update doc

25 files changed, 277 insertions, 12 deletions

diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip
index 4840aa7..2b892cf 100644
--- a/docs/source/auto_examples/auto_examples_jupyter.zip
+++ b/docs/source/auto_examples/auto_examples_jupyter.zip
diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip
index 6e73f21..9799061 100644
--- a/docs/source/auto_examples/auto_examples_python.zip
+++ b/docs/source/auto_examples/auto_examples_python.zip
diff --git a/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_001.png b/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_001.png
index 43b50e8..c31761d 100644
--- a/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_001.png
+++ b/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_001.png
diff --git a/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_002.png b/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_002.png
index 5651ebd..f6c5d0e 100644
--- a/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_002.png
+++ b/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_002.png
diff --git a/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_003.png b/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_003.png
index b6ca0f4..8f5dfb9 100644
--- a/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_003.png
+++ b/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_003.png
diff --git a/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_004.png b/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_004.png
index ab54a41..309565f 100644
--- a/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_004.png
+++ b/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_004.png
diff --git a/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_005.png b/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_005.png
index 24453e1..6ba8a56 100644
--- a/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_005.png
+++ b/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_005.png
diff --git a/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_006.png b/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_006.png
index d1b00e7..7fa0f98 100644
--- a/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_006.png
+++ b/docs/source/auto_examples/images/sphx_glr_plot_OT_2D_samples_006.png
diff --git a/docs/source/auto_examples/images/sphx_glr_plot_WDA_001.png b/docs/source/auto_examples/images/sphx_glr_plot_WDA_001.png
new file mode 100644
index 0000000..c577317
--- /dev/null
+++ b/docs/source/auto_examples/images/sphx_glr_plot_WDA_001.png
diff --git a/docs/source/auto_examples/images/sphx_glr_plot_optim_OTreg_003.png b/docs/source/auto_examples/images/sphx_glr_plot_optim_OTreg_003.png
index e12ebc6..7ffcc14 100644
--- a/docs/source/auto_examples/images/sphx_glr_plot_optim_OTreg_003.png
+++ b/docs/source/auto_examples/images/sphx_glr_plot_optim_OTreg_003.png
diff --git a/docs/source/auto_examples/images/sphx_glr_plot_optim_OTreg_004.png b/docs/source/auto_examples/images/sphx_glr_plot_optim_OTreg_004.png
index 1d57b8d..2a72060 100644
--- a/docs/source/auto_examples/images/sphx_glr_plot_optim_OTreg_004.png
+++ b/docs/source/auto_examples/images/sphx_glr_plot_optim_OTreg_004.png
diff --git a/docs/source/auto_examples/images/sphx_glr_plot_optim_OTreg_005.png b/docs/source/auto_examples/images/sphx_glr_plot_optim_OTreg_005.png
index 4f5f2d4..e70a6de 100644
--- a/docs/source/auto_examples/images/sphx_glr_plot_optim_OTreg_005.png
+++ b/docs/source/auto_examples/images/sphx_glr_plot_optim_OTreg_005.png
diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png
index 0ff6768..d64d50b 100644
--- a/docs/source/auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png
+++ b/docs/source/auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png
diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png
new file mode 100644
index 0000000..2597600
--- /dev/null
+++ b/docs/source/auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png
diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png
index cbc8e0f..3015582 100644
--- a/docs/source/auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png
+++ b/docs/source/auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png
diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst
index 6fac8c0..868ca76 100644
--- a/docs/source/auto_examples/index.rst
+++ b/docs/source/auto_examples/index.rst
@@ -23,6 +23,26 @@ POT Examples
 
 .. raw:: html
 
+    <div class="sphx-glr-thumbcontainer" tooltip="@author: rflamary ">
+
+.. only:: html
+
+    .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png
+
+        :ref:`sphx_glr_auto_examples_plot_WDA.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /auto_examples/plot_WDA
+
+.. raw:: html
+
     <div class="sphx-glr-thumbcontainer" tooltip=" ">
 
 .. only:: html
diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb
index e8ec1d1..7d42ba7 100644
--- a/docs/source/auto_examples/plot_OT_2D_samples.ipynb
+++ b/docs/source/auto_examples/plot_OT_2D_samples.ipynb
@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nn=20 # nb samples\n\nmu_s=np.array([0,0])\ncov_s=np.array([[1,0],[0,1]])\n\nmu_t=np.array([4,4])\ncov_t=np.array([[1,-.8],[-.8,1]])\n\nxs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\nxt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n\na,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM=ot.dist(xs,xt)\nM/=M.max()\n\n#%% plot samples\n\npl.figure(1)\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('Source and traget distributions')\n\npl.figure(2)\npl.imshow(M,interpolation='nearest')\npl.title('Cost matrix M')\n\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\npl.imshow(G0,interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')\n\n\n#%% sinkhorn\n\n# reg term\nlambd=5e-3\n\nGs=ot.sinkhorn(a,b,M,lambd)\n\npl.figure(5)\npl.imshow(Gs,interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')"
+        "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nn=2 # nb samples\n\nmu_s=np.array([0,0])\ncov_s=np.array([[1,0],[0,1]])\n\nmu_t=np.array([4,4])\ncov_t=np.array([[1,-.8],[-.8,1]])\n\nxs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\nxt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n\na,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM=ot.dist(xs,xt)\nM/=M.max()\n\n#%% plot samples\n\npl.figure(1)\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('Source and traget distributions')\n\npl.figure(2)\npl.imshow(M,interpolation='nearest')\npl.title('Cost matrix M')\n\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\npl.imshow(G0,interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')\n\n\n#%% sinkhorn\n\n# reg term\nlambd=5e-3\n\nGs=ot.sinkhorn(a,b,M,lambd)\n\npl.figure(5)\npl.imshow(Gs,interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')"
       ], 
       "outputs": [], 
       "metadata": {
diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py
index 6c39ad4..3b95083 100644
--- a/docs/source/auto_examples/plot_OT_2D_samples.py
+++ b/docs/source/auto_examples/plot_OT_2D_samples.py
@@ -13,7 +13,7 @@ import ot
 
 #%% parameters and data generation
 
-n=20 # nb samples
+n=2 # nb samples
 
 mu_s=np.array([0,0])
 cov_s=np.array([[1,0],[0,1]])
diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst
index bc86cb8..01e5f31 100644
--- a/docs/source/auto_examples/plot_OT_2D_samples.rst
+++ b/docs/source/auto_examples/plot_OT_2D_samples.rst
@@ -58,7 +58,7 @@
 
     #%% parameters and data generation
 
-    n=20 # nb samples
+    n=2 # nb samples
 
     mu_s=np.array([0,0])
     cov_s=np.array([[1,0],[0,1]])
@@ -122,7 +122,7 @@
     pl.legend(loc=0)
     pl.title('OT matrix Sinkhorn with samples')
 
-**Total running time of the script:** ( 0 minutes  1.051 seconds)
+**Total running time of the script:** ( 0 minutes  0.406 seconds)
 
 
 
diff --git a/docs/source/auto_examples/plot_WDA.ipynb b/docs/source/auto_examples/plot_WDA.ipynb
new file mode 100644
index 0000000..6d641a7
--- /dev/null
+++ b/docs/source/auto_examples/plot_WDA.ipynb
@@ -0,0 +1,54 @@
+{
+  "nbformat_minor": 0, 
+  "nbformat": 4, 
+  "cells": [
+    {
+      "execution_count": null, 
+      "cell_type": "code", 
+      "source": [
+        "%matplotlib inline"
+      ], 
+      "outputs": [], 
+      "metadata": {
+        "collapsed": false
+      }
+    }, 
+    {
+      "source": [
+        "\n# WAsserstein Discriminant Analysis\n\n\n@author: rflamary\n\n"
+      ], 
+      "cell_type": "markdown", 
+      "metadata": {}
+    }, 
+    {
+      "execution_count": null, 
+      "cell_type": "code", 
+      "source": [
+        "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\nfrom ot.dr import wda\n\n\n#%% parameters\n\nn=1000 # nb samples in source and target datasets\nnz=0.2\nxs,ys=ot.datasets.get_data_classif('3gauss',n,nz)\nxt,yt=ot.datasets.get_data_classif('3gauss',n,nz)\n\nnbnoise=8\n\nxs=np.hstack((xs,np.random.randn(n,nbnoise)))\nxt=np.hstack((xt,np.random.randn(n,nbnoise)))\n\n#%% plot samples\n\npl.figure(1)\n\n\npl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples')\npl.legend(loc=0)\npl.title('Discriminant dimensions')\n\n\n#%% plot distributions and loss matrix\np=2\nreg=1\nk=10\nmaxiter=100\n\nP,proj = wda(xs,ys,p,reg,k,maxiter=maxiter)\n\n#%% plot samples\n\nxsp=proj(xs)\nxtp=proj(xt)\n\npl.figure(1,(10,5))\n\npl.subplot(1,2,1)\npl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples')\npl.legend(loc=0)\npl.title('Projected training samples')\n\n\npl.subplot(1,2,2)\npl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples')\npl.legend(loc=0)\npl.title('Projected test samples')"
+      ], 
+      "outputs": [], 
+      "metadata": {
+        "collapsed": false
+      }
+    }
+  ], 
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 2", 
+      "name": "python2", 
+      "language": "python"
+    }, 
+    "language_info": {
+      "mimetype": "text/x-python", 
+      "nbconvert_exporter": "python", 
+      "name": "python", 
+      "file_extension": ".py", 
+      "version": "2.7.12", 
+      "pygments_lexer": "ipython2", 
+      "codemirror_mode": {
+        "version": 2, 
+        "name": "ipython"
+      }
+    }
+  }
+}
\ No newline at end of file
diff --git a/docs/source/auto_examples/plot_WDA.py b/docs/source/auto_examples/plot_WDA.py
new file mode 100644
index 0000000..94b7ef4
--- /dev/null
+++ b/docs/source/auto_examples/plot_WDA.py
@@ -0,0 +1,63 @@
+# -*- coding: utf-8 -*-
+"""
+=================================
+WAsserstein Discriminant Analysis
+=================================
+
+@author: rflamary
+"""
+
+import numpy as np
+import matplotlib.pylab as pl
+import ot
+from ot.datasets import get_1D_gauss as gauss
+from ot.dr import wda
+
+
+#%% parameters
+
+n=1000 # nb samples in source and target datasets
+nz=0.2
+xs,ys=ot.datasets.get_data_classif('3gauss',n,nz)
+xt,yt=ot.datasets.get_data_classif('3gauss',n,nz)
+
+nbnoise=8
+
+xs=np.hstack((xs,np.random.randn(n,nbnoise)))
+xt=np.hstack((xt,np.random.randn(n,nbnoise)))
+
+#%% plot samples
+
+pl.figure(1)
+
+
+pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples')
+pl.legend(loc=0)
+pl.title('Discriminant dimensions')
+
+
+#%% plot distributions and loss matrix
+p=2
+reg=1
+k=10
+maxiter=100
+
+P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter)
+
+#%% plot samples
+
+xsp=proj(xs)
+xtp=proj(xt)
+
+pl.figure(1,(10,5))
+
+pl.subplot(1,2,1)
+pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples')
+pl.legend(loc=0)
+pl.title('Projected training samples')
+
+
+pl.subplot(1,2,2)
+pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples')
+pl.legend(loc=0)
+pl.title('Projected test samples')
diff --git a/docs/source/auto_examples/plot_WDA.rst b/docs/source/auto_examples/plot_WDA.rst
new file mode 100644
index 0000000..379a133
--- /dev/null
+++ b/docs/source/auto_examples/plot_WDA.rst
@@ -0,0 +1,127 @@
+
+
+.. _sphx_glr_auto_examples_plot_WDA.py:
+
+
+=================================
+WAsserstein Discriminant Analysis
+=================================
+
+@author: rflamary
+
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_WDA_001.png
+    :align: center
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out::
+
+    Compiling cost function...
+    Computing gradient of cost function...
+     iter              cost val         grad. norm
+        1   +7.5272200933021116e-01 8.85804426e-01
+        2   +2.5764223980223788e-01 3.04501586e-01
+        3   +1.6018169776696620e-01 1.78298483e-01
+        4   +1.4560944642106255e-01 1.42133298e-01
+        5   +1.0243843483991794e-01 1.23342675e-01
+        6   +7.8856617504010643e-02 1.05379766e-01
+        7   +7.7620851864404483e-02 1.04044062e-01
+        8   +7.3160520861018416e-02 8.33770034e-02
+        9   +6.6999294576662857e-02 2.87368977e-02
+       10   +6.6250206928793964e-02 1.72155066e-03
+       11   +6.6247631521353170e-02 2.43806911e-04
+       12   +6.6247596955965438e-02 1.40066459e-04
+       13   +6.6247580176638649e-02 4.77471577e-06
+       14   +6.6247580163923028e-02 3.00484279e-06
+       15   +6.6247580159235792e-02 1.91039983e-06
+       16   +6.6247580156889613e-02 9.56038747e-07
+    Terminated - min grad norm reached after 16 iterations, 7.78 seconds.
+
+
+
+
+|
+
+
+.. code-block:: python
+
+
+    import numpy as np
+    import matplotlib.pylab as pl
+    import ot
+    from ot.datasets import get_1D_gauss as gauss
+    from ot.dr import wda
+
+
+    #%% parameters
+
+    n=1000 # nb samples in source and target datasets
+    nz=0.2
+    xs,ys=ot.datasets.get_data_classif('3gauss',n,nz)
+    xt,yt=ot.datasets.get_data_classif('3gauss',n,nz)
+
+    nbnoise=8
+
+    xs=np.hstack((xs,np.random.randn(n,nbnoise)))
+    xt=np.hstack((xt,np.random.randn(n,nbnoise)))
+
+    #%% plot samples
+
+    pl.figure(1)
+
+
+    pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples')
+    pl.legend(loc=0)
+    pl.title('Discriminant dimensions')
+
+
+    #%% plot distributions and loss matrix
+    p=2
+    reg=1
+    k=10
+    maxiter=100
+
+    P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter)
+
+    #%% plot samples
+
+    xsp=proj(xs)
+    xtp=proj(xt)
+
+    pl.figure(1,(10,5))
+
+    pl.subplot(1,2,1)
+    pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples')
+    pl.legend(loc=0)
+    pl.title('Projected training samples')
+
+
+    pl.subplot(1,2,2)
+    pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples')
+    pl.legend(loc=0)
+    pl.title('Projected test samples')
+
+**Total running time of the script:** ( 0 minutes  14.134 seconds)
+
+
+
+.. container:: sphx-glr-footer
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Python source code: plot_WDA.py <plot_WDA.py>`
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Jupyter notebook: plot_WDA.ipynb <plot_WDA.ipynb>`
+
+.. rst-class:: sphx-glr-signature
+
+    `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_
diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb
index 250ea72..00d4702 100644
--- a/docs/source/auto_examples/plot_optim_OTreg.ipynb
+++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb
@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std\nb=ot.datasets.get_1D_gauss(n,m=60,s=10)\n\n# loss matrix\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\nM/=M.max()\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n\n#%% Example with Frobenius norm regularization\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg=1e-1\n\nGl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg')\n\n#%% Example with entropic regularization\n\ndef f(G): return np.sum(G*np.log(G))\ndef df(G): return np.log(G)+1\n\nreg=1e-3\n\nGe=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(4)\not.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg')\n\n#%% Example with Frobenius norm + entropic regularization with gcg\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg1=1e-1\nreg2=1e-1\n\nGel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True)\n\npl.figure(5)\not.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg')"
+        "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std\nb=ot.datasets.get_1D_gauss(n,m=60,s=10)\n\n# loss matrix\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\nM/=M.max()\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n\n#%% Example with Frobenius norm regularization\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg=1e-1\n\nGl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg')\n\n#%% Example with entropic regularization\n\ndef f(G): return np.sum(G*np.log(G))\ndef df(G): return np.log(G)+1\n\nreg=1e-3\n\nGe=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(4)\not.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg')\n\n#%% Example with Frobenius norm + entropic regularization with gcg\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg1=1e-3\nreg2=1e-1\n\nGel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True)\n\npl.figure(5)\not.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg')"
       ], 
       "outputs": [], 
       "metadata": {
diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py
index 3c4d3f4..c585445 100644
--- a/docs/source/auto_examples/plot_optim_OTreg.py
+++ b/docs/source/auto_examples/plot_optim_OTreg.py
@@ -64,7 +64,7 @@ ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg')
 def f(G): return 0.5*np.sum(G**2)
 def df(G): return G
 
-reg1=1e-1
+reg1=1e-3
 reg2=1e-1
 
 Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True)
diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst
index d6397ba..0dff327 100644
--- a/docs/source/auto_examples/plot_optim_OTreg.rst
+++ b/docs/source/auto_examples/plot_optim_OTreg.rst
@@ -483,10 +483,11 @@ Regularized OT with generic solver
       200|1.607143e-01|-2.151971e-10
     It.  |Loss        |Delta loss
     --------------------------------
-        0|-4.988764e-01|0.000000e+00
-        1|-4.993932e-01|-1.034993e-03
-        2|-4.993933e-01|-9.845917e-08
-        3|-4.993933e-01|-9.206594e-12
+        0|1.693084e-01|0.000000e+00
+        1|1.610121e-01|-5.152589e-02
+        2|1.609378e-01|-4.622297e-04
+        3|1.609284e-01|-5.830043e-05
+        4|1.609284e-01|-1.111580e-12
 
 
 
@@ -554,14 +555,14 @@ Regularized OT with generic solver
     def f(G): return 0.5*np.sum(G**2)
     def df(G): return G
 
-    reg1=1e-1
+    reg1=1e-3
     reg2=1e-1
 
     Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True)
 
     pl.figure(5)
     ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg')
-**Total running time of the script:** ( 0 minutes  2.358 seconds)
+**Total running time of the script:** ( 0 minutes  2.422 seconds)