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| author | André Nusser <andre.nusser@googlemail.com> | 2019-03-16 17:30:08 +0100 | 
|---|---|---|
| committer | André Nusser <andre.nusser@googlemail.com> | 2019-05-11 14:54:51 +0200 | 
| commit | 50b011c4740a5ec5338903b1d8b5fbb4b42f3df3 (patch) | |
| tree | 3dcbe828b627c34cdba06818ecdd109160f5eeff /src | |
| parent | 61f443f24ce9f9a99d78cea70a53654716d1f8fb (diff) | |
Variable renaming.
Diffstat (limited to 'src')
| -rw-r--r-- | src/sample_selection.cc | 16 | 
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)  		{ | 
