def evaluate_tree_alpha_beta(self,
currentNode: Node,
parentEvaluator: int = None) -> None:
if currentNode.is_leaf:
currentNode.evaluator_value = 1 if self.is_player_first == (
currentNode.depth % 2 == 1) else -1
else:
child_evaluate_values = []
current_evaluation: int = None
is_current_max = currentNode.depth % 2 == 0
for child in currentNode.children:
self.evaluate_tree_alpha_beta(child, current_evaluation)
if child.evaluator_value:
if parentEvaluator:
skipped: bool = (
child.evaluator_value >= parentEvaluator
) if is_current_max else (
parentEvaluator <= child.evaluator_value)
if skipped:
return
if not current_evaluation or (is_current_max ==
child.evaluator_value >
current_evaluation):
current_evaluation = child.evaluator_value
child_evaluate_values.append(child.evaluator_value)
evaluate = max(child_evaluate_values) if is_current_max else min(
child_evaluate_values)
currentNode.evaluator_value = evaluate
self.nodesVisited += 1
def evaluate_tree_min_max(self, currentNode: Node) -> None:
if currentNode.is_leaf:
currentNode.evaluator_value = 1 if self.is_player_first == (
currentNode.depth % 2 == 1) else -1
else:
child_evaluate_values = []
for child in currentNode.children:
self.evaluate_tree_min_max(child)
child_evaluate_values.append(child.evaluator_value)
evaluate = max(child_evaluate_values) if (
currentNode.depth % 2 == 0) else min(child_evaluate_values)
currentNode.evaluator_value = evaluate
self.nodesVisited += 1
def evaluateTreeMinMax(self, currentNode: Node):
if currentNode.is_leaf:
currentNode.evaluator_value = 1 if (currentNode.depth %
2 == 1) else -1
else:
childEvaluateValues = []
for child in currentNode.children:
self.evaluateTreeMinMax(child)
childEvaluateValues.append(child.evaluator_value)
evaluate = max(childEvaluateValues) if (
currentNode.depth % 2 == 0) else min(childEvaluateValues)
currentNode.evaluator_value = evaluate
self.nodesVisited += 1
