def convert_rail_to_trapezium_event(railway_system, rail_key):
a = railway_system[rail_key][0].a
beginning = railway_system[rail_key][0].b
ending = railway_system[rail_key][1].a
b = railway_system[rail_key][1].b
return TemporalEventTrapezium(a, b, beginning, ending)
def animate(self, t):
interval = self.interval
B = TemporalEventTrapezium(interval[t], interval[t] + self.event_b_length_total,
interval[t] + self.event_b_length_beginning,
interval[t] + self.event_b_length_middle)
plt.figure()
B.plot().show()
a_b = (self.event_a * B).to_list()
b_c = (B * self.event_c).to_list()
self.line_b.set_data(B, B.membership_function)
artists = []
for rect, h in zip(self.rects_a_b, a_b):
rect.set_height(h)
artists.append(rect)
for rect, h in zip(self.rects_b_c, b_c):
rect.set_height(h)
artists.append(rect)
artists.append(self.line_a)
artists.append(self.line_b)
artists.append(self.line_c)
return artists
def animate(self, t):
interval = self.interval
B = TemporalEventTrapezium(interval[t],
interval[t] + self.event_b_length_total,
interval[t] + self.event_b_length_beginning,
interval[t] + self.event_b_length_middle)
plt.figure()
B.plot().show()
a_b = (self.event_a * B).to_list()
b_c = (B * self.event_c).to_list()
self.line_b.set_data(B, B.membership_function)
artists = []
for rect, h in zip(self.rects_a_b, a_b):
rect.set_height(h)
artists.append(rect)
for rect, h in zip(self.rects_b_c, b_c):
rect.set_height(h)
artists.append(rect)
artists.append(self.line_a)
artists.append(self.line_b)
artists.append(self.line_c)
return artists
artists = []
for rect, h in zip(self.rects_a_b, a_b):
rect.set_height(h)
artists.append(rect)
for rect, h in zip(self.rects_b_c, b_c):
rect.set_height(h)
artists.append(rect)
artists.append(self.line_a)
artists.append(self.line_b)
artists.append(self.line_c)
return artists
def show(self):
fr = len(self.interval) - 1
anim = animation.FuncAnimation(self.fig,
self.animate,
init_func=self.init,
frames=fr,
interval=fr,
blit=True)
self.plt.show()
if __name__ == '__main__':
anim = Animation(TemporalEventTrapezium(4, 8, 5, 7),
TemporalEventTrapezium(0, 10, 6, 9),
TemporalEventTrapezium(0.5, 11, 1, 3))
# anim.show()
__author__ = 'sebastian'
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
if __name__ == '__main__':
from spatiotemporal.temporal_events.trapezium import generate_random_events
from spatiotemporal.temporal_events.util import compute_railway_strength
import numpy
from spatiotemporal.temporal_events import RelationFormulaConvolution
search_tree = DepthFirstSearchComposition()
formula = RelationFormulaConvolution()
# A, B, C = generate_random_events(3)
# for event in [A, B, C]:
# p = ''
# for point in [event.a, event.b, event.beginning, event.ending]:
# p += str((point - A.a) / (A.beginning - A.a)) + ', '
# print p
A = TemporalEventTrapezium(0, 30, 10, 20)
B = TemporalEventTrapezium(1, 9, 2, 8)
C = TemporalEventTrapezium(0, 30, 10, 20)
actual_solution = (A * C).to_vector()
print 'Actual\n', actual_solution
goal = []
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])
goal.append(relation)
search_tree.add_relation(a_key, portion_index_a, b_key, portion_index_b, relation)
__author__ = 'sebastian'
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()
from spatiotemporal.temporal_events.trapezium import TemporalEventTrapezium
# f = CombinationFormulaGeometricMeanTrapezium()
# rf = RelationFormulaGeometricMean()
# a, b_beg, b_end = uniform(5, 2), uniform(1, 3), uniform(8, 4)
# [a_h], b_end_h = f.unpack(rf.compare(a, b_beg), rf.compare(a, b_end))
#
# print a_h.args, b_end_h.args
# print rf.compare(a, b_beg), rf.compare(a, b_end)
# print rf.compare(a_h, uniform_reference), rf.compare(a_h, b_end_h)
#
# quit()
A = TemporalEventTrapezium(2, 7, 5, 6)
B = TemporalEventTrapezium(1, 6, 3, 4)
C = TemporalEventTrapezium(0, 9, 7, 8)
f = CombinationFormulaGeometricMeanTrapezium()
rf = RelationFormulaGeometricMean()
# print rf.compare(f.unpack((1, 0, 0))[0], uniform(0, 1))
A_, B_beg, B_end, C_ = A.distribution_beginning, B.distribution_beginning, B.distribution_ending, C.distribution_beginning
# A, B, C = C.distribution_beginning, B.distribution_beginning, A.distribution_beginning
print f.combine(rf.compare(A_, B_beg),
rf.compare(A_, B_end),
rf.compare(B_beg, C_),
rf.compare(B_end, C_))
print rf.compare(A_, C_)