def test_distance_of_0_0_from_flat_line_yintercept_is_zero(self):
x = LineModel(0, 1, -3)
test_point = Point(0, 0)
distance_actual = x.compute_distance(test_point)
distance_expected = 3
self.assertEqual(distance_actual, distance_expected)
pass
def test_distance_of_1_0_from_slope_45degrees_yintercept_is_zero(self):
x = LineModel(-1, 1, 0)
test_point = Point(1, 0)
distance_actual = x.compute_distance(test_point)
distance_expected = 1 / math.sqrt(2)
self.assertEqual(distance_actual, distance_expected)
pass
def test_distance_of_origin_from_slope_45degrees_yintercept_is_zero(self):
x = LineModel(-1, 1, 0)
test_origin = Point(0, 0)
distance_actual = x.compute_distance(test_origin)
distance_expected = 0
self.assertEqual(distance_actual, distance_expected)
pass
def compute_average_distance(self, model: LineModel,
points: list) -> float:
lst_distances = list()
for p in points:
distance = model.compute_distance(p)
lst_distances.append(distance)
mean = statistics.mean(lst_distances)
return mean
def compute_mean_squared_distance(self, model: LineModel,
points: list) -> float:
lst_distances = list()
for p in points:
distance = model.compute_distance(p)
lst_distances.append(distance**2)
sum_of_squared_distances = sum(lst_distances)
sqroot = sum_of_squared_distances**0.5
mean = sqroot / len(lst_distances)
return mean
def get_inliers_from_model(self, model: LineModel,
points_old_inliers: list) -> list:
lst_inliers = list()
for p in self.points:
if ((p in points_old_inliers) == True):
continue
distance_from_model: float = model.compute_distance(p)
if (distance_from_model > self.threshold_error):
continue
lst_inliers.append(p)
return lst_inliers