remove laplace from jcpot

2 files changed, 5 insertions, 132 deletions

diff --git a/examples/plot_otda_jcpot.py b/examples/plot_otda_jcpot.py
index 316fa8b..c495690 100644
--- a/examples/plot_otda_jcpot.py
+++ b/examples/plot_otda_jcpot.py
@@ -115,7 +115,7 @@ pl.axis('off')
 ##############################################################################
 # Instantiate JCPOT adaptation algorithm and fit it
 # ----------------------------------------------------------------------------
-otda = ot.da.JCPOTTransport(reg_e=1e-2, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True)
+otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True)
 otda.fit(all_Xr, all_Yr, xt)
 
 ws1 = otda.proportions_.dot(otda.log_['D2'][0])
@@ -126,8 +126,8 @@ pl.clf()
 plot_ax(dec1, 'Source 1')
 plot_ax(dec2, 'Source 2')
 plot_ax(dect, 'Target')
-print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-2), xs1, ys1, xt)
-print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-2), xs2, ys2, xt)
+print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt)
+print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt)
 pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)
 pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)
 pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)
@@ -154,8 +154,8 @@ pl.clf()
 plot_ax(dec1, 'Source 1')
 plot_ax(dec2, 'Source 2')
 plot_ax(dect, 'Target')
-print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-2), xs1, ys1, xt)
-print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-2), xs2, ys2, xt)
+print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt)
+print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt)
 pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)
 pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)
 pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)
diff --git a/examples/plot_otda_laplacian.py b/examples/plot_otda_laplacian.py
deleted file mode 100644
index 965380c..0000000
--- a/examples/plot_otda_laplacian.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-========================
-OT for domain adaptation
-========================
-
-This example introduces a domain adaptation in a 2D setting and OTDA
-approache with Laplacian regularization.
-
-"""
-
-# Authors: Ievgen Redko <ievgen.redko@univ-st-etienne.fr>
-
-# License: MIT License
-
-import matplotlib.pylab as pl
-import ot
-
-##############################################################################
-# Generate data
-# -------------
-
-n_source_samples = 150
-n_target_samples = 150
-
-Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)
-Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)
-
-
-##############################################################################
-# Instantiate the different transport algorithms and fit them
-# -----------------------------------------------------------
-
-# EMD Transport
-ot_emd = ot.da.EMDTransport()
-ot_emd.fit(Xs=Xs, Xt=Xt)
-
-# Sinkhorn Transport
-ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01)
-ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
-
-# EMD Transport with Laplacian regularization
-ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1)
-ot_emd_laplace.fit(Xs=Xs, Xt=Xt)
-
-# transport source samples onto target samples
-transp_Xs_emd = ot_emd.transform(Xs=Xs)
-transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)
-transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs)
-
-##############################################################################
-# Fig 1 : plots source and target samples
-# ---------------------------------------
-
-pl.figure(1, figsize=(10, 5))
-pl.subplot(1, 2, 1)
-pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
-pl.xticks([])
-pl.yticks([])
-pl.legend(loc=0)
-pl.title('Source  samples')
-
-pl.subplot(1, 2, 2)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
-pl.xticks([])
-pl.yticks([])
-pl.legend(loc=0)
-pl.title('Target samples')
-pl.tight_layout()
-
-
-##############################################################################
-# Fig 2 : plot optimal couplings and transported samples
-# ------------------------------------------------------
-
-param_img = {'interpolation': 'nearest'}
-
-pl.figure(2, figsize=(15, 8))
-pl.subplot(2, 3, 1)
-pl.imshow(ot_emd.coupling_, **param_img)
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nEMDTransport')
-
-pl.figure(2, figsize=(15, 8))
-pl.subplot(2, 3, 2)
-pl.imshow(ot_sinkhorn.coupling_, **param_img)
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nSinkhornTransport')
-
-pl.subplot(2, 3, 3)
-pl.imshow(ot_emd_laplace.coupling_, **param_img)
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nEMDLaplaceTransport')
-
-pl.subplot(2, 3, 4)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.3)
-pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.xticks([])
-pl.yticks([])
-pl.title('Transported samples\nEmdTransport')
-pl.legend(loc="lower left")
-
-pl.subplot(2, 3, 5)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.3)
-pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.xticks([])
-pl.yticks([])
-pl.title('Transported samples\nSinkhornTransport')
-
-pl.subplot(2, 3, 6)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.3)
-pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.xticks([])
-pl.yticks([])
-pl.title('Transported samples\nEMDLaplaceTransport')
-pl.tight_layout()
-
-pl.show()