def testAllNPCs():
masterCombination = Colorcombination(random.sample(range(0, numberOfColors), lengthOfGuess))
gameCoordinator = GameCoordinator(lengthOfGuess, numberOfColors, masterCombination, maximumNumberOfGuesses)
print("\nStart all performance tests.\n")
print("NPC_random performance test")
npc = NPC_random(lengthOfGuess, numberOfColors, gameCoordinator.attempts)
npc.strategy = npc.strategy_automaticRandom
gameCoordinator.player = npc
startPerformanceTests(gameCoordinator)
print("NPC_hardCoded performance test")
npc = NPC_hardCoded(lengthOfGuess, numberOfColors, gameCoordinator.attempts)
npc.strategy = npc.strategy_automaticImprovedRandom
gameCoordinator.player = npc
startPerformanceTests(gameCoordinator)
print("NPC_csp performance test ")
npc = NPC_csp(lengthOfGuess, numberOfColors, gameCoordinator.attempts)
npc.strategy = npc.strategy_getNextPossibleCombination
npc.cspSolvingStrategy = constraint.BacktrackingSolver()
gameCoordinator.player = npc
startPerformanceTests(gameCoordinator, npc.resetProblems)
print("\nSucessfully performed all performance tests.")
def __init__(self, class_of_problems, solver=constraint.BacktrackingSolver()):
#logging.basicConfig(filename='stats_{}.log'.format(class_of_problems), level=logging.INFO)
self.path= 'data/{}/data'.format(class_of_problems)
self.parser_func = parser.PARSE[class_of_problems]
self.class_of_problems = class_of_problems
self.output_file = 'stats_{}_{}.log'.format(class_of_problems, solver.name)
self.solver = solver
def create_csp_problem(self,
solver=constraint.BacktrackingSolver(),
symmetry_break=False):
p = constraint.Problem(solver)
p.addVariables(self.nodes(), self.colors())
self.add_edge_constraint(p)
return p
def solve(problem,
solver=constraint.BacktrackingSolver(),
symmetry_break=False):
p = problem.create_csp_problem(solver, symmetry_break)
fixed = p.getSolution()
problem2 = copy.deepcopy(problem)
problem2.fixed = fixed
return problem2
def __init__(self, lengthOfGuess, numberOfColors, attempts):
self.__lengthOfGuess = lengthOfGuess
self.__numberOfColors = numberOfColors
self.__attempts = attempts
self.__cspVariables = []
self.__cspVariablesStrings = []
self.cspSolvingStrategy = constraint.BacktrackingSolver()
self.__cspProblem = self.__createBasicCSP()
self.strategy = self.strategy_getNextPossibleCombination
def solve2(problem,
solver=constraint.BacktrackingSolver(),
queue=None,
symmetry_break=False):
p = problem.create_csp_problem(solver, symmetry_break)
fixed = p.getSolution()
problem2 = copy.deepcopy(problem)
problem2.fixed = fixed
queue.put(problem2)
def puzzle_as_CP(fixed, boxsize, solver=constraint.BacktrackingSolver()):
"""puzzle_as_CP(fixed, boxsize) -> constraint.Problem
Returns a constraint problem representing a Sudoku puzzle, based on
'fixed' cell dictionary."""
p = empty_puzzle_as_CP(boxsize, solver)
for cell in fixed:
p.addConstraint(constraint.ExactSumConstraint(fixed[cell]), [cell])
return p
def solve_all(problem,
solver=constraint.BacktrackingSolver(),
symmetry_break=False):
p = problem.create_csp_problem(solver, symmetry_break)
fixed_list = p.getSolutions()
problems = []
for fixed in fixed_list:
problem2 = copy.deepcopy(problem)
problem2.fixed = fixed
problems.append(problem2)
return problems
def empty_puzzle_as_CP(boxsize, solver=constraint.BacktrackingSolver()):
"""empty_puzzle(boxsize) -> constraint.Problem
Returns a constraint problem representing an empty Sudoku puzzle of
box-dimension 'boxsize'."""
p = constraint.Problem(solver)
p.addVariables(cells(boxsize), symbols(boxsize))
add_row_constraints(p, boxsize)
add_col_constraints(p, boxsize)
add_box_constraints(p, boxsize)
return p
def create_csp_problem(self,
solver=constraint.BacktrackingSolver(),
symmetry_break=False):
p = constraint.Problem(solver)
p.addVariables(self.rows(), self.cols())
p.addConstraint(constraint.AllDifferentConstraint(), self.rows())
self.add_diag_left_constraints(p)
self.add_diag_right_constraints(p)
if symmetry_break:
#self.add_axes_sym_constraints(p)
#self.add_rotation_sym_constraints(p)
self.sym_constraints(p)
return p
def solve_with_timeout(problem,
solver=constraint.BacktrackingSolver(),
timeout_sec=300,
symmetry_break=False):
def solve():
finished = True
queue = Queue()
proc = Process(target=solve2,
args=(problem, solver, queue, symmetry_break))
proc.start()
try:
problem2 = queue.get(timeout=timeout_sec)
except:
proc.terminate()
problem2 = copy.deepcopy(problem)
finished = False
finally:
return problem2, finished
return solve
def solve(board):
problem = constraint.Problem(constraint.BacktrackingSolver())
# Build Variables
for y in range(SIZE):
for x in range(SIZE):
if board[y,x] == -1:
problem.addVariable(str([y,x]), [1, 100])
# Constraints for variables near numbers
for y in range(SIZE):
for x in range(SIZE):
if board[y,x] in [0,1,2,3,4]:
val = 0
adj_vars = []
if y>0 and board[y-1,x] == -1: adj_vars.append(str([y-1,x]))
if y<SIZE-1 and board[y+1,x] == -1: adj_vars.append(str([y+1,x]))
if x>0 and board[y,x-1] == -1: adj_vars.append(str([y,x-1]))
if x<SIZE-1 and board[y,x+1] == -1: adj_vars.append(str([y,x+1]))
val += 100*board[y,x] + len(adj_vars) - board[y,x]
problem.addConstraint(constraint.ExactSumConstraint(val), adj_vars)
# Sum of each scan must be less than 200, since only 1 light per scan
# For horizontal scans
for y in range(SIZE):
curr_scan = []
for x in range(SIZE):
if board[y,x] == -1: curr_scan.append(str([y,x]))
else:
if len(curr_scan) > 0:
problem.addConstraint(constraint.MaxSumConstraint(199), curr_scan)
curr_scan = []
if len(curr_scan) > 0:
problem.addConstraint(constraint.MaxSumConstraint(199), curr_scan)
# For vertical scans
for x in range(SIZE):
curr_scan = []
for y in range(SIZE):
if board[y,x] == -1: curr_scan.append(str([y,x]))
else:
if len(curr_scan) > 0:
problem.addConstraint(constraint.MaxSumConstraint(199), curr_scan)
curr_scan = []
if len(curr_scan) > 0:
problem.addConstraint(constraint.MaxSumConstraint(199), curr_scan)
# For each point, pair of scans passing through point must contain 100 at least once
scans = []
for y in range(SIZE):
for x in range(SIZE):
if board[y,x] != -1: continue
scan = []
scan.append(str([y,x]))
# Up
for i in reversed(range(y)):
if board[i,x] >= 0: break
else: scan.append(str([i,x]))
# Down
for i in range(y+1,SIZE):
if board[i,x] >= 0: break
else: scan.append(str([i,x]))
# Left
for i in reversed(range(x)):
if board[y,i] >= 0: break
else: scan.append(str([y,i]))
# Right
for i in range(x+1,SIZE):
if board[y,i] >= 0: break
else: scan.append(str([y,i]))
scan.sort()
if scan not in scans: scans.append(scan)
for scan in scans:
problem.addConstraint(constraint.SomeInSetConstraint([100]), scan)
solution = problem.getSolution()
for y in range(SIZE):
for x in range(SIZE):
if str([y,x]) in solution:
board[y,x] = -1 if solution[str([y,x])] == 1 else 100
return board
def is_solved(self):
p = self.create_csp_problem(solver=constraint.BacktrackingSolver(),
symmetry_break=False)
return p.isSolution(self.fixed)
def create_csp_problem(self,
solver=constraint.BacktrackingSolver(),
symmetry_break=False):
pass
def solve_as_CP(fixed, boxsize, solver=constraint.BacktrackingSolver()):
"""Use constraint programming to solve Sudoku puzzle of dimension 'boxsize'
with 'fixed' cells."""
return puzzle_as_CP(fixed, boxsize, solver).getSolution()
#new_csp_object = csp_problems.solve(csp_object, solver=solver)
end_time = time.time()
res = {'filename':filename, 'time':round(end_time-start_time, 5), 'solver':self.solver.name, 'solved':new_csp_object.is_solved()}
self.write(str(res))
except:
self.write('{} encountered an error'.format(filename))
continue
print('processing for {} finished'.format(filename))
return True
if __name__ == '__main__':
if True:
# Launch all experiments
solvers = [constraint.BacktrackingSolver(),
constraint.NMCSSolver(level=1),
constraint.NMCSSolver(level=2),
constraint.NRPA(level=1, playouts=100, iterative=True, heuristics=True)]
problems = [('sudoku',False),
('nqueens-early', False),
('nqueens-early', True),
('nqueens-late', True),
('coloring', False)
]
for solver in solvers:
for pb, symmetric_break in problems:
exp = Experiment(pb, solver=solver)
exp.launch(None, timeout=300, symmetry_break=symmetric_break)
if False:
