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Merge remote-tracking branch 'remotes/nodchip/master' into trainer

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noobpwnftw
2020-09-19 15:41:30 +08:00
parent 26f63fe741 61bc8d12d3
commit 411adab149
57 changed files with 9136 additions and 1469 deletions
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91
src/uci.cpp
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@@ -33,6 +33,10 @@
#include "tt.h"
#include "uci.h"
#include "learn/gensfen.h"
#include "learn/learn.h"
#include "learn/convert.h"
using namespace std;
extern vector<string> setup_bench(const Position&, istream&);
@@ -40,25 +44,6 @@ extern vector<string> setup_bench(const Position&, istream&);
// FEN string of the initial position, normal chess
const char* StartFEN = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1";
// Command to automatically generate a game record
#if defined (EVAL_LEARN)
namespace Learner
{
// Automatic generation of teacher position
void gen_sfen(Position& pos, istringstream& is);
// Learning from the generated game record
void learn(Position& pos, istringstream& is);
// A pair of reader and evaluation value. Returned by Learner::search(),Learner::qsearch().
typedef std::pair<Value, std::vector<Move> > ValueAndPV;
ValueAndPV qsearch(Position& pos);
ValueAndPV search(Position& pos, int depth_, size_t multiPV = 1, uint64_t nodesLimit = 0);
}
#endif
void test_cmd(Position& pos, istringstream& is)
{
// Initialize as it may be searched.
@@ -70,7 +55,7 @@ void test_cmd(Position& pos, istringstream& is)
if (param == "nnue") Eval::NNUE::TestCommand(pos, is);
}
namespace UCI {
namespace {
// position() is called when engine receives the "position" UCI command.
// The function sets up the position described in the given FEN string ("fen")
@@ -225,42 +210,41 @@ namespace UCI {
<< "\nNodes/second : " << 1000 * nodes / elapsed << endl;
}
// The win rate model returns the probability (per mille) of winning given an eval
// and a game-ply. The model fits rather accurately the LTC fishtest statistics.
int win_rate_model(Value v, int ply) {
// Return win rate in per mille (rounded to nearest)
return int(0.5 + win_rate_model_double(v, ply));
}
// The win rate model returns the probability (per mille) of winning given an eval
// and a game-ply. The model fits rather accurately the LTC fishtest statistics.
double win_rate_model_double(double v, int ply) {
// The model captures only up to 240 plies, so limit input (and rescale)
double m = std::min(240, ply) / 64.0;
// Coefficients of a 3rd order polynomial fit based on fishtest data
// for two parameters needed to transform eval to the argument of a
// logistic function.
double as[] = {-8.24404295, 64.23892342, -95.73056462, 153.86478679};
double bs[] = {-3.37154371, 28.44489198, -56.67657741, 72.05858751};
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 eval to centipawns with limited range
double x = std::clamp(double(100 * v) / PawnValueEg, -1000.0, 1000.0);
// Return win rate in per mille
return 1000.0 / (1 + std::exp((a - x) / b));
}
} // namespace
// The win rate model returns the probability (per mille) of winning given an eval
// and a game-ply. The model fits rather accurately the LTC fishtest statistics.
int UCI::win_rate_model(Value v, int ply) {
// Return win rate in per mille (rounded to nearest)
return int(0.5 + win_rate_model_double(v, ply));
}
// The win rate model returns the probability (per mille) of winning given an eval
// and a game-ply. The model fits rather accurately the LTC fishtest statistics.
double UCI::win_rate_model_double(double v, int ply) {
// The model captures only up to 240 plies, so limit input (and rescale)
double m = std::min(240, ply) / 64.0;
// Coefficients of a 3rd order polynomial fit based on fishtest data
// for two parameters needed to transform eval to the argument of a
// logistic function.
double as[] = {-8.24404295, 64.23892342, -95.73056462, 153.86478679};
double bs[] = {-3.37154371, 28.44489198, -56.67657741, 72.05858751};
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 eval to centipawns with limited range
double x = std::clamp(double(100 * v) / PawnValueEg, -1000.0, 1000.0);
// Return win rate in per mille
return 1000.0 / (1 + std::exp((a - x) / b));
}
// --------------------
// Call qsearch(),search() directly for testing
// --------------------
#if defined(EVAL_LEARN)
void qsearch_cmd(Position& pos)
{
cout << "qsearch : ";
@@ -292,8 +276,6 @@ void search_cmd(Position& pos, istringstream& is)
cout << endl;
}
#endif
/// UCI::loop() waits for a command from stdin, parses it and calls the appropriate
/// function. Also intercepts EOF from stdin to ensure gracefully exiting if the
/// GUI dies unexpectedly. When called with some command line arguments, e.g. to
@@ -349,16 +331,15 @@ void UCI::loop(int argc, char* argv[]) {
else if (token == "d") sync_cout << pos << sync_endl;
else if (token == "eval") trace_eval(pos);
else if (token == "compiler") sync_cout << compiler_info() << sync_endl;
#if defined (EVAL_LEARN)
else if (token == "gensfen") Learner::gen_sfen(pos, is);
else if (token == "learn") Learner::learn(pos, is);
else if (token == "convert") Learner::convert(is);
// Command to call qsearch(),search() directly for testing
else if (token == "qsearch") qsearch_cmd(pos);
else if (token == "search") search_cmd(pos, is);
#endif
// test command
else if (token == "test") test_cmd(pos, is);
else