wroking make!

1 files changed, 32 insertions, 36 deletions

diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst
index e05e591..c472c6a 100644
--- a/docs/source/auto_examples/plot_OT_2D_samples.rst
+++ b/docs/source/auto_examples/plot_OT_2D_samples.rst
@@ -7,7 +7,6 @@
 2D Optimal transport between empirical distributions
 ====================================================
 
-@author: rflamary
 
 
 
@@ -46,69 +45,64 @@
             :scale: 47
 
 
-.. rst-class:: sphx-glr-script-out
 
- Out::
-
-    ('Warning: numerical errors at iteration', 0)
-
-
-
-
-|
 
 
 .. code-block:: python
 
 
+    # Author: Remi Flamary <remi.flamary@unice.fr>
+    #
+    # License: MIT License
+
     import numpy as np
     import matplotlib.pylab as pl
     import ot
 
     #%% parameters and data generation
 
-    n=50 # nb samples
+    n = 50  # nb samples
 
-    mu_s=np.array([0,0])
-    cov_s=np.array([[1,0],[0,1]])
+    mu_s = np.array([0, 0])
+    cov_s = np.array([[1, 0], [0, 1]])
 
-    mu_t=np.array([4,4])
-    cov_t=np.array([[1,-.8],[-.8,1]])
+    mu_t = np.array([4, 4])
+    cov_t = np.array([[1, -.8], [-.8, 1]])
 
-    xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)
-    xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)
+    xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)
+    xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t)
 
-    a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples
+    a, b = np.ones((n,)) / n, np.ones((n,)) / n  # uniform distribution on samples
 
     # loss matrix
-    M=ot.dist(xs,xt)
-    M/=M.max()
+    M = ot.dist(xs, xt)
+    M /= M.max()
 
     #%% plot samples
 
     pl.figure(1)
-    pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')
-    pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')
+    pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+    pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
     pl.legend(loc=0)
-    pl.title('Source and traget distributions')
+    pl.title('Source and target distributions')
 
     pl.figure(2)
-    pl.imshow(M,interpolation='nearest')
+    pl.imshow(M, interpolation='nearest')
     pl.title('Cost matrix M')
 
 
     #%% EMD
 
-    G0=ot.emd(a,b,M)
+    G0 = ot.emd(a, b, M)
 
     pl.figure(3)
-    pl.imshow(G0,interpolation='nearest')
+    pl.imshow(G0, interpolation='nearest')
     pl.title('OT matrix G0')
 
     pl.figure(4)
-    ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])
-    pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')
-    pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')
+    ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])
+    pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+    pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
     pl.legend(loc=0)
     pl.title('OT matrix with samples')
 
@@ -116,22 +110,24 @@
     #%% sinkhorn
 
     # reg term
-    lambd=5e-4
+    lambd = 1e-3
 
-    Gs=ot.sinkhorn(a,b,M,lambd)
+    Gs = ot.sinkhorn(a, b, M, lambd)
 
     pl.figure(5)
-    pl.imshow(Gs,interpolation='nearest')
+    pl.imshow(Gs, interpolation='nearest')
     pl.title('OT matrix sinkhorn')
 
     pl.figure(6)
-    ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])
-    pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')
-    pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')
+    ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])
+    pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
+    pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
     pl.legend(loc=0)
     pl.title('OT matrix Sinkhorn with samples')
 
-**Total running time of the script:** ( 0 minutes  0.623 seconds)
+    pl.show()
+
+**Total running time of the script:** ( 0 minutes  2.908 seconds)