def logistic_radial_kernel(distance, bandwidth=1.0):
"""Logistic radial kernel.
Parameters
----------
distance : array-like
Array of non-negative real values.
bandwidth : float, optional (default=1.0)
Positive scale parameter of the kernel.
Returns
-------
weight : array-like
Array of non-negative real values of the same shape than
parameter 'distance'.
References
----------
https://en.wikipedia.org/wiki/Kernel_(statistics)
"""
distance = _check_distance(distance)
bandwidth = _check_bandwidth(bandwidth)
scaled_distance = distance / bandwidth
weight = 1 / (gs.exp(scaled_distance) + 2 + gs.exp(-scaled_distance))
return weight
def orbit_data(self):
point = gs.array([[gs.exp(4.0), 0.0], [0.0, gs.exp(2.0)]])
sqrt = gs.array([[gs.exp(2.0), 0.0], [0.0, gs.exp(1.0)]])
identity = GeneralLinear(2).identity
time = gs.linspace(0.0, 1.0, 3)
smoke_data = [
dict(
n=2,
point=point,
base_point=identity,
time=time,
expected=gs.array([identity, sqrt, point]),
),
dict(
n=2,
point=[point, point],
base_point=identity,
time=time,
expected=[
gs.array([identity, sqrt, point]),
gs.array([identity, sqrt, point]),
],
),
]
return self.generate_tests(smoke_data)
def test_gaussian_radial_kernel(self):
"""Test the gaussian radial kernel."""
distance = gs.array([[1], [2]], dtype=float)
bandwidth = 2
weight = gaussian_radial_kernel(distance=distance, bandwidth=bandwidth)
result = weight
expected = gs.array([[gs.exp(-1 / 8)], [gs.exp(-1 / 2)]], dtype=float)
self.assertAllClose(expected, result, atol=gs.atol)
def norm_factor_gradient(self, variances):
"""Compute normalization factor and its gradient.
Compute normalization factor given current variance
and dimensionality.
Parameters
----------
variances : array-like, shape=[n]
Value of variance.
Returns
-------
norm_factor : array-like, shape=[n]
Normalisation factor.
norm_factor_gradient : array-like, shape=[n]
Gradient of the normalization factor.
"""
variances = gs.transpose(gs.to_ndarray(variances, to_ndim=2))
dim_range = gs.arange(0, self.dim, 1.0)
alpha = self._compute_alpha(dim_range)
binomial_coefficient = gs.ones(self.dim)
binomial_coefficient[1:] = (self.dim - 1 + 1 - dim_range[1:]) / dim_range[1:]
binomial_coefficient = gs.cumprod(binomial_coefficient)
beta = ((-gs.ones(self.dim)) ** dim_range) * binomial_coefficient
sigma_repeated = gs.repeat(variances, self.dim, -1)
prod_alpha_sigma = gs.einsum("ij,j->ij", sigma_repeated, alpha)
term_2 = gs.exp((prod_alpha_sigma) ** 2) * (1 + gs.erf(prod_alpha_sigma))
term_1 = gs.sqrt(gs.pi / 2.0) * (1.0 / (2 ** (self.dim - 1)))
term_2 = gs.einsum("ij,j->ij", term_2, beta)
norm_factor = term_1 * variances * gs.sum(term_2, axis=-1, keepdims=True)
grad_term_1 = 1 / variances
grad_term_21 = 1 / gs.sum(term_2, axis=-1, keepdims=True)
grad_term_211 = (
gs.exp((prod_alpha_sigma) ** 2)
* (1 + gs.erf(prod_alpha_sigma))
* gs.einsum("ij,j->ij", sigma_repeated, alpha**2)
* 2
)
grad_term_212 = gs.repeat(
gs.expand_dims((2 / gs.sqrt(gs.pi)) * alpha, axis=0),
variances.shape[0],
axis=0,
)
grad_term_22 = grad_term_211 + grad_term_212
grad_term_22 = gs.einsum("ij, j->ij", grad_term_22, beta)
grad_term_22 = gs.sum(grad_term_22, axis=-1, keepdims=True)
norm_factor_gradient = grad_term_1 + (grad_term_21 * grad_term_22)
return gs.squeeze(norm_factor), gs.squeeze(norm_factor_gradient)
def test_laplacian_radial_kernel(self):
"""Test the Laplacian radial kernel."""
distance = gs.array([[1], [2]], dtype=float)
bandwidth = 2
weight = laplacian_radial_kernel(distance=distance,
bandwidth=bandwidth)
result = weight
expected = gs.array([[gs.exp(-1 / 2)], [gs.exp(-1.0)]], dtype=float)
self.assertAllClose(expected, result, atol=TOLERANCE)
def test_logistic_radial_kernel(self):
"""Test the logistic radial kernel."""
distance = gs.array([[1], [2]], dtype=float)
bandwidth = 2
weight = logistic_radial_kernel(distance=distance, bandwidth=bandwidth)
result = weight
expected = gs.array([[1 / (gs.exp(1 / 2) + 2 + gs.exp(-1 / 2))],
[1 / (gs.exp(1.0) + 2 + gs.exp(-1.0))]])
self.assertAllClose(expected, result, atol=TOLERANCE)
def test_orbit(self):
point = gs.array([[gs.exp(4.), 0.], [0., gs.exp(2.)]])
sqrt = gs.array([[gs.exp(2.), 0.], [0., gs.exp(1.)]])
idty = GeneralLinear(2).identity
path = GeneralLinear(2).orbit(point)
time = gs.linspace(0., 1., 3)
result = path(time)
expected = gs.array([idty, sqrt, point])
self.assertAllClose(result, expected)
def test_orbit_vectorization(self):
point = gs.array([[gs.exp(4.), 0.], [0., gs.exp(2.)]])
sqrt = gs.array([[gs.exp(2.), 0.], [0., gs.exp(1.)]])
identity = GeneralLinear(2).identity
path = GeneralLinear(2).orbit(gs.stack([point] * 2), identity)
time = gs.linspace(0., 1., 3)
result = path(time)
expected = gs.array([identity, sqrt, point])
expected = gs.stack([expected] * 2)
self.assertAllClose(result, expected)
def weighted_gmm_pdf(mixture_coefficients,
mesh_data,
means,
variances,
metric):
"""Return the probability density function of a GMM.
Parameters
----------
mixture_coefficients : array-like, shape=[n_gaussians,]
Coefficients of the Gaussian mixture model.
mesh_data : array-like, shape=[n_precision, dim]
Points at which the GMM probability density is computed.
means : array-like, shape=[n_gaussians, dim]
Means of each component of the GMM.
variances : array-like, shape=[n_gaussians,]
Variances of each component of the GMM.
metric : function
Distance function associated with the used metric.
Returns
-------
weighted_pdf : array-like, shape=[n_precision, n_gaussians,]
Probability density function computed for each point of
the mesh data, for each component of the GMM.
"""
distance_to_mean = metric.dist_broadcast(mesh_data, means)
variances_units = gs.expand_dims(variances, 0)
variances_units = gs.repeat(
variances_units, distance_to_mean.shape[0], axis=0)
distribution_normal = gs.exp(
-(distance_to_mean ** 2) / (2 * variances_units ** 2))
zeta_sigma = PI_2_3 * variances
zeta_sigma = zeta_sigma * gs.exp(
(variances ** 2 / 2) * gs.erf(variances / gs.sqrt(2)))
result_num = gs.expand_dims(mixture_coefficients, 0)
result_num = gs.repeat(
result_num, len(distribution_normal), axis=0)
result_num = result_num * distribution_normal
result_denum = gs.expand_dims(zeta_sigma, 0)
result_denum = gs.repeat(
result_denum, len(distribution_normal), axis=0)
weighted_pdf = result_num / result_denum
return weighted_pdf
def test_sigmoid_radial_kernel(self):
"""Test the sigmoid radial kernel."""
distance = gs.array([[1], [2]], dtype=float)
bandwidth = 2
weight = sigmoid_radial_kernel(distance=distance, bandwidth=bandwidth)
result = weight
expected = gs.array(
[
[1 / (gs.exp(1 / 2) + gs.exp(-1 / 2))],
[1 / (gs.exp(1.0) + gs.exp(-1.0))],
],
dtype=float,
)
self.assertAllClose(expected, result, atol=gs.atol)
def normalization_factor(self, variances):
"""Return normalization factor.
Parameters
----------
variances : array-like, shape=[n,]
Array of equally distant values of the
variance precision time.
Returns
-------
norm_func : array-like, shape=[n,]
Normalisation factor for all given variances.
"""
binomial_coefficient = None
n_samples = variances.shape[0]
expand_variances = gs.expand_dims(variances, axis=0)
expand_variances = gs.repeat(expand_variances, self.dim, axis=0)
if binomial_coefficient is None:
dim_range = gs.arange(self.dim)
dim_range[0] = 1
n_fact = dim_range.prod()
k_fact = gs.concatenate([
gs.expand_dims(dim_range[:i].prod(), 0)
for i in range(1, dim_range.shape[0] + 1)
], 0)
nmk_fact = gs.flip(k_fact, 0)
binomial_coefficient = n_fact / (k_fact * nmk_fact)
binomial_coefficient = gs.expand_dims(binomial_coefficient, -1)
binomial_coefficient = gs.repeat(binomial_coefficient,
n_samples,
axis=1)
range_ = gs.expand_dims(gs.arange(self.dim), -1)
range_ = gs.repeat(range_, n_samples, axis=1)
ones_ = gs.expand_dims(gs.ones(self.dim), -1)
ones_ = gs.repeat(ones_, n_samples, axis=1)
alternate_neg = (-ones_)**(range_)
erf_arg = ((
(self.dim - 1) - 2 * range_) * expand_variances) / gs.sqrt(2)
exp_arg = ((((self.dim - 1) - 2 * range_) * expand_variances) /
gs.sqrt(2))**2
norm_func_1 = (1 + gs.erf(erf_arg)) * gs.exp(exp_arg)
norm_func_2 = binomial_coefficient * norm_func_1
norm_func_3 = alternate_neg * norm_func_2
norm_func = NORMALIZATION_FACTOR_CST * variances * \
norm_func_3.sum(0) * (1 / (2 ** (self.dim - 1)))
return norm_func
def _exp(tangent_vec, base_point):
circle_center = (base_point[0] +
base_point[1] * tangent_vec[1] / tangent_vec[0])
circle_radius = gs.sqrt((circle_center - base_point[0])**2 +
base_point[1]**2)
moebius_d = 1
moebius_c = 1 / (2 * circle_radius)
moebius_b = circle_center - circle_radius
moebius_a = (circle_center + circle_radius) * moebius_c
point_complex = base_point[0] + 1j * base_point[1]
tangent_vec_complex = tangent_vec[0] + 1j * tangent_vec[1]
point_moebius = (1j * (moebius_d * point_complex - moebius_b) /
(moebius_c * point_complex - moebius_a))
tangent_vec_moebius = (
-1j * tangent_vec_complex *
(1j * moebius_c * point_moebius + moebius_d)**2)
end_point_moebius = point_moebius * gs.exp(
tangent_vec_moebius / point_moebius)
end_point_complex = (
moebius_a * 1j * end_point_moebius +
moebius_b) / (moebius_c * 1j * end_point_moebius + moebius_d)
end_point_expected = gs.hstack(
[np.real(end_point_complex),
np.imag(end_point_complex)])
return end_point_expected
def exp(self, tangent_vec, base_point, **kwargs):
"""Compute the Cholesky exponential map.
Compute the Riemannian exponential at point base_point
of tangent vector tangent_vec wrt the Cholesky metric.
This gives a lower triangular matrix with positive elements.
Parameters
----------
tangent_vec : array-like, shape=[..., n, n]
Tangent vector at base point.
base_point : array-like, shape=[..., n, n]
Base point.
Returns
-------
exp : array-like, shape=[..., n, n]
Riemannian exponential.
"""
sl_base_point = Matrices.to_strictly_lower_triangular(base_point)
sl_tangent_vec = Matrices.to_strictly_lower_triangular(tangent_vec)
diag_base_point = Matrices.diagonal(base_point)
diag_tangent_vec = Matrices.diagonal(tangent_vec)
diag_product_expm = gs.exp(gs.divide(diag_tangent_vec, diag_base_point))
sl_exp = sl_base_point + sl_tangent_vec
diag_exp = gs.vec_to_diag(diag_base_point * diag_product_expm)
exp = sl_exp + diag_exp
return exp
def laplacian_radial_kernel(distance, bandwidth=1.0):
"""Laplacian radial kernel.
Parameters
----------
distance : array-like
Array of non-negative real values.
bandwidth : float, optional (default=1.0)
Positive scale parameter of the kernel.
Returns
-------
weight : array-like
Array of non-negative real values of the same shape than
parameter 'distance'.
Returns
-------
http://crsouza.com/2010/03/17/
kernel-functions-for-machine-learning-applications/
https://data-flair.training/blogs/svm-kernel-functions/
"""
distance = _check_distance(distance)
bandwidth = _check_bandwidth(bandwidth)
scaled_distance = distance / bandwidth
weight = gs.exp(-scaled_distance)
return weight
def bump_radial_kernel(distance, bandwidth=1.0):
"""Bump radial kernel.
Parameters
----------
distance : array-like
Array of non-negative real values.
bandwidth : float, optional (default=1.0)
Positive scale parameter of the kernel.
Returns
-------
weight : array-like
Array of non-negative real values of the same shape than
parameter 'distance'.
References
----------
https://en.wikipedia.org/wiki/Radial_basis_function
"""
distance = _check_distance(distance)
bandwidth = _check_bandwidth(bandwidth)
scaled_distance = distance / bandwidth
weight = gs.where(
scaled_distance < 1,
gs.exp(-1 / (1 - scaled_distance**2)),
gs.zeros(distance.shape, dtype=float),
)
return weight
def test_exp(self):
point = gs.array([1., 1.])
tangent_vec = gs.array([2., 1.])
end_point = self.metric.exp(tangent_vec, point)
circle_center = point[0] + point[1] * tangent_vec[1] / tangent_vec[0]
circle_radius = gs.sqrt((circle_center - point[0])**2 + point[1]**2)
moebius_d = 1
moebius_c = 1 / (2 * circle_radius)
moebius_b = circle_center - circle_radius
moebius_a = (circle_center + circle_radius) * moebius_c
point_complex = point[0] + 1j * point[1]
tangent_vec_complex = tangent_vec[0] + 1j * tangent_vec[1]
point_moebius = 1j * (moebius_d * point_complex - moebius_b)\
/ (moebius_c * point_complex - moebius_a)
tangent_vec_moebius = -1j * tangent_vec_complex * (
1j * moebius_c * point_moebius + moebius_d)**2
end_point_moebius = point_moebius * gs.exp(
tangent_vec_moebius / point_moebius)
end_point_complex = (moebius_a * 1j * end_point_moebius + moebius_b)\
/ (moebius_c * 1j * end_point_moebius + moebius_d)
end_point_expected = gs.hstack(
[np.real(end_point_complex),
np.imag(end_point_complex)])
self.assertAllClose(end_point, end_point_expected)
def gaussian_radial_kernel(distance, bandwidth=1.0):
"""Gaussian radial kernel.
Parameters
----------
distance : array-like
Array of non-negative real values.
bandwidth : float, optional (default=1.0)
Positive scale parameter of the kernel.
Returns
-------
weight : array-like
Array of non-negative real values of the same shape than
parameter 'distance'.
References
----------
https://en.wikipedia.org/wiki/Kernel_(statistics)
https://en.wikipedia.org/wiki/Radial_basis_function
"""
distance = _check_distance(distance)
bandwidth = _check_bandwidth(bandwidth)
scaled_distance = distance / bandwidth
weight = gs.exp(-(scaled_distance**2) / 2)
return weight
def threshold(random_v):
"""Compute the acceptance threshold."""
squared_norm = gs.sum(random_v ** 2, axis=-1)
sinc = utils.taylor_exp_even_func(
squared_norm, utils.sinc_close_0) ** (dim - 1)
threshold_val = sinc * gs.exp(squared_norm * (dim - 1) / 2 / gs.pi)
return threshold_val, squared_norm ** .5
def test_exp_vectorization(self):
point = gs.array([[1.0, 1.0], [1.0, 1.0]])
tangent_vec = gs.array([[2.0, 1.0], [2.0, 1.0]])
result = self.metric.exp(tangent_vec, point)
point = point[0]
tangent_vec = tangent_vec[0]
circle_center = point[0] + point[1] * tangent_vec[1] / tangent_vec[0]
circle_radius = gs.sqrt((circle_center - point[0])**2 + point[1]**2)
moebius_d = 1
moebius_c = 1 / (2 * circle_radius)
moebius_b = circle_center - circle_radius
moebius_a = (circle_center + circle_radius) * moebius_c
point_complex = point[0] + 1j * point[1]
tangent_vec_complex = tangent_vec[0] + 1j * tangent_vec[1]
point_moebius = (1j * (moebius_d * point_complex - moebius_b) /
(moebius_c * point_complex - moebius_a))
tangent_vec_moebius = (-1j * tangent_vec_complex *
(1j * moebius_c * point_moebius + moebius_d)**2)
end_point_moebius = point_moebius * gs.exp(
tangent_vec_moebius / point_moebius)
end_point_complex = (moebius_a * 1j * end_point_moebius +
moebius_b) / (moebius_c * 1j * end_point_moebius +
moebius_d)
end_point_expected = gs.hstack(
[np.real(end_point_complex),
np.imag(end_point_complex)])
expected = gs.stack([end_point_expected, end_point_expected])
self.assertAllClose(result, expected)
def random_von_mises_fisher(self, kappa=10, n_samples=1):
"""Sample in the 2-sphere with the von Mises distribution.
Sample in the 2-sphere with the von Mises distribution centered in the
north pole.
Parameters
----------
kappa : int, optional
n_samples : int, optional
Returns
-------
point : array-like
"""
if self.dimension != 2:
raise NotImplementedError(
'Sampling from the von Mises Fisher distribution'
'is only implemented in dimension 2.')
angle = 2. * gs.pi * gs.random.rand(n_samples)
angle = gs.to_ndarray(angle, to_ndim=2, axis=1)
unit_vector = gs.hstack((gs.cos(angle), gs.sin(angle)))
scalar = gs.random.rand(n_samples)
coord_z = 1. + 1. / kappa * gs.log(scalar + (1. - scalar) *
gs.exp(gs.array(-2. * kappa)))
coord_z = gs.to_ndarray(coord_z, to_ndim=2, axis=1)
coord_xy = gs.sqrt(1. - coord_z**2) * unit_vector
point = gs.hstack((coord_xy, coord_z))
return point
def summand(k):
exp_arg = -((dim - 1 - 2 * k)**2) / 2 / variances
erf_arg_2 = (gs.pi * variances -
(dim - 1 - 2 * k) * 1j) / gs.sqrt(2 * variances)
sign = (-1.0)**k
comb_2 = gs.comb(k, dim - 1)
return sign * comb_2 * gs.exp(exp_arg) * gs.real(gs.erf(erf_arg_2))
def aux_differential_power(power, tangent_vec, base_point):
"""Compute the differential of the matrix power.
Auxiliary function to the functions differential_power and
inverse_differential_power.
Parameters
----------
power : float
Power function to differentiate.
tangent_vec : array_like, shape=[..., n, n]
Tangent vector at base point.
base_point : array_like, shape=[..., n, n]
Base point.
Returns
-------
eigvectors : array-like, shape=[..., n, n]
transp_eigvectors : array-like, shape=[..., n, n]
numerator : array-like, shape=[..., n, n]
denominator : array-like, shape=[..., n, n]
temp_result : array-like, shape=[..., n, n]
"""
eigvalues, eigvectors = gs.linalg.eigh(base_point)
if power == 0:
powered_eigvalues = gs.log(eigvalues)
elif power == math.inf:
powered_eigvalues = gs.exp(eigvalues)
else:
powered_eigvalues = eigvalues ** power
denominator = eigvalues[..., :, None] - eigvalues[..., None, :]
numerator = powered_eigvalues[..., :, None] - powered_eigvalues[..., None, :]
if power == 0:
numerator = gs.where(denominator == 0, gs.ones_like(numerator), numerator)
denominator = gs.where(
denominator == 0, eigvalues[..., :, None], denominator
)
elif power == math.inf:
numerator = gs.where(
denominator == 0, powered_eigvalues[..., :, None], numerator
)
denominator = gs.where(
denominator == 0, gs.ones_like(numerator), denominator
)
else:
numerator = gs.where(
denominator == 0, power * powered_eigvalues[..., :, None], numerator
)
denominator = gs.where(
denominator == 0, eigvalues[..., :, None], denominator
)
transp_eigvectors = Matrices.transpose(eigvectors)
temp_result = Matrices.mul(transp_eigvectors, tangent_vec, eigvectors)
return (eigvectors, transp_eigvectors, numerator, denominator, temp_result)
def test_inverse_differential_exp(self):
base_point = gs.array([[1., 0., 0.], [0., 1., 0.], [0., 0., -1.]])
x = gs.exp(1)
y = gs.sinh(1)
tangent_vec = gs.array([[x, x, y], [x, x, y], [y, y, 1 / x]])
result = self.space.inverse_differential_exp(tangent_vec, base_point)
expected = gs.array([[[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]])
self.assertAllClose(result, expected)
def test_bump_radial_kernel(self):
"""Test the bump radial kernel."""
distance = gs.array([[1 / 2], [2]], dtype=float)
bandwidth = 1
weight = bump_radial_kernel(distance=distance, bandwidth=bandwidth)
result = weight
expected = gs.array([[gs.exp(-1 / (3 / 4))], [0]], dtype=float)
self.assertAllClose(expected, result, atol=gs.atol)
def gaussian(x, mu, sig):
a = (x - mu)**2 / (2 * (sig**2))
b = 1 / (sig * (gs.sqrt(2 * gs.pi)))
f = b * gs.exp(-a)
l2_norm = gs.sqrt(gs.trapz(f**2, x))
f_sinf = f / l2_norm
return gs.array([f_sinf])
def test_inverse_differential_exp(self):
"""Test of inverse_differential_exp method."""
base_point = gs.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0]])
x = gs.exp(1.0)
y = gs.sinh(1.0)
tangent_vec = gs.array([[x, x, y], [x, x, y], [y, y, 1.0 / x]])
result = self.space.inverse_differential_exp(tangent_vec, base_point)
expected = gs.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
self.assertAllClose(result, expected)
def summand(k):
exp_arg = -((dim - 1 - 2 * k)**2) / 2 / variances
erf_arg_1 = (dim - 1 - 2 * k) * 1j / gs.sqrt(2 * variances)
erf_arg_2 = (gs.pi * variances -
(dim - 1 - 2 * k) * 1j) / gs.sqrt(2 * variances)
sign = (-1.0)**k
comb = gs.comb(dim - 1, k)
erf_terms = gs.imag(gs.erf(erf_arg_2) + gs.erf(erf_arg_1))
return sign * comb * gs.exp(exp_arg) * erf_terms
def test_differential_exp(self):
"""Test of differential_exp method."""
base_point = gs.array([[1., 0., 0.], [0., 1., 0.], [0., 0., -1.]])
tangent_vec = gs.array([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]])
result = self.space.differential_exp(tangent_vec, base_point)
x = gs.exp(1.)
y = gs.sinh(1.)
expected = gs.array([[x, x, y], [x, x, y], [y, y, 1 / x]])
self.assertAllClose(result, expected)
def grad_log_sigmoid(vector):
"""Gradient of log sigmoid function.
Parameters
----------
vector : array-like, shape=[n_samples, dim]
Returns
-------
gradient : array-like, shape=[n_samples, dim]
"""
return 1 / (1 + gs.exp(vector))
def log_test_data(self):
smoke_data = [
dict(
n=2,
point=[[EULER, 0.0], [2.0, EULER**3]],
base_point=[[EULER**3, 0.0], [4.0, EULER**4]],
expected=[[-2.0 * EULER**3, 0.0], [-2.0, -1 * EULER**4]],
),
dict(
n=2,
point=[
[[gs.exp(-2.0), 0.0], [0.0, gs.exp(2.0)]],
[[gs.exp(-3.0), 0.0], [2.0, gs.exp(3.0)]],
],
base_point=[[[1.0, 0.0], [-1.0, 1.0]], [[1.0, 0.0], [0.0,
1.0]]],
expected=[[[-2.0, 0.0], [1.0, 2.0]], [[-3.0, 0.0], [2.0,
3.0]]],
),
]
return self.generate_tests(smoke_data)
