fixed argument functions

Author: Kilian Fatras

ot/stochastic.py

@@ -435,7 +435,7 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None,
 ##############################################################################
 
 
-def batch_grad_dual(M, reg, a, b, alpha, beta, batch_size, batch_alpha,
+def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha,
                     batch_beta):
     '''
     Computes the partial gradient of F_\W_varepsilon
@@ -528,7 +528,7 @@ def batch_grad_dual(M, reg, a, b, alpha, beta, batch_size, batch_alpha,
     return grad_alpha, grad_beta
 
 
-def sgd_entropic_regularization(M, reg, a, b, batch_size, numItermax, lr):
+def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr):
     '''
     Compute the sgd algorithm to solve the regularized discrete measures
         optimal transport dual problem
@@ -612,7 +612,7 @@ def sgd_entropic_regularization(M, reg, a, b, batch_size, numItermax, lr):
         k = np.sqrt(cur_iter / 100 + 1)
         batch_alpha = np.random.choice(n_source, batch_size, replace=False)
         batch_beta = np.random.choice(n_target, batch_size, replace=False)
-        update_alpha, update_beta = batch_grad_dual(M, reg, a, b, cur_alpha,
+        update_alpha, update_beta = batch_grad_dual(a, b, M, reg, cur_alpha,
                                                     cur_beta, batch_size,
                                                     batch_alpha, batch_beta)
         cur_alpha += (lr / k) * update_alpha
@@ -698,7 +698,7 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1,
                       arXiv preprint arxiv:1711.02283.
     '''
 
-    opt_alpha, opt_beta = sgd_entropic_regularization(M, reg, a, b, batch_size,
+    opt_alpha, opt_beta = sgd_entropic_regularization(a, b, M, reg, batch_size,
                                                       numItermax, lr)
     pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) *
           a[:, None] * b[None, :])

test/test_stochastic.py

@@ -196,8 +196,8 @@ def test_dual_sgd_sinkhorn():
     reg = 1
     batch_size = 30
 
-    a = ot.datasets.get_1D_gauss(n, m=15, s=5)  # m= mean, s= std
-    b = ot.datasets.get_1D_gauss(n, m=15, s=5)
+    a = ot.datasets.get_1D_gauss(n, 15, 5)  # m= mean, s= std
+    b = ot.datasets.get_1D_gauss(n, 15, 5)
     X_source = np.arange(n, dtype=np.float64)
     Y_target = np.arange(n, dtype=np.float64)
     M = ot.dist(X_source.reshape((n, 1)), Y_target.reshape((n, 1)))