// The coefficients of a third-order polynomial fit is based on the fishtest data
// for two parameters that need to transform eval to the argument of a logistic
// function.
- double as[] = { 0.50379905, -4.12755858, 18.95487051, 152.00733652};
- double bs[] = {-1.71790378, 10.71543602, -17.05515898, 41.15680404};
+ constexpr double as[] = { 1.04790516, -8.58534089, 39.42615625, 316.17524816};
+ constexpr double bs[] = { -3.57324784, 22.28816201, -35.47480551, 85.60617701 };
+
+ // Enforce that NormalizeToPawnValue corresponds to a 50% win rate at ply 64
+ static_assert(UCI::NormalizeToPawnValue == int(as[0] + as[1] + as[2] + as[3]));
+
double a = (((as[0] * m + as[1]) * m + as[2]) * m) + as[3];
double b = (((bs[0] * m + bs[1]) * m + bs[2]) * m) + bs[3];
// Transform the eval to centipawns with limited range
- double x = std::clamp(double(100 * v) / PawnValueEg, -2000.0, 2000.0);
+ double x = std::clamp(double(v), -4000.0, 4000.0);
// Return the win rate in per mille units rounded to the nearest value
return int(0.5 + 1000 / (1 + std::exp((a - x) / b)));
stringstream ss;
if (abs(v) < VALUE_MATE_IN_MAX_PLY)
- ss << "cp " << v * 100 / PawnValueEg;
+ ss << "cp " << v * 100 / NormalizeToPawnValue;
else
ss << "mate " << (v > 0 ? VALUE_MATE - v + 1 : -VALUE_MATE - v) / 2;