pep8

1 files changed, 15 insertions, 18 deletions

diff --git a/ot/stochastic.py b/ot/stochastic.py
index 31c99be..98537d9 100644
--- a/ot/stochastic.py
+++ b/ot/stochastic.py
@@ -78,8 +78,8 @@ def coordinate_gradient(b, M, reg, beta, i):
     '''
 
     r = M[i, :] - beta
-    exp_beta = np.exp(-r/reg) * b
-    khi = exp_beta/(np.sum(exp_beta))
+    exp_beta = np.exp(-r / reg) * b
+    khi = exp_beta / (np.sum(exp_beta))
     return b - khi
 
 
@@ -164,7 +164,7 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=0.1):
         cur_coord_grad = a[i] * coordinate_gradient(b, M, reg, cur_beta, i)
         sum_stored_gradient += (cur_coord_grad - stored_gradient[i])
         stored_gradient[i] = cur_coord_grad
-        cur_beta += lr * (1./n_source) * sum_stored_gradient
+        cur_beta += lr * (1. / n_source) * sum_stored_gradient
     return cur_beta
 
 
@@ -246,8 +246,8 @@ def averaged_sgd_entropic_transport(b, M, reg, numItermax=300000, lr=1):
         k = cur_iter + 1
         i = np.random.randint(n_source)
         cur_coord_grad = coordinate_gradient(b, M, reg, cur_beta, i)
-        cur_beta += (lr/np.sqrt(k)) * cur_coord_grad
-        ave_beta = (1./k) * cur_beta + (1 - 1./k) * ave_beta
+        cur_beta += (lr / np.sqrt(k)) * cur_coord_grad
+        ave_beta = (1. / k) * cur_beta + (1 - 1. / k) * ave_beta
     return ave_beta
 
 
@@ -316,12 +316,11 @@ def c_transform_entropic(b, M, reg, beta):
     '''
 
     n_source = np.shape(M)[0]
-    n_target = np.shape(M)[1]
     alpha = np.zeros(n_source)
     for i in range(n_source):
         r = M[i, :] - beta
         min_r = np.min(r)
-        exp_beta = np.exp(-(r - min_r)/reg) * b
+        exp_beta = np.exp(-(r - min_r) / reg) * b
         alpha[i] = min_r - reg * np.log(np.sum(exp_beta))
     return alpha
 
@@ -407,8 +406,6 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=0.1,
                      Advances in Neural Information Processing Systems (2016),
                       arXiv preprint arxiv:1605.08527.
     '''
-    n_source = 7
-    n_target = 4
     if method.lower() == "sag":
         opt_beta = sag_entropic_transport(a, b, M, reg, numItermax, lr)
     elif method.lower() == "asgd":
@@ -418,7 +415,7 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=0.1,
         return None
 
     opt_alpha = c_transform_entropic(b, M, reg, opt_beta)
-    pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :])/reg) *
+    pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) *
           a[:, None] * b[None, :])
 
     if log:
@@ -511,8 +508,8 @@ def grad_dF_dalpha(M, reg, alpha, beta, batch_size, batch_alpha, batch_beta):
     grad_alpha = np.zeros(batch_size)
     grad_alpha[:] = batch_size
     for j in batch_beta:
-        grad_alpha -= np.exp((alpha[batch_alpha] + beta[j]
-                              - M[batch_alpha, j])/reg)
+        grad_alpha -= np.exp((alpha[batch_alpha] + beta[j] -
+                              M[batch_alpha, j]) / reg)
     return grad_alpha
 
 
@@ -594,7 +591,7 @@ def grad_dF_dbeta(M, reg, alpha, beta, batch_size, batch_alpha, batch_beta):
     grad_beta[:] = batch_size
     for i in batch_alpha:
         grad_beta -= np.exp((alpha[i] +
-                             beta[batch_beta] - M[i, batch_beta])/reg)
+                             beta[batch_beta] - M[i, batch_beta]) / reg)
     return grad_beta
 
 
@@ -681,10 +678,10 @@ def sgd_entropic_regularization(M, reg, batch_size, numItermax, lr,
             batch_beta = np.random.choice(n_target, batch_size, replace=False)
             grad_F_alpha = grad_dF_dalpha(M, reg, cur_alpha, cur_beta,
                                           batch_size, batch_alpha, batch_beta)
-            cur_alpha[batch_alpha] += (lr/k) * grad_F_alpha
+            cur_alpha[batch_alpha] += (lr / k) * grad_F_alpha
             grad_F_beta = grad_dF_dbeta(M, reg, cur_alpha, cur_beta,
                                         batch_size, batch_alpha, batch_beta)
-            cur_beta[batch_beta] += (lr/k) * grad_F_beta
+            cur_beta[batch_beta] += (lr / k) * grad_F_beta
 
     else:
         for cur_iter in range(numItermax):
@@ -695,8 +692,8 @@ def sgd_entropic_regularization(M, reg, batch_size, numItermax, lr,
                                           batch_size, batch_alpha, batch_beta)
             grad_F_beta = grad_dF_dbeta(M, reg, cur_alpha, cur_beta,
                                         batch_size, batch_alpha, batch_beta)
-            cur_alpha[batch_alpha] += (lr/k) * grad_F_alpha
-            cur_beta[batch_beta] += (lr/k) * grad_F_beta
+            cur_alpha[batch_alpha] += (lr / k) * grad_F_alpha
+            cur_beta[batch_beta] += (lr / k) * grad_F_beta
 
     return cur_alpha, cur_beta
 
@@ -779,7 +776,7 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1,
 
     opt_alpha, opt_beta = sgd_entropic_regularization(M, reg, batch_size,
                                                       numItermax, lr)
-    pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :])/reg) *
+    pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) *
           a[:, None] * b[None, :])
     if log:
         log = {}