# -*- coding: utf-8 -*-
"""
Solvers for the original linear program OT problem.
"""
# Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
import os
import multiprocessing
import sys
import numpy as np
import warnings
from . import cvx
from .cvx import barycenter
from .dmmot import dmmot_monge_1dgrid_loss, dmmot_monge_1dgrid_optimize
# import compiled emd
from .emd_wrap import emd_c, check_result, emd_1d_sorted
from .solver_1d import (emd_1d, emd2_1d, wasserstein_1d,
binary_search_circle, wasserstein_circle,
semidiscrete_wasserstein2_unif_circle)
from ..utils import dist, list_to_array
from ..utils import parmap
from ..backend import get_backend
__all__ = ['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', ' emd_1d_sorted',
'emd_1d', 'emd2_1d', 'wasserstein_1d', 'generalized_free_support_barycenter',
'binary_search_circle', 'wasserstein_circle', 'semidiscrete_wasserstein2_unif_circle',
'dmmot_monge_1dgrid_loss', 'dmmot_monge_1dgrid_optimize']
[docs]
def check_number_threads(numThreads):
"""Checks whether or not the requested number of threads has a valid value.
Parameters
----------
numThreads : int or str
The requested number of threads, should either be a strictly positive integer or "max" or None
Returns
-------
numThreads : int
Corrected number of threads
"""
if (numThreads is None) or (isinstance(numThreads, str) and numThreads.lower() == 'max'):
return -1
if (not isinstance(numThreads, int)) or numThreads < 1:
raise ValueError('numThreads should either be "max" or a strictly positive integer')
return numThreads
[docs]
def center_ot_dual(alpha0, beta0, a=None, b=None):
r"""Center dual OT potentials w.r.t. their weights
The main idea of this function is to find unique dual potentials
that ensure some kind of centering/fairness. The main idea is to find dual potentials that lead to the same final objective value for both source and targets (see below for more details). It will help having
stability when multiple calling of the OT solver with small changes.
Basically we add another constraint to the potential that will not
change the objective value but will ensure unicity. The constraint
is the following:
.. math::
\alpha^T \mathbf{a} = \beta^T \mathbf{b}
in addition to the OT problem constraints.
since :math:`\sum_i a_i=\sum_j b_j` this can be solved by adding/removing
a constant from both :math:`\alpha_0` and :math:`\beta_0`.
.. math::
c &= \frac{\beta_0^T \mathbf{b} - \alpha_0^T \mathbf{a}}{\mathbf{1}^T \mathbf{b} + \mathbf{1}^T \mathbf{a}}
\alpha &= \alpha_0 + c
\beta &= \beta_0 + c
Parameters
----------
alpha0 : (ns,) numpy.ndarray, float64
Source dual potential
beta0 : (nt,) numpy.ndarray, float64
Target dual potential
a : (ns,) numpy.ndarray, float64
Source histogram (uniform weight if empty list)
b : (nt,) numpy.ndarray, float64
Target histogram (uniform weight if empty list)
Returns
-------
alpha : (ns,) numpy.ndarray, float64
Source centered dual potential
beta : (nt,) numpy.ndarray, float64
Target centered dual potential
"""
# if no weights are provided, use uniform
if a is None:
a = np.ones(alpha0.shape[0]) / alpha0.shape[0]
if b is None:
b = np.ones(beta0.shape[0]) / beta0.shape[0]
# compute constant that balances the weighted sums of the duals
c = (b.dot(beta0) - a.dot(alpha0)) / (a.sum() + b.sum())
# update duals
alpha = alpha0 + c
beta = beta0 - c
return alpha, beta
[docs]
def estimate_dual_null_weights(alpha0, beta0, a, b, M):
r"""Estimate feasible values for 0-weighted dual potentials
The feasible values are computed efficiently but rather coarsely.
.. warning::
This function is necessary because the C++ solver in `emd_c`
discards all samples in the distributions with
zeros weights. This means that while the primal variable (transport
matrix) is exact, the solver only returns feasible dual potentials
on the samples with weights different from zero.
First we compute the constraints violations:
.. math::
\mathbf{V} = \alpha + \beta^T - \mathbf{M}
Next we compute the max amount of violation per row (:math:`\alpha`) and
columns (:math:`beta`)
.. math::
\mathbf{v^a}_i = \max_j \mathbf{V}_{i,j}
\mathbf{v^b}_j = \max_i \mathbf{V}_{i,j}
Finally we update the dual potential with 0 weights if a
constraint is violated
.. math::
\alpha_i = \alpha_i - \mathbf{v^a}_i \quad \text{ if } \mathbf{a}_i=0 \text{ and } \mathbf{v^a}_i>0
\beta_j = \beta_j - \mathbf{v^b}_j \quad \text{ if } \mathbf{b}_j=0 \text{ and } \mathbf{v^b}_j > 0
In the end the dual potentials are centered using function
:py:func:`ot.lp.center_ot_dual`.
Note that all those updates do not change the objective value of the
solution but provide dual potentials that do not violate the constraints.
Parameters
----------
alpha0 : (ns,) numpy.ndarray, float64
Source dual potential
beta0 : (nt,) numpy.ndarray, float64
Target dual potential
alpha0 : (ns,) numpy.ndarray, float64
Source dual potential
beta0 : (nt,) numpy.ndarray, float64
Target dual potential
a : (ns,) numpy.ndarray, float64
Source distribution (uniform weights if empty list)
b : (nt,) numpy.ndarray, float64
Target distribution (uniform weights if empty list)
M : (ns,nt) numpy.ndarray, float64
Loss matrix (c-order array with type float64)
Returns
-------
alpha : (ns,) numpy.ndarray, float64
Source corrected dual potential
beta : (nt,) numpy.ndarray, float64
Target corrected dual potential
"""
# binary indexing of non-zeros weights
asel = a != 0
bsel = b != 0
# compute dual constraints violation
constraint_violation = alpha0[:, None] + beta0[None, :] - M
# Compute largest violation per line and columns
aviol = np.max(constraint_violation, 1)
bviol = np.max(constraint_violation, 0)
# update corrects violation of
alpha_up = -1 * ~asel * np.maximum(aviol, 0)
beta_up = -1 * ~bsel * np.maximum(bviol, 0)
alpha = alpha0 + alpha_up
beta = beta0 + beta_up
return center_ot_dual(alpha, beta, a, b)
[docs]
def emd(a, b, M, numItermax=100000, log=False, center_dual=True, numThreads=1, check_marginals=True):
r"""Solves the Earth Movers distance problem and returns the OT matrix
.. math::
\gamma = \mathop{\arg \min}_\gamma \quad \langle \gamma, \mathbf{M} \rangle_F
s.t. \ \gamma \mathbf{1} = \mathbf{a}
\gamma^T \mathbf{1} = \mathbf{b}
\gamma \geq 0
where :
- :math:`\mathbf{M}` is the metric cost matrix
- :math:`\mathbf{a}` and :math:`\mathbf{b}` are the sample weights
.. warning:: Note that the :math:`\mathbf{M}` matrix in numpy needs to be a C-order
numpy.array in float64 format. It will be converted if not in this
format
.. note:: This function is backend-compatible and will work on arrays
from all compatible backends. But the algorithm uses the C++ CPU backend
which can lead to copy overhead on GPU arrays.
.. note:: This function will cast the computed transport plan to the data type
of the provided input with the following priority: :math:`\mathbf{a}`,
then :math:`\mathbf{b}`, then :math:`\mathbf{M}` if marginals are not provided.
Casting to an integer tensor might result in a loss of precision.
If this behaviour is unwanted, please make sure to provide a
floating point input.
.. note:: An error will be raised if the vectors :math:`\mathbf{a}` and :math:`\mathbf{b}` do not sum to the same value.
Uses the algorithm proposed in :ref:`[1] <references-emd>`.
Parameters
----------
a : (ns,) array-like, float
Source histogram (uniform weight if empty list)
b : (nt,) array-like, float
Target histogram (uniform weight if empty list)
M : (ns,nt) array-like, float
Loss matrix (c-order array in numpy with type float64)
numItermax : int, optional (default=100000)
The maximum number of iterations before stopping the optimization
algorithm if it has not converged.
log: bool, optional (default=False)
If True, returns a dictionary containing the cost and dual variables.
Otherwise returns only the optimal transportation matrix.
center_dual: boolean, optional (default=True)
If True, centers the dual potential using function
:py:func:`ot.lp.center_ot_dual`.
numThreads: int or "max", optional (default=1, i.e. OpenMP is not used)
If compiled with OpenMP, chooses the number of threads to parallelize.
"max" selects the highest number possible.
check_marginals: bool, optional (default=True)
If True, checks that the marginals mass are equal. If False, skips the
check.
Returns
-------
gamma: array-like, shape (ns, nt)
Optimal transportation matrix for the given
parameters
log: dict, optional
If input log is true, a dictionary containing the
cost and dual variables and exit status
Examples
--------
Simple example with obvious solution. The function emd accepts lists and
perform automatic conversion to numpy arrays
>>> import ot
>>> a=[.5,.5]
>>> b=[.5,.5]
>>> M=[[0.,1.],[1.,0.]]
>>> ot.emd(a, b, M)
array([[0.5, 0. ],
[0. , 0.5]])
.. _references-emd:
References
----------
.. [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011,
December). Displacement interpolation using Lagrangian mass transport.
In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM.
See Also
--------
ot.bregman.sinkhorn : Entropic regularized OT
ot.optim.cg : General regularized OT
"""
# convert to numpy if list
a, b, M = list_to_array(a, b, M)
a0, b0, M0 = a, b, M
if len(a0) != 0:
type_as = a0
elif len(b0) != 0:
type_as = b0
else:
type_as = M0
nx = get_backend(M0, a0, b0)
# convert to numpy
M, a, b = nx.to_numpy(M, a, b)
# ensure float64
a = np.asarray(a, dtype=np.float64)
b = np.asarray(b, dtype=np.float64)
M = np.asarray(M, dtype=np.float64, order='C')
# if empty array given then use uniform distributions
if len(a) == 0:
a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0]
if len(b) == 0:
b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1]
assert (a.shape[0] == M.shape[0] and b.shape[0] == M.shape[1]), \
"Dimension mismatch, check dimensions of M with a and b"
# ensure that same mass
if check_marginals:
np.testing.assert_almost_equal(a.sum(0),
b.sum(0), err_msg='a and b vector must have the same sum',
decimal=6)
b = b * a.sum() / b.sum()
asel = a != 0
bsel = b != 0
numThreads = check_number_threads(numThreads)
G, cost, u, v, result_code = emd_c(a, b, M, numItermax, numThreads)
if center_dual:
u, v = center_ot_dual(u, v, a, b)
if np.any(~asel) or np.any(~bsel):
u, v = estimate_dual_null_weights(u, v, a, b, M)
result_code_string = check_result(result_code)
if not nx.is_floating_point(type_as):
warnings.warn(
"Input histogram consists of integer. The transport plan will be "
"casted accordingly, possibly resulting in a loss of precision. "
"If this behaviour is unwanted, please make sure your input "
"histogram consists of floating point elements.",
stacklevel=2
)
if log:
log = {}
log['cost'] = cost
log['u'] = nx.from_numpy(u, type_as=type_as)
log['v'] = nx.from_numpy(v, type_as=type_as)
log['warning'] = result_code_string
log['result_code'] = result_code
return nx.from_numpy(G, type_as=type_as), log
return nx.from_numpy(G, type_as=type_as)
[docs]
def emd2(a, b, M, processes=1,
numItermax=100000, log=False, return_matrix=False,
center_dual=True, numThreads=1, check_marginals=True):
r"""Solves the Earth Movers distance problem and returns the loss
.. math::
\min_\gamma \quad \langle \gamma, \mathbf{M} \rangle_F
s.t. \ \gamma \mathbf{1} = \mathbf{a}
\gamma^T \mathbf{1} = \mathbf{b}
\gamma \geq 0
where :
- :math:`\mathbf{M}` is the metric cost matrix
- :math:`\mathbf{a}` and :math:`\mathbf{b}` are the sample weights
.. note:: This function is backend-compatible and will work on arrays
from all compatible backends. But the algorithm uses the C++ CPU backend
which can lead to copy overhead on GPU arrays.
.. note:: This function will cast the computed transport plan and
transportation loss to the data type of the provided input with the
following priority: :math:`\mathbf{a}`, then :math:`\mathbf{b}`,
then :math:`\mathbf{M}` if marginals are not provided.
Casting to an integer tensor might result in a loss of precision.
If this behaviour is unwanted, please make sure to provide a
floating point input.
.. note:: An error will be raised if the vectors :math:`\mathbf{a}` and :math:`\mathbf{b}` do not sum to the same value.
Uses the algorithm proposed in :ref:`[1] <references-emd2>`.
Parameters
----------
a : (ns,) array-like, float64
Source histogram (uniform weight if empty list)
b : (nt,) array-like, float64
Target histogram (uniform weight if empty list)
M : (ns,nt) array-like, float64
Loss matrix (for numpy c-order array with type float64)
processes : int, optional (default=1)
Nb of processes used for multiple emd computation (deprecated)
numItermax : int, optional (default=100000)
The maximum number of iterations before stopping the optimization
algorithm if it has not converged.
log: boolean, optional (default=False)
If True, returns a dictionary containing dual
variables. Otherwise returns only the optimal transportation cost.
return_matrix: boolean, optional (default=False)
If True, returns the optimal transportation matrix in the log.
center_dual: boolean, optional (default=True)
If True, centers the dual potential using function
:py:func:`ot.lp.center_ot_dual`.
numThreads: int or "max", optional (default=1, i.e. OpenMP is not used)
If compiled with OpenMP, chooses the number of threads to parallelize.
"max" selects the highest number possible.
check_marginals: bool, optional (default=True)
If True, checks that the marginals mass are equal. If False, skips the
check.
Returns
-------
W: float, array-like
Optimal transportation loss for the given parameters
log: dict
If input log is true, a dictionary containing dual
variables and exit status
Examples
--------
Simple example with obvious solution. The function emd accepts lists and
perform automatic conversion to numpy arrays
>>> import ot
>>> a=[.5,.5]
>>> b=[.5,.5]
>>> M=[[0.,1.],[1.,0.]]
>>> ot.emd2(a,b,M)
0.0
.. _references-emd2:
References
----------
.. [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W.
(2011, December). Displacement interpolation using Lagrangian mass
transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p.
158). ACM.
See Also
--------
ot.bregman.sinkhorn : Entropic regularized OT
ot.optim.cg : General regularized OT
"""
a, b, M = list_to_array(a, b, M)
a0, b0, M0 = a, b, M
if len(a0) != 0:
type_as = a0
elif len(b0) != 0:
type_as = b0
else:
type_as = M0
nx = get_backend(M0, a0, b0)
# convert to numpy
M, a, b = nx.to_numpy(M, a, b)
a = np.asarray(a, dtype=np.float64)
b = np.asarray(b, dtype=np.float64)
M = np.asarray(M, dtype=np.float64, order='C')
# if empty array given then use uniform distributions
if len(a) == 0:
a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0]
if len(b) == 0:
b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1]
assert (a.shape[0] == M.shape[0] and b.shape[0] == M.shape[1]), \
"Dimension mismatch, check dimensions of M with a and b"
# ensure that same mass
if check_marginals:
np.testing.assert_almost_equal(a.sum(0),
b.sum(0,keepdims=True), err_msg='a and b vector must have the same sum',
decimal=6)
b = b * a.sum(0) / b.sum(0,keepdims=True)
asel = a != 0
numThreads = check_number_threads(numThreads)
if log or return_matrix:
def f(b):
bsel = b != 0
G, cost, u, v, result_code = emd_c(a, b, M, numItermax, numThreads)
if center_dual:
u, v = center_ot_dual(u, v, a, b)
if np.any(~asel) or np.any(~bsel):
u, v = estimate_dual_null_weights(u, v, a, b, M)
result_code_string = check_result(result_code)
log = {}
if not nx.is_floating_point(type_as):
warnings.warn(
"Input histogram consists of integer. The transport plan will be "
"casted accordingly, possibly resulting in a loss of precision. "
"If this behaviour is unwanted, please make sure your input "
"histogram consists of floating point elements.",
stacklevel=2
)
G = nx.from_numpy(G, type_as=type_as)
if return_matrix:
log['G'] = G
log['u'] = nx.from_numpy(u, type_as=type_as)
log['v'] = nx.from_numpy(v, type_as=type_as)
log['warning'] = result_code_string
log['result_code'] = result_code
cost = nx.set_gradients(nx.from_numpy(cost, type_as=type_as),
(a0, b0, M0), (log['u'] - nx.mean(log['u']),
log['v'] - nx.mean(log['v']), G))
return [cost, log]
else:
def f(b):
bsel = b != 0
G, cost, u, v, result_code = emd_c(a, b, M, numItermax, numThreads)
if center_dual:
u, v = center_ot_dual(u, v, a, b)
if np.any(~asel) or np.any(~bsel):
u, v = estimate_dual_null_weights(u, v, a, b, M)
if not nx.is_floating_point(type_as):
warnings.warn(
"Input histogram consists of integer. The transport plan will be "
"casted accordingly, possibly resulting in a loss of precision. "
"If this behaviour is unwanted, please make sure your input "
"histogram consists of floating point elements.",
stacklevel=2
)
G = nx.from_numpy(G, type_as=type_as)
cost = nx.set_gradients(nx.from_numpy(cost, type_as=type_as),
(a0, b0, M0), (nx.from_numpy(u - np.mean(u), type_as=type_as),
nx.from_numpy(v - np.mean(v), type_as=type_as), G))
check_result(result_code)
return cost
if len(b.shape) == 1:
return f(b)
nb = b.shape[1]
if processes > 1:
warnings.warn(
"The 'processes' parameter has been deprecated. "
"Multiprocessing should be done outside of POT."
)
res = list(map(f, [b[:, i].copy() for i in range(nb)]))
return res
[docs]
def free_support_barycenter(measures_locations, measures_weights, X_init, b=None, weights=None, numItermax=100,
stopThr=1e-7, verbose=False, log=None, numThreads=1):
r"""
Solves the free support (locations of the barycenters are optimized, not the weights) Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Wasserstein distance), formally:
.. math::
\min_\mathbf{X} \quad \sum_{i=1}^N w_i W_2^2(\mathbf{b}, \mathbf{X}, \mathbf{a}_i, \mathbf{X}_i)
where :
- :math:`w \in \mathbb{(0, 1)}^{N}`'s are the barycenter weights and sum to one
- `measure_weights` denotes the :math:`\mathbf{a}_i \in \mathbb{R}^{k_i}`: empirical measures weights (on simplex)
- `measures_locations` denotes the :math:`\mathbf{X}_i \in \mathbb{R}^{k_i, d}`: empirical measures atoms locations
- :math:`\mathbf{b} \in \mathbb{R}^{k}` is the desired weights vector of the barycenter
This problem is considered in :ref:`[20] <references-free-support-barycenter>` (Algorithm 2).
There are two differences with the following codes:
- we do not optimize over the weights
- we do not do line search for the locations updates, we use i.e. :math:`\theta = 1` in
:ref:`[20] <references-free-support-barycenter>` (Algorithm 2). This can be seen as a discrete
implementation of the fixed-point algorithm of
:ref:`[43] <references-free-support-barycenter>` proposed in the continuous setting.
Parameters
----------
measures_locations : list of N (k_i,d) array-like
The discrete support of a measure supported on :math:`k_i` locations of a `d`-dimensional space
(:math:`k_i` can be different for each element of the list)
measures_weights : list of N (k_i,) array-like
Numpy arrays where each numpy array has :math:`k_i` non-negatives values summing to one
representing the weights of each discrete input measure
X_init : (k,d) array-like
Initialization of the support locations (on `k` atoms) of the barycenter
b : (k,) array-like
Initialization of the weights of the barycenter (non-negatives, sum to 1)
weights : (N,) array-like
Initialization of the coefficients of the barycenter (non-negatives, sum to 1)
numItermax : int, optional
Max number of iterations
stopThr : float, optional
Stop threshold on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
numThreads: int or "max", optional (default=1, i.e. OpenMP is not used)
If compiled with OpenMP, chooses the number of threads to parallelize.
"max" selects the highest number possible.
Returns
-------
X : (k,d) array-like
Support locations (on k atoms) of the barycenter
.. _references-free-support-barycenter:
References
----------
.. [20] Cuturi, Marco, and Arnaud Doucet. "Fast computation of Wasserstein barycenters." International Conference on Machine Learning. 2014.
.. [43] Álvarez-Esteban, Pedro C., et al. "A fixed-point approach to barycenters in Wasserstein space." Journal of Mathematical Analysis and Applications 441.2 (2016): 744-762.
"""
nx = get_backend(*measures_locations, *measures_weights, X_init)
iter_count = 0
N = len(measures_locations)
k = X_init.shape[0]
d = X_init.shape[1]
if b is None:
b = nx.ones((k,), type_as=X_init) / k
if weights is None:
weights = nx.ones((N,), type_as=X_init) / N
X = X_init
log_dict = {}
displacement_square_norms = []
displacement_square_norm = stopThr + 1.
while (displacement_square_norm > stopThr and iter_count < numItermax):
T_sum = nx.zeros((k, d), type_as=X_init)
for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights, weights):
M_i = dist(X, measure_locations_i)
T_i = emd(b, measure_weights_i, M_i, numThreads=numThreads)
T_sum = T_sum + weight_i * 1. / b[:, None] * nx.dot(T_i, measure_locations_i)
displacement_square_norm = nx.sum((T_sum - X) ** 2)
if log:
displacement_square_norms.append(displacement_square_norm)
X = T_sum
if verbose:
print('iteration %d, displacement_square_norm=%f\n', iter_count, displacement_square_norm)
iter_count += 1
if log:
log_dict['displacement_square_norms'] = displacement_square_norms
return X, log_dict
else:
return X
[docs]
def generalized_free_support_barycenter(X_list, a_list, P_list, n_samples_bary, Y_init=None, b=None, weights=None,
numItermax=100, stopThr=1e-7, verbose=False, log=None, numThreads=1, eps=0):
r"""
Solves the free support generalized Wasserstein barycenter problem: finding a barycenter (a discrete measure with
a fixed amount of points of uniform weights) whose respective projections fit the input measures.
More formally:
.. math::
\min_\gamma \quad \sum_{i=1}^p w_i W_2^2(\nu_i, \mathbf{P}_i\#\gamma)
where :
- :math:`\gamma = \sum_{l=1}^n b_l\delta_{y_l}` is the desired barycenter with each :math:`y_l \in \mathbb{R}^d`
- :math:`\mathbf{b} \in \mathbb{R}^{n}` is the desired weights vector of the barycenter
- The input measures are :math:`\nu_i = \sum_{j=1}^{k_i}a_{i,j}\delta_{x_{i,j}}`
- The :math:`\mathbf{a}_i \in \mathbb{R}^{k_i}` are the respective empirical measures weights (on the simplex)
- The :math:`\mathbf{X}_i \in \mathbb{R}^{k_i, d_i}` are the respective empirical measures atoms locations
- :math:`w = (w_1, \cdots w_p)` are the barycenter coefficients (on the simplex)
- Each :math:`\mathbf{P}_i \in \mathbb{R}^{d, d_i}`, and :math:`P_i\#\nu_i = \sum_{j=1}^{k_i}a_{i,j}\delta_{P_ix_{i,j}}`
As show by :ref:`[42] <references-generalized-free-support-barycenter>`,
this problem can be re-written as a Wasserstein Barycenter problem,
which we solve using the free support method :ref:`[20] <references-generalized-free-support-barycenter>`
(Algorithm 2).
Parameters
----------
X_list : list of p (k_i,d_i) array-like
Discrete supports of the input measures: each consists of :math:`k_i` locations of a `d_i`-dimensional space
(:math:`k_i` can be different for each element of the list)
a_list : list of p (k_i,) array-like
Measure weights: each element is a vector (k_i) on the simplex
P_list : list of p (d_i,d) array-like
Each :math:`P_i` is a linear map :math:`\mathbb{R}^{d} \rightarrow \mathbb{R}^{d_i}`
n_samples_bary : int
Number of barycenter points
Y_init : (n_samples_bary,d) array-like
Initialization of the support locations (on `k` atoms) of the barycenter
b : (n_samples_bary,) array-like
Initialization of the weights of the barycenter measure (on the simplex)
weights : (p,) array-like
Initialization of the coefficients of the barycenter (on the simplex)
numItermax : int, optional
Max number of iterations
stopThr : float, optional
Stop threshold on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
numThreads: int or "max", optional (default=1, i.e. OpenMP is not used)
If compiled with OpenMP, chooses the number of threads to parallelize.
"max" selects the highest number possible.
eps: Stability coefficient for the change of variable matrix inversion
If the :math:`\mathbf{P}_i^T` matrices don't span :math:`\mathbb{R}^d`, the problem is ill-defined and a matrix
inversion will fail. In this case one may set eps=1e-8 and get a solution anyway (which may make little sense)
Returns
-------
Y : (n_samples_bary,d) array-like
Support locations (on n_samples_bary atoms) of the barycenter
.. _references-generalized-free-support-barycenter:
References
----------
.. [20] Cuturi, M. and Doucet, A.. "Fast computation of Wasserstein barycenters." International Conference on Machine Learning. 2014.
.. [42] Delon, J., Gozlan, N., and Saint-Dizier, A.. Generalized Wasserstein barycenters between probability measures living on different subspaces. arXiv preprint arXiv:2105.09755, 2021.
"""
nx = get_backend(*X_list, *a_list, *P_list)
d = P_list[0].shape[1]
p = len(X_list)
if weights is None:
weights = nx.ones(p, type_as=X_list[0]) / p
# variable change matrix to reduce the problem to a Wasserstein Barycenter (WB)
A = eps * nx.eye(d, type_as=X_list[0]) # if eps nonzero: will force the invertibility of A
for (P_i, lambda_i) in zip(P_list, weights):
A = A + lambda_i * P_i.T @ P_i
B = nx.inv(nx.sqrtm(A))
Z_list = [x @ Pi @ B.T for (x, Pi) in zip(X_list, P_list)] # change of variables -> (WB) problem on Z
if Y_init is None:
Y_init = nx.randn(n_samples_bary, d, type_as=X_list[0])
if b is None:
b = nx.ones(n_samples_bary, type_as=X_list[0]) / n_samples_bary # not optimized
out = free_support_barycenter(Z_list, a_list, Y_init, b, numItermax=numItermax,
stopThr=stopThr, verbose=verbose, log=log, numThreads=numThreads)
if log: # unpack
Y, log_dict = out
else:
Y = out
log_dict = None
Y = Y @ B.T # return to the Generalized WB formulation
if log:
return Y, log_dict
else:
return Y