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authorAndré Nusser <andre.nusser@googlemail.com>2019-03-16 17:30:08 +0100
committerAndré Nusser <andre.nusser@googlemail.com>2019-05-11 14:54:51 +0200
commit50b011c4740a5ec5338903b1d8b5fbb4b42f3df3 (patch)
tree3dcbe828b627c34cdba06818ecdd109160f5eeff /src/sample_selection.cc
parent61f443f24ce9f9a99d78cea70a53654716d1f8fb (diff)
Variable renaming.
Diffstat (limited to 'src/sample_selection.cc')
-rw-r--r--src/sample_selection.cc16
1 files changed, 8 insertions, 8 deletions
diff --git a/src/sample_selection.cc b/src/sample_selection.cc
index 012888d..6e956df 100644
--- a/src/sample_selection.cc
+++ b/src/sample_selection.cc
@@ -176,17 +176,17 @@ const Sample* SampleSelection::getObjective(level_t level, std::size_t pos)
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);
- float alpha = 2.0;
- float beta = 1.0;
- float gamma = .05;
+ const float f_distance = 2.0;
+ const float f_recent = 1.0;
+ const float f_random = .05;
// 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(), 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() ? alpha*pow2(samples[up_index].power-lvl) : std::numeric_limits<float>::max());
- float down_value_lb = (up_index != 0 ? alpha*pow2(samples[down_index].power-lvl) : std::numeric_limits<float>::max());
+ float up_value_lb = (up_index < samples.size() ? f_distance*pow2(samples[up_index].power-lvl) : std::numeric_limits<float>::max());
+ float down_value_lb = (up_index != 0 ? f_distance*pow2(samples[down_index].power-lvl) : std::numeric_limits<float>::max());
std::size_t count = 0;
do
@@ -198,7 +198,7 @@ const Sample* SampleSelection::getObjective(level_t level, std::size_t pos)
if (up_index != samples.size()-1)
{
++up_index;
- up_value_lb = alpha*pow2(samples[up_index].power-lvl);
+ up_value_lb = f_distance*pow2(samples[up_index].power-lvl);
}
else
{
@@ -211,7 +211,7 @@ const Sample* SampleSelection::getObjective(level_t level, std::size_t pos)
if (down_index != 0)
{
--down_index;
- down_value_lb = alpha*pow2(samples[down_index].power-lvl);
+ down_value_lb = f_distance*pow2(samples[down_index].power-lvl);
}
else
{
@@ -222,7 +222,7 @@ const Sample* SampleSelection::getObjective(level_t level, std::size_t pos)
auto random = rand.floatInRange(0.,1.);
auto distance = samples[current_index].power - lvl;
auto recent = (float)settings.samplerate/std::max<std::size_t>(pos - last[current_index], 1);
- auto value = alpha*pow2(distance) + beta*pow2(recent) + gamma*random;
+ auto value = f_distance*pow2(distance) + f_recent*pow2(recent) + f_random*random;
if (value < value_opt)
{