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Files in /eval, /extra, & /learn - comments translated from Japanese to English
This commit is contained in:
FireFather
2020-06-28 03:12:55 +02:00
parent 2f8c692caa
commit aea08de018
54 changed files with 1903 additions and 1905 deletions
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src/eval/nnue/trainer/features/factorizer.h
+14 -14
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@@ -1,4 +1,4 @@
// NNUE評価関数の特徴量変換クラステンプレート
// NNUE evaluation function feature conversion class template
#ifndef _NNUE_TRAINER_FEATURES_FACTORIZER_H_
#define _NNUE_TRAINER_FEATURES_FACTORIZER_H_
@@ -14,31 +14,31 @@ namespace NNUE {
namespace Features {
// 入力特徴量を学習用特徴量に変換するクラステンプレート
// デフォルトでは学習用特徴量は元の入力特徴量と同じとし、必要に応じて特殊化する
// Class template that converts input features into learning features
// By default, the learning feature is the same as the original input feature, and specialized as necessary
template <typename FeatureType>
class Factorizer {
public:
// 学習用特徴量の次元数を取得する
// Get the dimensionality of the learning feature
static constexpr IndexType GetDimensions() {
return FeatureType::kDimensions;
}
// 学習用特徴量のインデックスと学習率のスケールを取得する
// Get index of learning feature and scale of learning rate
static void AppendTrainingFeatures(
IndexType base_index, std::vector<TrainingFeature>* training_features) {
assert(base_index < FeatureType::kDimensions);
assert(base_index <FeatureType::kDimensions);
training_features->emplace_back(base_index);
}
};
// 学習用特徴量の情報
// Learning feature information
struct FeatureProperties {
bool active;
IndexType dimensions;
};
// 元の入力特徴量を学習用特徴量に追加する
// Add the original input features to the learning features
template <typename FeatureType>
IndexType AppendBaseFeature(
FeatureProperties properties, IndexType base_index,
@@ -49,7 +49,7 @@ IndexType AppendBaseFeature(
return properties.dimensions;
}
// 学習率のスケールが0でなければ他の種類の学習用特徴量を引き継ぐ
// If the learning rate scale is not 0, inherit other types of learning features
template <typename FeatureType>
IndexType InheritFeaturesIfRequired(
IndexType index_offset, FeatureProperties properties, IndexType base_index,
@@ -70,8 +70,8 @@ IndexType InheritFeaturesIfRequired(
return properties.dimensions;
}
// 学習用特徴量を追加せず、必要に応じてインデックスの差分を返す
// 対応する特徴量がない場合にInheritFeaturesIfRequired()の代わりに呼ぶ
// Return the index difference as needed, without adding learning features
// Call instead of InheritFeaturesIfRequired() if there are no corresponding features
IndexType SkipFeatures(FeatureProperties properties) {
if (!properties.active) {
return 0;
@@ -79,7 +79,7 @@ IndexType SkipFeatures(FeatureProperties properties) {
return properties.dimensions;
}
// 学習用特徴量の次元数を取得する
// Get the dimensionality of the learning feature
template <std::size_t N>
constexpr IndexType GetActiveDimensions(
const FeatureProperties (&properties)[N]) {
@@ -93,7 +93,7 @@ constexpr IndexType GetActiveDimensions(
return dimensions;
}
// 配列の要素数を取得する
// get the number of elements in the array
template <typename T, std::size_t N>
constexpr std::size_t GetArrayLength(const T (&/*array*/)[N]) {
return N;
@@ -107,4 +107,4 @@ constexpr std::size_t GetArrayLength(const T (&/*array*/)[N]) {
#endif // defined(EVAL_NNUE)
#endif
#endif