def test_place_queen(self):
nqb = NQueensBoard(5)
nqrf = NQResultFunction()
new_board = nqrf.result(
nqb, QueenAction(QueenAction.PLACE_QUEEN, XYLocation(1, 2)))
self.assertTrue(new_board.queen_exists_at_square(2, 1))
def test_move_queen(self):
nqb = NQueensBoard(5)
nqb.add_queen_at(XYLocation(3, 4))
nqrf = NQResultFunction()
new_board = nqrf.result(nqb, QueenAction(QueenAction.MOVE_QUEEN, XYLocation(3, 1)))
self.assertFalse(new_board.queen_exists_at_square(4, 3))
self.assertTrue(new_board.queen_exists_at_square(1, 3))
def test_remove_queen_action(self):
queens = (XYLocation(1, 3), XYLocation(1, 4), XYLocation(1, 0))
nqb = NQueensBoard(5)
nqb.set_board(queens)
nqrf = NQResultFunction()
new_board = nqrf.result(nqb, QueenAction(QueenAction.REMOVE_QUEEN, XYLocation(1, 4)))
self.assertEquals(2, new_board.get_number_of_queens_on_board())
def test_move_queen(self):
nqb = NQueensBoard(5)
nqb.add_queen_at(XYLocation(3, 4))
nqrf = NQResultFunction()
new_board = nqrf.result(
nqb, QueenAction(QueenAction.MOVE_QUEEN, XYLocation(3, 1)))
self.assertFalse(new_board.queen_exists_at_square(4, 3))
self.assertTrue(new_board.queen_exists_at_square(1, 3))
def test_remove_queen_action(self):
queens = (XYLocation(1, 3), XYLocation(1, 4), XYLocation(1, 0))
nqb = NQueensBoard(5)
nqb.set_board(queens)
nqrf = NQResultFunction()
new_board = nqrf.result(
nqb, QueenAction(QueenAction.REMOVE_QUEEN, XYLocation(1, 4)))
self.assertEquals(2, new_board.get_number_of_queens_on_board())
def main():
for i in range(0, NUMBER_OF_TESTS):
sa = SimulateAnnealingSearch(AttackingPairHeuristic())
random_board = create_random_board()
print("Test number: " + str(i))
print("Random init board:")
print(random_board)
# Create N queen problem with N queens on the board
p = Problem(random_board, NQCActionsFunction(), NQResultFunction(),
NQueensGoalTest())
# Search for a solution
actions = sa.search(p)
if not sa.failed():
print("Test succeeded")
print("Actions:")
for action in actions:
print(action)
else:
print("Search failed")
print("Result state:")
print(sa.last_state)
print("\n\n")
__author__ = 'Ivan Mushketik'
##
# Example of solving N-queens problem with help of DFS
# Size of the chess board
SIZE = 5
# Using tree DFS
ts = TreeSearch()
dfs = DepthFirstSearch(ts)
# Create empty board
empty_board = NQueensBoard(SIZE)
# Define N-queen board
p = Problem(empty_board, NQIActionsFunctions(), NQResultFunction(), NQueensGoalTest())
# Get list of actions to solve a problem
actions = dfs.search(p)
board_to_show = NQueensBoard(SIZE)
rf = NQResultFunction()
# Print each action and perform each action (putting new queen on a board)
for action in actions:
print(action)
board_to_show = rf.result(board_to_show, action)
# Print result board
print(board_to_show)
def test_place_queen(self):
nqb = NQueensBoard(5)
nqrf = NQResultFunction()
new_board = nqrf.result(nqb, QueenAction(QueenAction.PLACE_QUEEN, XYLocation(1, 2)))
self.assertTrue(new_board.queen_exists_at_square(2, 1))
