def __init__(self):
'''
'''
self.outerlines_heurestic = [[7, 6, 5, 4], [6, 4, 3, 3], [5, 3, 2, 2],
[4, 3, 2, 1]]
self.snake_heurestic = [[16, 15, 14, 13], [9, 10, 11, 12],
[8, 7, 6, 5], [1, 2, 3, 4]]
self.gradient_heurestic = [[7, 6, 5, 4], [6, 5, 4, 3], [5, 4, 3, 2],
[4, 3, 2, 1]]
self.directions = {1: "RIGHT", 2: "LEFT", 3: "UP", 4: "DOWN"}
self.logic = Gamelogic()
self.neighbours = list()
heapq.heapify(self.neighbours)
'''
'''
if depth == 0:
h = self.heuristic(board)
return h
if maximizing_player:
#Return value of maximum-valued child node
alpha = float("-inf")
for neighbour in self.get_neighbours(board):
alpha = max(alpha, self.expectimax(neighbour, depth - 1,
False))
elif maximizing_player == False:
# Return weighted average of all child nodes' values
alpha = 0
neighbours = self.logic.append_all_random_numbers(board)
for key in neighbours.keys():
for neighbour in neighbours[key]:
alpha += (self.probability_of_reaching_node(key, board) *
self.expectimax(neighbour, depth - 1, True))
#print("alpha")
return alpha
if __name__ == "__main__":
solver = Solver()
logic = Gamelogic()
print("-----------")
board = [[2, 0, 0, 0], [0, 2, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
neighbours = solver.get_neighbours(board)
#print(neighbours)
