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diff --git a/examples/others/plot_WDA.py b/examples/others/plot_WDA.py
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+# -*- coding: utf-8 -*-
+"""
+=================================
+Wasserstein Discriminant Analysis
+=================================
+
+This example illustrate the use of WDA as proposed in [11].
+
+
+[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016).
+Wasserstein Discriminant Analysis.
+
+"""
+
+# Author: Remi Flamary <remi.flamary@unice.fr>
+#
+# License: MIT License
+
+# sphinx_gallery_thumbnail_number = 2
+
+import numpy as np
+import matplotlib.pylab as pl
+
+from ot.dr import wda, fda
+
+
+##############################################################################
+# Generate data
+# -------------
+
+#%% parameters
+
+n = 1000  # nb samples in source and target datasets
+nz = 0.2
+
+np.random.seed(1)
+
+# 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)))
+
+##############################################################################
+# Plot data
+# ---------
+
+#%% 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()
+
+##############################################################################
+# Compute Fisher Discriminant Analysis
+# ------------------------------------
+
+#%% Compute FDA
+p = 2
+
+Pfda, projfda = fda(xs, ys, p)
+
+##############################################################################
+# Compute Wasserstein Discriminant Analysis
+# -----------------------------------------
+
+#%% Compute WDA
+p = 2
+reg = 1e0
+k = 10
+maxiter = 100
+
+P0 = np.random.randn(xs.shape[1], p)
+
+P0 /= np.sqrt(np.sum(P0**2, 0, keepdims=True))
+
+Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter, P0=P0)
+
+
+##############################################################################
+# Plot 2D projections
+# -------------------
+
+#%% plot samples
+
+xsp = projfda(xs)
+xtp = projfda(xt)
+
+xspw = projwda(xs)
+xtpw = projwda(xt)
+
+pl.figure(2)
+
+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 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(2, 2, 4)
+pl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples')
+pl.legend(loc=0)
+pl.title('Projected test samples WDA')
+pl.tight_layout()
+
+pl.show()