def create_spherical_dataset(self,
num_samples_cluster,
radius=1.0,
offset=4.0,
dtype=ht.float32,
random_state=1):
"""
Creates k=4 sperical clusters in 3D space along the space-diagonal
Parameters
----------
num_samples_cluster: int
Number of samples per cluster. Each process will create n // MPI_WORLD.size elements for each cluster
radius: float
Radius of the sphere
offset: float
Shift of the clusters along the axes. The 4 clusters will be positioned centered around c1=(offset, offset,offset),
c2=(2*offset,2*offset,2*offset), c3=(-offset, -offset, -offset) and c4=(2*offset, -2*offset, -2*offset)
dtype: ht.datatype
random_state: int
seed of the torch random number generator
"""
# contains num_samples
p = ht.MPI_WORLD.size
# create k sperical clusters with each n elements per cluster. Each process creates k * n/p elements
num_ele = num_samples_cluster // p
ht.random.seed(random_state)
# radius between 0 and 1
r = ht.random.rand(num_ele, split=0) * radius
# theta between 0 and pi
theta = ht.random.rand(num_ele, split=0) * 3.1415
# phi between 0 and 2pi
phi = ht.random.rand(num_ele, split=0) * 2 * 3.1415
# Cartesian coordinates
x = r * ht.sin(theta) * ht.cos(phi)
x.astype(dtype, copy=False)
y = r * ht.sin(theta) * ht.sin(phi)
y.astype(dtype, copy=False)
z = r * ht.cos(theta)
z.astype(dtype, copy=False)
cluster1 = ht.stack((x + offset, y + offset, z + offset), axis=1)
cluster2 = ht.stack((x + 2 * offset, y + 2 * offset, z + 2 * offset),
axis=1)
cluster3 = ht.stack((x - offset, y - offset, z - offset), axis=1)
cluster4 = ht.stack((x - 2 * offset, y - 2 * offset, z - 2 * offset),
axis=1)
data = ht.concatenate((cluster1, cluster2, cluster3, cluster4), axis=0)
# Note: enhance when shuffle is available
return data
def test_cos(self):
# base elements
elements = 30
comparison = torch.arange(elements, dtype=torch.float64).cos()
# cosine of float32
float32_tensor = ht.arange(elements, dtype=ht.float32)
float32_cos = ht.cos(float32_tensor)
self.assertIsInstance(float32_cos, ht.tensor)
self.assertEqual(float32_cos.dtype, ht.float32)
self.assertEqual(float32_cos.dtype, ht.float32)
self.assertTrue(
torch.allclose(float32_cos._tensor__array.type(torch.double),
comparison))
# cosine of float64
float64_tensor = ht.arange(elements, dtype=ht.float64)
float64_cos = ht.cos(float64_tensor)
self.assertIsInstance(float64_cos, ht.tensor)
self.assertEqual(float64_cos.dtype, ht.float64)
self.assertEqual(float64_cos.dtype, ht.float64)
self.assertTrue(
torch.allclose(float64_cos._tensor__array.type(torch.double),
comparison))
# cosine of ints, automatic conversion to intermediate floats
int32_tensor = ht.arange(elements, dtype=ht.int32)
int32_cos = ht.cos(int32_tensor)
self.assertIsInstance(int32_cos, ht.tensor)
self.assertEqual(int32_cos.dtype, ht.float64)
self.assertEqual(int32_cos.dtype, ht.float64)
self.assertTrue(
torch.allclose(float32_cos._tensor__array.type(torch.double),
comparison))
# cosine of longs, automatic conversion to intermediate floats
int64_tensor = ht.arange(elements, dtype=ht.int64)
int64_cos = ht.cos(int64_tensor)
self.assertIsInstance(int64_cos, ht.tensor)
self.assertEqual(int64_cos.dtype, ht.float64)
self.assertEqual(int64_cos.dtype, ht.float64)
self.assertTrue(
torch.allclose(int64_cos._tensor__array.type(torch.double),
comparison))
# check exceptions
with self.assertRaises(TypeError):
ht.cos([1, 2, 3])
with self.assertRaises(TypeError):
ht.cos('hello world')