def test_greedy_dfs(weights):
def compare(original, final, moves, row, column, assert_error_message):
for delta_r, delta_c in moves:
original.swap(row, column, row + delta_r, column + delta_c)
row += delta_r
column += delta_c
for r in range(original.rows):
for c in range(original.columns):
assert isinstance(original.cell(r, c),
type(final.cell(r, c))), assert_error_message
# solution should contain move list that gets you to the final board state starting from original board state
original = Board.create_randomized_board(5, 6)
_, moves, final_board = GreedyDfs(weights).solve(original, 20)
compare(original, final_board, moves[1:], moves[0][0], moves[0][1],
"Greedy DFS yielded an incorrect 4-way move list!")
# same with diagonals enabled
original = Board.create_randomized_board(5, 6)
h = GreedyDfs(weights)
h.diagonals = True
_, moves, final_board = h.solve(original, 20)
compare(original, final_board, moves[1:], moves[0][0], moves[0][1],
"Greedy DFS yielded an incorrect 8-way move list!")
def run(rows, columns, depth, weights):
def update_board_with_matches(board, matches):
for piece, clusters in matches:
for r, c in clusters:
board.update(r, c, type(piece))
b = Board.create_randomized_board(5, 6)
# b = Board([Fire, Wood, Water, Dark, Light, Light,
# Water, Fire, Water, Light, Heart, Light,
# Fire, Water, Dark, Heart, Heart, Wood,
# Light, Water, Light, Fire, Wood, Wood,
# Dark, Heart, Dark, Light, Heart, Light], 5, 6)
m = Board.create_empty_board(rows, columns)
update_board_with_matches(m, b.get_matches())
h = GreedyDfs(weights) if False else PrunedBfs(weights)
h.diagonals = True
start = time.time()
moves = h.solve(b, depth)
performance = time.time() - start
print('---------------------------------------------')
print(b)
print(m)
print('run time : ' + str(performance))
print('best move score : ' + str(moves[0]))
print('start : ' + str(moves[1][0]))
print('moves : ' + str(moves[1][1:]))
print(moves[2])
m = Board.create_empty_board(rows, columns)
update_board_with_matches(m, moves[2].get_matches())
print(m)
def test_greedy_dfs(weights):
def compare(original, final, moves, row, column, assert_error_message):
for delta_r, delta_c in moves:
original.swap(row, column, row + delta_r, column + delta_c)
row += delta_r
column += delta_c
for r in range(original.rows):
for c in range(original.columns):
assert isinstance(original.cell(r, c), type(final.cell(r, c))), assert_error_message
# solution should contain move list that gets you to the final board state starting from original board state
original = Board.create_randomized_board(5, 6)
_, moves, final_board = GreedyDfs(weights).solve(original, 20)
compare(original, final_board, moves[1:], moves[0][0], moves[0][1], "Greedy DFS yielded an incorrect 4-way move list!")
# same with diagonals enabled
original = Board.create_randomized_board(5, 6)
h = GreedyDfs(weights)
h.diagonals = True
_, moves, final_board = h.solve(original, 20)
compare(original, final_board, moves[1:], moves[0][0], moves[0][1], "Greedy DFS yielded an incorrect 8-way move list!")
from pazudorasolver.heuristics.pruned_bfs import PrunedBfs
weights = {
Fire.symbol: 1.0,
Wood.symbol: 1.0,
Water.symbol: 1.0,
Dark.symbol: 1.0,
Light.symbol: 1.0,
Heart.symbol: 1.0,
Poison.symbol: 0.5,
Jammer.symbol: 0.5,
Unknown.symbol: 0.0
}
board = Board.create_randomized_board(5, 6)
matches = board.get_matches()
print(board)
print(matches)
# try GreedyDfs heuristic
solver1 = GreedyDfs(weights)
solution = solver1.solve(board, 50)
print(solution)
# try PrunedBfs heuristic
solver2 = PrunedBfs(weights)
solution = solver2.solve(board, 50)
print(solution)
Heart.symbol: 1.0,
Poison.symbol: 0.5,
Jammer.symbol: 0.5,
Unknown.symbol: 0.0
}
#board = Board.create_randomized_board(5, 6)
piece_list = [
Fire, Wood, Water, Dark, Light, Heart, Fire, Water, Dark, Light, Heart,
Fire, Fire, Water, Dark, Heart, Heart, Wood, Light, Water, Light, Fire,
Wood, Wood, Dark, Water, Dark, Light, Light, Light
]
number_of_rows = 5
number_of_columns = 6
board = Board(piece_list, number_of_rows, number_of_columns)
matches = board.get_matches()
print(board)
print(matches)
solver1 = GreedyDfs(weights)
solution = solver1.solve(board, 100)
print(solution)
solver2 = PrunedBfs(weights)
solution = solver2.solve(board, 100)
print(solution)
