def call_partition_trajectory(trajectory_point_list):
if len(trajectory_point_list) < 2:
raise ValueError("didn't provide a trajectory with enough points")
traj_line_iterable_getter = TrajectoryLineSegmentIteratorGetter(trajectory_point_list)
cum_dist_getter_func = \
cummulative_distance_function_getter_adapter(perp_distance_func=lambda x, y: perpendicular_distance(x, y), \
angle_distance_func=lambda x, y: \
angular_distance(x, y), \
accumulator_wrapper=lambda x: \
-(math.pow(2, 32) - 1) if x == 0 else math.log(x, 2), \
accumulator_func_getter=get_number_list_reducer_that_returns_each_midway_val)
partition_from_index_getter = get_partition_from_index_creator(get_line_segment_from_points)
partition_cost_computer_func = part_cost_computer_adapter(part_cost_func=partition_cost_computer, \
line_segment_iterable_getter=traj_line_iterable_getter.get_iterable, \
partition_line_getter=partition_from_index_getter, \
distance_func_computer_getter=cum_dist_getter_func, \
line_segment_creator=get_line_segment_from_points)
no_par_cost_computer_func = no_part_cost_computer_adapter(no_part_cost_func=no_partition_cost_computer, \
line_segment_iterable_getter=traj_line_iterable_getter.get_iterable, \
line_segment_creator=get_line_segment_from_points)
return partition_trajectory(trajectory_point_list=trajectory_point_list, \
partition_cost_func=partition_cost_computer_func, \
no_partition_cost_func=no_par_cost_computer_func, \
get_model_cost_computer_func=get_model_cost_computer, \
individual_line_seg_model_cost_computer=individual_line_seg_model_cost_computer)
def encoding_cost(self, start, end):
self.check_indice_args(start, end)
approximation_line = LineSegment.from_points([self.points[start], self.points[end]])
total_perp = 0.0
total_angular = 0.0
for i in xrange(start, end):
line_seg = LineSegment.from_points([self.points[i], self.points[i + 1]])
total_perp += perpendicular_distance(approximation_line, line_seg)
total_angular += angular_distance(approximation_line, line_seg)
if total_perp < 1.0:
total_perp = 1.0
if total_angular < 1.0:
total_angular = 1.0
return math.log(total_perp, 2) + math.log(total_angular, 2)
def encoding_cost(self, start, end):
self.check_indice_args(start, end)
approximation_line = LineSegment.from_points([self.points[start], self.points[end]])
total_perp = 0.0
total_angular = 0.0
for i in xrange(start, end):
line_seg = LineSegment.from_points([self.points[i], self.points[i + 1]])
total_perp += perpendicular_distance(approximation_line, line_seg)
total_angular += angular_distance(approximation_line, line_seg)
if total_perp < 1.0:
total_perp = 1.0
if total_angular < 1.0:
total_angular = 1.0
return math.log(total_perp, 2) + math.log(total_angular, 2)
def distance_to_candidate(self, other_candidate):
if other_candidate == None or other_candidate.line_segment == None or self.line_segment == None:
raise Exception()
return perpendicular_distance(self.line_segment, other_candidate.line_segment) + \
angular_distance(self.line_segment, other_candidate.line_segment) + \
parrallel_distance(self.line_segment, other_candidate.line_segment)
def distance_to_candidate(self, other_candidate):
if other_candidate == None or other_candidate.line_segment == None or self.line_segment == None:
raise Exception()
return perpendicular_distance(self.line_segment, other_candidate.line_segment) + \
angular_distance(self.line_segment, other_candidate.line_segment) + \
parrallel_distance(self.line_segment, other_candidate.line_segment)
