add test free support barycenter algorithm + cleaning

4 files changed, 121 insertions, 97 deletions

diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py
index 5b08507..b2e62c8 100644
--- a/examples/plot_free_support_barycenter.py
+++ b/examples/plot_free_support_barycenter.py
@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 """
 ====================================================
-2D Wasserstein barycenters of distributions
+2D free support Wasserstein barycenters of distributions
 ====================================================
 
 Illustration of 2D Wasserstein barycenters if discributions that are weighted
@@ -15,7 +15,8 @@ sum of diracs.
 
 import numpy as np
 import matplotlib.pylab as pl
-import ot.plot
+import ot
+
 
 ##############################################################################
 # Generate data
@@ -28,16 +29,16 @@ measures_weights = []
 
 for i in range(N):
 
-    n = np.random.randint(low=1, high=20)  # nb samples
+    n_i = np.random.randint(low=1, high=20)  # nb samples
 
-    mu = np.random.normal(0., 4., (d,))
+    mu_i = np.random.normal(0., 4., (d,))  # Gaussian mean
 
-    A = np.random.rand(d, d)
-    cov = np.dot(A, A.transpose())
+    A_i = np.random.rand(d, d)
+    cov_i = np.dot(A_i, A_i.transpose())  # Gaussian covariance matrix
 
-    x_i = ot.datasets.make_2D_samples_gauss(n, mu, cov)
-    b_i = np.random.uniform(0., 1., (n,))
-    b_i = b_i / np.sum(b_i)
+    x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i)  # Dirac locations
+    b_i = np.random.uniform(0., 1., (n_i,))
+    b_i = b_i / np.sum(b_i)  # Dirac weights
 
     measures_locations.append(x_i)
     measures_weights.append(b_i)
@@ -47,19 +48,17 @@ for i in range(N):
 # Compute free support barycenter
 # -------------
 
-k = 10
-X_init = np.random.normal(0., 1., (k, d))
-b = np.ones((k,)) / k
+k = 10 # number of Diracs of the barycenter
+X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations
+b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized)
 
-X = ot.lp.cvx.free_support_barycenter(measures_locations, measures_weights, X_init, b)
+X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b)
 
 
 ##############################################################################
 # Plot data
 # ---------
 
-#%% plot samples
-
 pl.figure(1)
 for (x_i, b_i) in zip(measures_locations, measures_weights):
     color = np.random.randint(low=1, high=10 * N)
diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py
index 4c0d170..96bf6de 100644
--- a/ot/lp/__init__.py
+++ b/ot/lp/__init__.py
@@ -17,8 +17,9 @@ from .import cvx
 from .emd_wrap import emd_c, check_result
 from ..utils import parmap
 from .cvx import barycenter
+from ..utils import dist
 
-__all__=['emd', 'emd2', 'barycenter', 'cvx']
+__all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx']
 
 
 def emd(a, b, M, numItermax=100000, log=False):
@@ -216,3 +217,92 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(),
 
     res = parmap(f, [b[:, i] for i in range(nb)], processes)
     return res
+
+
+
+def free_support_barycenter(measures_locations, measures_weights, X_init, b=None, weights=None, numItermax=100, stopThr=1e-7, verbose=False, log=None):
+    """
+    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)
+
+    The function solves the Wasserstein barycenter problem when the barycenter measure is constrained to be supported on k atoms.
+    This problem is considered in [1] (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. theta = 1 in [1] (Algorithm 2). This can be seen as a discrete implementation of the fixed-point algorithm of [2] proposed in the continuous setting.
+
+    Parameters
+    ----------
+    measures_locations : list of (k_i,d) np.ndarray
+        The discrete support of a measure supported on k_i locations of a d-dimensional space (k_i can be different for each element of the list)
+    measures_weights : list of (k_i,) np.ndarray
+        Numpy arrays where each numpy array has k_i non-negatives values summing to one representing the weights of each discrete input measure
+
+    X_init : (k,d) np.ndarray
+        Initialization of the support locations (on k atoms) of the barycenter
+    b : (k,) np.ndarray
+        Initialization of the weights of the barycenter (non-negatives, sum to 1)
+    weights : (k,) np.ndarray
+        Initialization of the coefficients of the barycenter (non-negatives, sum to 1)
+
+    numItermax : int, optional
+        Max number of iterations
+    stopThr : float, optional
+        Stop threshol on error (>0)
+    verbose : bool, optional
+        Print information along iterations
+    log : bool, optional
+        record log if True
+
+    Returns
+    -------
+    X : (k,d) np.ndarray
+        Support locations (on k atoms) of the barycenter
+
+    References
+    ----------
+
+    .. [1] Cuturi, Marco, and Arnaud Doucet. "Fast computation of Wasserstein barycenters." International Conference on Machine Learning. 2014.
+
+    .. [2]  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.
+
+    """
+
+    iter_count = 0
+
+    N = len(measures_locations)
+    k = X_init.shape[0]
+    d = X_init.shape[1]
+    if b is None:
+        b = np.ones((k,))/k
+    if weights is None:
+        weights = np.ones((N,)) / N
+
+    X = X_init
+
+    displacement_square_norms = []
+    displacement_square_norm = stopThr + 1.
+
+    while ( displacement_square_norm > stopThr and iter_count < numItermax ):
+
+        T_sum = np.zeros((k, d))
+
+        for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights, weights.tolist()):
+
+            M_i = dist(X, measure_locations_i)
+            T_i = emd(b, measure_weights_i, M_i)
+            T_sum = T_sum + weight_i * np.reshape(1. / b, (-1, 1)) * np.matmul(T_i, measure_locations_i)
+
+        displacement_square_norm = np.sum(np.square(T_sum-X))
+        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:
+        return X, displacement_square_norms
+    else:
+        return X
\ No newline at end of file
diff --git a/ot/lp/cvx.py b/ot/lp/cvx.py
index c097f58..8e763be 100644
--- a/ot/lp/cvx.py
+++ b/ot/lp/cvx.py
@@ -10,7 +10,7 @@ LP solvers for optimal transport using cvxopt
 import numpy as np
 import scipy as sp
 import scipy.sparse as sps
-import ot
+
 
 try:
     import cvxopt
@@ -145,83 +145,3 @@ def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-po
         return b, sol
     else:
         return b
-
-
-def free_support_barycenter(measures_locations, measures_weights, X_init, b, weights=None, numItermax=100, stopThr=1e-6, verbose=False):
-    """
-    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)
-
-    The function solves the Wasserstein barycenter problem when the barycenter measure is constrained to be supported on k atoms.
-    This problem is considered in [1] (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. theta = 1 in [1] (Algorithm 2). This can be seen as a discrete implementation of the fixed-point algorithm of [2] proposed in the continuous setting.
-
-    Parameters
-    ----------
-    data_positions : list of (k_i,d) np.ndarray
-        The discrete support of a measure supported on k_i locations of a d-dimensional space (k_i can be different for each element of the list)
-    data_weights : list of (k_i,) np.ndarray
-        Numpy arrays where each numpy array has k_i non-negatives values summing to one representing the weights of each discrete input measure
-
-    X_init : (k,d) np.ndarray
-        Initialization of the support locations (on k atoms) of the barycenter
-    b : (k,) np.ndarray
-        Initialization of the weights of the barycenter (non-negatives, sum to 1)
-    weights : (k,) np.ndarray
-        Initialization of the coefficients of the barycenter (non-negatives, sum to 1)
-
-    numItermax : int, optional
-        Max number of iterations
-    stopThr : float, optional
-        Stop threshol on error (>0)
-    verbose : bool, optional
-        Print information along iterations
-    log : bool, optional
-        record log if True
-
-    Returns
-    -------
-    X : (k,d) np.ndarray
-        Support locations (on k atoms) of the barycenter
-
-    References
-    ----------
-
-    .. [1] Cuturi, Marco, and Arnaud Doucet. "Fast computation of Wasserstein barycenters." International Conference on Machine Learning. 2014.
-
-    .. [2]  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.
-
-    """
-
-    iter_count = 0
-
-    d = X_init.shape[1]
-    k = b.size
-    N = len(measures_locations)
-
-    if not weights:
-        weights = np.ones((N,)) / N
-
-    X = X_init
-
-    displacement_square_norm = stopThr + 1.
-
-    while (displacement_square_norm > stopThr and iter_count < numItermax):
-
-        T_sum = np.zeros((k, d))
-
-        for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights, weights.tolist()):
-
-            M_i = ot.dist(X, measure_locations_i)
-            T_i = ot.emd(b, measure_weights_i, M_i)
-            T_sum = T_sum + weight_i * np.reshape(1. / b, (-1, 1)) * np.matmul(T_i, measure_locations_i)
-
-        displacement_square_norm = np.sum(np.square(X - T_sum))
-        X = T_sum
-
-        if verbose:
-            print('iteration %d, displacement_square_norm=%f\n', iter_count, displacement_square_norm)
-
-        iter_count += 1
-
-    return X
diff --git a/test/test_ot.py b/test/test_ot.py
index 399e549..dafc03f 100644
--- a/test/test_ot.py
+++ b/test/test_ot.py
@@ -135,6 +135,21 @@ def test_lp_barycenter():
     np.testing.assert_allclose(bary.sum(), 1)
 
 
+def test_free_support_barycenter():
+
+    measures_locations = [np.array([-1.]).reshape((1,1)), np.array([1.]).reshape((1,1))]
+    measures_weights = [np.array([1.]), np.array([1.])]
+
+    X_init = np.array([-12.]).reshape((1,1))
+
+    # obvious barycenter location between two diracs
+    bar_locations = np.array([0.]).reshape((1,1))
+
+    X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init)
+
+    np.testing.assert_allclose(X, bar_locations, rtol=1e-5, atol=1e-7)
+
+
 @pytest.mark.skipif(not ot.lp.cvx.cvxopt, reason="No cvxopt available")
 def test_lp_barycenter_cvxopt():