From a54775103541ea37f54269de1ba1e1396a6d7b30 Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Fri, 24 Apr 2020 17:32:57 +0200
Subject: exmaples in sections

---
 examples/plot_WDA.py | 129 ---------------------------------------------------
 1 file changed, 129 deletions(-)
 delete mode 100644 examples/plot_WDA.py

(limited to 'examples/plot_WDA.py')

diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py
deleted file mode 100644
index 5e17433..0000000
--- a/examples/plot_WDA.py
+++ /dev/null
@@ -1,129 +0,0 @@
-# -*- 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
-
-# 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
-
-Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter)
-
-
-##############################################################################
-# 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()
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