def Solve(self, problem):
figures, solutions = setup(problem)
scores = []
# compare diagonals
if problem.problemType == '3x3' and transformation.equality(figures[0], figures[4]) \
and transformation.equality(figures[1], figures[5]) \
and transformation.equality(figures[3], figures[7]):
print('diagonal production rule chosen')
m = comparison.compare_diagonal(scores, figures, solutions, problem)
scores = utility.get_score(m, problem)
elif comparison.compare_top_corners(figures) and comparison.compare_bottom_bc_ef(figures):
print('top shape / bottom shape production rule chosen')
m = comparison.compare_top_bottom(scores, figures, solutions, problem)
scores = utility.get_score(m, problem)
elif 'E' in problem.name.split(' ')[2]:
print('xor, union, intersect solver')
scores = image_op_solver(figures, solutions, problem)
# standard generate and test
else:
print('generate & test solver')
# generate our initial semantic network to test against
init_network = create_semantic_network(figures, problem)
scores = generate_and_test(init_network, scores, figures, solutions, problem)
given = scores.index(max(scores)) + 1
actual = problem.checkAnswer(scores)
print('given answer: ' + str(given))
print('actual answer: ' + str(actual))
print()
return scores
def generate_and_test(init_network, scores, figures, solutions, problem):
for i, solution in enumerate(solutions):
if problem.problemType == '3x3':
# compare init_network with generated solutions
H_A = create_relationship_diagram([figures[6], figures[7]],
'horizontal')
H_B = create_relationship_diagram([figures[7], solution],
'horizontal')
H = utility.union(H_A, H_B)
V_A = create_relationship_diagram([figures[2], figures[5]],
'vertical')
V_B = create_relationship_diagram([figures[5], solution],
'vertical')
V = utility.union(V_A, V_B)
D = create_relationship_diagram([figures[4], solution], 'diagonal')
else:
H = create_relationship_diagram([figures[2], solution],
'horizontal', '2x2')
V = create_relationship_diagram([figures[1], solution], 'vertical',
'2x2')
D = None
score = agent_compare(init_network, H, V, D, problem, i + 1)
scores.append(score)
scores = utility.normalize_scores(scores, problem)
print(scores)
if 1.0 not in scores:
if problem.problemType == '3x3':
m_diagonal = comparison.compare_diagonal(scores, figures,
solutions, problem)
print('comparing diagonals to finalize score')
possible_scores = [m_diagonal]
m = min(possible_scores, key=lambda t: t[1])
scores = utility.get_score(m, problem)
else:
m_union = comparison.compare_union(scores, figures, solutions,
problem)
print('comparing union to finalize score')
possible_scores = [m_union]
m = min(possible_scores, key=lambda t: t[1])
scores = utility.get_score(m, problem)
return scores
def generate_and_test(init_network, scores, figures, solutions, problem):
for i, solution in enumerate(solutions):
if problem.problemType == '3x3':
# compare init_network with generated solutions
H_A = create_relationship_diagram([figures[6], figures[7]], 'horizontal')
H_B = create_relationship_diagram([figures[7], solution], 'horizontal')
H = utility.union(H_A, H_B)
V_A = create_relationship_diagram([figures[2], figures[5]], 'vertical')
V_B = create_relationship_diagram([figures[5], solution], 'vertical')
V = utility.union(V_A, V_B)
D = create_relationship_diagram([figures[4], solution], 'diagonal')
else:
H = create_relationship_diagram([figures[2], solution], 'horizontal', '2x2')
V = create_relationship_diagram([figures[1], solution], 'vertical', '2x2')
D = None
score = agent_compare(init_network, H, V, D, problem, i + 1)
scores.append(score)
scores = utility.normalize_scores(scores, problem)
print(scores)
if 1.0 not in scores:
if problem.problemType == '3x3':
m_diagonal = comparison.compare_diagonal(scores, figures, solutions, problem)
print('comparing diagonals to finalize score')
possible_scores = [m_diagonal]
m = min(possible_scores, key=lambda t: t[1])
scores = utility.get_score(m, problem)
else:
m_union = comparison.compare_union(scores, figures, solutions, problem)
print('comparing union to finalize score')
possible_scores = [m_union]
m = min(possible_scores, key=lambda t: t[1])
scores = utility.get_score(m, problem)
return scores
def Solve(self, problem):
figures, solutions = setup(problem)
scores = []
# compare diagonals
if problem.problemType == '3x3' and transformation.equality(figures[0], figures[4]) \
and transformation.equality(figures[1], figures[5]) \
and transformation.equality(figures[3], figures[7]):
print('diagonal production rule chosen')
m = comparison.compare_diagonal(scores, figures, solutions,
problem)
scores = utility.get_score(m, problem)
elif comparison.compare_top_corners(
figures) and comparison.compare_bottom_bc_ef(figures):
print('top shape / bottom shape production rule chosen')
m = comparison.compare_top_bottom(scores, figures, solutions,
problem)
scores = utility.get_score(m, problem)
elif 'E' in problem.name.split(' ')[2]:
print('xor, union, intersect solver')
scores = image_op_solver(figures, solutions, problem)
# standard generate and test
else:
print('generate & test solver')
# generate our initial semantic network to test against
init_network = create_semantic_network(figures, problem)
scores = generate_and_test(init_network, scores, figures,
solutions, problem)
given = scores.index(max(scores)) + 1
actual = problem.checkAnswer(scores)
print('given answer: ' + str(given))
print('actual answer: ' + str(actual))
print()
return scores