void BJPlayer::reset(const BJShoe & shoe, BJRules & rules,
BJStrategy & strategy, BJProgress & progress) {
// Forget about any cards already dealt from the shoe, so shoe.reset(hand) will
// work.
this->shoe = shoe;
numHands = 0;
for (int card = 1; card <= 10; card++) {
playerHands[numHands].cards[card - 1] =
playerHands[numHands].hitHand[card - 1] = 0;
this->shoe.setTotalCards(card,shoe.getCards(card));
}
// Remember resplit rules when enumerating player hands.
for (int pairCard = 1; pairCard <= 10; pairCard++) {
resplit[pairCard - 1] = rules.getResplit(pairCard);
}
// Enumerate all possible player hands.
currentHand.reset();
countHands(numHands++, 1);
linkHands();
// Compute dealer probabilities for each hand. This takes the most time, so
// keep the caller updated on the progress.
computeDealer(rules, progress);
// Compute expected values for standing, doubling down, and hitting (in that
// order, so all required values will be available when needed).
linkHandCounts();
computeStand();
computeDoubleDown();
computeHit(rules, strategy);
// Compute expected values for splitting pairs. Re-link original hands by
// count for future use.
computeSplit(rules, strategy);
linkHandCounts();
// Blackjack pays 3:2, so correct the value for standing on this hand. We wait
// to do this until after computing E(split) since a blackjack after splitting
// a pair only pays even money.
correctStandBlackjack();
// Compute overall expected values, condition individual hands on no dealer
// blackjack, and finalize progress indicator.
computeOverall(rules, strategy);
conditionNoBlackjack();
progress.indicate(100);
}
bool DecisionTreeClusterNode::computeBestSplitBestRandomSplit( const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError ){
return computeSplit( numSplittingSteps, trainingData, features, classLabels, featureIndex, minError);
}
