Merge pull request #27 from rflamary/autonb

auto notebooks + release update (fixes #16)

1 files changed, 175 insertions, 61 deletions

diff --git a/docs/source/auto_examples/plot_WDA.rst b/docs/source/auto_examples/plot_WDA.rst
index 540555d..2d83123 100644
--- a/docs/source/auto_examples/plot_WDA.rst
+++ b/docs/source/auto_examples/plot_WDA.rst
@@ -7,108 +7,222 @@
 Wasserstein Discriminant Analysis
 =================================
 
-@author: rflamary
+This example illustrate the use of WDA as proposed in [11].
 
 
+[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016).
+Wasserstein Discriminant Analysis.
 
 
-.. image:: /auto_examples/images/sphx_glr_plot_WDA_001.png
-    :align: center
 
 
-.. rst-class:: sphx-glr-script-out
+.. code-block:: python
 
- Out::
 
-    Compiling cost function...
-    Computing gradient of cost function...
-     iter              cost val         grad. norm
-        1   +5.2427396265941129e-01 8.16627951e-01
-        2   +1.7904850059627236e-01 1.91366819e-01
-        3   +1.6985797253002377e-01 1.70940682e-01
-        4   +1.3903474972292729e-01 1.28606342e-01
-        5   +7.4961734618782416e-02 6.41973980e-02
-        6   +7.1900245222486239e-02 4.25693592e-02
-        7   +7.0472023318269614e-02 2.34599232e-02
-        8   +6.9917568641317152e-02 5.66542766e-03
-        9   +6.9885086242452696e-02 4.05756115e-04
-       10   +6.9884967432653489e-02 2.16836017e-04
-       11   +6.9884923649884148e-02 5.74961622e-05
-       12   +6.9884921818258436e-02 3.83257203e-05
-       13   +6.9884920459612282e-02 9.97486224e-06
-       14   +6.9884920414414409e-02 7.33567875e-06
-       15   +6.9884920388431387e-02 5.23889187e-06
-       16   +6.9884920385183902e-02 4.91959084e-06
-       17   +6.9884920373983223e-02 3.56451669e-06
-       18   +6.9884920369701245e-02 2.88858709e-06
-       19   +6.9884920361621208e-02 1.82294279e-07
-    Terminated - min grad norm reached after 19 iterations, 9.65 seconds.
+    # Author: Remi Flamary <remi.flamary@unice.fr>
+    #
+    # License: MIT License
 
+    import numpy as np
+    import matplotlib.pylab as pl
 
+    from ot.dr import wda, fda
 
 
-|
 
 
-.. 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
+
+
+Generate data
+-------------
+
+
+
+.. code-block:: python
 
 
     #%% 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)
+    n = 1000  # nb samples in source and target datasets
+    nz = 0.2
+
+    # generate circle dataset
+    t = np.random.rand(n) * 2 * np.pi
+    ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1
+    xs = np.concatenate(
+        (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)
+    xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2)
+
+    t = np.random.rand(n) * 2 * np.pi
+    yt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1
+    xt = np.concatenate(
+        (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)
+    xt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2)
+
+    nbnoise = 8
+
+    xs = np.hstack((xs, np.random.randn(n, nbnoise)))
+    xt = np.hstack((xt, np.random.randn(n, nbnoise)))
 
-    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')
+
+Plot data
+---------
+
+
+
+.. code-block:: python
+
+
+    #%% plot samples
+    pl.figure(1, figsize=(6.4, 3.5))
+
+    pl.subplot(1, 2, 1)
+    pl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples')
     pl.legend(loc=0)
     pl.title('Discriminant dimensions')
 
+    pl.subplot(1, 2, 2)
+    pl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples')
+    pl.legend(loc=0)
+    pl.title('Other dimensions')
+    pl.tight_layout()
+
+
 
-    #%% plot distributions and loss matrix
-    p=2
-    reg=1
-    k=10
-    maxiter=100
 
-    P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter)
+.. image:: /auto_examples/images/sphx_glr_plot_WDA_001.png
+    :align: center
+
+
+
+
+Compute Fisher Discriminant Analysis
+------------------------------------
+
+
+
+.. code-block:: python
+
+
+    #%% Compute FDA
+    p = 2
+
+    Pfda, projfda = fda(xs, ys, p)
+
+
+
+
+
+
+
+Compute Wasserstein Discriminant Analysis
+-----------------------------------------
+
+
+
+.. code-block:: python
+
+
+    #%% Compute WDA
+    p = 2
+    reg = 1e0
+    k = 10
+    maxiter = 100
+
+    Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter)
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out::
+
+    Compiling cost function...
+    Computing gradient of cost function...
+     iter              cost val         grad. norm
+        1   +9.0167295050534191e-01 2.28422652e-01
+        2   +4.8324990550878105e-01 4.89362707e-01
+        3   +3.4613154515357075e-01 2.84117562e-01
+        4   +2.5277108387195002e-01 1.24888750e-01
+        5   +2.4113858393736629e-01 8.07491482e-02
+        6   +2.3642108593032782e-01 1.67612140e-02
+        7   +2.3625721372202199e-01 7.68640008e-03
+        8   +2.3625461994913738e-01 7.42200784e-03
+        9   +2.3624493441436939e-01 6.43534105e-03
+       10   +2.3621901383686217e-01 2.17960585e-03
+       11   +2.3621854258326572e-01 2.03306749e-03
+       12   +2.3621696458678049e-01 1.37118721e-03
+       13   +2.3621569489873540e-01 2.76368907e-04
+       14   +2.3621565599232983e-01 1.41898134e-04
+       15   +2.3621564465487518e-01 5.96602069e-05
+       16   +2.3621564232556647e-01 1.08709521e-05
+       17   +2.3621564230277003e-01 9.17855656e-06
+       18   +2.3621564224857586e-01 1.73728345e-06
+       19   +2.3621564224748123e-01 1.17770019e-06
+       20   +2.3621564224658587e-01 2.16179383e-07
+    Terminated - min grad norm reached after 20 iterations, 9.20 seconds.
+
+
+Plot 2D projections
+-------------------
+
+
+
+.. code-block:: python
+
 
     #%% plot samples
 
-    xsp=proj(xs)
-    xtp=proj(xt)
+    xsp = projfda(xs)
+    xtp = projfda(xt)
+
+    xspw = projwda(xs)
+    xtpw = projwda(xt)
 
-    pl.figure(1,(10,5))
+    pl.figure(2)
 
-    pl.subplot(1,2,1)
-    pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples')
+    pl.subplot(2, 2, 1)
+    pl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples')
     pl.legend(loc=0)
-    pl.title('Projected training samples')
+    pl.title('Projected training samples FDA')
 
+    pl.subplot(2, 2, 2)
+    pl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples')
+    pl.legend(loc=0)
+    pl.title('Projected test samples FDA')
+
+    pl.subplot(2, 2, 3)
+    pl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples')
+    pl.legend(loc=0)
+    pl.title('Projected training samples WDA')
 
-    pl.subplot(1,2,2)
-    pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples')
+    pl.subplot(2, 2, 4)
+    pl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples')
     pl.legend(loc=0)
-    pl.title('Projected test samples')
+    pl.title('Projected test samples WDA')
+    pl.tight_layout()
+
+    pl.show()
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_WDA_003.png
+    :align: center
+
+
+
 
-**Total running time of the script:** ( 0 minutes  16.902 seconds)
+**Total running time of the script:** ( 0 minutes  16.182 seconds)