Sample from the diffusion kernel on any n-sphere
Project description
SphereDiff
Purpose
The purpose of this package is to sample from the isotropic diffusion kernel on the surface of an n-dimensional unit sphere, where n is an arbitrary natural number dimension greater than or equal to 3.
Installation
From a repository checkout
pip install --user .
From PyPI
pip install --user spherediff
Use
The user may sample from the isotropic Gaussian distribution on the unit
n-sphere using the sample_spherical_kernel
function, which may be
imported as follows:
>> from spherediff.sample import sample_spherical_kernel
This function takes three arguments and one additional, optional argument. The first is n, the dimension of the space in which the n-sphere is embedded. The second is a numpy array of shape (N, n) consisting of the n-dimensional unit vectors at which to center the distributions from which the samples are to be generated. The third is a numpy array of shape (N,) consisting of the scalar variance parameters of each distribution from which to generate samples. The fourth is a boolean flag that determines whether sampling should be done on the full surface of the n-sphere (if False) or on the hemisphere with reflecting boundary conditions for the diffusion kernel.
Example output from sample_spherical_kernel
is:
>>> import numpy as np
>>> from spherediff.sample import sample_spherical_kernel
>>> np.random.seed(42)
>>> means = np.random.randn(5, 3)
>>> means /= np.linalg.norm(means, axis=1, keepdims=True)
>>> means
array([[ 0.60000205, -0.1670153 , 0.78237039],
[ 0.97717133, -0.15023209, -0.15022156],
[ 0.86889694, 0.42224942, -0.25830898],
[ 0.63675162, -0.5438697 , -0.54658314],
[ 0.09351637, -0.73946664, -0.66666616]])
>>> vars = 0.1 * np.ones(5)
>>> sample_spherical_kernel(3, means, vars)
array([[ 0.30027556, -0.53104481, 0.79235472],
[ 0.91657116, -0.39288942, 0.07439905],
[ 0.81325411, 0.41495422, -0.40795926],
[ 0.39907791, -0.44171124, -0.80350981],
[ 0.16422958, -0.76019121, -0.62860001]])
>>> sample_spherical_kernel(3, means, vars, hemisphere=True)
array([[ 0.92723597, 0.02336567, 0.37374791],
[ 0.99421791, -0.03878944, -0.10013055],
[ 0.15771025, 0.6492883 , -0.74401087],
[ 0.2418101 , -0.41127436, -0.87885225],
[-0.11192408, -0.71437847, -0.69075061]])
The user may also compute the score function of the isotropic Gaussian
distribution on the unit n-sphere, defined as the Riemannian gradient
of the logarithm of the probability density. This may be done using the
score_spherical_kernel
function, which may be imported as follows:
from spherediff.score import score_spherical_kernel
This function takes four arguments. The first is n, the dimension of the space in which the n-sphere is embedded. The second is a numpy array of shape (N, n) consisting of the n-dimensional unit vectors at which to evaluate the score function. The third is a numpy array of shape (N, n) consisting of the n-dimensional unit vectors at which to center the distributions of which the corresponding score functions will be evaluated. The fourth is a numpy array of shape (N,) consisting of the scalar variance parameters of each distribution.
Example output from score_spherical_kernel
is:
>>> import numpy as np
>>> from spherediff.sample import sample_spherical_kernel
>>> from spherediff.score import score_spherical_kernel
>>> np.random.seed(42)
>>> means = np.random.randn(5, 3)
>>> means /= np.linalg.norm(means, axis=1, keepdims=True)
>>> means
array([[ 0.60000205, -0.1670153 , 0.78237039],
[ 0.97717133, -0.15023209, -0.15022156],
[ 0.86889694, 0.42224942, -0.25830898],
[ 0.63675162, -0.5438697 , -0.54658314],
[ 0.09351637, -0.73946664, -0.66666616]])
>>> vars = 0.1 * np.ones(5)
>>> x = sample_spherical_kernel(3, means, vars)
>>> x
array([[ 0.30027556, -0.53104481, 0.79235472],
[ 0.91657116, -0.39288942, 0.07439905],
[ 0.81325411, 0.41495422, -0.40795926],
[ 0.39907791, -0.44171124, -0.80350981],
[ 0.16422958, -0.76019121, -0.62860001]])
>>> score_spherical_kernel(3, x, means, vars)
array([[ 0.45383257, -1.4484151 , -0.34370955],
[ 2.57268711, 3.50638512, -2.19627206],
[ 0.33420178, 5.16225326, -0.64669865],
[ 2.62797084, 2.20685896, -1.97070763],
[-3.68698876, -0.78777185, -3.71349795]])
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