// subtracts totalEloError from expectedElo
public static double CalculateGoodness(
Player sideToMove,
EnumArray <GameLevel, AggregatedEntry> aggregatedEntries,
ChessDBCNScore score,
Options options
)
{
const double maxAllowedPlayerEloDiff = 400;
const double maxCalculatedEloDiff = 800;
bool useEval = options.EvalWeight > 0.0;
bool useGames = options.GamesWeight > 0.0;
AggregatedEntry totalEntry = new AggregatedEntry();
foreach (KeyValuePair <GameLevel, AggregatedEntry> e in aggregatedEntries)
{
totalEntry.Combine(e.Value);
}
if (useGames && totalEntry.Count == 0)
{
return(0.0);
}
if (useEval && score == null && Math.Abs(totalEntry.TotalEloDiff / (double)totalEntry.Count) > maxAllowedPlayerEloDiff)
{
return(0.0);
}
double gamesWeight = options.GamesWeight;
double evalWeight = options.EvalWeight;
double calculateAdjustedPerf(AggregatedEntry e)
{
if (e.Count > 0)
{
ulong? lowNThreshold = options.IncreaseErrorBarForLowN ? (ulong?)options.LowN : null;
ulong totalWins = e.WinCount;
ulong totalDraws = e.DrawCount;
ulong totalLosses = e.Count - totalWins - totalDraws;
double totalEloError = EloCalculator.Clamp(
EloCalculator.EloError99pct(totalWins, totalDraws, totalLosses, lowNThreshold),
2 * maxCalculatedEloDiff
);
double totalPerf = (totalWins + totalDraws * options.DrawScore) / e.Count;
double expectedTotalPerf = EloCalculator.GetExpectedPerformance(e.TotalEloDiff / (double)e.Count);
if (sideToMove == Player.Black)
{
totalPerf = 1.0 - totalPerf;
expectedTotalPerf = 1.0 - expectedTotalPerf;
}
double adjustedPerf = EloCalculator.GetAdjustedPerformance(totalPerf, expectedTotalPerf, maxCalculatedEloDiff);
double expectedElo =
EloCalculator.Clamp(
EloCalculator.Clamp(
EloCalculator.GetEloFromPerformance(adjustedPerf),
maxCalculatedEloDiff
) - totalEloError,
maxCalculatedEloDiff);
adjustedPerf = EloCalculator.GetExpectedPerformance(expectedElo);
return(adjustedPerf);
}
else
{
return(0.0);
}
}
double adjustedGamesPerf = calculateAdjustedPerf(totalEntry);
double gamesGoodness = EloCalculator.Clamp(
EloCalculator.GetEloFromPerformance(adjustedGamesPerf),
maxCalculatedEloDiff
) * gamesWeight;
// If eval is not present then assume 0.5 but reduce it for moves with low game count.
// The idea is that we don't want missing eval to penalize common moves.
double evalGoodness =
(
score != null
? EloCalculator.GetEloFromPerformance(score.Perf)
: -EloCalculator.EloError99pct(
totalEntry.WinCount,
totalEntry.DrawCount,
totalEntry.LossCount
)
) * evalWeight;
double weightSum = gamesWeight + evalWeight;
double goodness = EloCalculator.GetExpectedPerformance((gamesGoodness + evalGoodness) / weightSum);
return(goodness);
}
// multiplies games weight by e^-(elo_error/EloErrorHalfWeight)
public static double CalculateGoodnessPrioritizeEval(
Player sideToMove,
EnumArray <GameLevel, AggregatedEntry> aggregatedEntries,
ChessDBCNScore score,
Options options
)
{
const double eloErrorHalfWeight = 100;
const double maxAllowedPlayerEloDiff = 400;
const double maxCalculatedEloDiff = 800;
AggregatedEntry totalEntry = new AggregatedEntry();
foreach (KeyValuePair <GameLevel, AggregatedEntry> e in aggregatedEntries)
{
totalEntry.Combine(e.Value);
}
if (score == null && Math.Abs(totalEntry.TotalEloDiff / (double)totalEntry.Count) > maxAllowedPlayerEloDiff)
{
return(0.0);
}
double evalWeight = options.EvalWeight;
Tuple <double, double> calculateAdjustedPerf(AggregatedEntry e)
{
ulong? lowNThreshold = options.IncreaseErrorBarForLowN ? (ulong?)options.LowN : null;
ulong totalWins = e.WinCount;
ulong totalDraws = e.DrawCount;
ulong totalLosses = e.Count - totalWins - totalDraws;
double totalEloError = EloCalculator.Clamp(
EloCalculator.EloError99pct(totalWins, totalDraws, totalLosses, lowNThreshold),
2 * maxCalculatedEloDiff
);
double gw = options.GamesWeight * Math.Exp(-(totalEloError / eloErrorHalfWeight));
if (e.Count > 0)
{
double totalPerf = (totalWins + totalDraws * options.DrawScore) / e.Count;
double expectedTotalPerf = EloCalculator.GetExpectedPerformance(e.TotalEloDiff / (double)e.Count);
if (sideToMove == Player.Black)
{
totalPerf = 1.0 - totalPerf;
expectedTotalPerf = 1.0 - expectedTotalPerf;
}
double adjustedPerf = EloCalculator.GetAdjustedPerformance(totalPerf, expectedTotalPerf, maxCalculatedEloDiff);
if (score == null)
{
// If the score is null there's nothing to moderate the bad empirical data.
// So we apply the old method to reduce the QI of this move based on elo error.
double expectedElo =
EloCalculator.Clamp(
EloCalculator.Clamp(
EloCalculator.GetEloFromPerformance(adjustedPerf),
maxCalculatedEloDiff
) - totalEloError,
maxCalculatedEloDiff);
adjustedPerf = EloCalculator.GetExpectedPerformance(expectedElo);
}
return(new Tuple <double, double>(gw, adjustedPerf));
}
else
{
return(new Tuple <double, double>(0, 0));
}
}
(double gamesWeight, double adjustedGamesPerf) = calculateAdjustedPerf(totalEntry);
double gamesGoodness = EloCalculator.Clamp(
EloCalculator.GetEloFromPerformance(adjustedGamesPerf),
maxCalculatedEloDiff
) * gamesWeight;
// If eval is not present then assume 0.5 but reduce it for moves with low game count.
// The idea is that we don't want missing eval to penalize common moves.
double evalGoodness =
(
score != null
? EloCalculator.GetEloFromPerformance(score.Perf)
: -EloCalculator.EloError99pct(
totalEntry.WinCount,
totalEntry.DrawCount,
totalEntry.LossCount
)
) * evalWeight;
double weightSum = gamesWeight + evalWeight;
double goodness = EloCalculator.GetExpectedPerformance((gamesGoodness + evalGoodness) / weightSum);
return(goodness);
}
