def test_calculate_normal_3d(self):
test_1 = np.array([[1, 0, 0], [0, 1, 0]])
out = calculate_normal(test_1)
self.assertTrue((out != 0).any)
self.assertTrue(self.arr_eq(np.matmul(test_1, out.T), np.array([0,
0])))
test_2 = np.array([[3, 9, 1], [-8, 22, 12]])
out = calculate_normal(test_2)
self.assertTrue((out != 0).any)
self.assertTrue(self.arr_eq(np.matmul(test_2, out.T), np.array([0,
0])))
def test_calculate_normal_2d(self):
test_1 = np.array([1, 2])
out = calculate_normal(test_1)
self.assertTrue((out != 0).any)
self.assertEqual(np.dot(test_1, out), 0)
test_2 = np.array([1000, -333])
out = calculate_normal(test_2)
self.assertTrue((out != 0).any)
self.assertEqual(np.dot(test_2, out), 0)
test_3 = np.array([[1, 20]])
out = calculate_normal(test_3)
self.assertTrue((out != 0).any)
self.assertEqual(np.dot(test_3, out), 0)
continue
yield np.array([x, y])
yield np.array([-x, y])
if __name__ == "__main__":
import aistats.utils as aiu
dims = [1, 1, 1, 1, 1]
en = IterEnumerator(dims) #NaiveEnumerator([10, 4, 3])
dist_normals, points = set(), 0 # 12150, 278k
for point in en.generate_points():
points += 1
if points % 10000 == 0:
print(
f"Point n-tuples: {points}\nDist_points: {len(dist_normals)}")
normal = aiu.calculate_normal(point)
nmlzd = aiu.normalize_vector(normal)
dist_normals.add(tuple(nmlzd))
dist_normals.add(tuple(-nmlzd))
#print(dist_normals)
print(f"Point n-tuples: {points}\nDist_points: {len(dist_normals)}")
print("NAIVE")
en = NaiveEnumerator(dims) # NaiveEnumerator([10, 4, 3])
dist_normals2, points2 = set(), 0 # 12150, 278k
for point in en.generate_points():
points2 += 1
if points2 % 10000 == 0:
print(
f"Point n-tuples: {points2}\nDist_normals: {len(dist_normals2)}"
)
def neighbour_normal(self) -> np.ndarray:
neighbours = self.neighbour_matrix()
normal = aiu.calculate_normal(neighbours)
return aiu.normalize_vector(normal)
def generate_normals(self):
for points in self.enumerator.generate_points():
normal = aiu.calculate_normal(points)
yield aiu.normalize_vector(normal)