Doc+armijo

1 files changed, 20 insertions, 19 deletions

diff --git a/ot/gromov.py b/ot/gromov.py
index 44248d1..5a57dc8 100644
--- a/ot/gromov.py
+++ b/ot/gromov.py
@@ -33,12 +33,12 @@ def init_matrix(C1, C2, p, q, loss_fun='square_loss'):
         * C2 : Metric cost matrix in the target space

         * T : A coupling between those two spaces

 

-    The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as :

+    The square-loss function L(a,b)=|a-b|^2 is read as :

         L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with :

-            * f1(a)=(a^2)/2

-            * f2(b)=(b^2)/2

+            * f1(a)=(a^2)

+            * f2(b)=(b^2)

             * h1(a)=a

-            * h2(b)=b

+            * h2(b)=2*b

 

     The kl-loss function L(a,b)=a*log(a/b)-a+b is read as :

         L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with :

@@ -269,7 +269,7 @@ def update_kl_loss(p, lambdas, T, Cs):
     return np.exp(np.divide(tmpsum, ppt))

 

 

-def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs):

+def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs):

     """

     Returns the gromov-wasserstein transport between (C1,p) and (C2,q)

 

@@ -307,8 +307,8 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs)
         Print information along iterations

     log : bool, optional

         record log if True

-    amijo : bool, optional

-        If True the steps of the line-search is found via an amijo research. Else closed form is used.

+    armijo : bool, optional

+        If True the steps of the line-search is found via an armijo research. Else closed form is used.

         If there is convergence issues use False.

     **kwargs : dict

         parameters can be directly pased to the ot.optim.cg solver

@@ -344,14 +344,14 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs)
         return gwggrad(constC, hC1, hC2, G)

 

     if log:

-        res, log = cg(p, q, 0, 1, f, df, G0, log=True, amijo=amijo, C1=C1, C2=C2, constC=constC, **kwargs)

+        res, log = cg(p, q, 0, 1, f, df, G0, log=True, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)

         log['gw_dist'] = gwloss(constC, hC1, hC2, res)

         return res, log

     else:

-        return cg(p, q, 0, 1, f, df, G0, amijo=amijo, C1=C1, C2=C2, constC=constC, **kwargs)

+        return cg(p, q, 0, 1, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)

 

 

-def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, amijo=False, **kwargs):

+def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, **kwargs):

     """

     Computes the FGW distance between two graphs see [3]

     .. math::

@@ -363,6 +363,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5,
     - M is the (ns,nt) metric cost matrix

     - :math:`f` is the regularization term ( and df is its gradient)

     - a and b are source and target weights (sum to 1)

+    - L is a loss function to account for the misfit between the similarity matrices

     The algorithm used for solving the problem is conditional gradient as discussed in  [1]_

     Parameters

     ----------

@@ -386,8 +387,8 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5,
         Print information along iterations

     log : bool, optional

         record log if True

-    amijo : bool, optional

-        If True the steps of the line-search is found via an amijo research. Else closed form is used.

+    armijo : bool, optional

+        If True the steps of the line-search is found via an armijo research. Else closed form is used.

         If there is convergence issues use False.

     **kwargs : dict

         parameters can be directly pased to the ot.optim.cg solver

@@ -415,10 +416,10 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5,
     def df(G):

         return gwggrad(constC, hC1, hC2, G)

 

-    return cg(p, q, M, alpha, f, df, G0, amijo=amijo, C1=C1, C2=C2, constC=constC, **kwargs)

+    return cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)

 

 

-def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs):

+def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs):

     """

     Returns the gromov-wasserstein discrepancy between (C1,p) and (C2,q)

 

@@ -456,8 +457,8 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs
         Print information along iterations

     log : bool, optional

         record log if True

-    amijo : bool, optional

-        If True the steps of the line-search is found via an amijo research. Else closed form is used.

+    armijo : bool, optional

+        If True the steps of the line-search is found via an armijo research. Else closed form is used.

         If there is convergence issues use False.

     Returns

     -------

@@ -487,7 +488,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs
 

     def df(G):

         return gwggrad(constC, hC1, hC2, G)

-    res, log = cg(p, q, 0, 1, f, df, G0, log=True, amijo=amijo, C1=C1, C2=C2, constC=constC, **kwargs)

+    res, log = cg(p, q, 0, 1, f, df, G0, log=True, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)

     log['gw_dist'] = gwloss(constC, hC1, hC2, res)

     log['T'] = res

     if log:

@@ -890,7 +891,7 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_
                     p=None, loss_fun='square_loss', max_iter=100, tol=1e-9,

                     verbose=False, log=True, init_C=None, init_X=None):

     """

-    Compute the fgw barycenter as presented eq (5) in [3].

+    Compute the fgw barycenter as presented eq (5) in [24].

     ----------

     N : integer

         Desired number of samples of the target barycenter

@@ -1065,7 +1066,7 @@ def update_sructure_matrix(p, lambdas, T, Cs):
 

 def update_feature_matrix(lambdas, Ys, Ts, p):

     """

-    Updates the feature with respect to the S Ts couplings. See "Solving the barycenter problem with Block Coordinate Descent (BCD)" in [3]

+    Updates the feature with respect to the S Ts couplings. See "Solving the barycenter problem with Block Coordinate Descent (BCD)" in [24]

     calculated at each iteration

     Parameters

     ----------