def energy(self):
the_whole_enchilada(point_iterable_list=self.state.get_input_trajectories(), \
epsilon=self.state.get_epsilon(), \
min_neighbors=0, \
min_num_trajectories_in_cluster=1, \
min_vertical_lines=100, \
min_prev_dist=100, \
clusters_hook=self.state.compute_entropy)
return self.state.get_entropy()
def energy(self):
the_whole_enchilada(point_iterable_list=self.state.get_input_trajectories(), \
epsilon=self.state.get_epsilon(), \
min_neighbors=0, \
min_num_trajectories_in_cluster=1, \
min_vertical_lines=100, \
min_prev_dist=100, \
clusters_hook=self.state.compute_entropy)
return self.state.get_entropy()
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_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_hooks_get_called_with_correct_stuff(self):
part_hook_called = [False]
clusters_hook_called = [False]
def test_partition_hook(points):
expected_partitions = iter([self.create_simple_line_seg((0, 0), (4, 0)), \
self.create_simple_line_seg((0, 1), (4, 1)), \
self.create_simple_line_seg((0, 3), (4, 3))])
i = 0
for traj in points:
exp = expected_partitions.next()
self.verify_lines_almost_equal(exp, traj.line_segment)
i += 1
self.assertRaises(StopIteration, expected_partitions.next)
part_hook_called[0] = True
def test_clusters_hook(clusters):
cluster_one = [self.create_simple_line_seg((0, 0), (4, 0)), \
self.create_simple_line_seg((0, 1), (4, 1))]
cluster_two = [self.create_simple_line_seg((0, 3), (4, 3))]
expected_clusters = [cluster_one, cluster_two]
def line_seg_groups_equal(a, b):
for actual in a:
if not any(map(lambda x: self.lines_almost_equal(x, actual), b)):
return False
return len(a) == len(b)
def cluster_expected(cluster):
for exp in expected_clusters:
if line_seg_groups_equal(exp, cluster):
expected_clusters.remove(exp)
return True
return False
for actual_cluster in clusters:
actual_traj_set = map(lambda x: x.line_segment, \
actual_cluster.get_trajectory_line_segments())
self.assertTrue(cluster_expected(actual_traj_set), \
" cluster: " + str(actual_traj_set) + \
" is not expected. here are the expected " + \
" line seg clusters: " + str(expected_clusters))
self.assertTrue(len(expected_clusters) == 0, " failed, still " + \
" have this left in expected: " + str(expected_clusters))
clusters_hook_called[0] = True
points = [[Point(0, 0), Point(2, 0), Point(4, 0)], \
[Point(0, 1), Point(2, 1), Point(4, 1)], \
[Point(0, 3), Point(2, 3), Point(4, 3)]]
expected = [[Point(0, 0.5), Point(4, 0.5)], \
[Point(0, 3), Point(4, 3)]]
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=1.0, \
partitioned_points_hook=test_partition_hook, \
clusters_hook=test_clusters_hook)
self.verify_iterable_works_more_than_once(iterable=res, list_ob=expected)
self.assertTrue(part_hook_called[0])
self.assertTrue(clusters_hook_called[0])
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_one_long_line_joins_two_short_lines(self):
points = [[Point(0, 1), Point(20, 1), Point(40, 1), Point(60, 1), Point(80, 1)], \
[Point(0, 0), Point(20, 0)], \
[Point(60, 2), Point(80, 2)]]
expected = [[Point(0, 0.5), Point(20, 0.5), Point(60, 1.5), Point(80, 1.5)]]
res = the_whole_enchilada(point_iterable_list=points, \
epsilon=1, min_neighbors=2, min_num_trajectories_in_cluster=3, min_vertical_lines=2, min_prev_dist=10.0)
self.verify_iterable_works_more_than_once(iterable=res, list_ob=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_four_points_in_a_row_diagonally(self):
points = [[Point(0, 0), Point(10, 10), Point(20, 20), Point(30, 30)]]
expected = [[Point(0, 0), Point(30, 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=math.sqrt(2.0) - DECIMAL_MAX_DIFF_FOR_EQUALITY)
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_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_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_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_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_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 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_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_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_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 run_deer_stuff():
print "hello"
file = os.path.dirname(__file__) + "\\deer_1995.tra"
points = read_test_file(file)
traj_res = the_whole_enchilada(point_iterable_list=points, epsilon=40, min_neighbors=7, \
min_num_trajectories_in_cluster=2, min_vertical_lines=7, min_prev_dist=0.0)
print "heres the output: " + str(traj_res)
print "about to print out the lines"
for traj in traj_res:
print "A new average trajectory:"
for point in traj:
print " x: " + str(point.x) + ", y: " + str(point.y)
print "done"
def run_deer_stuff():
print "hello"
file = os.path.dirname(__file__) + "\\deer_1995.tra"
points = read_test_file(file)
traj_res = the_whole_enchilada(point_iterable_list=points, epsilon=40, min_neighbors=7, \
min_num_trajectories_in_cluster=2, min_vertical_lines=7, min_prev_dist=0.0)
print "heres the output: " + str(traj_res)
print "about to print out the lines"
for traj in traj_res:
print "A new average trajectory:"
for point in traj:
print " x: " + str(point.x) + ", y: " + str(point.y)
print "done"
def test_one_long_line_joins_two_short_lines(self):
points = [[Point(0, 1), Point(20, 1), Point(40, 1), Point(60, 1), Point(80, 1)], \
[Point(0, 0), Point(20, 0)], \
[Point(60, 2), Point(80, 2)]]
expected = [[
Point(0, 0.5),
Point(20, 0.5),
Point(60, 1.5),
Point(80, 1.5)
]]
res = the_whole_enchilada(point_iterable_list=points, \
epsilon=1, min_neighbors=2, min_num_trajectories_in_cluster=3, min_vertical_lines=2, min_prev_dist=10.0)
self.verify_iterable_works_more_than_once(iterable=res,
list_ob=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_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_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_hooks_get_called_with_correct_stuff(self):
part_hook_called = [False]
clusters_hook_called = [False]
def test_partition_hook(points):
expected_partitions = iter([self.create_simple_line_seg((0, 0), (4, 0)), \
self.create_simple_line_seg((0, 1), (4, 1)), \
self.create_simple_line_seg((0, 3), (4, 3))])
i = 0
for traj in points:
exp = expected_partitions.next()
self.verify_lines_almost_equal(exp, traj.line_segment)
i += 1
self.assertRaises(StopIteration, expected_partitions.next)
part_hook_called[0] = True
def test_clusters_hook(clusters):
cluster_one = [self.create_simple_line_seg((0, 0), (4, 0)), \
self.create_simple_line_seg((0, 1), (4, 1))]
cluster_two = [self.create_simple_line_seg((0, 3), (4, 3))]
expected_clusters = [cluster_one, cluster_two]
def line_seg_groups_equal(a, b):
for actual in a:
if not any(
map(lambda x: self.lines_almost_equal(x, actual),
b)):
return False
return len(a) == len(b)
def cluster_expected(cluster):
for exp in expected_clusters:
if line_seg_groups_equal(exp, cluster):
expected_clusters.remove(exp)
return True
return False
for actual_cluster in clusters:
actual_traj_set = map(lambda x: x.line_segment, \
actual_cluster.get_trajectory_line_segments())
self.assertTrue(cluster_expected(actual_traj_set), \
" cluster: " + str(actual_traj_set) + \
" is not expected. here are the expected " + \
" line seg clusters: " + str(expected_clusters))
self.assertTrue(len(expected_clusters) == 0, " failed, still " + \
" have this left in expected: " + str(expected_clusters))
clusters_hook_called[0] = True
points = [[Point(0, 0), Point(2, 0), Point(4, 0)], \
[Point(0, 1), Point(2, 1), Point(4, 1)], \
[Point(0, 3), Point(2, 3), Point(4, 3)]]
expected = [[Point(0, 0.5), Point(4, 0.5)], \
[Point(0, 3), Point(4, 3)]]
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=1.0, \
partitioned_points_hook=test_partition_hook, \
clusters_hook=test_clusters_hook)
self.verify_iterable_works_more_than_once(iterable=res,
list_ob=expected)
self.assertTrue(part_hook_called[0])
self.assertTrue(clusters_hook_called[0])
