Corrections on Gromov

1 files changed, 0 insertions, 20 deletions

diff --git a/ot/gromov.py b/ot/gromov.py
index 1726f5e..82e3fd3 100644
--- a/ot/gromov.py
+++ b/ot/gromov.py
@@ -40,7 +40,6 @@ def tensor_square_loss(C1, C2, T):
     function as the loss function of Gromow-Wasserstein discrepancy.

 

     Where :

-

         C1 : Metric cost matrix in the source space

         C2 : Metric cost matrix in the target space

         T : A coupling between those two spaces

@@ -61,13 +60,10 @@ def tensor_square_loss(C1, C2, T):
     T : ndarray, shape (ns, nt)

          Coupling between source and target spaces

 

-

     Returns

     -------

     tens : ndarray, shape (ns, nt)

            \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result

-

-

     """

 

     C1 = np.asarray(C1, dtype=np.float64)

@@ -119,7 +115,6 @@ def tensor_kl_loss(C1, C2, T):
     T :  ndarray, shape (ns, nt)

          Coupling between source and target spaces

 

-

     Returns

     -------

     tens : ndarray, shape (ns, nt)

@@ -127,7 +122,6 @@ def tensor_kl_loss(C1, C2, T):
 

     References

     ----------

-

     .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices."  International Conference on Machine Learning (ICML). 2016.

 

     """

@@ -159,7 +153,6 @@ def update_square_loss(p, lambdas, T, Cs):
     Updates C according to the L2 Loss kernel with the S Ts couplings

     calculated at each iteration

 

-

     Parameters

     ----------

     p  : ndarray, shape (N,)

@@ -174,8 +167,6 @@ def update_square_loss(p, lambdas, T, Cs):
     ----------

     C : ndarray, shape (nt,nt)

         updated C matrix

-

-

     """

     tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))])

     ppt = np.outer(p, p)

@@ -202,8 +193,6 @@ def update_kl_loss(p, lambdas, T, Cs):
     ----------

     C : ndarray, shape (ns,ns)

         updated C matrix

-

-

     """

     tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))])

     ppt = np.outer(p, p)

@@ -229,7 +218,6 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9,
              \GW\geq 0

 

     Where :

-

         C1 : Metric cost matrix in the source space

         C2 : Metric cost matrix in the target space

         p  : distribution in the source space

@@ -237,7 +225,6 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9,
         L  : loss function to account for the misfit between the similarity matrices

         H  : entropy

 

-

     Parameters

     ----------

     C1 : ndarray, shape (ns, ns)

@@ -261,13 +248,11 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9,
     log : bool, optional

         record log if True

 

-

     Returns

     -------

     T : ndarray, shape (ns, nt)

         coupling between the two spaces that minimizes :

             \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T))

-

     """

 

     C1 = np.asarray(C1, dtype=np.float64)

@@ -322,9 +307,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9
     .. math::

         \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T))

 

-

     Where :

-

         C1 : Metric cost matrix in the source space

         C2 : Metric cost matrix in the target space

         p  : distribution in the source space

@@ -332,7 +315,6 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9
         L  : loss function to account for the misfit between the similarity matrices

         H  : entropy

 

-

     Parameters

     ----------

     C1 : ndarray, shape (ns, ns)

@@ -360,7 +342,6 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9
     -------

     gw_dist : float

         Gromov-Wasserstein distance

-

     """

 

     if log:

@@ -428,7 +409,6 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000,
     -------

     C : ndarray, shape (N, N)

         Similarity matrix in the barycenter space (permutated arbitrarily)

-

     """

 

     S = len(Cs)