def test_bulk_get_min_distance(self):
"""Verify that bulk version returns the same as regular version.
This function checks that the bulk version of get_min_distance
on a number of randomly generated points returns the same
result as the regular version.
"""
for i in range(50):
dim = np.random.randint(1, 20)
num_points_1 = np.random.randint(10, 50)
num_points_2 = np.random.randint(10, 50)
points = np.random.uniform(-100, 100, size=(num_points_1,dim))
other_points = np.random.uniform(-100, 100,
size=(num_points_2,dim))
dist1 = [ru.get_min_distance(point, other_points)
for point in points]
dist2 = ru.bulk_get_min_distance(points, other_points)
for j in range(num_points_1):
msg='Failed random test {:d} point {:d}'.format(i, j)
self.assertAlmostEqual(dist1[j], dist2[j], 12, msg=msg)
def test_bulk_get_min_distance(self):
"""Verify that bulk version returns the same as regular version.
This function checks that the bulk version of get_min_distance
on a number of randomly generated points returns the same
result as the regular version.
"""
for i in range(50):
dim = np.random.randint(1, 20)
num_points_1 = np.random.randint(10, 50)
num_points_2 = np.random.randint(10, 50)
points = np.random.uniform(-100, 100, size=(num_points_1, dim))
other_points = np.random.uniform(-100,
100,
size=(num_points_2, dim))
dist1 = [
ru.get_min_distance(point, other_points) for point in points
]
dist2 = ru.bulk_get_min_distance(points, other_points)
for j in range(num_points_1):
msg = 'Failed random test {:d} point {:d}'.format(i, j)
self.assertAlmostEqual(dist1[j], dist2[j], 12, msg=msg)
def addPoints(pointFilePath, valueFilePath, model):
points, values = readPoints(pointFilePath, valueFilePath)
#Check that points have correct dimensionality
assert len(points[0]) == len(
model.l_lower), "Model has %d dimensions, but point file has %d" % (
len(model.l_lower), len(points[0]))
#Scale points
points_scaled = rbfopt_utils.bulk_transform_domain(model.l_settings,
model.l_lower,
model.l_upper, points)
#Add the points (last entry is for fast evals)
#add_node(self, point, orig_point, value, is_fast)
for i in range(len(points)):
#Distance test
if (rbfopt_utils.get_min_distance(points[i], model.all_node_pos) >
model.l_settings.min_dist):
model.add_node(points_scaled[i], points[i], values[i])
else:
print("Point too close to add to model!")
def test_get_min_distance(self):
"""Test some extreme cases for get_min_distance."""
self.assertEqual(
ru.get_min_distance(
np.array([i for i in range(5)]),
np.array([[i + j for i in range(5)] for j in range(10)])), 0.0)
def test_get_min_distance(self):
"""Test some extreme cases for get_min_distance."""
self.assertEqual(ru.get_min_distance(np.array([i for i in range(5)]),
np.array([[i+j for i in range(5)]
for j in range(10)])), 0.0)