def __init__(self, distribution_beginning, distribution_ending,
bins=50, relation_formula=None):
if not isinstance(distribution_beginning, rv_frozen):
raise TypeError("'distribution_beginning' should be a scipy frozen distribution")
if not isinstance(distribution_ending, rv_frozen):
raise TypeError("'distribution_ending' should be a scipy frozen distribution")
self._distribution_beginning = distribution_beginning
self._distribution_ending = distribution_ending
a, beginning = calculate_bounds_of_probability_distribution(distribution_beginning)
ending, b = calculate_bounds_of_probability_distribution(distribution_ending)
self._beginning = UnixTime(beginning)
self._ending = UnixTime(ending)
self.membership_function = MembershipFunction(self)
bins_beginning = bins / 2
bins_ending = bins - bins_beginning
self.interval_beginning = TimeInterval(a, beginning, bins_beginning)
self.interval_ending = TimeInterval(ending, b, bins_ending)
list.__init__(self, self.interval_beginning + self.interval_ending)
TimeInterval.__init__(self, a, b, bins)
if relation_formula is None:
relation_formula = RelationFormulaGeometricMean()
elif not isinstance(relation_formula, BaseRelationFormula):
raise TypeError("'relation_formula' should be of type 'BaseRelationFormula'")
relation_formula.bounds[distribution_beginning] = self.a, self.beginning
relation_formula.bounds[distribution_ending] = self.ending, self.b
self._formula_creator = FormulaCreator(relation_formula)
from spatiotemporal.temporal_events.trapezium import TemporalEventTrapezium
from spatiotemporal.temporal_events.relation_formulas import FormulaCreator
from spatiotemporal.temporal_events.composition.non_linear_least_squares import DecompositionFitter
import matplotlib.pyplot as plt
all_relations = "pmoFDseSdfOMP"
a = TemporalEventTrapezium(1, 12, 4, 8)
b = TemporalEventTrapezium(9, 17, 13, 15)
# compute relations between events
temporal_relations = a * b
print("Relations: {0}".format(temporal_relations.to_list()))
# print degree for every relation
for relation in all_relations:
print(relation, temporal_relations[relation])
# plot events
a.plot(show_distributions=True).ylim(ymin=-0.1, ymax=1.1)
b.plot(show_distributions=True).figure()
plt.show()
# from the 13 relations, learns parameters for all combinations of the
# before, same, and after relationships between the beginning and
# ending distributions of the two intervals
formula = FormulaCreator(DecompositionFitter(temporal_relations))
# from these relationships, computes the 13 relations again
relations_estimate = formula.calculate_relations()
print("Estimated relations: {0}".format(relations_estimate.to_list()))
