def compose(relation_a_b, relation_b_c):
strengths_by_solution = {}
# Reverse engineer the (before, same, after) relations
formula_a_b = DecompositionFitter(relation_a_b)
formula_b_c = DecompositionFitter(relation_b_c)
# formula.compare now calculates (before, same, after) for each relation
# Initiating the DFS tree
search_tree = DepthFirstSearchComposition()
# DecompositionFitter uses 'beginning' and 'ending' as keys for compare method (as opposed to probability
# distribution in an actual RelationFormula), facilitating the access by a dict:
dist_key_by_index = {0: 'beginning', 1: 'ending'}
for temporal_event_1_key, temporal_event_2_key, formula in [
('A', 'B', formula_a_b), ('B', 'C', formula_b_c)
]:
# portion_index = 0 means beginning and portion_index = 1 means ending
for portion_index_1 in [0, 1]:
for portion_index_2 in [0, 1]:
# Adding the known (before, same, aster) relations (formula.compare) to our algorithm
search_tree.add_relation(
temporal_event_1_key, portion_index_1,
temporal_event_2_key, portion_index_2,
formula.compare(dist_key_by_index[portion_index_1],
dist_key_by_index[portion_index_2]))
# Initiating a RailwaySystem instance as the starting point of DFS
rails = RailwaySystem()
rails.add_rail('A')
rails.add_rail('B')
rails.add_rail('C')
# Executing DFS
solutions = search_tree.find_solutions(rails)
# Now we need to see how reliable the solutions are
strength_total, strength_per_solution = compute_railway_strength(solutions,
goals=[
('A',
'C')
])
# If the total strength of the solutions is zero, we can conclude that the inputs were not composable
if strength_total == 0:
return [], 0
# If not, we return the solutions with their corresponding truth value
# Some solution might be repeated, we only pass one instance of these solution but
# add up their truth values for that single instance, we do this by some dict tricks
for railway_system in solutions:
railway_system.compress()
A = convert_rail_to_trapezium_event(railway_system, 'A')
C = convert_rail_to_trapezium_event(railway_system, 'C')
solution = A * C
if solution in strengths_by_solution:
strengths_by_solution[solution] += strength_per_solution
else:
strengths_by_solution[solution] = strength_per_solution
result = []
for solution in strengths_by_solution:
strength = strengths_by_solution[solution]
result.append((solution, strength))
return result, strength_total
for event_key in events:
rails.add_rail(event_key)
solutions = search_tree.find_solutions(rails)
average_solution = numpy.zeros(13)
print "Solutions"
for railway_system in solutions:
railway_system.compress()
estimate = []
A = convert_rail_to_trapezium_event(railway_system, "A")
B = convert_rail_to_trapezium_event(railway_system, "B")
C = convert_rail_to_trapezium_event(railway_system, "C")
solution = (A * C).to_vector()
print solution
average_solution += solution / len(solutions)
events = {"A": A, "B": B, "C": C}
for a_key, b_key in [("A", "B"), ("B", "C")]:
a, b = events[a_key], events[b_key]
for portion_index_a in [0, 1]:
for portion_index_b in [0, 1]:
relation = formula.compare(a[portion_index_a], b[portion_index_b])
estimate.append(relation)
# print goal - numpy.array(estimate)
print compute_railway_strength(solutions, goals=[("A", "C")])
print "Average\n", average_solution
print "Error\n", actual_solution - average_solution
rails.add_rail(event_key)
solutions = search_tree.find_solutions(rails)
average_solution = numpy.zeros(13)
print 'Solutions'
for railway_system in solutions:
railway_system.compress()
estimate = []
A = convert_rail_to_trapezium_event(railway_system, 'A')
B = convert_rail_to_trapezium_event(railway_system, 'B')
C = convert_rail_to_trapezium_event(railway_system, 'C')
solution = (A * C).to_vector()
print solution
average_solution += solution / len(solutions)
events = {'A': A, 'B': B, 'C': C}
for a_key, b_key in [('A', 'B'), ('B', 'C')]:
a, b = events[a_key], events[b_key]
for portion_index_a in [0, 1]:
for portion_index_b in [0, 1]:
relation = formula.compare(a[portion_index_a],
b[portion_index_b])
estimate.append(relation)
# print goal - numpy.array(estimate)
print compute_railway_strength(solutions, goals=[('A', 'C')])
print 'Average\n', average_solution
print 'Error\n', actual_solution - average_solution
for event_key in events:
rails.add_rail(event_key)
solutions = search_tree.find_solutions(rails)
average_solution = numpy.zeros(13)
print 'Solutions'
for railway_system in solutions:
railway_system.compress()
estimate = []
A = convert_rail_to_trapezium_event(railway_system, 'A')
B = convert_rail_to_trapezium_event(railway_system, 'B')
C = convert_rail_to_trapezium_event(railway_system, 'C')
solution = (A * C).to_vector()
print solution
average_solution += solution / len(solutions)
events = {'A': A, 'B': B, 'C': C}
for a_key, b_key in [('A', 'B'), ('B', 'C')]:
a, b = events[a_key], events[b_key]
for portion_index_a in [0, 1]:
for portion_index_b in [0, 1]:
relation = formula.compare(a[portion_index_a], b[portion_index_b])
estimate.append(relation)
# print goal - numpy.array(estimate)
print compute_railway_strength(solutions)
print 'Average\n', average_solution
print 'Error\n', actual_solution - average_solution
