private EstimatedAD estimateADPreFLop(PlayerStats playerStats, DifferencePair[] sortedOpponents, PreFlopParams preFlopParams)
{
Debug.Assert(playerStats != null);
var priorBetRaise = new HistDistribution(_options.priorNumBins);
var priorCheckCall = new HistDistribution(_options.priorNumBins);
var priorFold = new HistDistribution(_options.priorNumBins);
var cumulativeActionStats = new ActionStats();
int k = 0;
for (int i = 0; (i < sortedOpponents.Length) &&
(k < _options.maxSimilarPlayers) &&
(k == 0 || sortedOpponents[i].Difference < _options.maxDifference); i++)
{
int playerInd = sortedOpponents[i].Index;
if (_baseModels[playerInd].VPIP.Sigma < _options.maxBaseStatsSigma)
{
ActionStats similarOponentStats = _fullStatsList[playerInd].getPreFlopStats(preFlopParams);
if (similarOponentStats.totalSamples() > _options.minSamples)
{
priorBetRaise.AddSample(similarOponentStats.betRaiseProbability());
priorCheckCall.AddSample(similarOponentStats.checkCallProbability());
priorFold.AddSample(similarOponentStats.foldProbability());
k++;
}
else
{
cumulativeActionStats.append(similarOponentStats);
}
if (cumulativeActionStats.totalSamples() > _options.minSamples)
{
priorBetRaise.AddSample(cumulativeActionStats.betRaiseProbability());
priorCheckCall.AddSample(cumulativeActionStats.checkCallProbability());
priorFold.AddSample(cumulativeActionStats.foldProbability());
cumulativeActionStats.clear();
k++;
}
}
}
priorBetRaise.Normalize();
priorCheckCall.Normalize();
priorFold.Normalize();
// Update prior
ActionStats startStats = playerStats.getPreFlopStats(preFlopParams);
var estBetRaise = estimateGaussian(priorBetRaise, startStats.BetRaiseSamples, startStats.totalSamples());
var estCheckCall = estimateGaussian(priorCheckCall, startStats.CheckCallSamples, startStats.totalSamples());
var estFold = estimateGaussian(priorFold, startStats.FoldSamples, startStats.totalSamples());
if (preFlopParams.ForcedAction())
{
var totalMean = estBetRaise.Mean + estCheckCall.Mean + estFold.Mean;
var scale = 1.0f / totalMean;
estBetRaise = estBetRaise.Scale(scale);
estCheckCall = estCheckCall.Scale(scale);
estFold = estFold.Scale(scale);
}
else
{
var totalMean = estBetRaise.Mean + estCheckCall.Mean;
var scale = 1.0f / totalMean;
estBetRaise = estBetRaise.Scale(scale);
estCheckCall = estCheckCall.Scale(scale);
estFold = new GaussianDistribution(0.0f, 0.0f);
}
return(new EstimatedAD(estBetRaise, estCheckCall, estFold, k, startStats.totalSamples()));
}
