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| author | Rémi Flamary <remi.flamary@gmail.com> | 2017-08-31 09:28:37 +0200 |
|---|---|---|
| committer | Rémi Flamary <remi.flamary@gmail.com> | 2017-08-31 09:28:37 +0200 |
| commit | 212f3889b1114026765cda0134e02766daa82af2 (patch) | |
| tree | f9ea2d2566d1544b3409152f8ebbc8ca706c96e2 /examples/plot_otda_mapping.py | |
| parent | ec67362de5ec785e3871eac75a8aa477857092c4 (diff) | |
update tests
Diffstat (limited to 'examples/plot_otda_mapping.py')
| -rw-r--r-- | examples/plot_otda_mapping.py | 27 |
1 files changed, 13 insertions, 14 deletions
diff --git a/examples/plot_otda_mapping.py b/examples/plot_otda_mapping.py index 09d2cb4..e0da2d8 100644 --- a/examples/plot_otda_mapping.py +++ b/examples/plot_otda_mapping.py @@ -6,7 +6,7 @@ OT mapping estimation for domain adaptation [8] This example presents how to use MappingTransport to estimate at the same time both the coupling transport and approximate the transport map with either -a linear or a kernelized mapping as introduced in [8] +a linear or a kernelized mapping as introduced in [8]. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", @@ -43,6 +43,17 @@ Xt, yt = ot.datasets.get_data_classif( Xt[yt == 2] *= 3 Xt = Xt + 4 +############################################################################## +# plot data +############################################################################## + +pl.figure(1, (10, 5)) +pl.clf() +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.legend(loc=0) +pl.title('Source and target distributions') + ############################################################################## # Instantiate the different transport algorithms and fit them @@ -76,19 +87,7 @@ transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) ############################################################################## -# plot data -############################################################################## - -pl.figure(1, (10, 5)) -pl.clf() -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.legend(loc=0) -pl.title('Source and target distributions') - - -############################################################################## -# plot transported samples +# Plot transported samples ############################################################################## pl.figure(2) |