autopep+remove sinkhorn+add simtype

1 files changed, 8 insertions, 30 deletions

diff --git a/examples/plot_otda_laplacian.py b/examples/plot_otda_laplacian.py
index d9ae280..965380c 100644
--- a/examples/plot_otda_laplacian.py
+++ b/examples/plot_otda_laplacian.py
@@ -5,7 +5,7 @@ OT for domain adaptation
 ========================
 
 This example introduces a domain adaptation in a 2D setting and OTDA
-approaches with Laplacian regularization.
+approache with Laplacian regularization.
 
 """
 
@@ -36,22 +36,17 @@ ot_emd = ot.da.EMDTransport()
 ot_emd.fit(Xs=Xs, Xt=Xt)
 
 # Sinkhorn Transport
-ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.5)
+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)
 
-# Sinkhorn Transport with Laplacian regularization
-ot_sinkhorn_laplace = ot.da.SinkhornLaplaceTransport(reg_e=.5, reg_lap=100, reg_src=1)
-ot_sinkhorn_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)
-transp_Xs_sinkhorn_laplace = ot_sinkhorn_laplace.transform(Xs=Xs)
 
 ##############################################################################
 # Fig 1 : plots source and target samples
@@ -80,35 +75,27 @@ pl.tight_layout()
 
 param_img = {'interpolation': 'nearest'}
 
-n_plots = 2
-
 pl.figure(2, figsize=(15, 8))
-pl.subplot(2, 2*n_plots, 1)
+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, 2*n_plots, 2)
+pl.subplot(2, 3, 2)
 pl.imshow(ot_sinkhorn.coupling_, **param_img)
 pl.xticks([])
 pl.yticks([])
 pl.title('Optimal coupling\nSinkhornTransport')
 
-pl.subplot(2, 2*n_plots, 3)
+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, 2*n_plots, 4)
-pl.imshow(ot_emd_laplace.coupling_, **param_img)
-pl.xticks([])
-pl.yticks([])
-pl.title('Optimal coupling\nSinkhornLaplaceTransport')
-
-pl.subplot(2, 2*n_plots, 5)
+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,
@@ -118,7 +105,7 @@ pl.yticks([])
 pl.title('Transported samples\nEmdTransport')
 pl.legend(loc="lower left")
 
-pl.subplot(2, 2*n_plots, 6)
+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,
@@ -127,7 +114,7 @@ pl.xticks([])
 pl.yticks([])
 pl.title('Transported samples\nSinkhornTransport')
 
-pl.subplot(2, 2*n_plots, 7)
+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,
@@ -135,15 +122,6 @@ pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys,
 pl.xticks([])
 pl.yticks([])
 pl.title('Transported samples\nEMDLaplaceTransport')
-
-pl.subplot(2, 2*n_plots, 8)
-pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-           label='Target samples', alpha=0.3)
-pl.scatter(transp_Xs_sinkhorn_laplace[:, 0], transp_Xs_sinkhorn_laplace[:, 1], c=ys,
-           marker='+', label='Transp samples', s=30)
-pl.xticks([])
-pl.yticks([])
-pl.title('Transported samples\nSinkhornLaplaceTransport')
 pl.tight_layout()
 
 pl.show()