uint64_t num, nodes = 0, cnt = 1;
vector<string> list = setup_bench(pos, args);
- num = count_if(list.begin(), list.end(), [](string s) { return s.find("go ") == 0 || s.find("eval") == 0; });
+ num = count_if(list.begin(), list.end(), [](const string& s) { return s.find("go ") == 0 || s.find("eval") == 0; });
TimePoint elapsed = now();
// 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.
- constexpr double as[] = { -0.58270499, 2.68512549, 15.24638015, 344.49745382};
- constexpr double bs[] = { -2.65734562, 15.96509799, -20.69040836, 73.61029937 };
+ constexpr double as[] = { 0.33677609, -4.30175627, 33.08810557, 365.60223431};
+ constexpr double bs[] = { -2.50471102, 14.23235405, -14.33066859, 71.42705250 };
// 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]));
stringstream ss;
- if (abs(v) < VALUE_MATE_IN_MAX_PLY)
+ if (abs(v) < VALUE_TB_WIN_IN_MAX_PLY)
ss << "cp " << v * 100 / NormalizeToPawnValue;
+ else if (abs(v) < VALUE_MATE_IN_MAX_PLY)
+ {
+ const int ply = VALUE_MATE_IN_MAX_PLY - 1 - std::abs(v); // recompute ss->ply
+ ss << "cp " << (v > 0 ? 20000 - ply : -20000 + ply);
+ }
else
ss << "mate " << (v > 0 ? VALUE_MATE - v + 1 : -VALUE_MATE - v) / 2;