addressed AG comments + adding random seed

5 files changed, 21 insertions, 14 deletions

diff --git a/examples/da/plot_otda_classes.py b/examples/da/plot_otda_classes.py
index e5c82fb..6870fa4 100644
--- a/examples/da/plot_otda_classes.py
+++ b/examples/da/plot_otda_classes.py
@@ -15,8 +15,10 @@ approaches currently supported in POT.
 # License: MIT License
 
 import matplotlib.pylab as pl
+import numpy as np
 import ot
 
+np.random.seed(42)
 
 # number of source and target points to generate
 ns = 150
diff --git a/examples/da/plot_otda_color_images.py b/examples/da/plot_otda_color_images.py
index bca7350..805d0b0 100644
--- a/examples/da/plot_otda_color_images.py
+++ b/examples/da/plot_otda_color_images.py
@@ -20,9 +20,10 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
 import numpy as np
 from scipy import ndimage
 import matplotlib.pylab as pl
-
 import ot
 
+np.random.seed(42)
+
 
 def im2mat(I):
     """Converts and image to matrix (one pixel per line)"""
diff --git a/examples/da/plot_otda_d2.py b/examples/da/plot_otda_d2.py
index 1d2192f..8833eb2 100644
--- a/examples/da/plot_otda_d2.py
+++ b/examples/da/plot_otda_d2.py
@@ -19,17 +19,19 @@ of what the transport methods are doing.
 # License: MIT License
 
 import matplotlib.pylab as pl
+import numpy as np
 import ot
 
-# number of source and target points to generate
-ns = 150
-nt = 150
+np.random.seed(42)
 
-Xs, ys = ot.datasets.get_data_classif('3gauss', ns)
-Xt, yt = ot.datasets.get_data_classif('3gauss2', nt)
+n_samples_source = 150
+n_samples_target = 150
+
+Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)
+Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)
 
 # Cost matrix
-M = ot.dist(Xs, Xt)
+M = ot.dist(Xs, Xt, metric='sqeuclidean')
 
 # Instantiate the different transport algorithms and fit them
 
diff --git a/examples/da/plot_otda_mapping.py b/examples/da/plot_otda_mapping.py
index 6d83507..aea7f09 100644
--- a/examples/da/plot_otda_mapping.py
+++ b/examples/da/plot_otda_mapping.py
@@ -23,7 +23,7 @@ import matplotlib.pylab as pl
 import ot
 
 
-np.random.seed(0)
+np.random.seed(42)
 
 ##############################################################################
 # generate
@@ -31,10 +31,11 @@ np.random.seed(0)
 
 n = 100  # nb samples in source and target datasets
 theta = 2 * np.pi / 20
-nz = 0.1
-Xs, ys = ot.datasets.get_data_classif('gaussrot', n, nz=nz)
-Xs_new, _ = ot.datasets.get_data_classif('gaussrot', n, nz=nz)
-Xt, yt = ot.datasets.get_data_classif('gaussrot', n, theta=theta, nz=nz)
+noise_level = 0.1
+Xs, ys = ot.datasets.get_data_classif('gaussrot', n, nz=noise_level)
+Xs_new, _ = ot.datasets.get_data_classif('gaussrot', n, nz=noise_level)
+Xt, yt = ot.datasets.get_data_classif(
+    'gaussrot', n, theta=theta, nz=noise_level)
 
 # one of the target mode changes its variance (no linear mapping)
 Xt[yt == 2] *= 3
@@ -46,8 +47,7 @@ ot_mapping_linear = ot.da.MappingTransport(
     kernel="linear", mu=1e0, eta=1e-8, bias=True,
     max_iter=20, verbose=True)
 
-ot_mapping_linear.fit(
-    Xs=Xs, Xt=Xt)
+ot_mapping_linear.fit(Xs=Xs, Xt=Xt)
 
 # for original source samples, transform applies barycentric mapping
 transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)
diff --git a/examples/da/plot_otda_mapping_colors_images.py b/examples/da/plot_otda_mapping_colors_images.py
index 4209020..6c024ea 100644
--- a/examples/da/plot_otda_mapping_colors_images.py
+++ b/examples/da/plot_otda_mapping_colors_images.py
@@ -23,6 +23,8 @@ from scipy import ndimage
 import matplotlib.pylab as pl
 import ot
 
+np.random.seed(42)
+
 
 def im2mat(I):
     """Converts and image to matrix (one pixel per line)"""