def test_i_consistency_one(self):
rand_var = random.choice(tuple(self.const_problem1.get_variables()))
rand_var.assign(random.choice(rand_var.domain))
self.assertRaises(AssertionError, csp.i_consistency,
self.const_problem1, 17)
self.const_problem1.unassign_all_variables()
res = csp.i_consistency(self.const_problem1, 1)
self.assertTrue(res)
with_history=True)
measure_performance(2, "pc2_n_queens_problem", pc2_n_queens_problem,
"simulated_annealing", 100000, 0.5, 0.99999)
measure_performance(2, "pc2_n_queens_problem", pc2_n_queens_problem,
"random_restart_first_choice_hill_climbing", 100, 100,
10)
general_genetic_n_queens_problem = csp.GeneralGeneticConstraintProblem(
pc2_n_queens_problem, 0.1)
measure_performance(2, "pc2_n_queens_problem",
general_genetic_n_queens_problem,
"genetic_local_search", 100, 100, 0.1)
two_consistency_n_queens_problem = copy.deepcopy(n_queens_problem)
two_consistency_n_queens_problem.unassign_all_variables()
two_consistency_start_time = time.process_time()
is_two_consistent = csp.i_consistency(two_consistency_n_queens_problem, 2)
two_consistency_end_time = time.process_time()
if is_two_consistent:
print()
print()
print("-" * 145)
print("using 2-consistency as a preprocessing stage which took",
two_consistency_end_time - two_consistency_start_time, "seconds")
print("-" * 145)
measure_performance(1,
"two_consistency_n_queens_problem",
two_consistency_n_queens_problem,
"backtracking_search",
with_history=True)
measure_performance(1,
"two_consistency_n_queens_problem",
100000, 0.5, 0.99999)
measure_performance(2, "pc2_verbal_arithmetic_problem",
pc2_verbal_arithmetic_problem,
"random_restart_first_choice_hill_climbing", 100, 100,
10)
general_genetic_verbal_arithmetic_problem = csp.GeneralGeneticConstraintProblem(
pc2_verbal_arithmetic_problem, 0.1)
measure_performance(2, "pc2_verbal_arithmetic_problem",
general_genetic_verbal_arithmetic_problem,
"genetic_local_search", 100, 100, 0.1)
two_consistency_verbal_arithmetic_problem = copy.deepcopy(
verbal_arithmetic_problem)
two_consistency_verbal_arithmetic_problem.unassign_all_variables()
two_consistency_start_time = time.process_time()
is_two_consistent = csp.i_consistency(
two_consistency_verbal_arithmetic_problem, 2)
two_consistency_end_time = time.process_time()
if is_two_consistent:
print()
print()
print("-" * 145)
print("using 2-consistency as a preprocessing stage which took",
two_consistency_end_time - two_consistency_start_time, "seconds")
print("-" * 145)
measure_performance(1,
"two_consistency_verbal_arithmetic_problem",
two_consistency_verbal_arithmetic_problem,
"backtracking_search",
with_history=True)
measure_performance(1,
"two_consistency_verbal_arithmetic_problem",
pc2_car_assembly_problem, "simulated_annealing",
100000, 0.5, 0.99999)
measure_performance(2, "pc2_car_assembly_problem",
pc2_car_assembly_problem,
"random_restart_first_choice_hill_climbing", 10, 10,
10)
general_genetic_car_assembly_problem = csp.GeneralGeneticConstraintProblem(
pc2_car_assembly_problem, 0.1)
measure_performance(2, "pc2_car_assembly_problem",
general_genetic_car_assembly_problem,
"genetic_local_search", 100, 100, 0.1)
two_consistency_car_assembly_problem = copy.deepcopy(car_assembly_problem)
two_consistency_car_assembly_problem.unassign_all_variables()
two_consistency_start_time = time.process_time()
is_two_consistent = csp.i_consistency(two_consistency_car_assembly_problem, 2)
two_consistency_end_time = time.process_time()
if is_two_consistent:
print()
print()
print("-" * 145)
print("using 2-consistency as a preprocessing stage which took",
two_consistency_end_time - two_consistency_start_time, "seconds")
print("-" * 145)
measure_performance(1,
"two_consistency_car_assembly_problem",
two_consistency_car_assembly_problem,
"backtracking_search",
with_history=True)
measure_performance(1,
"two_consistency_car_assembly_problem",
pc2_map_coloring_problem, "simulated_annealing",
100000, 0.5, 0.99999)
measure_performance(2, "pc2_map_coloring_problem",
pc2_map_coloring_problem,
"random_restart_first_choice_hill_climbing", 100, 100,
10)
general_genetic_map_coloring_problem = csp.GeneralGeneticConstraintProblem(
pc2_map_coloring_problem, 0.1)
measure_performance(2, "pc2_map_coloring_problem",
general_genetic_map_coloring_problem,
"genetic_local_search", 100, 100, 0.1)
two_consistency_map_coloring_problem = copy.deepcopy(map_coloring_problem)
two_consistency_map_coloring_problem.unassign_all_variables()
two_consistency_start_time = time.process_time()
is_two_consistent = csp.i_consistency(two_consistency_map_coloring_problem, 2)
two_consistency_end_time = time.process_time()
if is_two_consistent:
print()
print()
print("-" * 145)
print("using 2-consistency as a preprocessing stage which took",
two_consistency_end_time - two_consistency_start_time, "seconds")
print("-" * 145)
measure_performance(1,
"two_consistency_map_coloring_problem",
two_consistency_map_coloring_problem,
"backtracking_search",
with_history=True)
measure_performance(1,
"two_consistency_map_coloring_problem",
def test_i_consistency_seven(self):
res = csp.i_consistency(self.const_problem3, 2)
self.assertTrue(res)
def test_i_consistency_five(self):
res = csp.i_consistency(self.const_problem2, 2)
self.assertFalse(res)
def test_i_consistency_four(self):
res = csp.i_consistency(self.const_problem2, 1)
self.assertTrue(res)
def test_i_consistency_three(self):
res = csp.i_consistency(self.const_problem1, 3)
self.assertFalse(res)
with_history=True)
measure_performance(2, "pc2_einstein_problem", pc2_einstein_problem,
"simulated_annealing", 100000, 0.5, 0.99999)
measure_performance(2, "pc2_einstein_problem", pc2_einstein_problem,
"random_restart_first_choice_hill_climbing", 100, 100,
10)
general_genetic_pc2_einstein_problem = csp.GeneralGeneticConstraintProblem(
pc2_einstein_problem, 0.1)
measure_performance(2, "pc2_einstein_problem",
general_genetic_pc2_einstein_problem,
"genetic_local_search", 100, 100, 0.1)
two_consistency_einstein_problem = copy.deepcopy(einstein_problem)
two_consistency_einstein_problem.unassign_all_variables()
two_consistency_start_time = time.process_time()
is_two_consistent = csp.i_consistency(two_consistency_einstein_problem, 2)
two_consistency_end_time = time.process_time()
if is_two_consistent:
print()
print()
print("-" * 145)
print("using 2-consistency as a preprocessing stage which took",
two_consistency_end_time - two_consistency_start_time, "seconds")
print("-" * 145)
measure_performance(1,
"two_consistency_einstein_problem",
two_consistency_einstein_problem,
"backtracking_search",
with_history=True)
measure_performance(1,
"two_consistency_einstein_problem",
measure_performance(2, "pc2_magic_square_problem", pc2_magic_square_problem,
"min_conflicts", 100000, with_history=True)
measure_performance(2, "pc2_magic_square_problem", pc2_magic_square_problem,
"constraints_weighting", 10000, with_history=True)
measure_performance(2, "pc2_magic_square_problem", pc2_magic_square_problem,
"simulated_annealing", 100000, 0.5, 0.99999)
measure_performance(2, "pc2_magic_square_problem", pc2_magic_square_problem,
"random_restart_first_choice_hill_climbing", 100, 100, 10)
general_genetic_pc2_magic_square_problem = csp.GeneralGeneticConstraintProblem(pc2_magic_square_problem, 0.1)
measure_performance(2, "general_genetic_pc2_magic_square_problem", general_genetic_pc2_magic_square_problem,
"genetic_local_search", 1000, 1000, 0.1)
two_consistency_magic_square_problem = copy.deepcopy(magic_square_problem)
two_consistency_magic_square_problem.unassign_all_variables()
two_consistency_start_time = time.process_time()
is_two_consistent = csp.i_consistency(two_consistency_magic_square_problem, 2)
two_consistency_end_time = time.process_time()
if is_two_consistent:
print()
print()
print("-" * 145)
print("using 2-consistency as a preprocessing stage which took",
two_consistency_end_time - two_consistency_start_time, "seconds")
print("-" * 145)
measure_performance(1, "two_consistency_magic_square_problem", two_consistency_magic_square_problem,
"backtracking_search", with_history=True)
measure_performance(1, "two_consistency_magic_square_problem", two_consistency_magic_square_problem,
"backtracking_search", inference=csp.forward_check, with_history=True)
measure_performance(1, "two_consistency_magic_square_problem", two_consistency_magic_square_problem,
"heuristic_backtracking_search", with_history=True)
measure_performance(1, "two_consistency_magic_square_problem", two_consistency_magic_square_problem,
