A simple yet efficient implementation of transformation from Euclidian to Hyperspherical (N-spherical) space
The library uses this Wikipedia article as a basis
Package listing:
- layers
import torch
from pytorch_hypersphere.layers import ToHyperSphere, ToEuclidean
ths = ToHyperSphere(16) # initialize transformation layer
te = ToEuclidean(16) # initialize transformation layer
x_eucl = torch.randn((4, 16)) # random floats in euclidian space
x_sphere = ths(x_eucl) # transformation to hyperspherical
x_eucl_2 = te(x_sphere) # transformation back to euclidean- functional
import torch
from pytorch_hypersphere.functional import to_hypersphere, to_euclidean
x_eucl = torch.randn((4, 16)) # random floats in euclidian space
x_sphere = to_hypersphere(x_eucl) # transformation to hyperspherical
x_eucl_2 = to_euclidean(x_sphere) # transformation back to euclidean- rand
from pytorch_hypersphere.random import euclidean_randn_spherical, nsphere_randn_spherical
random_points_on_sphere_in_euclidean = euclidean_randn_spherical(shape=(4, 16), stretch_coefficient=2) # generate points randomly distributed on a sphere, in euclidean coordinates, with radius of 2
random_points_on_sphere_in_nsphere = nsphere_randn_spherical(shape=(4, 16), stretch_coefficient=1) # generate points randomly distributed on a sphere, in spherical coordinates, with radius of 1