection.h"
#include <hugin.hpp>
#include "powerlist.h"
#include "random.h"
#include "settings.h"
#include <algorithm>
namespace
{
float pow2(float f)
{
return f*f;
}
} // end anonymous namespace
SampleSelection::SampleSelection(Settings& settings, Random& rand, const PowerList& powerlist)
: settings(settings), rand(rand), powerlist(powerlist)
{
}
void SampleSelection::finalise()
{
last.assign(powerlist.getPowerListItems().size(), 0);
}
// FIXME: For the position, weird hacks via the powerlist are necessary. Refactor!
// FIXME: bad variable naming
const Sample* SampleSelection::get(level_t level, float position, float openness, std::size_t pos)
{
const auto& samples = powerlist.getPowerListItems();
if(!samples.size())
{
return nullptr; // No samples to choose from.
}
std::size_t index_opt = 0;
float power_opt{0.f};
float pos_opt{0.f};
float openness_opt{0.f};
float value_opt{std::numeric_limits<float>::max()};
// the following three values are mostly for debugging
float close_opt = 0.;
float closepos_opt = 0.;
float random_opt = 0.;
float diverse_opt = 0.;
// Note the magic values in front of the settings factors.
const float f_close = 4.*settings.sample_selection_f_close.load();
const float f_position = 1000.*settings.sample_selection_f_position.load(); // FIXME: huge factor for now
const float f_openness = 1000.*settings.sample_selection_f_openness.load(); // FIXME: huge factor for now
const float f_diverse = (1./2.)*settings.sample_selection_f_diverse.load();
const float f_random = (1./3.)*settings.sample_selection_f_random.load();
float power_range = powerlist.getMaxPower() - powerlist.getMinPower();
// If all power values are the same then power_range is invalid but we want
// to avoid division by zero.
if (power_range == 0.) { power_range = 1.0; }
// start with most promising power value and then stop when reaching far values
// which cannot become opt anymore
auto closest_it = std::lower_bound(samples.begin(), samples.end(), level);
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;
if (up_index < samples.size()) {
auto const close_up = (samples[up_index].power-level)/power_range;
up_value_lb = f_close*pow2(close_up);
}
else {
--up_index;
up_value_lb = std::numeric_limits<float>::max();
}
auto const close_down = (samples[down_index].power-level)/power_range;
float down_value_lb = (up_index != 0 ? f_close*pow2(close_down) : std::numeric_limits<float>::max());
std::size_t count = 0;
do
{
DEBUG(rand, "%d %d", (int)up_index, (int)down_index);
assert(down_index <= up_index);
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-level)/power_range);
}
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-level)/power_range);
}
else
{
down_value_lb = std::numeric_limits<float>::max();
}
}
auto random = rand.floatInRange(0.,1.);
auto close = (samples[current_index].power - level)/power_range;
auto diverse = 1./(1. + (float)(pos - last[current_index])/settings.samplerate);
auto closepos = samples[current_index].sample->getPosition() - position;
auto closeopenness = samples[current_index].sample->getOpenness() - openness;
// note that the value below for close and closepos is actually the weighted squared l2 distance in 2d
auto value =
f_close*pow2(close)
+ f_position*pow2(closepos)
+ f_openness*pow2(closeopenness)
+ f_diverse*diverse
+ f_random*random;
if (value < value_opt)
{
index_opt = current_index;
power_opt = samples[current_index].power;
pos_opt = samples[current_index].sample->getPosition();
openness_opt = samples[current_index].sample->getOpenness();
value_opt = value;
random_opt = random;
close_opt = close;
diverse_opt = diverse;
closepos_opt = closepos;
}
++count;
}
while (up_value_lb <= value_opt || down_value_lb <= value_opt);
DEBUG(rand, "Chose sample with index: %d, value: %f, power: %f, position: %f, openness: %f, random: %f, close: %f, diverse: %f, closepos: %f, count: %d", (int)index_opt, value_opt, power_opt, pos_opt, openness_opt, random_opt, close_opt, diverse_opt, closepos_opt, (int)count);
last[index_opt] = pos;
return samples[index_opt].sample;
}
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