From 4a585de94109102c89bcd7ad43e35772e1027cd2 Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Fri, 16 Feb 2018 11:58:59 +0100
Subject: update examples

---
 ot/gromov.py | 109 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 108 insertions(+), 1 deletion(-)

(limited to 'ot/gromov.py')

diff --git a/ot/gromov.py b/ot/gromov.py
index 8d08397..e4dd112 100644
--- a/ot/gromov.py
+++ b/ot/gromov.py
@@ -590,7 +590,7 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon,
         return logv['gw_dist']
 
 
-def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon,
+def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon,
                        max_iter=1000, tol=1e-9, verbose=False, log=False, init_C=None):
     """
     Returns the gromov-wasserstein barycenters of S measured similarity matrices
@@ -696,3 +696,110 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon,
         cpt += 1
 
     return C
+
+
+def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, 
+                       max_iter=1000, tol=1e-9, verbose=False, log=False, init_C=None):
+    """
+    Returns the gromov-wasserstein barycenters of S measured similarity matrices
+
+    (Cs)_{s=1}^{s=S}
+
+    The function solves the following optimization problem with block 
+    coordinate descent:
+
+    .. math::
+        C = argmin_C\in R^NxN \sum_s \lambda_s GW(C,Cs,p,ps)
+
+
+    Where :
+
+        Cs : metric cost matrix
+        ps  : distribution
+
+    Parameters
+    ----------
+    N  : Integer
+         Size of the targeted barycenter
+    Cs : list of S np.ndarray(ns,ns)
+         Metric cost matrices
+    ps : list of S np.ndarray(ns,)
+         sample weights in the S spaces
+    p  : ndarray, shape(N,)
+         weights in the targeted barycenter
+    lambdas : list of float
+              list of the S spaces' weights
+    loss_fun :  tensor-matrix multiplication function based on specific loss function
+    update : function(p,lambdas,T,Cs) that updates C according to a specific Kernel
+             with the S Ts couplings calculated at each iteration
+    max_iter : int, optional
+        Max number of iterations
+    tol : float, optional
+        Stop threshol on error (>0)
+    verbose : bool, optional
+        Print information along iterations
+    log : bool, optional
+        record log if True
+    init_C : bool, ndarray, shape(N,N)
+             random initial value for the C matrix provided by user
+
+    Returns
+    -------
+    C : ndarray, shape (N, N)
+        Similarity matrix in the barycenter space (permutated arbitrarily)
+        
+    References
+    ----------
+    .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
+    "Gromov-Wasserstein averaging of kernel and distance matrices."
+    International Conference on Machine Learning (ICML). 2016.        
+        
+    """
+
+    S = len(Cs)
+
+    Cs = [np.asarray(Cs[s], dtype=np.float64) for s in range(S)]
+    lambdas = np.asarray(lambdas, dtype=np.float64)
+
+    # Initialization of C : random SPD matrix (if not provided by user)
+    if init_C is None:
+        xalea = np.random.randn(N, 2)
+        C = dist(xalea, xalea)
+        C /= C.max()
+    else:
+        C = init_C
+
+    cpt = 0
+    err = 1
+
+    error = []
+
+    while(err > tol and cpt < max_iter):
+        Cprev = C
+
+        T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, 
+                                numItermax=max_iter, stopThr=1e-5, verbose=verbose, log=log) for s in range(S)]
+        if loss_fun == 'square_loss':
+            C = update_square_loss(p, lambdas, T, Cs)
+
+        elif loss_fun == 'kl_loss':
+            C = update_kl_loss(p, lambdas, T, Cs)
+
+        if cpt % 10 == 0:
+            # we can speed up the process by checking for the error only all
+            # the 10th iterations
+            err = np.linalg.norm(C - Cprev)
+            error.append(err)
+
+            if log:
+                log['err'].append(err)
+
+            if verbose:
+                if cpt % 200 == 0:
+                    print('{:5s}|{:12s}'.format(
+                        'It.', 'Err') + '\n' + '-' * 19)
+                print('{:5d}|{:8e}|'.format(cpt, err))
+
+        cpt += 1
+
+    return C
\ No newline at end of file
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