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PascalCase -> snake_case for consistency with the rest of the codebase.

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This commit is contained in:
Tomasz Sobczyk
2020-10-14 22:42:58 +02:00
committed by nodchip
parent 2398d34e87
commit 146a6b056e
37 changed files with 844 additions and 737 deletions
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22
src/nnue/trainer/features/factorizer.h
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@@ -14,12 +14,12 @@ namespace Eval::NNUE::Features {
class Factorizer {
public:
// Get the dimensionality of the learning feature
static constexpr IndexType GetDimensions() {
static constexpr IndexType get_dimensions() {
return FeatureType::kDimensions;
}
// Get index of learning feature and scale of learning rate
static void AppendTrainingFeatures(
static void append_training_features(
IndexType base_index, std::vector<TrainingFeature>* training_features) {
assert(base_index <FeatureType::kDimensions);
@@ -35,7 +35,7 @@ namespace Eval::NNUE::Features {
// Add the original input features to the learning features
template <typename FeatureType>
IndexType AppendBaseFeature(
IndexType append_base_feature(
FeatureProperties properties, IndexType base_index,
std::vector<TrainingFeature>* training_features) {
@@ -47,7 +47,7 @@ namespace Eval::NNUE::Features {
// If the learning rate scale is not 0, inherit other types of learning features
template <typename FeatureType>
IndexType InheritFeaturesIfRequired(
IndexType inherit_features_if_required(
IndexType index_offset, FeatureProperties properties, IndexType base_index,
std::vector<TrainingFeature>* training_features) {
@@ -55,17 +55,17 @@ namespace Eval::NNUE::Features {
return 0;
}
assert(properties.dimensions == Factorizer<FeatureType>::GetDimensions());
assert(properties.dimensions == Factorizer<FeatureType>::get_dimensions());
assert(base_index < FeatureType::kDimensions);
const auto start = training_features->size();
Factorizer<FeatureType>::AppendTrainingFeatures(
Factorizer<FeatureType>::append_training_features(
base_index, training_features);
for (auto i = start; i < training_features->size(); ++i) {
auto& feature = (*training_features)[i];
assert(feature.GetIndex() < Factorizer<FeatureType>::GetDimensions());
feature.ShiftIndex(index_offset);
assert(feature.get_index() < Factorizer<FeatureType>::get_dimensions());
feature.shift_index(index_offset);
}
return properties.dimensions;
@@ -73,7 +73,7 @@ namespace Eval::NNUE::Features {
// Return the index difference as needed, without adding learning features
// Call instead of InheritFeaturesIfRequired() if there are no corresponding features
IndexType SkipFeatures(FeatureProperties properties) {
IndexType skip_features(FeatureProperties properties) {
if (!properties.active)
return 0;
@@ -82,7 +82,7 @@ namespace Eval::NNUE::Features {
// Get the dimensionality of the learning feature
template <std::size_t N>
constexpr IndexType GetActiveDimensions(
constexpr IndexType get_active_dimensions(
const FeatureProperties (&properties)[N]) {
static_assert(N > 0, "");
@@ -100,7 +100,7 @@ namespace Eval::NNUE::Features {
// get the number of elements in the array
template <typename T, std::size_t N>
constexpr std::size_t GetArrayLength(const T (&/*array*/)[N]) {
constexpr std::size_t get_array_length(const T (&/*array*/)[N]) {
return N;
}