# -*- coding: utf-8 -*-
"""
Optimal transport for Gaussian distributions
"""
# Author: Theo Gnassounou <theo.gnassounou@inria.fr>
# Remi Flamary <remi.flamary@polytehnique.edu>
#
# License: MIT License
import warnings
from .backend import get_backend
from .utils import dots, is_all_finite, list_to_array
[docs]
def bures_wasserstein_mapping(ms, mt, Cs, Ct, log=False):
r"""Return OT linear operator between samples.
The function estimates the optimal linear operator that aligns the two
empirical distributions. This is equivalent to estimating the closed
form mapping between two Gaussian distributions :math:`\mathcal{N}(\mu_s,\Sigma_s)`
and :math:`\mathcal{N}(\mu_t,\Sigma_t)` as proposed in
:ref:`[1] <references-OT-mapping-linear>` and discussed in remark 2.29 in
:ref:`[2] <references-OT-mapping-linear>`.
The linear operator from source to target :math:`M`
.. math::
M(\mathbf{x})= \mathbf{A} \mathbf{x} + \mathbf{b}
where :
.. math::
\mathbf{A} &= \Sigma_s^{-1/2} \left(\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2} \right)^{1/2}
\Sigma_s^{-1/2}
\mathbf{b} &= \mu_t - \mathbf{A} \mu_s
Parameters
----------
ms : array-like (d,)
mean of the source distribution
mt : array-like (d,)
mean of the target distribution
Cs : array-like (d,d)
covariance of the source distribution
Ct : array-like (d,d)
covariance of the target distribution
log : bool, optional
record log if True
Returns
-------
A : (d, d) array-like
Linear operator
b : (1, d) array-like
bias
log : dict
log dictionary return only if log==True in parameters
.. _references-OT-mapping-linear:
References
----------
.. [1] Knott, M. and Smith, C. S. "On the optimal mapping of
distributions", Journal of Optimization Theory and Applications
Vol 43, 1984
.. [2] Peyré, G., & Cuturi, M. (2017). "Computational Optimal
Transport", 2018.
"""
ms, mt, Cs, Ct = list_to_array(ms, mt, Cs, Ct)
nx = get_backend(ms, mt, Cs, Ct)
Cs12 = nx.sqrtm(Cs)
Cs12inv = nx.inv(Cs12)
M0 = nx.sqrtm(dots(Cs12, Ct, Cs12))
A = dots(Cs12inv, M0, Cs12inv)
b = mt - nx.dot(ms, A)
if log:
log = {}
log['Cs12'] = Cs12
log['Cs12inv'] = Cs12inv
return A, b, log
else:
return A, b
[docs]
def empirical_bures_wasserstein_mapping(xs, xt, reg=1e-6, ws=None,
wt=None, bias=True, log=False):
r"""Return OT linear operator between samples.
The function estimates the optimal linear operator that aligns the two
empirical distributions. This is equivalent to estimating the closed
form mapping between two Gaussian distributions :math:`\mathcal{N}(\mu_s,\Sigma_s)`
and :math:`\mathcal{N}(\mu_t,\Sigma_t)` as proposed in
:ref:`[1] <references-OT-mapping-linear>` and discussed in remark 2.29 in
:ref:`[2] <references-OT-mapping-linear>`.
The linear operator from source to target :math:`M`
.. math::
M(\mathbf{x})= \mathbf{A} \mathbf{x} + \mathbf{b}
where :
.. math::
\mathbf{A} &= \Sigma_s^{-1/2} \left(\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2} \right)^{1/2}
\Sigma_s^{-1/2}
\mathbf{b} &= \mu_t - \mathbf{A} \mu_s
Parameters
----------
xs : array-like (ns,d)
samples in the source domain
xt : array-like (nt,d)
samples in the target domain
reg : float,optional
regularization added to the diagonals of covariances (>0)
ws : array-like (ns,1), optional
weights for the source samples
wt : array-like (ns,1), optional
weights for the target samples
bias: boolean, optional
estimate bias :math:`\mathbf{b}` else :math:`\mathbf{b} = 0` (default:True)
log : bool, optional
record log if True
Returns
-------
A : (d, d) array-like
Linear operator
b : (1, d) array-like
bias
log : dict
log dictionary return only if log==True in parameters
.. _references-OT-mapping-linear:
References
----------
.. [1] Knott, M. and Smith, C. S. "On the optimal mapping of
distributions", Journal of Optimization Theory and Applications
Vol 43, 1984
.. [2] Peyré, G., & Cuturi, M. (2017). "Computational Optimal
Transport", 2018.
"""
xs, xt = list_to_array(xs, xt)
nx = get_backend(xs, xt)
is_input_finite = is_all_finite(xs, xt)
d = xs.shape[1]
if bias:
mxs = nx.mean(xs, axis=0)[None, :]
mxt = nx.mean(xt, axis=0)[None, :]
xs = xs - mxs
xt = xt - mxt
else:
mxs = nx.zeros((1, d), type_as=xs)
mxt = nx.zeros((1, d), type_as=xs)
if ws is None:
ws = nx.ones((xs.shape[0], 1), type_as=xs) / xs.shape[0]
if wt is None:
wt = nx.ones((xt.shape[0], 1), type_as=xt) / xt.shape[0]
Cs = nx.dot((xs * ws).T, xs) / nx.sum(ws) + reg * nx.eye(d, type_as=xs)
Ct = nx.dot((xt * wt).T, xt) / nx.sum(wt) + reg * nx.eye(d, type_as=xt)
if log:
A, b, log = bures_wasserstein_mapping(mxs, mxt, Cs, Ct, log=log)
else:
A, b = bures_wasserstein_mapping(mxs, mxt, Cs, Ct)
if is_input_finite and not is_all_finite(A, b):
warnings.warn(
"Numerical errors were encountered in ot.gaussian.empirical_bures_wasserstein_mapping. "
"Consider increasing the regularization parameter `reg`.")
if log:
log['Cs'] = Cs
log['Ct'] = Ct
return A, b, log
else:
return A, b
[docs]
def bures_wasserstein_distance(ms, mt, Cs, Ct, log=False):
r"""Return Bures Wasserstein distance between samples.
The function estimates the Bures-Wasserstein distance between two
empirical distributions source :math:`\mu_s` and target :math:`\mu_t`,
discussed in remark 2.31 :ref:`[1] <references-bures-wasserstein-distance>`.
The Bures Wasserstein distance between source and target distribution :math:`\mathcal{W}`
.. math::
\mathcal{W}(\mu_s, \mu_t)_2^2= \left\lVert \mathbf{m}_s - \mathbf{m}_t \right\rVert^2 + \mathcal{B}(\Sigma_s, \Sigma_t)^{2}
where :
.. math::
\mathbf{B}(\Sigma_s, \Sigma_t)^{2} = \text{Tr}\left(\Sigma_s + \Sigma_t - 2 \sqrt{\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2}} \right)
Parameters
----------
ms : array-like (d,)
mean of the source distribution
mt : array-like (d,)
mean of the target distribution
Cs : array-like (d,d)
covariance of the source distribution
Ct : array-like (d,d)
covariance of the target distribution
log : bool, optional
record log if True
Returns
-------
W : float
Bures Wasserstein distance
log : dict
log dictionary return only if log==True in parameters
.. _references-bures-wasserstein-distance:
References
----------
.. [1] Peyré, G., & Cuturi, M. (2017). "Computational Optimal
Transport", 2018.
"""
ms, mt, Cs, Ct = list_to_array(ms, mt, Cs, Ct)
nx = get_backend(ms, mt, Cs, Ct)
Cs12 = nx.sqrtm(Cs)
B = nx.trace(Cs + Ct - 2 * nx.sqrtm(dots(Cs12, Ct, Cs12)))
W = nx.sqrt(nx.maximum(nx.norm(ms - mt)**2 + B, 0))
if log:
log = {}
log['Cs12'] = Cs12
return W, log
else:
return W
[docs]
def empirical_bures_wasserstein_distance(xs, xt, reg=1e-6, ws=None,
wt=None, bias=True, log=False):
r"""Return Bures Wasserstein distance from mean and covariance of distribution.
The function estimates the Bures-Wasserstein distance between two
empirical distributions source :math:`\mu_s` and target :math:`\mu_t`,
discussed in remark 2.31 :ref:`[1] <references-bures-wasserstein-distance>`.
The Bures Wasserstein distance between source and target distribution :math:`\mathcal{W}`
.. math::
\mathcal{W}(\mu_s, \mu_t)_2^2= \left\lVert \mathbf{m}_s - \mathbf{m}_t \right\rVert^2 + \mathcal{B}(\Sigma_s, \Sigma_t)^{2}
where :
.. math::
\mathbf{B}(\Sigma_s, \Sigma_t)^{2} = \text{Tr}\left(\Sigma_s + \Sigma_t - 2 \sqrt{\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2}} \right)
Parameters
----------
xs : array-like (ns,d)
samples in the source domain
xt : array-like (nt,d)
samples in the target domain
reg : float,optional
regularization added to the diagonals of covariances (>0)
ws : array-like (ns), optional
weights for the source samples
wt : array-like (ns), optional
weights for the target samples
bias: boolean, optional
estimate bias :math:`\mathbf{b}` else :math:`\mathbf{b} = 0` (default:True)
log : bool, optional
record log if True
Returns
-------
W : float
Bures Wasserstein distance
log : dict
log dictionary return only if log==True in parameters
.. _references-bures-wasserstein-distance:
References
----------
.. [1] Peyré, G., & Cuturi, M. (2017). "Computational Optimal
Transport", 2018.
"""
xs, xt = list_to_array(xs, xt)
nx = get_backend(xs, xt)
d = xs.shape[1]
if bias:
mxs = nx.mean(xs, axis=0)[None, :]
mxt = nx.mean(xt, axis=0)[None, :]
xs = xs - mxs
xt = xt - mxt
else:
mxs = nx.zeros((1, d), type_as=xs)
mxt = nx.zeros((1, d), type_as=xs)
if ws is None:
ws = nx.ones((xs.shape[0], 1), type_as=xs) / xs.shape[0]
if wt is None:
wt = nx.ones((xt.shape[0], 1), type_as=xt) / xt.shape[0]
Cs = nx.dot((xs * ws).T, xs) / nx.sum(ws) + reg * nx.eye(d, type_as=xs)
Ct = nx.dot((xt * wt).T, xt) / nx.sum(wt) + reg * nx.eye(d, type_as=xt)
if log:
W, log = bures_wasserstein_distance(mxs, mxt, Cs, Ct, log=log)
log['Cs'] = Cs
log['Ct'] = Ct
return W, log
else:
W = bures_wasserstein_distance(mxs, mxt, Cs, Ct)
return W
[docs]
def bures_wasserstein_barycenter(m, C, weights=None, num_iter=1000, eps=1e-7, log=False):
r"""Return OT linear operator between samples.
The function estimates the optimal barycenter of the
empirical distributions. This is equivalent to resolving the fixed point
algorithm for multiple Gaussian distributions :math:`\left{\mathcal{N}(\mu,\Sigma)\right}_{i=1}^n`
:ref:`[1] <references-OT-mapping-linear-barycenter>`.
The barycenter still following a Gaussian distribution :math:`\mathcal{N}(\mu_b,\Sigma_b)`
where :
.. math::
\mu_b = \sum_{i=1}^n w_i \mu_i
And the barycentric covariance is the solution of the following fixed-point algorithm:
.. math::
\Sigma_b = \sum_{i=1}^n w_i \left(\Sigma_b^{1/2}\Sigma_i^{1/2}\Sigma_b^{1/2}\right)^{1/2}
Parameters
----------
m : array-like (k,d)
mean of k distributions
C : array-like (k,d,d)
covariance of k distributions
weights : array-like (k), optional
weights for each distribution
num_iter : int, optional
number of iteration for the fixed point algorithm
eps : float, optional
tolerance for the fixed point algorithm
log : bool, optional
record log if True
Returns
-------
mb : (d,) array-like
mean of the barycenter
Cb : (d, d) array-like
covariance of the barycenter
log : dict
log dictionary return only if log==True in parameters
.. _references-OT-mapping-linear-barycenter:
References
----------
.. [1] M. Agueh and G. Carlier, "Barycenters in the Wasserstein space",
SIAM Journal on Mathematical Analysis, vol. 43, no. 2, pp. 904-924,
2011.
"""
nx = get_backend(*C, *m,)
if weights is None:
weights = nx.ones(C.shape[0], type_as=C[0]) / C.shape[0]
# Compute the mean barycenter
mb = nx.sum(m * weights[:, None], axis=0)
# Init the covariance barycenter
Cb = nx.mean(C * weights[:, None, None], axis=0)
for it in range(num_iter):
# fixed point update
Cb12 = nx.sqrtm(Cb)
Cnew = Cb12 @ C @ Cb12
C_ = []
for i in range(len(C)):
C_.append(nx.sqrtm(Cnew[i]))
Cnew = nx.stack(C_, axis=0)
Cnew *= weights[:, None, None]
Cnew = nx.sum(Cnew, axis=0)
# check convergence
diff = nx.norm(Cb - Cnew)
if diff <= eps:
break
Cb = Cnew
else:
print("Dit not converge.")
if log:
log = {}
log['num_iter'] = it
log['final_diff'] = diff
return mb, Cb, log
else:
return mb, Cb
[docs]
def empirical_bures_wasserstein_barycenter(
X, reg=1e-6, weights=None, num_iter=1000, eps=1e-7,
w=None, bias=True, log=False
):
r"""Return OT linear operator between samples.
The function estimates the optimal barycenter of the
empirical distributions. This is equivalent to resolving the fixed point
algorithm for multiple Gaussian distributions :math:`\left{\mathcal{N}(\mu,\Sigma)\right}_{i=1}^n`
:ref:`[1] <references-OT-mapping-linear-barycenter>`.
The barycenter still following a Gaussian distribution :math:`\mathcal{N}(\mu_b,\Sigma_b)`
where :
.. math::
\mu_b = \sum_{i=1}^n w_i \mu_i
And the barycentric covariance is the solution of the following fixed-point algorithm:
.. math::
\Sigma_b = \sum_{i=1}^n w_i \left(\Sigma_b^{1/2}\Sigma_i^{1/2}\Sigma_b^{1/2}\right)^{1/2}
Parameters
----------
X : list of array-like (n,d)
samples in each distribution
reg : float,optional
regularization added to the diagonals of covariances (>0)
weights : array-like (n,), optional
weights for each distribution
num_iter : int, optional
number of iteration for the fixed point algorithm
eps : float, optional
tolerance for the fixed point algorithm
w : list of array-like (n,), optional
weights for each sample in each distribution
bias: boolean, optional
estimate bias :math:`\mathbf{b}` else :math:`\mathbf{b} = 0` (default:True)
log : bool, optional
record log if True
Returns
-------
mb : (d,) array-like
mean of the barycenter
Cb : (d, d) array-like
covariance of the barycenter
log : dict
log dictionary return only if log==True in parameters
.. _references-OT-mapping-linear-barycenter:
References
----------
.. [1] M. Agueh and G. Carlier, "Barycenters in the Wasserstein space",
SIAM Journal on Mathematical Analysis, vol. 43, no. 2, pp. 904-924,
2011.
"""
X = list_to_array(*X)
nx = get_backend(*X)
k = len(X)
d = [X[i].shape[1] for i in range(k)]
if bias:
m = [nx.mean(X[i], axis=0)[None, :] for i in range(k)]
X = [X[i] - m[i] for i in range(k)]
else:
m = [nx.zeros((1, d[i]), type_as=X[i]) for i in range(k)]
if w is None:
w = [nx.ones((X[i].shape[0], 1), type_as=X[i]) / X[i].shape[0] for i in range(k)]
C = [
nx.dot((X[i] * w[i]).T, X[i]) / nx.sum(w[i]) + reg * nx.eye(d[i], type_as=X[i])
for i in range(k)
]
m = nx.stack(m, axis=0)
C = nx.stack(C, axis=0)
if log:
mb, Cb, log = bures_wasserstein_barycenter(m, C, weights=weights, num_iter=num_iter, eps=eps, log=log)
return mb, Cb, log
else:
mb, Cb = bures_wasserstein_barycenter(m, C, weights=weights, num_iter=num_iter, eps=eps, log=log)
return mb, Cb
[docs]
def gaussian_gromov_wasserstein_distance(Cov_s, Cov_t, log=False):
r""" Return the Gaussian Gromov-Wasserstein value from [57].
This function return the closed form value of the Gaussian Gromov-Wasserstein
distance between two Gaussian distributions
:math:`\mathcal{N}(\mu_s,\Sigma_s)` and :math:`\mathcal{N}(\mu_t,\Sigma_t)`
when the OT plan is assumed to be also Gaussian. See [57] Theorem 4.1 for
more details.
Parameters
----------
Cov_s : array-like (ds,ds)
covariance of the source distribution
Cov_t : array-like (dt,dt)
covariance of the target distribution
Returns
-------
G : float
Gaussian Gromov-Wasserstein distance
.. _references-gaussien_gromov_wasserstein_distance:
References
----------
.. [57] Delon, J., Desolneux, A., & Salmona, A. (2022). Gromov–Wasserstein
distances between Gaussian distributions. Journal of Applied Probability,
59(4), 1178-1198.
"""
nx = get_backend(Cov_s, Cov_t)
# ensure that Cov_s is the largest covariance matrix
# that is m >= n
if Cov_s.shape[0] < Cov_t.shape[0]:
Cov_s, Cov_t = Cov_t, Cov_s
n = Cov_t.shape[0]
# compte and sort eigenvalues decerasingly
d_s = nx.flip(nx.sort(nx.eigh(Cov_s)[0]))
d_t = nx.flip(nx.sort(nx.eigh(Cov_t)[0]))
# compute the gaussien Gromov-Wasserstein distance
res = 4 * (nx.sum(d_s) - nx.sum(d_t))**2 + 8 * nx.sum((d_s[:n] - d_t)**2) + 8 * nx.sum((d_s[n:])**2)
if log:
log = {}
log['d_s'] = d_s
log['d_t'] = d_t
return nx.sqrt(res), log
else:
return nx.sqrt(res)
[docs]
def empirical_gaussian_gromov_wasserstein_distance(xs, xt, ws=None,
wt=None, log=False):
r"""Return Gaussian Gromov-Wasserstein distance between samples.
The function estimates the Gaussian Gromov-Wasserstein distance between two
Gaussien distributions source :math:`\mu_s` and target :math:`\mu_t`, whose
parameters are estimated from the provided samples :math:`\mathcal{X}_s` and
:math:`\mathcal{X}_t`. See [57] Theorem 4.1 for more details.
Parameters
----------
xs : array-like (ns,d)
samples in the source domain
xt : array-like (nt,d)
samples in the target domain
ws : array-like (ns,1), optional
weights for the source samples
wt : array-like (ns,1), optional
weights for the target samples
log : bool, optional
record log if True
Returns
-------
G : float
Gaussian Gromov-Wasserstein distance
.. _references-gaussien_gromov_wasserstein:
References
----------
.. [57] Delon, J., Desolneux, A., & Salmona, A. (2022). Gromov–Wasserstein
distances between Gaussian distributions. Journal of Applied Probability,
59(4), 1178-1198.
"""
xs, xt = list_to_array(xs, xt)
nx = get_backend(xs, xt)
if ws is None:
ws = nx.ones((xs.shape[0], 1), type_as=xs) / xs.shape[0]
if wt is None:
wt = nx.ones((xt.shape[0], 1), type_as=xt) / xt.shape[0]
mxs = nx.dot(ws.T, xs) / nx.sum(ws)
mxt = nx.dot(wt.T, xt) / nx.sum(wt)
xs = xs - mxs
xt = xt - mxt
Cs = nx.dot((xs * ws).T, xs) / nx.sum(ws)
Ct = nx.dot((xt * wt).T, xt) / nx.sum(wt)
if log:
G, log = gaussian_gromov_wasserstein_distance(Cs, Ct, log=log)
log['Cov_s'] = Cs
log['Cov_t'] = Ct
return G, log
else:
G = gaussian_gromov_wasserstein_distance(Cs, Ct)
return G
[docs]
def gaussian_gromov_wasserstein_mapping(mu_s, mu_t, Cov_s, Cov_t, sign_eigs=None, log=False):
r""" Return the Gaussian Gromov-Wasserstein mapping from [57].
This function return the closed form value of the Gaussian
Gromov-Wasserstein mapping between two Gaussian distributions
:math:`\mathcal{N}(\mu_s,\Sigma_s)` and :math:`\mathcal{N}(\mu_t,\Sigma_t)`
when the OT plan is assumed to be also Gaussian. See [57] Theorem 4.1 for
more details.
Parameters
----------
mu_s : array-like (ds,)
mean of the source distribution
mu_t : array-like (dt,)
mean of the target distribution
Cov_s : array-like (ds,ds)
covariance of the source distribution
Cov_t : array-like (dt,dt)
covariance of the target distribution
log : bool, optional
record log if True
Returns
-------
A : (dt, ds) array-like
Linear operator
b : (1, dt) array-like
bias
.. _references-gaussien_gromov_wasserstein_mapping:
References
----------
.. [57] Delon, J., Desolneux, A., & Salmona, A. (2022). Gromov–Wasserstein
distances between Gaussian distributions. Journal of Applied Probability,
59(4), 1178-1198.
"""
nx = get_backend(mu_s, mu_t, Cov_s, Cov_t)
n = Cov_t.shape[0]
m = Cov_s.shape[0]
# compte and sort eigenvalues/eigenvectors decreasingly
d_s, U_s = nx.eigh(Cov_s)
id_s = nx.flip(nx.argsort(d_s))
d_s, U_s = d_s[id_s], U_s[:, id_s]
d_t, U_t = nx.eigh(Cov_t)
id_t = nx.flip(nx.argsort(d_t))
d_t, U_t = d_t[id_t], U_t[:, id_t]
if sign_eigs is None:
sign_eigs = nx.ones(min(m, n), type_as=mu_s)
if m >= n:
A = nx.concatenate((nx.diag(sign_eigs * nx.sqrt(d_t) / nx.sqrt(d_s[:n])), nx.zeros((n, m - n), type_as=mu_s)), axis=1).T
else:
A = nx.concatenate((nx.diag(sign_eigs * nx.sqrt(d_t[:m]) / nx.sqrt(d_s)), nx.zeros((n - m, m), type_as=mu_s)), axis=0).T
A = nx.dot(nx.dot(U_s, A), U_t.T)
# compute the gaussien Gromov-Wasserstein dis
b = mu_t - nx.dot(mu_s, A)
if log:
log = {}
log['d_s'] = d_s
log['d_t'] = d_t
log['U_s'] = U_s
log['U_t'] = U_t
return A, b, log
else:
return A, b
[docs]
def empirical_gaussian_gromov_wasserstein_mapping(xs, xt, ws=None,
wt=None, sign_eigs=None, log=False):
r"""Return Gaussian Gromov-Wasserstein mapping between samples.
The function estimates the Gaussian Gromov-Wasserstein mapping between two
Gaussien distributions source :math:`\mu_s` and target :math:`\mu_t`, whose
parameters are estimated from the provided samples :math:`\mathcal{X}_s` and
:math:`\mathcal{X}_t`. See [57] Theorem 4.1 for more details.
Parameters
----------
xs : array-like (ns,ds)
samples in the source domain
xt : array-like (nt,dt)
samples in the target domain
ws : array-like (ns,1), optional
weights for the source samples
wt : array-like (ns,1), optional
weights for the target samples
sign_eigs : array-like (min(ds,dt),) or string, optional
sign of the eigenvalues of the mapping matrix, by default all signs will
be positive. If 'skewness' is provided, the sign of the eigenvalues is
selected as the product of the sign of the skewness of the projected data.
log : bool, optional
record log if True
Returns
-------
A : (dt, ds) array-like
Linear operator
b : (1, dt) array-like
bias
.. _references-empirical_gaussian_gromov_wasserstein_mapping:
References
----------
.. [57] Delon, J., Desolneux, A., & Salmona, A. (2022). Gromov–Wasserstein
distances between Gaussian distributions. Journal of Applied Probability,
59(4), 1178-1198.
"""
xs, xt = list_to_array(xs, xt)
nx = get_backend(xs, xt)
m = xs.shape[1]
n = xt.shape[1]
if ws is None:
ws = nx.ones((xs.shape[0], 1), type_as=xs) / xs.shape[0]
if wt is None:
wt = nx.ones((xt.shape[0], 1), type_as=xt) / xt.shape[0]
# estimate mean and covariance
mu_s = nx.dot(ws.T, xs) / nx.sum(ws)
mu_t = nx.dot(wt.T, xt) / nx.sum(wt)
xs = xs - mu_s
xt = xt - mu_t
Cov_s = nx.dot((xs * ws).T, xs) / nx.sum(ws)
Cov_t = nx.dot((xt * wt).T, xt) / nx.sum(wt)
# compte and sort eigenvalues/eigenvectors decreasingly
d_s, U_s = nx.eigh(Cov_s)
id_s = nx.flip(nx.argsort(d_s))
d_s, U_s = d_s[id_s], U_s[:, id_s]
d_t, U_t = nx.eigh(Cov_t)
id_t = nx.flip(nx.argsort(d_t))
d_t, U_t = d_t[id_t], U_t[:, id_t]
# select the sign of the eigenvalues
if sign_eigs is None:
sign_eigs = nx.ones(min(m, n), type_as=mu_s)
elif sign_eigs == 'skewness':
size = min(m, n)
skew_s = nx.sum((nx.dot(xs, U_s[:, :size]))**3 * ws, axis=0)
skew_t = nx.sum((nx.dot(xt, U_t[:, :size]))**3 * wt, axis=0)
sign_eigs = nx.sign(skew_t * skew_s)
if m >= n:
A = nx.concatenate((nx.diag(sign_eigs * nx.sqrt(d_t) / nx.sqrt(d_s[:n])), nx.zeros((n, m - n), type_as=mu_s)), axis=1).T
else:
A = nx.concatenate((nx.diag(sign_eigs * nx.sqrt(d_t[:m]) / nx.sqrt(d_s)), nx.zeros((n - m, m), type_as=mu_s)), axis=0).T
A = nx.dot(nx.dot(U_s, A), U_t.T)
# compute the gaussien Gromov-Wasserstein dis
b = mu_t - nx.dot(mu_s, A)
if log:
log = {}
log['d_s'] = d_s
log['d_t'] = d_t
log['U_s'] = U_s
log['U_t'] = U_t
log['Cov_s'] = Cov_s
log['Cov_t'] = Cov_t
return A, b, log
else:
return A, b