debug sgd dual

12 files changed, 37 insertions, 41 deletions

diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py
index 90325c9..f33e2a4 100644
--- a/examples/plot_OT_1D.py
+++ b/examples/plot_OT_1D.py
@@ -17,7 +17,7 @@ import numpy as np
 import matplotlib.pylab as pl
 import ot
 import ot.plot
-from ot.datasets import get_1D_gauss as gauss
+from ot.datasets import make_1D_gauss as gauss
 
 ##############################################################################
 # Generate data
diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py
index 9818ec5..bb952a0 100644
--- a/examples/plot_OT_2D_samples.py
+++ b/examples/plot_OT_2D_samples.py
@@ -16,6 +16,7 @@ sum of diracs. The OT matrix is plotted with the samples.
 import numpy as np
 import matplotlib.pylab as pl
 import ot
+import ot.plot
 
 ##############################################################################
 # Generate data
@@ -31,8 +32,8 @@ cov_s = np.array([[1, 0], [0, 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.make_2D_samples_gauss(n, mu_s, cov_s)
+xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)
 
 a, b = np.ones((n,)) / n, np.ones((n,)) / n  # uniform distribution on samples
 
diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py
index ecf640c..5ed9f3f 100644
--- a/examples/plot_barycenter_1D.py
+++ b/examples/plot_barycenter_1D.py
@@ -37,8 +37,8 @@ n = 100  # nb bins
 x = np.arange(n, dtype=np.float64)
 
 # Gaussian distributions
-a1 = ot.datasets.get_1D_gauss(n, m=20, s=5)  # m= mean, s= std
-a2 = ot.datasets.get_1D_gauss(n, m=60, s=8)
+a1 = ot.datasets.make_1D_gauss(n, m=20, s=5)  # m= mean, s= std
+a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)
 
 # creating matrix A containing all distributions
 A = np.vstack((a1, a2)).T
diff --git a/examples/plot_barycenter_lp_vs_entropic.py b/examples/plot_barycenter_lp_vs_entropic.py
index 6936bbb..b82765e 100644
--- a/examples/plot_barycenter_lp_vs_entropic.py
+++ b/examples/plot_barycenter_lp_vs_entropic.py
@@ -15,8 +15,6 @@ Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343.
 Iterative Bregman projections for regularized transportation problems
 SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
 
-
-
 """
 
 # Author: Remi Flamary <remi.flamary@unice.fr>
@@ -32,8 +30,8 @@ from matplotlib.collections import PolyCollection  # noqa
 
 #import ot.lp.cvx as cvx
 
-#
-# Generate data
+##############################################################################
+# Gaussian Data
 # -------------
 
 #%% parameters
@@ -47,8 +45,8 @@ x = np.arange(n, dtype=np.float64)
 
 # Gaussian distributions
 # Gaussian distributions
-a1 = ot.datasets.get_1D_gauss(n, m=20, s=5)  # m= mean, s= std
-a2 = ot.datasets.get_1D_gauss(n, m=60, s=8)
+a1 = ot.datasets.make_1D_gauss(n, m=20, s=5)  # m= mean, s= std
+a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)
 
 # creating matrix A containing all distributions
 A = np.vstack((a1, a2)).T
@@ -58,9 +56,6 @@ n_distributions = A.shape[1]
 M = ot.utils.dist0(n)
 M /= M.max()
 
-#
-# Plot data
-# ---------
 
 #%% plot the distributions
 
@@ -70,10 +65,6 @@ for i in range(n_distributions):
 pl.title('Distributions')
 pl.tight_layout()
 
-#
-# Barycenter computation
-# ----------------------
-
 #%% barycenter computation
 
 alpha = 0.5  # 0<=alpha<=1
@@ -110,6 +101,10 @@ pl.tight_layout()
 
 problems.append([A, [bary_l2, bary_wass, bary_wass2]])
 
+##############################################################################
+# Dirac Data
+# ----------
+
 #%% parameters
 
 a1 = 1.0 * (x > 10) * (x < 50)
@@ -135,9 +130,6 @@ for i in range(n_distributions):
 pl.title('Distributions')
 pl.tight_layout()
 
-#
-# Barycenter computation
-# ----------------------
 
 #%% barycenter computation
 
@@ -207,9 +199,6 @@ for i in range(n_distributions):
 pl.title('Distributions')
 pl.tight_layout()
 
-#
-# Barycenter computation
-# ----------------------
 
 #%% barycenter computation
 
@@ -249,7 +238,7 @@ pl.title('Barycenters')
 pl.tight_layout()
 
 
-#
+##############################################################################
 # Final figure
 # ------------
 #
diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py
index 73b42c3..7ed2b01 100644
--- a/examples/plot_compute_emd.py
+++ b/examples/plot_compute_emd.py
@@ -17,7 +17,7 @@ ground metrics and plot their values for diffeent distributions.
 import numpy as np
 import matplotlib.pylab as pl
 import ot
-from ot.datasets import get_1D_gauss as gauss
+from ot.datasets import make_1D_gauss as gauss
 
 
 ##############################################################################
diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py
index 5cd40f6..deb2f86 100644
--- a/examples/plot_gromov.py
+++ b/examples/plot_gromov.py
@@ -38,7 +38,7 @@ mu_t = np.array([4, 4, 4])
 cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])

 

 

-xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)

+xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)

 P = sp.linalg.sqrtm(cov_t)

 xt = np.random.randn(n_samples, 3).dot(P) + mu_t

 

diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py
index 92df016..2c58def 100644
--- a/examples/plot_optim_OTreg.py
+++ b/examples/plot_optim_OTreg.py
@@ -42,8 +42,8 @@ n = 100  # nb bins
 x = np.arange(n, dtype=np.float64)
 
 # Gaussian distributions
-a = ot.datasets.get_1D_gauss(n, m=20, s=5)  # m= mean, s= std
-b = ot.datasets.get_1D_gauss(n, m=60, s=10)
+a = ot.datasets.make_1D_gauss(n, m=20, s=5)  # m= mean, s= std
+b = ot.datasets.make_1D_gauss(n, m=60, s=10)
 
 # loss matrix
 M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
diff --git a/examples/plot_otda_classes.py b/examples/plot_otda_classes.py
index b14c11a..c311fbd 100644
--- a/examples/plot_otda_classes.py
+++ b/examples/plot_otda_classes.py
@@ -25,8 +25,8 @@ import ot
 n_source_samples = 150
 n_target_samples = 150
 
-Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples)
-Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples)
+Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)
+Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)
 
 
 ##############################################################################
@@ -82,7 +82,7 @@ pl.tight_layout()
 # Fig 2 : plot optimal couplings and transported samples
 # ------------------------------------------------------
 
-param_img = {'interpolation': 'nearest', 'cmap': 'spectral'}
+param_img = {'interpolation': 'nearest'}
 
 pl.figure(2, figsize=(15, 8))
 pl.subplot(2, 4, 1)
diff --git a/examples/plot_otda_d2.py b/examples/plot_otda_d2.py
index 70beb35..cf22c2f 100644
--- a/examples/plot_otda_d2.py
+++ b/examples/plot_otda_d2.py
@@ -29,8 +29,8 @@ import ot.plot
 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)
+Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)
+Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)
 
 # Cost matrix
 M = ot.dist(Xs, Xt, metric='sqeuclidean')
diff --git a/examples/plot_otda_linear_mapping.py b/examples/plot_otda_linear_mapping.py
index 7a3b761..c65bd4f 100644
--- a/examples/plot_otda_linear_mapping.py
+++ b/examples/plot_otda_linear_mapping.py
@@ -1,11 +1,17 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
-Created on Tue Mar 20 14:31:15 2018
+============================
+Linear OT mapping estimation
+============================
+
 
-@author: rflamary
 """
 
+# Author: Remi Flamary <remi.flamary@unice.fr>
+#
+# License: MIT License
+
 import numpy as np
 import pylab as pl
 import ot
diff --git a/examples/plot_otda_mapping.py b/examples/plot_otda_mapping.py
index 167c3a1..5880adf 100644
--- a/examples/plot_otda_mapping.py
+++ b/examples/plot_otda_mapping.py
@@ -32,11 +32,11 @@ n_target_samples = 100
 theta = 2 * np.pi / 20
 noise_level = 0.1
 
-Xs, ys = ot.datasets.get_data_classif(
+Xs, ys = ot.datasets.make_data_classif(
     'gaussrot', n_source_samples, nz=noise_level)
-Xs_new, _ = ot.datasets.get_data_classif(
+Xs_new, _ = ot.datasets.make_data_classif(
     'gaussrot', n_source_samples, nz=noise_level)
-Xt, yt = ot.datasets.get_data_classif(
+Xt, yt = ot.datasets.make_data_classif(
     'gaussrot', n_target_samples, theta=theta, nz=noise_level)
 
 # one of the target mode changes its variance (no linear mapping)
diff --git a/examples/plot_otda_semi_supervised.py b/examples/plot_otda_semi_supervised.py
index 7963aef..8a67720 100644
--- a/examples/plot_otda_semi_supervised.py
+++ b/examples/plot_otda_semi_supervised.py
@@ -29,8 +29,8 @@ import ot
 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)
+Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)
+Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)
 
 
 ##############################################################################