no more pep8 problem, need cleanup plots in test and examples

1 files changed, 10 insertions, 10 deletions

diff --git a/ot/da.py b/ot/da.py
index ee73ec8..c688654 100644
--- a/ot/da.py
+++ b/ot/da.py
@@ -330,17 +330,17 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False,
     if bias:
         xs1 = np.hstack((xs, np.ones((ns, 1))))
         xstxs = xs1.T.dot(xs1)
-        I = np.eye(d + 1)
-        I[-1] = 0
-        I0 = I[:, :-1]
+        Id = np.eye(d + 1)
+        Id[-1] = 0
+        I0 = Id[:, :-1]
 
         def sel(x):
             return x[:-1, :]
     else:
         xs1 = xs
         xstxs = xs1.T.dot(xs1)
-        I = np.eye(d)
-        I0 = I
+        Id = np.eye(d)
+        I0 = Id
 
         def sel(x):
             return x
@@ -361,7 +361,7 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False,
     def solve_L(G):
         """ solve L problem with fixed G (least square)"""
         xst = ns * G.dot(xt)
-        return np.linalg.solve(xstxs + eta * I, xs1.T.dot(xst) + eta * I0)
+        return np.linalg.solve(xstxs + eta * Id, xs1.T.dot(xst) + eta * I0)
 
     def solve_G(L, G0):
         """Update G with CG algorithm"""
@@ -520,8 +520,8 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian',
     K = kernel(xs, xs, method=kerneltype, sigma=sigma)
     if bias:
         K1 = np.hstack((K, np.ones((ns, 1))))
-        I = np.eye(ns + 1)
-        I[-1] = 0
+        Id = np.eye(ns + 1)
+        Id[-1] = 0
         Kp = np.eye(ns + 1)
         Kp[:ns, :ns] = K
 
@@ -535,14 +535,14 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian',
 
     else:
         K1 = K
-        I = np.eye(ns)
+        Id = np.eye(ns)
 
         # ls regul
         # K0 = K1.T.dot(K1)+eta*I
         # Kreg=I
 
         # proper kernel ridge
-        K0 = K + eta * I
+        K0 = K + eta * Id
         Kreg = K
 
     if log: