minimax_with_depth_en.py

def minimax(node, depth): #node - current vertex in tree, means 'board configuration'. depth - recursion depth count.
    if node.isLeaf() or depth == 0: #if node appears to be leaf or recursion limit exceeded
        return evaluate_board(node) #some evaluation function for node in a tree (returns some value)
    children = getChildrenConfigurations(node) #some function to get all possible children nodes (i.e. possible configurations) from current node
    if node.type() == max_node: #if we define current node as max-node (i.e. we play with black stones and try to maximize cost)
        best_evaluation = MIN_VALUE #initialize with minimum possible value
        for child_node in children:
            cur_evalutaion = minimax(child_node, depth - 1) #evaluate "cost" of a possible move but recursively decrease depth
            if cur_evaluation > best_evaluation: #in case this move is more preferable, we memorize it
                best_evaluation = cur_evaluation
        return best_evaluation #return best cost found
    if node.type() == min_node: #if we define current node as min-node (i.e. we play with white stones and try to minimize cost)
        best_evaluation = MAX_VALUE #initialize with maximum possible value
        for child_node in children:
            cur_evalutaion = minimax(child_node, depth - 1) #evaluate "cost" of a possible move but recursively decrease depth
            if cur_evaluation < best_evaluation: #in case this move is more preferable, we memorize it
                best_evaluation = cur_evaluation
        return best_evaluation #return best cost found