def test_get_lower_cost_of_states_with_different_cost():
board = [[" "," ","C"],["Z"," "," "],[" ","Z"," "]]
a_test = A_Star(board, vision=2, carrots=2, MaxLastMovements=2)
a_test.open = [[1,2],[2,2]]
a_test.f = [3,2]
best, next_state = a_test.get_lower()
assert best == 2 and next_state == [2,2]
def test_h_considering_carrots():
board = [[" "," ","C"],["Z"," "," "],[" "," ","Z"]]
a_test = A_Star(board, vision=2, carrots=2, MaxLastMovements=2)
carrots_coordinates = a_test.carrots_in_sight(current_state=[0,2], board=board)
a_test.open = [[0,1],[1,2]]
h = a_test.h_with_carrots(carrots_coordinates, current=[0,2])
assert h == [3,1]
def test_h_not_considering_carrots():
board = [[" "," ","C"],["Z"," "," "],[" ","Z"," "]]
a_test = A_Star(board, vision=1, carrots=2, MaxLastMovements=2)
a_test.open = [[0,1],[1,0],[2,1],[1,2]]
a_test.LastMovements = [[1,0],[1,2],[0,1]]
h = a_test.h_without_carrots()
assert h == [10,5,1,5]
def test_total_cost():
board = [[" "," ","C"],["Z"," "," "],[" "," ","Z"]]
a_test = A_Star(board, vision=2, carrots=2, MaxLastMovements=2)
a_test.g = 1
a_test.open = [[0,2],[1,1],[2,2]]
a_test.LastMovements = [[0,2]]
a_test.calculate_f_score(current_state=[1,2], board=board)
assert a_test.f == [15,4,3]
def test_put_state_in_last_movements():
board = [[" "," ","C"],["Z"," "," "],[" ","Z"," "]]
a_test = A_Star(board, vision=2, carrots=2, MaxLastMovements=2)
a_test.put_Last_Movement([1,2])
assert a_test.LastMovements[-1] == [1,2]
def test_search_rabbit_on_board_at_start():
board = [[" "," ","C"],["Z"," "," "],[" ","Z"," "]]
a_test = A_Star(board, vision=2, carrots=2, MaxLastMovements=2)
[row, column] = a_test.search_start(board)
assert [row, column] == [0,2]
def test_neighbors_of_2_2():
board = [[" "," ","C"],["Z"," "," "],[" ","Z"," "]]
a_test = A_Star(board, vision=2, carrots=2, MaxLastMovements=2)
neigbors = a_test.search_neighbors(current_state=[2,2], board=board)
assert [2,1] in neigbors and [1,2] in neigbors
def test_4_5_is_in_a_board_of_3x3():
board = [[" "," ","C"],["Z"," "," "],[" ","Z"," "]]
a_test = A_Star(board, vision=2, carrots=2, MaxLastMovements=2)
result = a_test.is_in_board(x=-2, y=-1, board=board)
assert result == False
def test_0_carrots_in_sight():
board = [[" "," ","C"],["Z"," "," "],[" ","Z"," "]]
a_test = A_Star(board, vision=1, carrots=2, MaxLastMovements=2)
coordinates = a_test.carrots_in_sight(current_state=[0,2], board=board)
assert coordinates == []
# Checks algorithm selected parameters
if args.a_estrella:
if args.vision is None or args.zanahorias is None:
parser.error(
"The A* algorithm needs to know the vision field range and carrot's amount.")
exit(-1)
elif args.vision < 1 or args.zanahorias < 1:
parser.error(
"The A* algorithm needs to know the vision field range and carrot's amount. (>=1)")
exit(-1)
if args.movimientos_pasados is not None:
movimientos_pasados = args.movimientos_pasados
else:
movimientos_pasados = 5
algorithm = A_Star(board=None, vision=args.vision,
carrots=args.zanahorias, MaxLastMovements=movimientos_pasados)
elif args.genetico:
# Checks if just one direction is selected
cont_unique_flag = 0
direction = ""
unique_flags = ["derecha", "izquierda", "arriba", "abajo"]
for flag in unique_flags:
if args.__dict__[flag]:
cont_unique_flag += 1
direction = flag
if cont_unique_flag != 1:
parser.error("The genetic algorithm need just one direction.")
exit(-1)
if args.individuos is None or args.generaciones is None: