:orphan:
====================
Gallery of examples
====================
These self-contained examples showcase the features of the :mod:`geomloss` module.
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Kernel vs. Hausdorff vs. Sinkhorn
--------------------------------------
See the difference between
our **kernel**, **hausdorff** and **sinkhorn**
loss functions:
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.. image:: /_auto_examples/sinkhorn_multiscale/images/thumb/sphx_glr_plot_epsilon_scaling_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_sinkhorn_multiscale_plot_epsilon_scaling.py`
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1) Blur parameter, scaling strategy
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.. image:: /_auto_examples/sinkhorn_multiscale/images/thumb/sphx_glr_plot_kernel_truncation_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_sinkhorn_multiscale_plot_kernel_truncation.py`
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2) Kernel truncation, log-linear runtimes
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.. image:: /_auto_examples/sinkhorn_multiscale/images/thumb/sphx_glr_plot_optimal_transport_cluster_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_sinkhorn_multiscale_plot_optimal_transport_cluster.py`
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3) Optimal Transport in high dimension
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.. image:: /_auto_examples/sinkhorn_multiscale/images/thumb/sphx_glr_plot_transport_blur_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_sinkhorn_multiscale_plot_transport_blur.py`
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4) Sinkhorn vs. blurred Wasserstein distances
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Optimal Transport
----------------------
Use the
**sinkhorn** loss as an affordable, drop-in replacement
for the Wasserstein distance:
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.. image:: /_auto_examples/optimal_transport/images/thumb/sphx_glr_model_fitting_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_optimal_transport_model_fitting.py`
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Optimization routines
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.. image:: /_auto_examples/optimal_transport/images/thumb/sphx_glr_plot_interpolation_3D_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_optimal_transport_plot_interpolation_3D.py`
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Creating a fancy interpolation video between 3D meshes.
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.. image:: /_auto_examples/optimal_transport/images/thumb/sphx_glr_plot_optimal_transport_2D_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_optimal_transport_plot_optimal_transport_2D.py`
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Optimal Transport in 2D
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.. image:: /_auto_examples/optimal_transport/images/thumb/sphx_glr_plot_optimal_transport_color_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_optimal_transport_plot_optimal_transport_color.py`
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Color transfer with Optimal Transport
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.. image:: /_auto_examples/optimal_transport/images/thumb/sphx_glr_plot_optimal_transport_labels_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_optimal_transport_plot_optimal_transport_labels.py`
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Label transfer with Optimal Transport
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.. image:: /_auto_examples/optimal_transport/images/thumb/sphx_glr_plot_wasserstein_barycenters_1D_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_optimal_transport_plot_wasserstein_barycenters_1D.py`
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Wasserstein barycenters in 1D
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.. image:: /_auto_examples/optimal_transport/images/thumb/sphx_glr_plot_wasserstein_barycenters_2D_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_optimal_transport_plot_wasserstein_barycenters_2D.py`
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Wasserstein barycenters in 2D
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Performances
----------------
Select the hyper-parameters that are best suited to your data:
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.. image:: /_auto_examples/performances/images/thumb/sphx_glr_benchmarks_ot_solvers_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_performances_benchmarks_ot_solvers.py`
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Utility routines for benchmarks on OT solvers
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.. image:: /_auto_examples/performances/images/thumb/sphx_glr_plot_benchmarks_ot_3D_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_performances_plot_benchmarks_ot_3D.py`
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Wasserstein distances between large point clouds
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.. image:: /_auto_examples/performances/images/thumb/sphx_glr_plot_benchmarks_samplesloss_3D_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_performances_plot_benchmarks_samplesloss_3D.py`
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Benchmark SamplesLoss in 3D
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.. image:: /_auto_examples/performances/images/thumb/sphx_glr_plot_profile_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_performances_plot_profile.py`
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Profile the GeomLoss routines
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Scaling up to brain tractograms -- with Pierre Roussillon
-------------------------------------------------------------
Use unbalanced, regularized Optimal Transport to process
white matter fiber tracks.
The scripts presented below should allow you to reproduce
the experiments of the `Miccai 2019
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.. image:: /_auto_examples/brain_tractograms/images/thumb/sphx_glr_track_barycenter_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_brain_tractograms_track_barycenter.py`
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Create an atlas using Wasserstein barycenters
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.. image:: /_auto_examples/brain_tractograms/images/thumb/sphx_glr_tract_io_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_brain_tractograms_tract_io.py`
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Input-Output with brain tractograms
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.. image:: /_auto_examples/brain_tractograms/images/thumb/sphx_glr_transfer_labels_thumb.png
:alt:
:ref:`sphx_glr__auto_examples_brain_tractograms_transfer_labels.py`
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Transferring labels from a segmented atlas
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.. toctree::
:hidden:
:includehidden:
/./_auto_examples/comparisons/index.rst
/./_auto_examples/sinkhorn_multiscale/index.rst
/./_auto_examples/optimal_transport/index.rst
/./_auto_examples/performances/index.rst
/./_auto_examples/brain_tractograms/index.rst
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.. container:: sphx-glr-footer sphx-glr-footer-gallery
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download all examples in Python source code: _auto_examples_python.zip `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download all examples in Jupyter notebooks: _auto_examples_jupyter.zip `
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.. rst-class:: sphx-glr-signature
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