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CircleCI update of dev docs (768).
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‎master/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip‎

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‎master/_downloads/5798c188b81f705c8a7ba8550573b0ae/plot_gromov.py‎

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pl.title('Entropic Gromov Wasserstein')
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pl.show()
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#############################################################################
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#
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# Compute GW with a scalable stochastic method with any loss function
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# ----------------------------------------------------------------------
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def loss(x, y):
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return np.abs(x - y)
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pgw, plog = ot.gromov.pointwise_gromov_wasserstein(C1, C2, p, q, loss, max_iter=100,
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log=True)
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sgw, slog = ot.gromov.sampled_gromov_wasserstein(C1, C2, p, q, loss, epsilon=0.1, max_iter=100,
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log=True)
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print('Pointwise Gromov-Wasserstein distance estimated: ' + str(plog['gw_dist_estimated']))
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print('Variance estimated: ' + str(plog['gw_dist_std']))
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print('Sampled Gromov-Wasserstein distance: ' + str(slog['gw_dist_estimated']))
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print('Variance estimated: ' + str(slog['gw_dist_std']))
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pl.figure(1, (10, 5))
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pl.subplot(1, 2, 1)
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pl.imshow(pgw.toarray(), cmap='jet')
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pl.title('Pointwise Gromov Wasserstein')
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pl.subplot(1, 2, 2)
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pl.imshow(sgw, cmap='jet')
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pl.title('Sampled Gromov Wasserstein')
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pl.show()

‎master/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip‎

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‎master/_downloads/705e002e8bade030ed468515173797f7/plot_gromov.ipynb‎

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"source": [
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"p = ot.unif(n_samples)\nq = ot.unif(n_samples)\n\ngw0, log0 = ot.gromov.gromov_wasserstein(\n C1, C2, p, q, 'square_loss', verbose=True, log=True)\n\ngw, log = ot.gromov.entropic_gromov_wasserstein(\n C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n\n\nprint('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\nprint('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(gw0, cmap='jet')\npl.title('Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(gw, cmap='jet')\npl.title('Entropic Gromov Wasserstein')\n\npl.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Compute GW with a scalable stochastic method with any loss function\n\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def loss(x, y):\n return np.abs(x - y)\n\n\npgw, plog = ot.gromov.pointwise_gromov_wasserstein(C1, C2, p, q, loss, max_iter=100,\n log=True)\n\nsgw, slog = ot.gromov.sampled_gromov_wasserstein(C1, C2, p, q, loss, epsilon=0.1, max_iter=100,\n log=True)\n\nprint('Pointwise Gromov-Wasserstein distance estimated: ' + str(plog['gw_dist_estimated']))\nprint('Variance estimated: ' + str(plog['gw_dist_std']))\nprint('Sampled Gromov-Wasserstein distance: ' + str(slog['gw_dist_estimated']))\nprint('Variance estimated: ' + str(slog['gw_dist_std']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(pgw.toarray(), cmap='jet')\npl.title('Pointwise Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(sgw, cmap='jet')\npl.title('Sampled Gromov Wasserstein')\n\npl.show()"
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]
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}
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],
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"metadata": {

‎master/_images/sphx_glr_plot_OT_2D_samples_001.png‎

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‎master/_images/sphx_glr_plot_OT_2D_samples_003.png‎

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‎master/_images/sphx_glr_plot_OT_2D_samples_004.png‎

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‎master/_images/sphx_glr_plot_OT_2D_samples_005.png‎

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‎master/_images/sphx_glr_plot_OT_2D_samples_006.png‎

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