WDLConverter.hpp

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#pragma once

 
#include <algorithm>
#include <cmath>

class WDLConverter {
public:
  struct WDL {
    float win, draw, loss;
  };
 
  // Score ranges
  static constexpr int MAX_CP = 15000;
 
  // Lichess constant: 1 / 0.00368208 (exact, from WDLNormalizer.hpp)
  static constexpr float WDL_SCALE = 1.0f / 0.00368208f;
 
  // Aggression parameter: -1.0 = defensive, 0 = neutral, +1.0 = aggressive
  float aggression = 0.0f;

  int convert(float win, float draw, float loss) const {
    return convertWDL(win, draw, loss);
  }
 
private:
  int convertWDL(float win, float draw, float loss) const {
    // Aggression adjusts how draws affect win/loss perception
    // Positive aggression: draws feel like partial losses (fight for wins)
    // Negative aggression: draws feel like partial wins (play safe)
    float draw_to_win = 0.5f - aggression * 0.3f;
    draw_to_win = std::clamp(draw_to_win, 0.0f, 1.0f);
 
    // Adjust win/loss with draw allocation
    float win_adj = win + draw * draw_to_win;
    float loss_adj = loss + draw * (1.0f - draw_to_win);
 
    // The key ratio - this extracts the original sigmoid value
    // regardless of the 0.3 factor used in training
    float total = win_adj + loss_adj;
    if (total < 1e-6f) {
      return 0; // Draw-only position
    }
    float win_ratio = win_adj / total;
 
    // Clamp to avoid log(0) or log(inf)
    win_ratio = std::clamp(win_ratio, 1e-6f, 1.0f - 1e-6f);
 
    // Inverse sigmoid: cp = SCALE * ln(win_ratio / (1 - win_ratio))
    int cp =
        static_cast<int>(WDL_SCALE * std::log(win_ratio / (1.0f - win_ratio)));
 
    // Clamp to non-mate range
    return std::clamp(cp, -MAX_CP, MAX_CP);
  }
};