def test_creation(self):
self.assertRaises((Exception, ArgumentError), TrajectoryLineSegment, \
LineSegment.from_points([Point(0, 0), Point(1, 1)]), -1)
self.assertRaises((Exception, ArgumentError), TrajectoryLineSegment,
None, 1)
self.assertRaises((Exception, ArgumentError), TrajectoryLineSegment, \
LineSegment.from_points([Point(0, 0), Point(1, 1)]), None)
def test_three_lines(self):
traj_line_segments = [self.create_trajectory_line_seg((0.0, 0.0), (2.0, 0.0), 0), \
self.create_trajectory_line_seg(start=(0.0, 1.0), end=(2.0, 1.0), traj_id=2), \
self.create_trajectory_line_seg(start=(0.0, 2.0), end=(2.0, 2.0), traj_id=1)]
res = get_representative_line_from_trajectory_line_segments(traj_line_segments, 1, 0)
expected = [Point(0, 1), Point(2, 1)]
self.verify_iterable_works_more_than_once(iterable=res, list_ob=expected)
def test_two_line_segs(self):
points = [[Point(0, 1), Point(1, 1)], \
[Point(0, 0), Point(1, 0)]]
expected = [[Point(0, 0.5), (1, 0.5)]]
res = the_whole_enchilada(point_iterable_list=points, \
epsilon=100, min_neighbors=1, min_num_trajectories_in_cluster=2, min_vertical_lines=2, min_prev_dist=1.0)
self.verify_iterable_works_more_than_once(iterable=res,
list_ob=expected)
def test_simple_lines(self):
traj_line_segments = [self.create_trajectory_line_seg(start=(0.0, 0.0), end=(1.0, 0.0), traj_id=0), \
self.create_trajectory_line_seg(start=(0.0, 1.0), end=(1.0, 1.0), traj_id=1)]
res = get_representative_line_from_trajectory_line_segments(traj_line_segments, 1, 0)
expected = [Point(0, 0.5), Point(1, 0.5)]
for p in res:
self.assertEquals(p.__class__, Point)
self.verify_iterable_works_more_than_once(iterable=res, list_ob=expected)
def test_two_short_line_endpoints_get_picked(self):
lines = [LineSegment.from_points([Point(0, 0), Point(1, 0)]), \
LineSegment.from_points([Point(0, 1), Point(1, 1)])]
traj_lines = [TrajectoryLineSegment(lines[0], 0), \
TrajectoryLineSegment(lines[1], 1)]
res = get_representative_trajectory_average_inputs(trajectory_line_segments=traj_lines, \
min_prev_dist=1, min_lines=2)
expected = 2
self.assertEquals(len(res), expected)
def test_four_line_sets_should_result(self):
lines = [LineSegment.from_points([Point(0, 0), Point(1, 0)]), \
LineSegment.from_points([Point(0.5, 1), Point(1.5, 1)])]
traj_lines = [TrajectoryLineSegment(lines[0], 0), \
TrajectoryLineSegment(lines[1], 1)]
res = get_representative_trajectory_average_inputs(trajectory_line_segments=traj_lines, \
min_prev_dist=0.5, min_lines=1)
expected = 4
self.assertEquals(len(res), expected)
def test_single_line_seg(self):
points = [[Point(0, 0), Point(2, 0)]]
expected = [[Point(0, 0), Point(2, 0)]]
res = the_whole_enchilada(point_iterable_list=points, \
epsilon=1, \
min_neighbors=0, min_num_trajectories_in_cluster=1, \
min_vertical_lines=1, \
min_prev_dist=1)
self.verify_point_iterable_almost_equals_list(iterable=res,
expected_list=expected)
def test_three_vertical_points_in_a_row_small_spacing(self):
points = [[Point(0, 0), Point(0, 1.1), Point(0, 2.2)]]
expected = [[Point(0, 0), Point(0, 2.2)]]
res = the_whole_enchilada(point_iterable_list=points, \
epsilon=1, \
min_neighbors=0, min_num_trajectories_in_cluster=1, \
min_vertical_lines=1, \
min_prev_dist=1)
self.verify_point_iterable_almost_equals_list(iterable=res,
expected_list=expected)
def test_three_points_in_a_row_negative_diagonal(self):
points = [[Point(0, 20), Point(10, 10), Point(20, 0)]]
expected = [[Point(0, 20), Point(20, 0)]]
res = the_whole_enchilada(point_iterable_list=points, \
epsilon=0, \
min_neighbors=0, min_num_trajectories_in_cluster=1, \
min_vertical_lines=1, \
min_prev_dist=(2 * math.sqrt(2.0) - DECIMAL_MAX_DIFF_FOR_EQUALITY))
self.verify_point_iterable_almost_equals_list(iterable=res,
expected_list=expected)
def test_builds_graph_correctly_with_empty_trajectories(self):
trajectories = [[], \
[Point(0, 0), Point(1, 1), Point(2, 2)], \
[], \
[Point(10, 0), Point(11, 0), Point(12, 0)], \
[Point(0, 10), Point(0, 11), Point(0, 12)], \
[]]
filtered_trajs = []
cur_index = 0
for traj in trajectories:
if len(traj) > 0:
filtered_trajs.append(FilteredTrajectory(traj, cur_index))
cur_index += 1
pt_graph = build_point_graph(filtered_trajectories=filtered_trajs)
expected_neighbors = [set([1]), set([0, 2]), set([1]), \
set([4]), set([3, 5]), set([4]), \
set([7]), set([6, 8]), set([7])]
expected_trajectory_ids = [0, 0, 0, \
1, 1, 1, \
2, 2, 2]
self.verify_pt_graph(trajectories=trajectories, \
expected_neighbors=expected_neighbors, \
expected_trajectory_ids=expected_trajectory_ids, \
pt_graph=pt_graph)
def test_computes_components_correctly_when_all_in_same_component(self):
trajectories = [[], \
[Point(0, 0), Point(1, 1), Point(2, 2)], \
[], \
[Point(10, 0), Point(11, 0), Point(12, 0)], \
[Point(0, 10), Point(0, 11), Point(0, 12)], \
[]]
filtered_trajs = []
cur_index = 0
for traj in trajectories:
if len(traj) > 0:
filtered_trajs.append(FilteredTrajectory(traj, cur_index))
cur_index += 1
pt_graph = build_point_graph(filtered_trajectories=filtered_trajs)
def dummy_find_other_neighbors_func(pt_node, pt_graph):
other_neighbors_table = [[3], [], [], [0], [], [6], [5], [], []]
return other_neighbors_table[pt_node.index]
compute_graph_component_ids(pt_graph=pt_graph, \
find_other_neighbors_func=dummy_find_other_neighbors_func)
expected_component_ids = [0] * 9
for pt_node in pt_graph:
self.assertEquals(pt_node.graph_component_id, \
expected_component_ids[pt_node.index], \
"pt node comp id at index " + str(pt_node.index) + \
" is " + str(pt_node.graph_component_id) + \
" but expected " + \
str(expected_component_ids[pt_node.index]))
def test_partition_happens_with_three_points_in_a_row_horizontally(self):
points = [[Point(0, 0), Point(2, 0), Point(4, 0)]]
expected = [[Point(0, 0), Point(4, 0)]]
res = the_whole_enchilada(point_iterable_list=points, \
epsilon=0, \
min_neighbors=0, min_num_trajectories_in_cluster=1, \
min_vertical_lines=1, \
min_prev_dist=4)
self.verify_iterable_works_more_than_once(iterable=res,
list_ob=expected)
self.verify_point_iterable_almost_equals_list(iterable=res,
expected_list=expected)
def create_line(self,
horizontal_start,
horizontal_end,
line_set_ids,
trajectory_id,
orig_pos=None):
if orig_pos == None:
orig_pos = random.randint(0, 1000)
return {'trajectory_line_segment': \
TrajectoryLineSegment(LineSegment.from_points([Point(horizontal_start, random.randint(0, 1000)), \
Point(horizontal_end, random.randint(0, 1000))]), \
trajectory_id, orig_pos), \
'line_set_ids': line_set_ids}
class TestPoint(ClusterCandidate):
def __init__(self, x, y):
ClusterCandidate.__init__(self)
self.point = Point(x, y)
def distance_to_candidate(self, other_candidate):
return self.point.distance_to(other_candidate.point)
def read_test_file(file_name):
file = open(file_name)
num_dimensions = int(file.next())
if num_dimensions != 2:
raise Exception("didn't make this for this")
num_traj = int(file.next())
exp_id = 0
all_traj = []
for line in file:
tokens = line.split()
id = int(tokens[0])
num_points = int(tokens[1])
if id != exp_id:
raise Exception("something wrong")
traj = []
for i in filter(lambda x: x % 2 == 0, xrange(0, num_points)):
x = float(tokens[i + 2])
try:
y = float(tokens[i + 3])
except IndexError:
print "error on index " + str(i) + " with id " + str(id)
traj.append(Point(x, y))
all_traj.append(traj)
exp_id += 1
file.close()
return all_traj
def build_test_object(self, points, model_cost, encoding_cost, start, end):
traj = Trajectory(0)
traj.points.extend(map(lambda point: Point(point[0], point[1]),
points))
return {'trajectory': traj, 'model_cost': model_cost, \
'encoding_cost': encoding_cost, 'total_cost': model_cost + encoding_cost, \
'start': start, 'end': end}
class TestPoint(ClusterCandidate):
def __init__(self, x, y):
ClusterCandidate.__init__(self)
self.point = Point(x, y)
def distance_to_candidate(self, other_candidate):
return self.point.distance_to(other_candidate.point)
def get_representative_line_from_rotated_line_segments(trajectory_line_segments, min_vertical_lines, min_prev_dist):
inputs = get_representative_trajectory_average_inputs(trajectory_line_segments=trajectory_line_segments, \
min_prev_dist=min_prev_dist, min_lines=min_vertical_lines)
out = []
for line_seg_averaging_input in inputs:
vert_val = get_mean_vertical_coordinate_in_line_segments(line_seg_averaging_input)
out.append(Point(line_seg_averaging_input['horizontal_position'], vert_val))
return out
def test_parrallel_distance_joins_two_lines_segs(self):
points = [[Point(0, 0), Point(1, 0)], \
[Point(2, 0), Point(3, 0)]]
expected = [[Point(0, 0), Point(1, 0), Point(2, 0), Point(3, 0)]]
res = the_whole_enchilada(point_iterable_list=points, \
epsilon=1, \
min_neighbors=1, min_num_trajectories_in_cluster=2, \
min_vertical_lines=1, \
min_prev_dist=1)
self.verify_point_iterable_almost_equals_list(iterable=res,
expected_list=expected)
def test_reads_file(self):
expected = [[Point(1, 2), Point(3, 4)], \
[Point(2, 4), Point(6, 8)], \
[Point(1, 3), Point(5, 7)], \
[Point(9, 8)]]
res = read_test_file(os.path.dirname(__file__) + "\\dummy_input.txt")
exp_iter = iter(expected)
for traj in res:
exp_line = exp_iter.next()
self.assertListEqual(exp_line, traj)
def test_normal_turning_line(self):
points = [[
Point(0, 0),
Point(20, 20),
Point(40, 0),
Point(60, 20),
Point(80, 0)
]]
expected = [[
Point(0, 0),
Point(20, 20),
Point(40, 0),
Point(60, 20),
Point(80, 0)
]]
res = the_whole_enchilada(point_iterable_list=points, \
epsilon=100, \
min_neighbors=3, min_num_trajectories_in_cluster=1, \
min_vertical_lines=1, \
min_prev_dist=1)
self.verify_point_iterable_almost_equals_list(iterable=res,
expected_list=expected)
def test_two_vertical_line_segments(self):
points = [[Point(0, 0), Point(0, 1)], \
[Point(1, 0), Point(1, 1)]]
expected = [[Point(0.5, 0.0), Point(0.5, 1)]]
res = the_whole_enchilada(point_iterable_list=points, \
epsilon=100, min_neighbors=0, min_num_trajectories_in_cluster=1, min_vertical_lines=1, min_prev_dist=0)
self.verify_point_iterable_almost_equals_list(iterable=res,
expected_list=expected)
def test_trajectory_loops_around(self):
points = [[
Point(0, 1000.0),
Point(1000.0, 2000.0),
Point(2000.0, 1000.0),
Point(1000.0, 0),
Point(0, 1000.0)
]]
expected = [[Point(0, 1000), Point(1000, 1000), Point(2000, 1000)]]
res = the_whole_enchilada(point_iterable_list=points, \
epsilon=3000, \
min_neighbors=3, min_num_trajectories_in_cluster=1, \
min_vertical_lines=1, \
min_prev_dist=10)
self.verify_point_iterable_almost_equals_list(iterable=res,
expected_list=expected)
def test_two_45_degree_line_segments(self):
points = [[Point(0, 1), Point(1, 0)], \
[Point(1, 2), Point(2, 1)]]
expected = [[Point(0.5, 1.5), Point(1.5, 0.5)]]
res = the_whole_enchilada(point_iterable_list=points, \
epsilon=math.sqrt(2.0), min_neighbors=1, min_num_trajectories_in_cluster=2, \
min_vertical_lines=2, \
min_prev_dist=math.sqrt(2.0) - DECIMAL_MAX_DIFF_FOR_EQUALITY)
self.verify_point_iterable_almost_equals_list(iterable=res,
expected_list=expected)
def test_four_points_in_a_row_slight_diagonally(self):
points = [[
Point(0, 0),
Point(100, 10),
Point(200, 20),
Point(300, 30)
]]
expected = [[Point(0, 0), Point(300, 30)]]
res = the_whole_enchilada(point_iterable_list=points, \
epsilon=0, \
min_neighbors=0, min_num_trajectories_in_cluster=1, \
min_vertical_lines=1, \
min_prev_dist=1)
self.verify_point_iterable_almost_equals_list(iterable=res,
expected_list=expected)
def test_obvious_NOT_partition(self):
trajectory = [Point(0, 0), Point(1, 1), Point(2, 0)]
expected_par = [0, 1, 2]
self.verify_iterable_works_more_than_once(expected_par, \
call_partition_trajectory(trajectory_point_list=trajectory))
def __init__(self, x, y):
ClusterCandidate.__init__(self)
self.point = Point(x, y)
def test_doesnt_partition_big_diamond(self):
traj = [Point(0, 100), Point(100, 200), Point(200, 100), Point(100, 0), Point(0, 100)]
expected_par = [0, 1, 2, 3, 4]
self.verify_iterable_works_more_than_once(call_partition_trajectory(trajectory_point_list=traj), \
expected_par)
def test_three_points_in_a_row_diagonal_shorter(self):
traj = [Point(0, 0), Point(1, 1), Point(2, 2)]
expected_par = [0, 2]
self.verify_iterable_works_more_than_once(call_partition_trajectory(trajectory_point_list=traj), \
expected_par)
def test_three_points_horizontal_big_spacing(self):
traj = [Point(0, 0), Point(200, 0), Point(444, 0)]
expected_par = [0, 2]
self.verify_iterable_works_more_than_once(call_partition_trajectory(trajectory_point_list=traj), \
expected_par)
def test_not_enough_points(self):
self.assertRaises(ValueError, call_partition_trajectory, [Point(1, 1)])
self.assertRaises(ValueError, call_partition_trajectory, [])
def test_longer_obvious_partition(self):
trajectory = [Point(0, 0), Point(10000, 1), Point(20000, 0), Point(30000, 2), Point(40000, 1)]
expected_par = [0, 4]
self.verify_iterable_works_more_than_once(expected_par, \
call_partition_trajectory(trajectory_point_list=trajectory))