diff options
123 files changed, 10908 insertions, 1486 deletions
diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml new file mode 100644 index 0000000..cb3baf8 --- /dev/null +++ b/.github/workflows/pythonpackage.yml @@ -0,0 +1,30 @@ +name: Test Package + +on: [push] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + max-parallel: 4 + matrix: + python-version: [2.7, 3.5, 3.6, 3.7] + + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + - name: Lint with flake8 + run: | + pip install flake8 + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics @@ -59,6 +59,9 @@ coverage.xml *.mo *.pot +# xml +*.xml + # Django stuff: *.log local_settings.py @@ -103,3 +106,15 @@ ENV/ # coverage output folder cov_html/ + +docs/source/modules/generated/* +docs/source/_build/* + +# local debug folder +debug + +# vscode parameters +.vscode + +# pytest cahche +.pytest_cache
\ No newline at end of file diff --git a/.travis.yml b/.travis.yml index 90a0ff4..5b3a26e 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,36 +1,46 @@ +dist: xenial # required for Python >= 3.7 language: python matrix: -# allow_failures: -# - os: osx - include: -# - os: osx -# language: generic - - os: linux - sudo: required - python: 3.4 - - os: linux - sudo: required - python: 3.5 - - os: linux - sudo: required - python: 3.6 - - os: linux - sudo: required - python: 2.7 + # allow_failures: + # - os: osx + # - os: windows + include: + - os: linux + sudo: required + python: 3.5 + - os: linux + sudo: required + python: 3.6 + - os: linux + sudo: required + python: 3.7 + - os: linux + sudo: required + python: 2.7 + # - os: osx + # sudo: required + # language: generic + # - name: "Python 3.7.3 on Windows" + # os: windows # Windows 10.0.17134 N/A Build 17134 + # language: shell # 'language: python' is an error on Travis CI Windows + # before_install: choco install python + # env: PATH=/c/Python37:/c/Python37/Scripts:$PATH +# before_script: # configure a headless display to test plot generation +# - "export DISPLAY=:99.0" +# - sleep 3 # give xvfb some time to start before_install: - ./.travis/before_install.sh -before_script: # configure a headless display to test plot generation - - "export DISPLAY=:99.0" - - "sh -e /etc/init.d/xvfb start" - - sleep 3 # give xvfb some time to start # command to install dependencies install: - pip install -r requirements.txt - - pip install flake8 pytest pytest-cov + - pip install -U "numpy>=1.14" "scipy<1.3" # for numpy array formatting in doctests + scipy version: otherwise, pymanopt fails, cf <https://github.com/pymanopt/pymanopt/issues/77> + - pip install flake8 pytest "pytest-cov<2.6" - pip install . # command to run tests + check syntax style +services: + - xvfb script: - python setup.py develop - flake8 examples/ ot/ test/ - - python -m pytest -v test/ --cov=ot + - python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot # - py.test ot test diff --git a/MANIFEST.in b/MANIFEST.in index e0acb7a..df4e139 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,5 +1,5 @@ -graft ot/lp/ include README.md +include RELEASES.md include LICENSE include ot/lp/core.h include ot/lp/EMD.h @@ -3,6 +3,8 @@ PYTHON=python3 branch := $(shell git symbolic-ref --short -q HEAD) + + help : @echo "The following make targets are available:" @echo " help - print this message" @@ -13,6 +15,7 @@ help : @echo " sremove - remove the package (system with sudo)" @echo " clean - remove any temporary files" @echo " notebook - launch ipython notebook" + build : $(PYTHON) setup.py build @@ -42,19 +45,20 @@ pep8 : flake8 examples/ ot/ test/ test : FORCE pep8 - $(PYTHON) -m pytest -v test/ --cov=ot --cov-report html:cov_html + $(PYTHON) -m pytest -v test/ --doctest-modules --ignore ot/gpu/ --cov=ot --cov-report html:cov_html pytest : FORCE - $(PYTHON) -m pytest -v test/ --cov=ot + $(PYTHON) -m pytest -v test/ --doctest-modules --ignore ot/gpu/ --cov=ot -uploadpypi : - #python setup.py register - $(PYTHON) setup.py sdist upload -r pypi +release : + twine upload dist/* + +release_test : + twine upload --repository-url https://test.pypi.org/legacy/ dist/* rdoc : pandoc --from=markdown --to=rst --output=docs/source/readme.rst README.md - notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ @@ -73,5 +77,15 @@ autopep8 : aautopep8 : autopep8 -air test ot examples --jobs -1 + +wheels : + CIBW_BEFORE_BUILD="pip install numpy cython" cibuildwheel --platform linux --output-dir dist + +dist : wheels + $(PYTHON) setup.py sdist + + +pydocstyle : + pydocstyle ot FORCE : @@ -15,7 +15,8 @@ This open source Python library provide several solvers for optimization problem It provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] and greedy SInkhorn [22] with optional GPU implementation (requires cupy). +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy). +* Sinkhorn divergence [23] and entropic regularization OT from empirical data. * Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. * Non regularized Wasserstein barycenters [16] with LP solver (only small scale). * Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. @@ -26,6 +27,8 @@ It provides the following solvers: * Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) * Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) * Non regularized free support Wasserstein barycenters [20]. +* Unbalanced OT with KL relaxation distance and barycenter [10, 25]. +* Screening Sinkhorn Algorithm for OT [26]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -43,7 +46,7 @@ year={2017} ## Installation -The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for using the EMD solver and relies on the following Python modules: +The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules: - Numpy (>=1.11) - Scipy (>=1.0) @@ -52,6 +55,12 @@ The library has been tested on Linux, MacOSX and Windows. It requires a C++ comp #### Pip installation +Note that due to a limitation of pip, `cython` and `numpy` need to be installed +prior to installing POT. This can be done easily with +``` +pip install numpy cython +``` + You can install the toolbox through PyPI with: ``` pip install POT @@ -61,6 +70,8 @@ or get the very latest version by downloading it and then running: python setup.py install --user # for user install (no root) ``` + + #### Anaconda installation with conda-forge If you use the Anaconda python distribution, POT is available in [conda-forge](https://conda-forge.org). To install it and the required dependencies: @@ -142,17 +153,21 @@ Here is a list of the Python notebooks available [here](https://github.com/rflam * [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb) * [Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb) * [Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) - +* [Fused Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) +* [Fused Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb) You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). ## Acknowledgements -The contributors to this library are: +This toolbox has been created and is maintained by * [Rémi Flamary](http://remi.flamary.com/) * [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) + +The contributors to this library are + * [Alexandre Gramfort](http://alexandre.gramfort.net/) * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) * [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) @@ -163,6 +178,10 @@ The contributors to this library are: * Erwan Vautier (Gromov-Wasserstein) * [Kilian Fatras](https://kilianfatras.github.io/) * [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) +* [Vayer Titouan](https://tvayer.github.io/) +* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) +* [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) +* [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): @@ -230,3 +249,11 @@ You can also post bug reports and feature requests in Github issues. Make sure t [21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https://dl.acm.org/citation.cfm?id=2766963). ACM Transactions on Graphics (TOG), 34(4), 66. [22] J. Altschuler, J.Weed, P. Rigollet, (2017) [Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration](https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf), Advances in Neural Information Processing Systems (NIPS) 31 + +[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 + +[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML). + +[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). + +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). diff --git a/RELEASES.md b/RELEASES.md index a617441..66eee19 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -1,6 +1,74 @@ # POT Releases +## 0.6 Year 3 +*July 2019* + +This is the first official stable release of POT and this means a jump to 0.6! +The library has been used in +the wild for a while now and we have reached a state where a lot of fundamental +OT solvers are available and tested. It has been quite stable in the last months +but kept the beta flag in its Pypi classifiers until now. + +Note that this release will be the last one supporting officially Python 2.7 (See +https://python3statement.org/ for more reasons). For next release we will keep +the travis tests for Python 2 but will make them non necessary for merge in 2020. + +The features are never complete in a toolbox designed for solving mathematical +problems and research but with the new contributions we now implement algorithms and solvers +from 24 scientific papers (listed in the README.md file). New features include a +direct implementation of the [empirical Sinkhorn divergence](https://pot.readthedocs.io/en/latest/all.html#ot.bregman.empirical_sinkhorn_divergence) +, a new efficient (Cython implementation) solver for [EMD in 1D](https://pot.readthedocs.io/en/latest/all.html#ot.lp.emd_1d) +and corresponding [Wasserstein +1D](https://pot.readthedocs.io/en/latest/all.html#ot.lp.wasserstein_1d). We now also +have implementations for [Unbalanced OT](https://github.com/rflamary/POT/blob/master/notebooks/plot_UOT_1D.ipynb) +and a solver for [Unbalanced OT barycenters](https://github.com/rflamary/POT/blob/master/notebooks/plot_UOT_barycenter_1D.ipynb). +A new variant of Gromov-Wasserstein divergence called [Fused +Gromov-Wasserstein](https://pot.readthedocs.io/en/latest/all.html?highlight=fused_#ot.gromov.fused_gromov_wasserstein) + has been also contributed with exemples of use on [structured data](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) +and computing [barycenters of labeld graphs](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb). + + +A lot of work has been done on the documentation with several new +examples corresponding to the new features and a lot of corrections for the +docstrings. But the most visible change is a new +[quick start guide](https://pot.readthedocs.io/en/latest/quickstart.html) for +POT that gives several pointers about which function or classes allow to solve which +specific OT problem. When possible a link is provided to relevant examples. + +We will also provide with this release some pre-compiled Python wheels for Linux +64bit on +github and pip. This will simplify the install process that before required a C +compiler and numpy/cython already installed. + +Finally we would like to acknowledge and thank the numerous contributors of POT +that has helped in the past build the foundation and are still contributing to +bring new features and solvers to the library. + + + +#### Features + +* Add compiled manylinux 64bits wheels to pip releases (PR #91) +* Add quick start guide (PR #88) +* Make doctest work on travis (PR #90) +* Update documentation (PR #79, PR #84) +* Solver for EMD in 1D (PR #89) +* Solvers for regularized unbalanced OT (PR #87, PR#99) +* Solver for Fused Gromov-Wasserstein (PR #86) +* Add empirical Sinkhorn and empirical Sinkhorn divergences (PR #80) + + +#### Closed issues + +- Issue #59 fail when using "pip install POT" (new details in doc+ hopefully + wheels) +- Issue #85 Cannot run gpu modules +- Issue #75 Greenkhorn do not return log (solved in PR #76) +- Issue #82 Gromov-Wasserstein fails when the cost matrices are slightly different +- Issue #72 Macosx build problem + + ## 0.5.0 Year 2 *Sep 2018* diff --git a/docs/cache_nbrun b/docs/cache_nbrun index 575adc8..8a95023 100644 --- a/docs/cache_nbrun +++ b/docs/cache_nbrun @@ -1 +1 @@ -{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_barycenter_1D.ipynb": "5f6fb8aebd8e2e91ebc77c923cb112b3", "plot_stochastic.ipynb": "e2c520150378ae4635f74509f687fa01", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_1D_smooth.ipynb": "3a059103652225a0c78ea53895cf79e5", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_free_support_barycenter.ipynb": "246dd2feff4b233a4f1a553c5a202fdc", "plot_convolutional_barycenter.ipynb": "a72bb3716a1baaffd81ae267a673f9b6", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357"}
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\ No newline at end of file diff --git a/docs/source/all.rst b/docs/source/all.rst index 32930fd..c968aa1 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -43,7 +43,7 @@ ot.da .. automodule:: ot.da :members: - + ot.gpu -------- @@ -80,3 +80,9 @@ ot.stochastic .. automodule:: ot.stochastic :members: + +ot.unbalanced +------------- + +.. automodule:: ot.unbalanced + :members: diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip Binary files differindex 304bb06..901195a 100644 --- a/docs/source/auto_examples/auto_examples_jupyter.zip +++ b/docs/source/auto_examples/auto_examples_jupyter.zip diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip Binary files differindex 3be8a76..ded2613 100644 --- a/docs/source/auto_examples/auto_examples_python.zip +++ b/docs/source/auto_examples/auto_examples_python.zip diff --git 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a/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png +++ b/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index 259fca1..fe6702d 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -29,13 +29,13 @@ This is a gallery of all the POT example files. .. raw:: html - <div class="sphx-glr-thumbcontainer" tooltip="Illustrates the use of the generic solver for regularized OT with user-designed regularization ..."> + <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation of Unbalanced Optimal transport using a Kullback-Leibl..."> .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_UOT_1D_thumb.png - :ref:`sphx_glr_auto_examples_plot_optim_OTreg.py` + :ref:`sphx_glr_auto_examples_plot_UOT_1D.py` .. raw:: html @@ -45,17 +45,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_optim_OTreg + /auto_examples/plot_UOT_1D .. raw:: html - <div class="sphx-glr-thumbcontainer" tooltip="Illustration of 2D Wasserstein barycenters if discributions that are weighted sum of diracs."> + <div class="sphx-glr-thumbcontainer" tooltip="Illustrates the use of the generic solver for regularized OT with user-designed regularization ..."> .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_free_support_barycenter_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png - :ref:`sphx_glr_auto_examples_plot_free_support_barycenter.py` + :ref:`sphx_glr_auto_examples_plot_optim_OTreg.py` .. raw:: html @@ -65,17 +65,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_free_support_barycenter + /auto_examples/plot_optim_OTreg .. raw:: html - <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation of EMD, Sinkhorn and smooth OT plans and their visuali..."> + <div class="sphx-glr-thumbcontainer" tooltip="Illustration of 2D Wasserstein barycenters if discributions that are weighted sum of diracs."> .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_1D_smooth_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_free_support_barycenter_thumb.png - :ref:`sphx_glr_auto_examples_plot_OT_1D_smooth.py` + :ref:`sphx_glr_auto_examples_plot_free_support_barycenter.py` .. raw:: html @@ -85,17 +85,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_OT_1D_smooth + /auto_examples/plot_free_support_barycenter .. raw:: html - <div class="sphx-glr-thumbcontainer" tooltip="This example is designed to show how to use the Gromov-Wassertsein distance computation in POT...."> + <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation of EMD, Sinkhorn and smooth OT plans and their visuali..."> .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_1D_smooth_thumb.png - :ref:`sphx_glr_auto_examples_plot_gromov.py` + :ref:`sphx_glr_auto_examples_plot_OT_1D_smooth.py` .. raw:: html @@ -105,17 +105,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_gromov + /auto_examples/plot_OT_1D_smooth .. raw:: html - <div class="sphx-glr-thumbcontainer" tooltip="Illustration of 2D optimal transport between discributions that are weighted sum of diracs. The..."> + <div class="sphx-glr-thumbcontainer" tooltip="This example is designed to show how to use the Gromov-Wassertsein distance computation in POT...."> .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png - :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` + :ref:`sphx_glr_auto_examples_plot_gromov.py` .. raw:: html @@ -125,7 +125,7 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_OT_2D_samples + /auto_examples/plot_gromov .. raw:: html @@ -209,6 +209,26 @@ This is a gallery of all the POT example files. .. raw:: html + <div class="sphx-glr-thumbcontainer" tooltip="Illustration of 2D optimal transport between discributions that are weighted sum of diracs. The..."> + +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` + +.. raw:: html + + </div> + + +.. toctree:: + :hidden: + + /auto_examples/plot_OT_2D_samples + +.. raw:: html + <div class="sphx-glr-thumbcontainer" tooltip="This example is designed to show how to use the stochatic optimization algorithms for descrete ..."> .. only:: html @@ -229,7 +249,7 @@ This is a gallery of all the POT example files. .. raw:: html - <div class="sphx-glr-thumbcontainer" tooltip="This example presents a way of transferring colors between two image with Optimal Transport as ..."> + <div class="sphx-glr-thumbcontainer" tooltip="This example presents a way of transferring colors between two images with Optimal Transport as..."> .. only:: html @@ -289,6 +309,26 @@ This is a gallery of all the POT example files. .. raw:: html + <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation of regularized Wassersyein Barycenter as proposed in [..."> + +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_UOT_barycenter_1D_thumb.png + + :ref:`sphx_glr_auto_examples_plot_UOT_barycenter_1D.py` + +.. raw:: html + + </div> + + +.. toctree:: + :hidden: + + /auto_examples/plot_UOT_barycenter_1D + +.. raw:: html + <div class="sphx-glr-thumbcontainer" tooltip="This example presents how to use MappingTransport to estimate at the same time both the couplin..."> .. only:: html @@ -329,6 +369,26 @@ This is a gallery of all the POT example files. .. raw:: html + <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation of FGW for 1D measures[18]."> + +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_fgw_thumb.png + + :ref:`sphx_glr_auto_examples_plot_fgw.py` + +.. raw:: html + + </div> + + +.. toctree:: + :hidden: + + /auto_examples/plot_fgw + +.. raw:: html + <div class="sphx-glr-thumbcontainer" tooltip="This example introduces a domain adaptation in a 2D setting and the 4 OTDA approaches currently..."> .. only:: html @@ -409,6 +469,26 @@ This is a gallery of all the POT example files. .. raw:: html + <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the computation barycenter of labeled graphs using FGW"> + +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_fgw_thumb.png + + :ref:`sphx_glr_auto_examples_plot_barycenter_fgw.py` + +.. raw:: html + + </div> + + +.. toctree:: + :hidden: + + /auto_examples/plot_barycenter_fgw + +.. raw:: html + <div class="sphx-glr-thumbcontainer" tooltip="This example is designed to show how to use the Gromov-Wasserstein distance computation in POT...."> .. only:: html diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb index 26831f9..dad138b 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.ipynb +++ b/docs/source/auto_examples/plot_OT_2D_samples.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" + "# Author: Remi Flamary <remi.flamary@unice.fr>\n# Kilian Fatras <kilian.fatras@irisa.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" ] }, { @@ -100,6 +100,24 @@ "source": [ "#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGs = ot.sinkhorn(a, b, M, lambd)\n\npl.figure(5)\npl.imshow(Gs, interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')\n\npl.show()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Emprirical Sinkhorn\n----------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGes = ot.bregman.empirical_sinkhorn(xs, xt, lambd)\n\npl.figure(7)\npl.imshow(Ges, interpolation='nearest')\npl.title('OT matrix empirical sinkhorn')\n\npl.figure(8)\not.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn from samples')\n\npl.show()" + ] } ], "metadata": { @@ -118,7 +136,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py index bb952a0..63126ba 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.py +++ b/docs/source/auto_examples/plot_OT_2D_samples.py @@ -10,6 +10,7 @@ sum of diracs. The OT matrix is plotted with the samples. """ # Author: Remi Flamary <remi.flamary@unice.fr> +# Kilian Fatras <kilian.fatras@irisa.fr> # # License: MIT License @@ -100,3 +101,28 @@ pl.legend(loc=0) pl.title('OT matrix Sinkhorn with samples') pl.show() + + +############################################################################## +# Emprirical Sinkhorn +# ---------------- + +#%% sinkhorn + +# reg term +lambd = 1e-3 + +Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd) + +pl.figure(7) +pl.imshow(Ges, interpolation='nearest') +pl.title('OT matrix empirical sinkhorn') + +pl.figure(8) +ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.legend(loc=0) +pl.title('OT matrix Sinkhorn from samples') + +pl.show() diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst index 624ae3e..1f1d713 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ b/docs/source/auto_examples/plot_OT_2D_samples.rst @@ -17,6 +17,7 @@ sum of diracs. The OT matrix is plotted with the samples. # Author: Remi Flamary <remi.flamary@unice.fr> + # Kilian Fatras <kilian.fatras@irisa.fr> # # License: MIT License @@ -176,6 +177,8 @@ Compute Sinkhorn + + .. rst-class:: sphx-glr-horizontal @@ -192,7 +195,58 @@ Compute Sinkhorn -**Total running time of the script:** ( 0 minutes 3.027 seconds) +Emprirical Sinkhorn +---------------- + + + +.. code-block:: python + + + #%% sinkhorn + + # reg term + lambd = 1e-3 + + Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd) + + pl.figure(7) + pl.imshow(Ges, interpolation='nearest') + pl.title('OT matrix empirical sinkhorn') + + pl.figure(8) + ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + pl.legend(loc=0) + pl.title('OT matrix Sinkhorn from samples') + + pl.show() + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_013.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_014.png + :scale: 47 + + +.. rst-class:: sphx-glr-script-out + + Out:: + + Warning: numerical errors at iteration 0 + + +**Total running time of the script:** ( 0 minutes 2.616 seconds) diff --git a/docs/source/auto_examples/plot_UOT_1D.ipynb b/docs/source/auto_examples/plot_UOT_1D.ipynb new file mode 100644 index 0000000..c695306 --- /dev/null +++ b/docs/source/auto_examples/plot_UOT_1D.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# 1D Unbalanced optimal transport\n\n\nThis example illustrates the computation of Unbalanced Optimal transport\nusing a Kullback-Leibler relaxation.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Hicham Janati <hicham.janati@inria.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# make distributions unbalanced\nb *= 5.\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot distributions and loss matrix\n----------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n# plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Unbalanced Sinkhorn\n--------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Sinkhorn\n\nepsilon = 0.1 # entropy parameter\nalpha = 1. # Unbalanced KL relaxation parameter\nGs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn')\n\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}
\ No newline at end of file diff --git a/docs/source/auto_examples/plot_UOT_1D.py b/docs/source/auto_examples/plot_UOT_1D.py new file mode 100644 index 0000000..2ea8b05 --- /dev/null +++ b/docs/source/auto_examples/plot_UOT_1D.py @@ -0,0 +1,76 @@ +# -*- coding: utf-8 -*- +""" +=============================== +1D Unbalanced optimal transport +=============================== + +This example illustrates the computation of Unbalanced Optimal transport +using a Kullback-Leibler relaxation. +""" + +# Author: Hicham Janati <hicham.janati@inria.fr> +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +import ot.plot +from ot.datasets import make_1D_gauss as gauss + +############################################################################## +# Generate data +# ------------- + + +#%% parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a = gauss(n, m=20, s=5) # m= mean, s= std +b = gauss(n, m=60, s=10) + +# make distributions unbalanced +b *= 5. + +# loss matrix +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() + + +############################################################################## +# Plot distributions and loss matrix +# ---------------------------------- + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +pl.plot(x, a, 'b', label='Source distribution') +pl.plot(x, b, 'r', label='Target distribution') +pl.legend() + +# plot distributions and loss matrix + +pl.figure(2, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + + +############################################################################## +# Solve Unbalanced Sinkhorn +# -------------- + + +# Sinkhorn + +epsilon = 0.1 # entropy parameter +alpha = 1. # Unbalanced KL relaxation parameter +Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True) + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn') + +pl.show() diff --git a/docs/source/auto_examples/plot_UOT_1D.rst b/docs/source/auto_examples/plot_UOT_1D.rst new file mode 100644 index 0000000..8e618b4 --- /dev/null +++ b/docs/source/auto_examples/plot_UOT_1D.rst @@ -0,0 +1,173 @@ + + +.. _sphx_glr_auto_examples_plot_UOT_1D.py: + + +=============================== +1D Unbalanced optimal transport +=============================== + +This example illustrates the computation of Unbalanced Optimal transport +using a Kullback-Leibler relaxation. + + + +.. code-block:: python + + + # Author: Hicham Janati <hicham.janati@inria.fr> + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot + import ot.plot + from ot.datasets import make_1D_gauss as gauss + + + + + + + +Generate data +------------- + + + +.. code-block:: python + + + + #%% parameters + + n = 100 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + a = gauss(n, m=20, s=5) # m= mean, s= std + b = gauss(n, m=60, s=10) + + # make distributions unbalanced + b *= 5. + + # loss matrix + M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M /= M.max() + + + + + + + + +Plot distributions and loss matrix +---------------------------------- + + + +.. code-block:: python + + + #%% plot the distributions + + pl.figure(1, figsize=(6.4, 3)) + pl.plot(x, a, 'b', label='Source distribution') + pl.plot(x, b, 'r', label='Target distribution') + pl.legend() + + # plot distributions and loss matrix + + pl.figure(2, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_002.png + :scale: 47 + + + + +Solve Unbalanced Sinkhorn +-------------- + + + +.. code-block:: python + + + + # Sinkhorn + + epsilon = 0.1 # entropy parameter + alpha = 1. # Unbalanced KL relaxation parameter + Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True) + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn') + + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_006.png + :align: center + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Err + ------------------- + 0|1.838786e+00| + 10|1.242379e-01| + 20|2.581314e-03| + 30|5.674552e-05| + 40|1.252959e-06| + 50|2.768136e-08| + 60|6.116090e-10| + + +**Total running time of the script:** ( 0 minutes 0.259 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_UOT_1D.py <plot_UOT_1D.py>` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_UOT_1D.ipynb <plot_UOT_1D.ipynb>` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb b/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb new file mode 100644 index 0000000..e59cdc2 --- /dev/null +++ b/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb @@ -0,0 +1,126 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# 1D Wasserstein barycenter demo for Unbalanced distributions\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [10] for Unbalanced inputs.\n\n\n[10] Chizat, L., Peyr\u00e9, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Hicham Janati <hicham.janati@inria.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# make unbalanced dists\na2 *= 3.\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation\n----------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# non weighted barycenter computation\n\nweight = 0.5 # 0<=weight<=1\nweights = np.array([1 - weight, weight])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nalpha = 1.\n\nbary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycentric interpolation\n-------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# barycenter interpolation\n\nn_weight = 11\nweight_list = np.linspace(0, 1, n_weight)\n\n\nB_l2 = np.zeros((n, n_weight))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_weight):\n weight = weight_list[i]\n weights = np.array([1 - weight, weight])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n\n\n# plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = weight_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel(r'$\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = weight_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel(r'$\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}
\ No newline at end of file diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.py b/docs/source/auto_examples/plot_UOT_barycenter_1D.py new file mode 100644 index 0000000..c8d9d3b --- /dev/null +++ b/docs/source/auto_examples/plot_UOT_barycenter_1D.py @@ -0,0 +1,164 @@ +# -*- coding: utf-8 -*- +""" +=========================================================== +1D Wasserstein barycenter demo for Unbalanced distributions +=========================================================== + +This example illustrates the computation of regularized Wassersyein Barycenter +as proposed in [10] for Unbalanced inputs. + + +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + +""" + +# Author: Hicham Janati <hicham.janati@inria.fr> +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa +from matplotlib.collections import PolyCollection + +############################################################################## +# Generate data +# ------------- + +# parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) + +# make unbalanced dists +a2 *= 3. + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + +############################################################################## +# Plot data +# --------- + +# plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + +############################################################################## +# Barycenter computation +# ---------------------- + +# non weighted barycenter computation + +weight = 0.5 # 0<=weight<=1 +weights = np.array([1 - weight, weight]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +alpha = 1. + +bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + +############################################################################## +# Barycentric interpolation +# ------------------------- + +# barycenter interpolation + +n_weight = 11 +weight_list = np.linspace(0, 1, n_weight) + + +B_l2 = np.zeros((n, n_weight)) + +B_wass = np.copy(B_l2) + +for i in range(0, n_weight): + weight = weight_list[i] + weights = np.array([1 - weight, weight]) + B_l2[:, i] = A.dot(weights) + B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + + +# plot interpolation + +pl.figure(3) + +cmap = pl.cm.get_cmap('viridis') +verts = [] +zs = weight_list +for i, z in enumerate(zs): + ys = B_l2[:, i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel(r'$\alpha$') +ax.set_ylim3d(0, 1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max() * 1.01) +pl.title('Barycenter interpolation with l2') +pl.tight_layout() + +pl.figure(4) +cmap = pl.cm.get_cmap('viridis') +verts = [] +zs = weight_list +for i, z in enumerate(zs): + ys = B_wass[:, i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel(r'$\alpha$') +ax.set_ylim3d(0, 1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max() * 1.01) +pl.title('Barycenter interpolation with Wasserstein') +pl.tight_layout() + +pl.show() diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.rst b/docs/source/auto_examples/plot_UOT_barycenter_1D.rst new file mode 100644 index 0000000..ac17587 --- /dev/null +++ b/docs/source/auto_examples/plot_UOT_barycenter_1D.rst @@ -0,0 +1,261 @@ + + +.. _sphx_glr_auto_examples_plot_UOT_barycenter_1D.py: + + +=========================================================== +1D Wasserstein barycenter demo for Unbalanced distributions +=========================================================== + +This example illustrates the computation of regularized Wassersyein Barycenter +as proposed in [10] for Unbalanced inputs. + + +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + + + + +.. code-block:: python + + + # Author: Hicham Janati <hicham.janati@inria.fr> + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot + # necessary for 3d plot even if not used + from mpl_toolkits.mplot3d import Axes3D # noqa + from matplotlib.collections import PolyCollection + + + + + + + +Generate data +------------- + + + +.. code-block:: python + + + # parameters + + n = 100 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) + + # make unbalanced dists + a2 *= 3. + + # creating matrix A containing all distributions + A = np.vstack((a1, a2)).T + n_distributions = A.shape[1] + + # loss matrix + normalization + M = ot.utils.dist0(n) + M /= M.max() + + + + + + + +Plot data +--------- + + + +.. code-block:: python + + + # plot the distributions + + pl.figure(1, figsize=(6.4, 3)) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + pl.tight_layout() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_001.png + :align: center + + + + +Barycenter computation +---------------------- + + + +.. code-block:: python + + + # non weighted barycenter computation + + weight = 0.5 # 0<=weight<=1 + weights = np.array([1 - weight, weight]) + + # l2bary + bary_l2 = A.dot(weights) + + # wasserstein + reg = 1e-3 + alpha = 1. + + bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + + pl.figure(2) + pl.clf() + pl.subplot(2, 1, 1) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + + pl.subplot(2, 1, 2) + pl.plot(x, bary_l2, 'r', label='l2') + pl.plot(x, bary_wass, 'g', label='Wasserstein') + pl.legend() + pl.title('Barycenters') + pl.tight_layout() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_003.png + :align: center + + + + +Barycentric interpolation +------------------------- + + + +.. code-block:: python + + + # barycenter interpolation + + n_weight = 11 + weight_list = np.linspace(0, 1, n_weight) + + + B_l2 = np.zeros((n, n_weight)) + + B_wass = np.copy(B_l2) + + for i in range(0, n_weight): + weight = weight_list[i] + weights = np.array([1 - weight, weight]) + B_l2[:, i] = A.dot(weights) + B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + + + # plot interpolation + + pl.figure(3) + + cmap = pl.cm.get_cmap('viridis') + verts = [] + zs = weight_list + for i, z in enumerate(zs): + ys = B_l2[:, i] + verts.append(list(zip(x, ys))) + + ax = pl.gcf().gca(projection='3d') + + poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) + poly.set_alpha(0.7) + ax.add_collection3d(poly, zs=zs, zdir='y') + ax.set_xlabel('x') + ax.set_xlim3d(0, n) + ax.set_ylabel(r'$\alpha$') + ax.set_ylim3d(0, 1) + ax.set_zlabel('') + ax.set_zlim3d(0, B_l2.max() * 1.01) + pl.title('Barycenter interpolation with l2') + pl.tight_layout() + + pl.figure(4) + cmap = pl.cm.get_cmap('viridis') + verts = [] + zs = weight_list + for i, z in enumerate(zs): + ys = B_wass[:, i] + verts.append(list(zip(x, ys))) + + ax = pl.gcf().gca(projection='3d') + + poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) + poly.set_alpha(0.7) + ax.add_collection3d(poly, zs=zs, zdir='y') + ax.set_xlabel('x') + ax.set_xlim3d(0, n) + ax.set_ylabel(r'$\alpha$') + ax.set_ylim3d(0, 1) + ax.set_zlabel('') + ax.set_zlim3d(0, B_l2.max() * 1.01) + pl.title('Barycenter interpolation with Wasserstein') + pl.tight_layout() + + pl.show() + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_005.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_006.png + :scale: 47 + + + + +**Total running time of the script:** ( 0 minutes 0.344 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_UOT_barycenter_1D.py <plot_UOT_barycenter_1D.py>` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_UOT_barycenter_1D.ipynb <plot_UOT_barycenter_1D.ipynb>` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ diff --git a/docs/source/auto_examples/plot_barycenter_fgw.ipynb b/docs/source/auto_examples/plot_barycenter_fgw.ipynb new file mode 100644 index 0000000..28229b2 --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_fgw.ipynb @@ -0,0 +1,126 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n=================================\nPlot graphs' barycenter using FGW\n=================================\n\nThis example illustrates the computation barycenter of labeled graphs using FGW\n\nRequires networkx >=2\n\n.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n and Courty Nicolas\n \"Optimal Transport for structured data with application on graphs\"\n International Conference on Machine Learning (ICML). 2019.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Titouan Vayer <titouan.vayer@irisa.fr>\n#\n# License: MIT License\n\n#%% load libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport math\nfrom scipy.sparse.csgraph import shortest_path\nimport matplotlib.colors as mcol\nfrom matplotlib import cm\nfrom ot.gromov import fgw_barycenters\n#%% Graph functions\n\n\ndef find_thresh(C, inf=0.5, sup=3, step=10):\n \"\"\" Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected\n Tthe threshold is found by a linesearch between values \"inf\" and \"sup\" with \"step\" thresholds tested.\n The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix\n and the original matrix.\n Parameters\n ----------\n C : ndarray, shape (n_nodes,n_nodes)\n The structure matrix to threshold\n inf : float\n The beginning of the linesearch\n sup : float\n The end of the linesearch\n step : integer\n Number of thresholds tested\n \"\"\"\n dist = []\n search = np.linspace(inf, sup, step)\n for thresh in search:\n Cprime = sp_to_adjency(C, 0, thresh)\n SC = shortest_path(Cprime, method='D')\n SC[SC == float('inf')] = 100\n dist.append(np.linalg.norm(SC - C))\n return search[np.argmin(dist)], dist\n\n\ndef sp_to_adjency(C, threshinf=0.2, threshsup=1.8):\n \"\"\" Thresholds the structure matrix in order to compute an adjency matrix.\n All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0\n Parameters\n ----------\n C : ndarray, shape (n_nodes,n_nodes)\n The structure matrix to threshold\n threshinf : float\n The minimum value of distance from which the new value is set to 1\n threshsup : float\n The maximum value of distance from which the new value is set to 1\n Returns\n -------\n C : ndarray, shape (n_nodes,n_nodes)\n The threshold matrix. Each element is in {0,1}\n \"\"\"\n H = np.zeros_like(C)\n np.fill_diagonal(H, np.diagonal(C))\n C = C - H\n C = np.minimum(np.maximum(C, threshinf), threshsup)\n C[C == threshsup] = 0\n C[C != 0] = 1\n\n return C\n\n\ndef build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None):\n \"\"\" Create a noisy circular graph\n \"\"\"\n g = nx.Graph()\n g.add_nodes_from(list(range(N)))\n for i in range(N):\n noise = float(np.random.normal(mu, sigma, 1))\n if with_noise:\n g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)\n else:\n g.add_node(i, attr_name=math.sin(2 * i * math.pi / N))\n g.add_edge(i, i + 1)\n if structure_noise:\n randomint = np.random.randint(0, p)\n if randomint == 0:\n if i <= N - 3:\n g.add_edge(i, i + 2)\n if i == N - 2:\n g.add_edge(i, 0)\n if i == N - 1:\n g.add_edge(i, 1)\n g.add_edge(N, 0)\n noise = float(np.random.normal(mu, sigma, 1))\n if with_noise:\n g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)\n else:\n g.add_node(N, attr_name=math.sin(2 * N * math.pi / N))\n return g\n\n\ndef graph_colors(nx_graph, vmin=0, vmax=7):\n cnorm = mcol.Normalize(vmin=vmin, vmax=vmax)\n cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis')\n cpick.set_array([])\n val_map = {}\n for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items():\n val_map[k] = cpick.to_rgba(v)\n colors = []\n for node in nx_graph.nodes():\n colors.append(val_map[node])\n return colors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% circular dataset\n# We build a dataset of noisy circular graphs.\n# Noise is added on the structures by random connections and on the features by gaussian noise.\n\n\nnp.random.seed(30)\nX0 = []\nfor k in range(9):\n X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Plot graphs\n\nplt.figure(figsize=(8, 10))\nfor i in range(len(X0)):\n plt.subplot(3, 3, i + 1)\n g = X0[i]\n pos = nx.kamada_kawai_layout(g)\n nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100)\nplt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20)\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation\n----------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph\n# Features distances are the euclidean distances\nCs = [shortest_path(nx.adjacency_matrix(x)) for x in X0]\nps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]\nYs = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0]\nlambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel()\nsizebary = 15 # we choose a barycenter with 15 nodes\n\nA, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Barycenter\n-------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Create the barycenter\nbary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))\nfor i, v in enumerate(A.ravel()):\n bary.add_node(i, attr_name=v)\n\n#%%\npos = nx.kamada_kawai_layout(bary)\nnx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False)\nplt.suptitle('Barycenter', fontsize=20)\nplt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}
\ No newline at end of file diff --git a/docs/source/auto_examples/plot_barycenter_fgw.py b/docs/source/auto_examples/plot_barycenter_fgw.py new file mode 100644 index 0000000..77b0370 --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_fgw.py @@ -0,0 +1,184 @@ +# -*- coding: utf-8 -*- +""" +================================= +Plot graphs' barycenter using FGW +================================= + +This example illustrates the computation barycenter of labeled graphs using FGW + +Requires networkx >=2 + +.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + +""" + +# Author: Titouan Vayer <titouan.vayer@irisa.fr> +# +# License: MIT License + +#%% load libraries +import numpy as np +import matplotlib.pyplot as plt +import networkx as nx +import math +from scipy.sparse.csgraph import shortest_path +import matplotlib.colors as mcol +from matplotlib import cm +from ot.gromov import fgw_barycenters +#%% Graph functions + + +def find_thresh(C, inf=0.5, sup=3, step=10): + """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected + Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. + The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix + and the original matrix. + Parameters + ---------- + C : ndarray, shape (n_nodes,n_nodes) + The structure matrix to threshold + inf : float + The beginning of the linesearch + sup : float + The end of the linesearch + step : integer + Number of thresholds tested + """ + dist = [] + search = np.linspace(inf, sup, step) + for thresh in search: + Cprime = sp_to_adjency(C, 0, thresh) + SC = shortest_path(Cprime, method='D') + SC[SC == float('inf')] = 100 + dist.append(np.linalg.norm(SC - C)) + return search[np.argmin(dist)], dist + + +def sp_to_adjency(C, threshinf=0.2, threshsup=1.8): + """ Thresholds the structure matrix in order to compute an adjency matrix. + All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0 + Parameters + ---------- + C : ndarray, shape (n_nodes,n_nodes) + The structure matrix to threshold + threshinf : float + The minimum value of distance from which the new value is set to 1 + threshsup : float + The maximum value of distance from which the new value is set to 1 + Returns + ------- + C : ndarray, shape (n_nodes,n_nodes) + The threshold matrix. Each element is in {0,1} + """ + H = np.zeros_like(C) + np.fill_diagonal(H, np.diagonal(C)) + C = C - H + C = np.minimum(np.maximum(C, threshinf), threshsup) + C[C == threshsup] = 0 + C[C != 0] = 1 + + return C + + +def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None): + """ Create a noisy circular graph + """ + g = nx.Graph() + g.add_nodes_from(list(range(N))) + for i in range(N): + noise = float(np.random.normal(mu, sigma, 1)) + if with_noise: + g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise) + else: + g.add_node(i, attr_name=math.sin(2 * i * math.pi / N)) + g.add_edge(i, i + 1) + if structure_noise: + randomint = np.random.randint(0, p) + if randomint == 0: + if i <= N - 3: + g.add_edge(i, i + 2) + if i == N - 2: + g.add_edge(i, 0) + if i == N - 1: + g.add_edge(i, 1) + g.add_edge(N, 0) + noise = float(np.random.normal(mu, sigma, 1)) + if with_noise: + g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise) + else: + g.add_node(N, attr_name=math.sin(2 * N * math.pi / N)) + return g + + +def graph_colors(nx_graph, vmin=0, vmax=7): + cnorm = mcol.Normalize(vmin=vmin, vmax=vmax) + cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis') + cpick.set_array([]) + val_map = {} + for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items(): + val_map[k] = cpick.to_rgba(v) + colors = [] + for node in nx_graph.nodes(): + colors.append(val_map[node]) + return colors + +############################################################################## +# Generate data +# ------------- + +#%% circular dataset +# We build a dataset of noisy circular graphs. +# Noise is added on the structures by random connections and on the features by gaussian noise. + + +np.random.seed(30) +X0 = [] +for k in range(9): + X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3)) + +############################################################################## +# Plot data +# --------- + +#%% Plot graphs + +plt.figure(figsize=(8, 10)) +for i in range(len(X0)): + plt.subplot(3, 3, i + 1) + g = X0[i] + pos = nx.kamada_kawai_layout(g) + nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100) +plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20) +plt.show() + +############################################################################## +# Barycenter computation +# ---------------------- + +#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph +# Features distances are the euclidean distances +Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0] +ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0] +Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0] +lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel() +sizebary = 15 # we choose a barycenter with 15 nodes + +A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True) + +############################################################################## +# Plot Barycenter +# ------------------------- + +#%% Create the barycenter +bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) +for i, v in enumerate(A.ravel()): + bary.add_node(i, attr_name=v) + +#%% +pos = nx.kamada_kawai_layout(bary) +nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False) +plt.suptitle('Barycenter', fontsize=20) +plt.show() diff --git a/docs/source/auto_examples/plot_barycenter_fgw.rst b/docs/source/auto_examples/plot_barycenter_fgw.rst new file mode 100644 index 0000000..2c44a65 --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_fgw.rst @@ -0,0 +1,268 @@ + + +.. _sphx_glr_auto_examples_plot_barycenter_fgw.py: + + +================================= +Plot graphs' barycenter using FGW +================================= + +This example illustrates the computation barycenter of labeled graphs using FGW + +Requires networkx >=2 + +.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + + + + +.. code-block:: python + + + # Author: Titouan Vayer <titouan.vayer@irisa.fr> + # + # License: MIT License + + #%% load libraries + import numpy as np + import matplotlib.pyplot as plt + import networkx as nx + import math + from scipy.sparse.csgraph import shortest_path + import matplotlib.colors as mcol + from matplotlib import cm + from ot.gromov import fgw_barycenters + #%% Graph functions + + + def find_thresh(C, inf=0.5, sup=3, step=10): + """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected + Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. + The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix + and the original matrix. + Parameters + ---------- + C : ndarray, shape (n_nodes,n_nodes) + The structure matrix to threshold + inf : float + The beginning of the linesearch + sup : float + The end of the linesearch + step : integer + Number of thresholds tested + """ + dist = [] + search = np.linspace(inf, sup, step) + for thresh in search: + Cprime = sp_to_adjency(C, 0, thresh) + SC = shortest_path(Cprime, method='D') + SC[SC == float('inf')] = 100 + dist.append(np.linalg.norm(SC - C)) + return search[np.argmin(dist)], dist + + + def sp_to_adjency(C, threshinf=0.2, threshsup=1.8): + """ Thresholds the structure matrix in order to compute an adjency matrix. + All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0 + Parameters + ---------- + C : ndarray, shape (n_nodes,n_nodes) + The structure matrix to threshold + threshinf : float + The minimum value of distance from which the new value is set to 1 + threshsup : float + The maximum value of distance from which the new value is set to 1 + Returns + ------- + C : ndarray, shape (n_nodes,n_nodes) + The threshold matrix. Each element is in {0,1} + """ + H = np.zeros_like(C) + np.fill_diagonal(H, np.diagonal(C)) + C = C - H + C = np.minimum(np.maximum(C, threshinf), threshsup) + C[C == threshsup] = 0 + C[C != 0] = 1 + + return C + + + def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None): + """ Create a noisy circular graph + """ + g = nx.Graph() + g.add_nodes_from(list(range(N))) + for i in range(N): + noise = float(np.random.normal(mu, sigma, 1)) + if with_noise: + g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise) + else: + g.add_node(i, attr_name=math.sin(2 * i * math.pi / N)) + g.add_edge(i, i + 1) + if structure_noise: + randomint = np.random.randint(0, p) + if randomint == 0: + if i <= N - 3: + g.add_edge(i, i + 2) + if i == N - 2: + g.add_edge(i, 0) + if i == N - 1: + g.add_edge(i, 1) + g.add_edge(N, 0) + noise = float(np.random.normal(mu, sigma, 1)) + if with_noise: + g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise) + else: + g.add_node(N, attr_name=math.sin(2 * N * math.pi / N)) + return g + + + def graph_colors(nx_graph, vmin=0, vmax=7): + cnorm = mcol.Normalize(vmin=vmin, vmax=vmax) + cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis') + cpick.set_array([]) + val_map = {} + for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items(): + val_map[k] = cpick.to_rgba(v) + colors = [] + for node in nx_graph.nodes(): + colors.append(val_map[node]) + return colors + + + + + + + +Generate data +------------- + + + +.. code-block:: python + + + #%% circular dataset + # We build a dataset of noisy circular graphs. + # Noise is added on the structures by random connections and on the features by gaussian noise. + + + np.random.seed(30) + X0 = [] + for k in range(9): + X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3)) + + + + + + + +Plot data +--------- + + + +.. code-block:: python + + + #%% Plot graphs + + plt.figure(figsize=(8, 10)) + for i in range(len(X0)): + plt.subplot(3, 3, i + 1) + g = X0[i] + pos = nx.kamada_kawai_layout(g) + nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100) + plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20) + plt.show() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_fgw_001.png + :align: center + + + + +Barycenter computation +---------------------- + + + +.. code-block:: python + + + #%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph + # Features distances are the euclidean distances + Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0] + ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0] + Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0] + lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel() + sizebary = 15 # we choose a barycenter with 15 nodes + + A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True) + + + + + + + +Plot Barycenter +------------------------- + + + +.. code-block:: python + + + #%% Create the barycenter + bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) + for i, v in enumerate(A.ravel()): + bary.add_node(i, attr_name=v) + + #%% + pos = nx.kamada_kawai_layout(bary) + nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False) + plt.suptitle('Barycenter', fontsize=20) + plt.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_fgw_002.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 2.065 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_barycenter_fgw.py <plot_barycenter_fgw.py>` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_barycenter_fgw.ipynb <plot_barycenter_fgw.ipynb>` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ diff --git a/docs/source/auto_examples/plot_fgw.ipynb b/docs/source/auto_examples/plot_fgw.ipynb new file mode 100644 index 0000000..1b150bd --- /dev/null +++ b/docs/source/auto_examples/plot_fgw.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Plot Fused-gromov-Wasserstein\n\n\nThis example illustrates the computation of FGW for 1D measures[18].\n\n.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n and Courty Nicolas\n \"Optimal Transport for structured data with application on graphs\"\n International Conference on Machine Learning (ICML). 2019.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Titouan Vayer <titouan.vayer@irisa.fr>\n#\n# License: MIT License\n\nimport matplotlib.pyplot as pl\nimport numpy as np\nimport ot\nfrom ot.gromov import gromov_wasserstein, fused_gromov_wasserstein" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n# We create two 1D random measures\nn = 20 # number of points in the first distribution\nn2 = 30 # number of points in the second distribution\nsig = 1 # std of first distribution\nsig2 = 0.1 # std of second distribution\n\nnp.random.seed(0)\n\nphi = np.arange(n)[:, None]\nxs = phi + sig * np.random.randn(n, 1)\nys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1)\n\nphi2 = np.arange(n2)[:, None]\nxt = phi2 + sig * np.random.randn(n2, 1)\nyt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1)\nyt = yt[::-1, :]\n\np = ot.unif(n)\nq = ot.unif(n2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% plot the distributions\n\npl.close(10)\npl.figure(10, (7, 7))\n\npl.subplot(2, 1, 1)\n\npl.scatter(ys, xs, c=phi, s=70)\npl.ylabel('Feature value a', fontsize=20)\npl.title('$\\mu=\\sum_i \\delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1)\npl.xticks(())\npl.yticks(())\npl.subplot(2, 1, 2)\npl.scatter(yt, xt, c=phi2, s=70)\npl.xlabel('coordinates x/y', fontsize=25)\npl.ylabel('Feature value b', fontsize=20)\npl.title('$\\\\nu=\\sum_j \\delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1)\npl.yticks(())\npl.tight_layout()\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create structure matrices and across-feature distance matrix\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Structure matrices and across-features distance matrix\nC1 = ot.dist(xs)\nC2 = ot.dist(xt)\nM = ot.dist(ys, yt)\nw1 = ot.unif(C1.shape[0])\nw2 = ot.unif(C2.shape[0])\nGot = ot.emd([], [], M)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot matrices\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%%\ncmap = 'Reds'\npl.close(10)\npl.figure(10, (5, 5))\nfs = 15\nl_x = [0, 5, 10, 15]\nl_y = [0, 5, 10, 15, 20, 25]\ngs = pl.GridSpec(5, 5)\n\nax1 = pl.subplot(gs[3:, :2])\n\npl.imshow(C1, cmap=cmap, interpolation='nearest')\npl.title(\"$C_1$\", fontsize=fs)\npl.xlabel(\"$k$\", fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\npl.xticks(l_x)\npl.yticks(l_x)\n\nax2 = pl.subplot(gs[:3, 2:])\n\npl.imshow(C2, cmap=cmap, interpolation='nearest')\npl.title(\"$C_2$\", fontsize=fs)\npl.ylabel(\"$l$\", fontsize=fs)\n#pl.ylabel(\"$l$\",fontsize=fs)\npl.xticks(())\npl.yticks(l_y)\nax2.set_aspect('auto')\n\nax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1)\npl.imshow(M, cmap=cmap, interpolation='nearest')\npl.yticks(l_x)\npl.xticks(l_y)\npl.ylabel(\"$i$\", fontsize=fs)\npl.title(\"$M_{AB}$\", fontsize=fs)\npl.xlabel(\"$j$\", fontsize=fs)\npl.tight_layout()\nax3.set_aspect('auto')\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute FGW/GW\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Computing FGW and GW\nalpha = 1e-3\n\not.tic()\nGwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True)\not.toc()\n\n#%reload_ext WGW\nGg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualize transport matrices\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% visu OT matrix\ncmap = 'Blues'\nfs = 15\npl.figure(2, (13, 5))\npl.clf()\npl.subplot(1, 3, 1)\npl.imshow(Got, cmap=cmap, interpolation='nearest')\n#pl.xlabel(\"$y$\",fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\npl.xticks(())\n\npl.title('Wasserstein ($M$ only)')\n\npl.subplot(1, 3, 2)\npl.imshow(Gg, cmap=cmap, interpolation='nearest')\npl.title('Gromov ($C_1,C_2$ only)')\npl.xticks(())\npl.subplot(1, 3, 3)\npl.imshow(Gwg, cmap=cmap, interpolation='nearest')\npl.title('FGW ($M+C_1,C_2$)')\n\npl.xlabel(\"$j$\", fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\n\npl.tight_layout()\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}
\ No newline at end of file diff --git a/docs/source/auto_examples/plot_fgw.py b/docs/source/auto_examples/plot_fgw.py new file mode 100644 index 0000000..43efc94 --- /dev/null +++ b/docs/source/auto_examples/plot_fgw.py @@ -0,0 +1,173 @@ +# -*- coding: utf-8 -*- +""" +============================== +Plot Fused-gromov-Wasserstein +============================== + +This example illustrates the computation of FGW for 1D measures[18]. + +.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + +""" + +# Author: Titouan Vayer <titouan.vayer@irisa.fr> +# +# License: MIT License + +import matplotlib.pyplot as pl +import numpy as np +import ot +from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein + +############################################################################## +# Generate data +# --------- + +#%% parameters +# We create two 1D random measures +n = 20 # number of points in the first distribution +n2 = 30 # number of points in the second distribution +sig = 1 # std of first distribution +sig2 = 0.1 # std of second distribution + +np.random.seed(0) + +phi = np.arange(n)[:, None] +xs = phi + sig * np.random.randn(n, 1) +ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1) + +phi2 = np.arange(n2)[:, None] +xt = phi2 + sig * np.random.randn(n2, 1) +yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1) +yt = yt[::-1, :] + +p = ot.unif(n) +q = ot.unif(n2) + +############################################################################## +# Plot data +# --------- + +#%% plot the distributions + +pl.close(10) +pl.figure(10, (7, 7)) + +pl.subplot(2, 1, 1) + +pl.scatter(ys, xs, c=phi, s=70) +pl.ylabel('Feature value a', fontsize=20) +pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1) +pl.xticks(()) +pl.yticks(()) +pl.subplot(2, 1, 2) +pl.scatter(yt, xt, c=phi2, s=70) +pl.xlabel('coordinates x/y', fontsize=25) +pl.ylabel('Feature value b', fontsize=20) +pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1) +pl.yticks(()) +pl.tight_layout() +pl.show() + +############################################################################## +# Create structure matrices and across-feature distance matrix +# --------- + +#%% Structure matrices and across-features distance matrix +C1 = ot.dist(xs) +C2 = ot.dist(xt) +M = ot.dist(ys, yt) +w1 = ot.unif(C1.shape[0]) +w2 = ot.unif(C2.shape[0]) +Got = ot.emd([], [], M) + +############################################################################## +# Plot matrices +# --------- + +#%% +cmap = 'Reds' +pl.close(10) +pl.figure(10, (5, 5)) +fs = 15 +l_x = [0, 5, 10, 15] +l_y = [0, 5, 10, 15, 20, 25] +gs = pl.GridSpec(5, 5) + +ax1 = pl.subplot(gs[3:, :2]) + +pl.imshow(C1, cmap=cmap, interpolation='nearest') +pl.title("$C_1$", fontsize=fs) +pl.xlabel("$k$", fontsize=fs) +pl.ylabel("$i$", fontsize=fs) +pl.xticks(l_x) +pl.yticks(l_x) + +ax2 = pl.subplot(gs[:3, 2:]) + +pl.imshow(C2, cmap=cmap, interpolation='nearest') +pl.title("$C_2$", fontsize=fs) +pl.ylabel("$l$", fontsize=fs) +#pl.ylabel("$l$",fontsize=fs) +pl.xticks(()) +pl.yticks(l_y) +ax2.set_aspect('auto') + +ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1) +pl.imshow(M, cmap=cmap, interpolation='nearest') +pl.yticks(l_x) +pl.xticks(l_y) +pl.ylabel("$i$", fontsize=fs) +pl.title("$M_{AB}$", fontsize=fs) +pl.xlabel("$j$", fontsize=fs) +pl.tight_layout() +ax3.set_aspect('auto') +pl.show() + +############################################################################## +# Compute FGW/GW +# --------- + +#%% Computing FGW and GW +alpha = 1e-3 + +ot.tic() +Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True) +ot.toc() + +#%reload_ext WGW +Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True) + +############################################################################## +# Visualize transport matrices +# --------- + +#%% visu OT matrix +cmap = 'Blues' +fs = 15 +pl.figure(2, (13, 5)) +pl.clf() +pl.subplot(1, 3, 1) +pl.imshow(Got, cmap=cmap, interpolation='nearest') +#pl.xlabel("$y$",fontsize=fs) +pl.ylabel("$i$", fontsize=fs) +pl.xticks(()) + +pl.title('Wasserstein ($M$ only)') + +pl.subplot(1, 3, 2) +pl.imshow(Gg, cmap=cmap, interpolation='nearest') +pl.title('Gromov ($C_1,C_2$ only)') +pl.xticks(()) +pl.subplot(1, 3, 3) +pl.imshow(Gwg, cmap=cmap, interpolation='nearest') +pl.title('FGW ($M+C_1,C_2$)') + +pl.xlabel("$j$", fontsize=fs) +pl.ylabel("$i$", fontsize=fs) + +pl.tight_layout() +pl.show() diff --git a/docs/source/auto_examples/plot_fgw.rst b/docs/source/auto_examples/plot_fgw.rst new file mode 100644 index 0000000..aec725d --- /dev/null +++ b/docs/source/auto_examples/plot_fgw.rst @@ -0,0 +1,297 @@ + + +.. _sphx_glr_auto_examples_plot_fgw.py: + + +============================== +Plot Fused-gromov-Wasserstein +============================== + +This example illustrates the computation of FGW for 1D measures[18]. + +.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + + + + +.. code-block:: python + + + # Author: Titouan Vayer <titouan.vayer@irisa.fr> + # + # License: MIT License + + import matplotlib.pyplot as pl + import numpy as np + import ot + from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein + + + + + + + +Generate data +--------- + + + +.. code-block:: python + + + #%% parameters + # We create two 1D random measures + n = 20 # number of points in the first distribution + n2 = 30 # number of points in the second distribution + sig = 1 # std of first distribution + sig2 = 0.1 # std of second distribution + + np.random.seed(0) + + phi = np.arange(n)[:, None] + xs = phi + sig * np.random.randn(n, 1) + ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1) + + phi2 = np.arange(n2)[:, None] + xt = phi2 + sig * np.random.randn(n2, 1) + yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1) + yt = yt[::-1, :] + + p = ot.unif(n) + q = ot.unif(n2) + + + + + + + +Plot data +--------- + + + +.. code-block:: python + + + #%% plot the distributions + + pl.close(10) + pl.figure(10, (7, 7)) + + pl.subplot(2, 1, 1) + + pl.scatter(ys, xs, c=phi, s=70) + pl.ylabel('Feature value a', fontsize=20) + pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1) + pl.xticks(()) + pl.yticks(()) + pl.subplot(2, 1, 2) + pl.scatter(yt, xt, c=phi2, s=70) + pl.xlabel('coordinates x/y', fontsize=25) + pl.ylabel('Feature value b', fontsize=20) + pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1) + pl.yticks(()) + pl.tight_layout() + pl.show() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_fgw_010.png + :align: center + + + + +Create structure matrices and across-feature distance matrix +--------- + + + +.. code-block:: python + + + #%% Structure matrices and across-features distance matrix + C1 = ot.dist(xs) + C2 = ot.dist(xt) + M = ot.dist(ys, yt) + w1 = ot.unif(C1.shape[0]) + w2 = ot.unif(C2.shape[0]) + Got = ot.emd([], [], M) + + + + + + + +Plot matrices +--------- + + + +.. code-block:: python + + + #%% + cmap = 'Reds' + pl.close(10) + pl.figure(10, (5, 5)) + fs = 15 + l_x = [0, 5, 10, 15] + l_y = [0, 5, 10, 15, 20, 25] + gs = pl.GridSpec(5, 5) + + ax1 = pl.subplot(gs[3:, :2]) + + pl.imshow(C1, cmap=cmap, interpolation='nearest') + pl.title("$C_1$", fontsize=fs) + pl.xlabel("$k$", fontsize=fs) + pl.ylabel("$i$", fontsize=fs) + pl.xticks(l_x) + pl.yticks(l_x) + + ax2 = pl.subplot(gs[:3, 2:]) + + pl.imshow(C2, cmap=cmap, interpolation='nearest') + pl.title("$C_2$", fontsize=fs) + pl.ylabel("$l$", fontsize=fs) + #pl.ylabel("$l$",fontsize=fs) + pl.xticks(()) + pl.yticks(l_y) + ax2.set_aspect('auto') + + ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1) + pl.imshow(M, cmap=cmap, interpolation='nearest') + pl.yticks(l_x) + pl.xticks(l_y) + pl.ylabel("$i$", fontsize=fs) + pl.title("$M_{AB}$", fontsize=fs) + pl.xlabel("$j$", fontsize=fs) + pl.tight_layout() + ax3.set_aspect('auto') + pl.show() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_fgw_011.png + :align: center + + + + +Compute FGW/GW +--------- + + + +.. code-block:: python + + + #%% Computing FGW and GW + alpha = 1e-3 + + ot.tic() + Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True) + ot.toc() + + #%reload_ext WGW + Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True) + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 0|4.734462e+01|0.000000e+00|0.000000e+00 + 1|2.508258e+01|8.875498e-01|2.226204e+01 + 2|2.189329e+01|1.456747e-01|3.189297e+00 + 3|2.189329e+01|0.000000e+00|0.000000e+00 + Elapsed time : 0.0016989707946777344 s + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 0|4.683978e+04|0.000000e+00|0.000000e+00 + 1|3.860061e+04|2.134468e-01|8.239175e+03 + 2|2.182948e+04|7.682787e-01|1.677113e+04 + 3|2.182948e+04|0.000000e+00|0.000000e+00 + + +Visualize transport matrices +--------- + + + +.. code-block:: python + + + #%% visu OT matrix + cmap = 'Blues' + fs = 15 + pl.figure(2, (13, 5)) + pl.clf() + pl.subplot(1, 3, 1) + pl.imshow(Got, cmap=cmap, interpolation='nearest') + #pl.xlabel("$y$",fontsize=fs) + pl.ylabel("$i$", fontsize=fs) + pl.xticks(()) + + pl.title('Wasserstein ($M$ only)') + + pl.subplot(1, 3, 2) + pl.imshow(Gg, cmap=cmap, interpolation='nearest') + pl.title('Gromov ($C_1,C_2$ only)') + pl.xticks(()) + pl.subplot(1, 3, 3) + pl.imshow(Gwg, cmap=cmap, interpolation='nearest') + pl.title('FGW ($M+C_1,C_2$)') + + pl.xlabel("$j$", fontsize=fs) + pl.ylabel("$i$", fontsize=fs) + + pl.tight_layout() + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_fgw_004.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 1.468 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_fgw.py <plot_fgw.py>` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_fgw.ipynb <plot_fgw.ipynb>` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ diff --git a/docs/source/auto_examples/plot_otda_color_images.ipynb b/docs/source/auto_examples/plot_otda_color_images.ipynb index 2daf406..103bdec 100644 --- a/docs/source/auto_examples/plot_otda_color_images.ipynb +++ b/docs/source/auto_examples/plot_otda_color_images.ipynb @@ -1,144 +1,144 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "\n# OT for image color adaptation\n\n\nThis example presents a way of transferring colors between two image\nwith Optimal Transport as introduced in [6]\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport.\nSIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "%matplotlib inline" + ] + }, { - "execution_count": null, - "cell_type": "code", + "cell_type": "markdown", + "metadata": {}, "source": [ - "# Authors: Remi Flamary <remi.flamary@unice.fr>\n# Stanislas Chambon <stan.chambon@gmail.com>\n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" - ], - "outputs": [], + "\n# OT for image color adaptation\n\n\nThis example presents a way of transferring colors between two images\nwith Optimal Transport as introduced in [6]\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport.\nSIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary <remi.flamary@unice.fr>\n# Stanislas Chambon <stan.chambon@gmail.com>\n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts an image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot original image\n-------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(1, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Scatter plot of colors\n----------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(2, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(2, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# prediction between images (using out of sample prediction as in [6])\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\ntransp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n\ntransp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\ntransp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(transp_Xs_emd, I1.shape))\nI2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n\nI1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\nI2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# prediction between images (using out of sample prediction as in [6])\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\ntransp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\ntransp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(transp_Xs_emd, I1.shape))\nI2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n\nI1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\nI2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot new images\n---------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(3, figsize=(8, 4))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(2, 3, 2)\npl.imshow(I1t)\npl.axis('off')\npl.title('Image 1 Adapt')\n\npl.subplot(2, 3, 3)\npl.imshow(I1te)\npl.axis('off')\npl.title('Image 1 Adapt (reg)')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\n\npl.subplot(2, 3, 5)\npl.imshow(I2t)\npl.axis('off')\npl.title('Image 2 Adapt')\n\npl.subplot(2, 3, 6)\npl.imshow(I2te)\npl.axis('off')\npl.title('Image 2 Adapt (reg)')\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "pl.figure(3, figsize=(8, 4))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(2, 3, 2)\npl.imshow(I1t)\npl.axis('off')\npl.title('Image 1 Adapt')\n\npl.subplot(2, 3, 3)\npl.imshow(I1te)\npl.axis('off')\npl.title('Image 1 Adapt (reg)')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\n\npl.subplot(2, 3, 5)\npl.imshow(I2t)\npl.axis('off')\npl.title('Image 2 Adapt')\n\npl.subplot(2, 3, 6)\npl.imshow(I2te)\npl.axis('off')\npl.title('Image 2 Adapt (reg)')\npl.tight_layout()\n\npl.show()" + ] } - ], + ], "metadata": { "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" } - } + }, + "nbformat": 4, + "nbformat_minor": 0 }
\ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_color_images.py b/docs/source/auto_examples/plot_otda_color_images.py index e77aec0..62383a2 100644 --- a/docs/source/auto_examples/plot_otda_color_images.py +++ b/docs/source/auto_examples/plot_otda_color_images.py @@ -4,7 +4,7 @@ OT for image color adaptation ============================= -This example presents a way of transferring colors between two image +This example presents a way of transferring colors between two images with Optimal Transport as introduced in [6] [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). @@ -27,7 +27,7 @@ r = np.random.RandomState(42) def im2mat(I): - """Converts and image to matrix (one pixel per line)""" + """Converts an image to matrix (one pixel per line)""" return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) @@ -115,8 +115,8 @@ ot_sinkhorn.fit(Xs=Xs, Xt=Xt) transp_Xs_emd = ot_emd.transform(Xs=X1) transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) -transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) -transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) +transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2) I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) diff --git a/docs/source/auto_examples/plot_otda_color_images.rst b/docs/source/auto_examples/plot_otda_color_images.rst index 9c31ba7..ab0406e 100644 --- a/docs/source/auto_examples/plot_otda_color_images.rst +++ b/docs/source/auto_examples/plot_otda_color_images.rst @@ -7,7 +7,7 @@ OT for image color adaptation ============================= -This example presents a way of transferring colors between two image +This example presents a way of transferring colors between two images with Optimal Transport as introduced in [6] [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). @@ -34,7 +34,7 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. def im2mat(I): - """Converts and image to matrix (one pixel per line)""" + """Converts an image to matrix (one pixel per line)""" return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) @@ -168,8 +168,8 @@ Instantiate the different transport algorithms and fit them transp_Xs_emd = ot_emd.transform(Xs=X1) transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) - transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) - transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) + transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) + transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2) I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) @@ -235,11 +235,13 @@ Plot new images -**Total running time of the script:** ( 3 minutes 16.469 seconds) +**Total running time of the script:** ( 3 minutes 55.541 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -252,6 +254,9 @@ Plot new images :download:`Download Jupyter notebook: plot_otda_color_images.ipynb <plot_otda_color_images.ipynb>` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb index 56caa8a..baffef4 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb @@ -1,144 +1,144 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "\n# OT for image color adaptation with mapping estimation\n\n\nOT for domain adaptation with image color adaptation [6] with mapping\nestimation [8].\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),\n 1853-1882.\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n discrete optimal transport\", Neural Information Processing Systems (NIPS),\n 2016.\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Authors: Remi Flamary <remi.flamary@unice.fr>\n# Stanislas Chambon <stan.chambon@gmail.com>\n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary <remi.flamary@unice.fr>\n# Stanislas Chambon <stan.chambon@gmail.com>\n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Domain adaptation for pixel distribution transfer\n-------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\nImage_emd = minmax(mat2im(transp_Xs_emd, I1.shape))\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\nImage_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n\not_mapping_linear = ot.da.MappingTransport(\n mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\nX1tl = ot_mapping_linear.transform(Xs=X1)\nImage_mapping_linear = minmax(mat2im(X1tl, I1.shape))\n\not_mapping_gaussian = ot.da.MappingTransport(\n mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\nX1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping\nImage_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\nImage_emd = minmax(mat2im(transp_Xs_emd, I1.shape))\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\nImage_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n\not_mapping_linear = ot.da.MappingTransport(\n mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\nX1tl = ot_mapping_linear.transform(Xs=X1)\nImage_mapping_linear = minmax(mat2im(X1tl, I1.shape))\n\not_mapping_gaussian = ot.da.MappingTransport(\n mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\nX1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping\nImage_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot original images\n--------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(1, figsize=(6.4, 3))\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\npl.tight_layout()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(6.4, 3))\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\npl.tight_layout()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot pixel values distribution\n------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(2, figsize=(6.4, 5))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(2, figsize=(6.4, 5))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot transformed images\n-----------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(2, figsize=(10, 5))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Im. 1')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Im. 2')\n\npl.subplot(2, 3, 2)\npl.imshow(Image_emd)\npl.axis('off')\npl.title('EmdTransport')\n\npl.subplot(2, 3, 5)\npl.imshow(Image_sinkhorn)\npl.axis('off')\npl.title('SinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(Image_mapping_linear)\npl.axis('off')\npl.title('MappingTransport (linear)')\n\npl.subplot(2, 3, 6)\npl.imshow(Image_mapping_gaussian)\npl.axis('off')\npl.title('MappingTransport (gaussian)')\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "pl.figure(2, figsize=(10, 5))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Im. 1')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Im. 2')\n\npl.subplot(2, 3, 2)\npl.imshow(Image_emd)\npl.axis('off')\npl.title('EmdTransport')\n\npl.subplot(2, 3, 5)\npl.imshow(Image_sinkhorn)\npl.axis('off')\npl.title('SinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(Image_mapping_linear)\npl.axis('off')\npl.title('MappingTransport (linear)')\n\npl.subplot(2, 3, 6)\npl.imshow(Image_mapping_gaussian)\npl.axis('off')\npl.title('MappingTransport (gaussian)')\npl.tight_layout()\n\npl.show()" + ] } - ], + ], "metadata": { "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" } - } + }, + "nbformat": 4, + "nbformat_minor": 0 }
\ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.py b/docs/source/auto_examples/plot_otda_mapping_colors_images.py index 5f1e844..a20eca8 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.py +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.py @@ -77,7 +77,7 @@ Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) # SinkhornTransport ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) ot_sinkhorn.fit(Xs=Xs, Xt=Xt) -transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) ot_mapping_linear = ot.da.MappingTransport( diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst index 8394fb0..2afdc8a 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst @@ -104,7 +104,7 @@ Domain adaptation for pixel distribution transfer # SinkhornTransport ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) + transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) ot_mapping_linear = ot.da.MappingTransport( @@ -132,39 +132,39 @@ Domain adaptation for pixel distribution transfer It. |Loss |Delta loss -------------------------------- - 0|3.680518e+02|0.000000e+00 - 1|3.592439e+02|-2.393116e-02 - 2|3.590632e+02|-5.030248e-04 - 3|3.589698e+02|-2.601358e-04 - 4|3.589118e+02|-1.614977e-04 - 5|3.588724e+02|-1.097608e-04 - 6|3.588436e+02|-8.035205e-05 - 7|3.588215e+02|-6.141923e-05 - 8|3.588042e+02|-4.832627e-05 - 9|3.587902e+02|-3.909574e-05 - 10|3.587786e+02|-3.225418e-05 - 11|3.587688e+02|-2.712592e-05 - 12|3.587605e+02|-2.314041e-05 - 13|3.587534e+02|-1.991287e-05 - 14|3.587471e+02|-1.744348e-05 - 15|3.587416e+02|-1.544523e-05 - 16|3.587367e+02|-1.364654e-05 - 17|3.587323e+02|-1.230435e-05 - 18|3.587284e+02|-1.093370e-05 - 19|3.587276e+02|-2.052728e-06 + 0|3.680534e+02|0.000000e+00 + 1|3.592501e+02|-2.391854e-02 + 2|3.590682e+02|-5.061555e-04 + 3|3.589745e+02|-2.610227e-04 + 4|3.589167e+02|-1.611644e-04 + 5|3.588768e+02|-1.109242e-04 + 6|3.588482e+02|-7.972733e-05 + 7|3.588261e+02|-6.166174e-05 + 8|3.588086e+02|-4.871697e-05 + 9|3.587946e+02|-3.919056e-05 + 10|3.587830e+02|-3.228124e-05 + 11|3.587731e+02|-2.744744e-05 + 12|3.587648e+02|-2.334451e-05 + 13|3.587576e+02|-1.995629e-05 + 14|3.587513e+02|-1.761058e-05 + 15|3.587457e+02|-1.542568e-05 + 16|3.587408e+02|-1.366315e-05 + 17|3.587365e+02|-1.221732e-05 + 18|3.587325e+02|-1.102488e-05 + 19|3.587303e+02|-6.062107e-06 It. |Loss |Delta loss -------------------------------- - 0|3.784758e+02|0.000000e+00 - 1|3.646352e+02|-3.656911e-02 - 2|3.642861e+02|-9.574714e-04 - 3|3.641523e+02|-3.672061e-04 - 4|3.640788e+02|-2.020990e-04 - 5|3.640321e+02|-1.282701e-04 - 6|3.640002e+02|-8.751240e-05 - 7|3.639765e+02|-6.521203e-05 - 8|3.639582e+02|-5.007767e-05 - 9|3.639439e+02|-3.938917e-05 - 10|3.639323e+02|-3.187865e-05 + 0|3.784871e+02|0.000000e+00 + 1|3.646491e+02|-3.656142e-02 + 2|3.642975e+02|-9.642655e-04 + 3|3.641626e+02|-3.702413e-04 + 4|3.640888e+02|-2.026301e-04 + 5|3.640419e+02|-1.289607e-04 + 6|3.640097e+02|-8.831646e-05 + 7|3.639861e+02|-6.487612e-05 + 8|3.639679e+02|-4.994063e-05 + 9|3.639536e+02|-3.941436e-05 + 10|3.639419e+02|-3.209753e-05 Plot original images @@ -283,11 +283,13 @@ Plot transformed images -**Total running time of the script:** ( 2 minutes 52.212 seconds) +**Total running time of the script:** ( 3 minutes 14.206 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -300,6 +302,9 @@ Plot transformed images :download:`Download Jupyter notebook: plot_otda_mapping_colors_images.ipynb <plot_otda_mapping_colors_images.ipynb>` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ diff --git a/docs/source/auto_examples/plot_stochastic.ipynb b/docs/source/auto_examples/plot_stochastic.ipynb index c6f0013..7f6ff3d 100644 --- a/docs/source/auto_examples/plot_stochastic.ipynb +++ b/docs/source/auto_examples/plot_stochastic.ipynb @@ -33,25 +33,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM\n############################################################################\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(\"------------SEMI-DUAL PROBLEM------------\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "DISCRETE CASE\nSample two discrete measures for the discrete case\n---------------------------------------------\n\nDefine 2 discrete measures a and b, the points where are defined the source\nand the target measures and finally the cost matrix c.\n\n" + "COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM\n############################################################################\n############################################################################\n DISCRETE CASE:\n\n Sample two discrete measures for the discrete case\n ---------------------------------------------\n\n Define 2 discrete measures a and b, the points where are defined the source\n and the target measures and finally the cost matrix c.\n\n" ] }, { @@ -87,7 +69,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "SEMICONTINOUS CASE\nSample one general measure a, one discrete measures b for the semicontinous\ncase\n---------------------------------------------\n\nDefine one general measure a, one discrete measures b, the points where\nare defined the source and the target measures and finally the cost matrix c.\n\n" + "SEMICONTINOUS CASE:\n\nSample one general measure a, one discrete measures b for the semicontinous\ncase\n---------------------------------------------\n\nDefine one general measure a, one discrete measures b, the points where\nare defined the source and the target measures and finally the cost matrix c.\n\n" ] }, { @@ -202,25 +184,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM\n############################################################################\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(\"------------DUAL PROBLEM------------\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "SEMICONTINOUS CASE\nSample one general measure a, one discrete measures b for the semicontinous\ncase\n---------------------------------------------\n\nDefine one general measure a, one discrete measures b, the points where\nare defined the source and the target measures and finally the cost matrix c.\n\n" + "COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM\n############################################################################\n############################################################################\n SEMICONTINOUS CASE:\n\n Sample one general measure a, one discrete measures b for the semicontinous\n case\n ---------------------------------------------\n\n Define one general measure a, one discrete measures b, the points where\n are defined the source and the target measures and finally the cost matrix c.\n\n" ] }, { @@ -323,7 +287,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.7" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_stochastic.py b/docs/source/auto_examples/plot_stochastic.py index b9375d4..742f8d9 100644 --- a/docs/source/auto_examples/plot_stochastic.py +++ b/docs/source/auto_examples/plot_stochastic.py @@ -21,9 +21,9 @@ import ot.plot ############################################################################# # COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM ############################################################################# -print("------------SEMI-DUAL PROBLEM------------") ############################################################################# -# DISCRETE CASE +# DISCRETE CASE: +# # Sample two discrete measures for the discrete case # --------------------------------------------- # @@ -57,7 +57,8 @@ sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, print(sag_pi) ############################################################################# -# SEMICONTINOUS CASE +# SEMICONTINOUS CASE: +# # Sample one general measure a, one discrete measures b for the semicontinous # case # --------------------------------------------- @@ -139,9 +140,9 @@ pl.show() ############################################################################# # COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM ############################################################################# -print("------------DUAL PROBLEM------------") ############################################################################# -# SEMICONTINOUS CASE +# SEMICONTINOUS CASE: +# # Sample one general measure a, one discrete measures b for the semicontinous # case # --------------------------------------------- diff --git a/docs/source/auto_examples/plot_stochastic.rst b/docs/source/auto_examples/plot_stochastic.rst index a49bc05..d531045 100644 --- a/docs/source/auto_examples/plot_stochastic.rst +++ b/docs/source/auto_examples/plot_stochastic.rst @@ -34,29 +34,14 @@ algorithms for descrete and semicontinous measures from the POT library. COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM ############################################################################ +############################################################################ + DISCRETE CASE: + Sample two discrete measures for the discrete case + --------------------------------------------- - -.. code-block:: python - - print("------------SEMI-DUAL PROBLEM------------") - - - - -.. rst-class:: sphx-glr-script-out - - Out:: - - ------------SEMI-DUAL PROBLEM------------ - - -DISCRETE CASE -Sample two discrete measures for the discrete case ---------------------------------------------- - -Define 2 discrete measures a and b, the points where are defined the source -and the target measures and finally the cost matrix c. + Define 2 discrete measures a and b, the points where are defined the source + and the target measures and finally the cost matrix c. @@ -115,7 +100,8 @@ results. [4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]] -SEMICONTINOUS CASE +SEMICONTINOUS CASE: + Sample one general measure a, one discrete measures b for the semicontinous case --------------------------------------------- @@ -174,15 +160,15 @@ results. Out:: - [3.9018759 7.63059124 3.93260224 2.67274989 1.43888443 3.26904884 - 2.78748299] [-2.48511647 -2.43621119 -0.93585194 5.8571796 ] - [[2.56614773e-02 9.96758169e-02 1.75151781e-02 4.67049862e-06] - [1.21201047e-01 1.24433535e-02 1.28173754e-03 7.93100436e-03] - [3.58778167e-03 7.64232233e-02 6.28459924e-02 1.45441936e-07] - [2.63551754e-02 3.35577920e-02 8.25011211e-02 4.43054320e-04] - [9.24518246e-03 7.03074064e-04 1.00325744e-02 1.22876312e-01] - [2.03656325e-02 8.45420425e-04 1.73604569e-03 1.19910044e-01] - [4.17781688e-02 2.66463708e-02 7.18353075e-02 2.59729583e-03]] + [3.98220325 7.76235856 3.97645524 2.72051681 1.23219313 3.07696856 + 2.84476972] [-2.65544161 -2.50838395 -0.9397765 6.10360206] + [[2.34528761e-02 1.00491956e-01 1.89058354e-02 6.47543413e-06] + [1.16616747e-01 1.32074516e-02 1.45653361e-03 1.15764107e-02] + [3.16154850e-03 7.42892944e-02 6.54061055e-02 1.94426150e-07] + [2.33152216e-02 3.27486992e-02 8.61986263e-02 5.94595747e-04] + [6.34131496e-03 5.31975896e-04 8.12724003e-03 1.27856612e-01] + [1.41744829e-02 6.49096245e-04 1.42704389e-03 1.26606520e-01] + [3.73127657e-02 2.62526499e-02 7.57727161e-02 3.51901117e-03]] Compare the results with the Sinkhorn algorithm @@ -288,30 +274,15 @@ Plot Sinkhorn results COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM ############################################################################ +############################################################################ + SEMICONTINOUS CASE: + Sample one general measure a, one discrete measures b for the semicontinous + case + --------------------------------------------- - -.. code-block:: python - - print("------------DUAL PROBLEM------------") - - - - -.. rst-class:: sphx-glr-script-out - - Out:: - - ------------DUAL PROBLEM------------ - - -SEMICONTINOUS CASE -Sample one general measure a, one discrete measures b for the semicontinous -case ---------------------------------------------- - -Define one general measure a, one discrete measures b, the points where -are defined the source and the target measures and finally the cost matrix c. + Define one general measure a, one discrete measures b, the points where + are defined the source and the target measures and finally the cost matrix c. @@ -365,15 +336,15 @@ Call ot.solve_dual_entropic and plot the results. Out:: - [ 1.29325617 5.0435082 1.30996326 0.05538236 -1.08113283 0.73711558 - 0.18086364] [0.08840343 0.17710082 1.68604226 8.37377551] - [[2.47763879e-02 1.00144623e-01 1.77492330e-02 4.25988443e-06] - [1.19568278e-01 1.27740478e-02 1.32714202e-03 7.39121816e-03] - [3.41581121e-03 7.57137404e-02 6.27992039e-02 1.30808430e-07] - [2.52245323e-02 3.34219732e-02 8.28754229e-02 4.00582912e-04] - [9.75329554e-03 7.71824343e-04 1.11085400e-02 1.22456628e-01] - [2.12304276e-02 9.17096580e-04 1.89946234e-03 1.18084973e-01] - [4.04179693e-02 2.68253041e-02 7.29410047e-02 2.37369404e-03]] + [0.92449986 2.75486107 1.07923806 0.02741145 0.61355413 1.81961594 + 0.12072562] [0.33831611 0.46806842 1.5640451 4.96947652] + [[2.20001105e-02 9.26497883e-02 1.08654588e-02 9.78995555e-08] + [1.55669974e-02 1.73279561e-03 1.19120878e-04 2.49058251e-05] + [3.48198483e-03 8.04151063e-02 4.41335396e-02 3.45115752e-09] + [3.14927954e-02 4.34760520e-02 7.13338154e-02 1.29442395e-05] + [6.81836550e-02 5.62182457e-03 5.35386584e-02 2.21568095e-02] + [8.04671052e-02 3.62163462e-03 4.96331605e-03 1.15837801e-02] + [4.88644009e-02 3.37903481e-02 6.07955004e-02 7.42743505e-05]] Compare the results with the Sinkhorn algorithm @@ -448,7 +419,7 @@ Plot Sinkhorn results -**Total running time of the script:** ( 0 minutes 22.857 seconds) +**Total running time of the script:** ( 0 minutes 20.889 seconds) diff --git a/docs/source/conf.py b/docs/source/conf.py index 433eca6..d29b829 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -15,7 +15,10 @@ import sys import os import re -import sphinx_gallery +try: + import sphinx_gallery +except ImportError: + print("warning sphinx-gallery not installed") # !!!! allow readthedoc compilation try: @@ -65,6 +68,8 @@ extensions = [ #'sphinx_gallery.gen_gallery', ] +napoleon_numpy_docstring = True + # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] @@ -81,7 +86,7 @@ master_doc = 'index' # General information about the project. project = u'POT Python Optimal Transport' -copyright = u'2016-2018, Rémi Flamary, Nicolas Courty' +copyright = u'2016-2019, Rémi Flamary, Nicolas Courty' author = u'Rémi Flamary, Nicolas Courty' # The version info for the project you're documenting, acts as replacement for @@ -323,7 +328,10 @@ texinfo_documents = [ # Example configuration for intersphinx: refer to the Python standard library. -intersphinx_mapping = {'https://docs.python.org/': None} +intersphinx_mapping = {'python': ('https://docs.python.org/3', None), + 'numpy': ('http://docs.scipy.org/doc/numpy/', None), + 'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None), + 'matplotlib': ('http://matplotlib.sourceforge.net/', None)} sphinx_gallery_conf = { 'examples_dirs': ['../../examples','../../examples/da'], diff --git a/docs/source/index.rst b/docs/source/index.rst index b8eabcb..9078d35 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -10,9 +10,10 @@ Contents -------- .. toctree:: - :maxdepth: 3 + :maxdepth: 2 self + quickstart all auto_examples/index diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst new file mode 100644 index 0000000..978eaff --- /dev/null +++ b/docs/source/quickstart.rst @@ -0,0 +1,923 @@ + +Quick start guide +================= + +In the following we provide some pointers about which functions and classes +to use for different problems related to optimal transport (OT) and machine +learning. We refer when we can to concrete examples in the documentation that +are also available as notebooks on the POT Github. + +This document is not a tutorial on numerical optimal transport. For this we strongly +recommend to read the very nice book [15]_ . + + +Optimal transport and Wasserstein distance +------------------------------------------ + +.. note:: + In POT, most functions that solve OT or regularized OT problems have two + versions that return the OT matrix or the value of the optimal solution. For + instance :any:`ot.emd` return the OT matrix and :any:`ot.emd2` return the + Wassertsein distance. This approach has been implemented in practice for all + solvers that return an OT matrix (even Gromov-Wasserstsein) + +Solving optimal transport +^^^^^^^^^^^^^^^^^^^^^^^^^ + +The optimal transport problem between discrete distributions is often expressed +as + +.. math:: + \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 + +where : + +- :math:`M\in\mathbb{R}_+^{m\times n}` is the metric cost matrix defining the cost to move mass from bin :math:`a_i` to bin :math:`b_j`. +- :math:`a` and :math:`b` are histograms on the simplex (positive, sum to 1) that represent the +weights of each samples in the source an target distributions. + +Solving the linear program above can be done using the function :any:`ot.emd` +that will return the optimal transport matrix :math:`\gamma^*`: + +.. code:: python + + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + T=ot.emd(a,b,M) # exact linear program + +The method implemented for solving the OT problem is the network simplex, it is +implemented in C from [1]_. It has a complexity of :math:`O(n^3)` but the +solver is quite efficient and uses sparsity of the solution. + +.. hint:: + Examples of use for :any:`ot.emd` are available in : + + - :any:`auto_examples/plot_OT_2D_samples` + - :any:`auto_examples/plot_OT_1D` + - :any:`auto_examples/plot_OT_L1_vs_L2` + + +Computing Wasserstein distance +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The value of the OT solution is often more of interest than the OT matrix : + +.. math:: + OT(a,b)=\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 + + +It can computed from an already estimated OT matrix with +:code:`np.sum(T*M)` or directly with the function :any:`ot.emd2`. + +.. code:: python + + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + W=ot.emd2(a,b,M) # Wasserstein distance / EMD value + +Note that the well known `Wasserstein distance +<https://en.wikipedia.org/wiki/Wasserstein_metric>`_ between distributions a and +b is defined as + + + .. math:: + + W_p(a,b)=(\min_\gamma \sum_{i,j}\gamma_{i,j}\|x_i-y_j\|_p)^\frac{1}{p} + + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 + +This means that if you want to compute the :math:`W_2` you need to compute the +square root of :any:`ot.emd2` when providing +:code:`M=ot.dist(xs,xt)` that use the squared euclidean distance by default. Computing +the :math:`W_1` wasserstein distance can be done directly with :any:`ot.emd2` +when providing :code:`M=ot.dist(xs,xt, metric='euclidean')` to use the euclidean +distance. + + +.. hint:: + An example of use for :any:`ot.emd2` is available in : + + - :any:`auto_examples/plot_compute_emd` + + +Special cases +^^^^^^^^^^^^^ + +Note that the OT problem and the corresponding Wasserstein distance can in some +special cases be computed very efficiently. + +For instance when the samples are in 1D, then the OT problem can be solved in +:math:`O(n\log(n))` by using a simple sorting. In this case we provide the +function :any:`ot.emd_1d` and :any:`ot.emd2_1d` to return respectively the OT +matrix and value. Note that since the solution is very sparse the :code:`sparse` +parameter of :any:`ot.emd_1d` allows for solving and returning the solution for +very large problems. Note that in order to compute directly the :math:`W_p` +Wasserstein distance in 1D we provide the function :any:`ot.wasserstein_1d` that +takes :code:`p` as a parameter. + +Another special case for estimating OT and Monge mapping is between Gaussian +distributions. In this case there exists a close form solution given in Remark +2.29 in [15]_ and the Monge mapping is an affine function and can be +also computed from the covariances and means of the source and target +distributions. In the case when the finite sample dataset is supposed gaussian, we provide +:any:`ot.da.OT_mapping_linear` that returns the parameters for the Monge +mapping. + + +Regularized Optimal Transport +----------------------------- + +Recent developments have shown the interest of regularized OT both in terms of +computational and statistical properties. +We address in this section the regularized OT problems that can be expressed as + +.. math:: + \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + \lambda\Omega(\gamma) + + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 + + +where : + +- :math:`M\in\mathbb{R}_+^{m\times n}` is the metric cost matrix defining the cost to move mass from bin :math:`a_i` to bin :math:`b_j`. +- :math:`a` and :math:`b` are histograms (positive, sum to 1) that represent the weights of each samples in the source an target distributions. +- :math:`\Omega` is the regularization term. + +We discuss in the following specific algorithms that can be used depending on +the regularization term. + + +Entropic regularized OT +^^^^^^^^^^^^^^^^^^^^^^^ + +This is the most common regularization used for optimal transport. It has been +proposed in the ML community by Marco Cuturi in his seminal paper [2]_. This +regularization has the following expression + +.. math:: + \Omega(\gamma)=\sum_{i,j}\gamma_{i,j}\log(\gamma_{i,j}) + + +The use of the regularization term above in the optimization problem has a very +strong impact. First it makes the problem smooth which leads to new optimization +procedures such as the well known Sinkhorn algorithm [2]_ or L-BFGS (see +:any:`ot.smooth` ). Next it makes the problem +strictly convex meaning that there will be a unique solution. Finally the +solution of the resulting optimization problem can be expressed as: + +.. math:: + + \gamma_\lambda^*=\text{diag}(u)K\text{diag}(v) + +where :math:`u` and :math:`v` are vectors and :math:`K=\exp(-M/\lambda)` where +the :math:`\exp` is taken component-wise. In order to solve the optimization +problem, on can use an alternative projection algorithm called Sinkhorn-Knopp that can be very +efficient for large values if regularization. + +The Sinkhorn-Knopp algorithm is implemented in :any:`ot.sinkhorn` and +:any:`ot.sinkhorn2` that return respectively the OT matrix and the value of the +linear term. Note that the regularization parameter :math:`\lambda` in the +equation above is given to those functions with the parameter :code:`reg`. + + >>> import ot + >>> a=[.5,.5] + >>> b=[.5,.5] + >>> M=[[0.,1.],[1.,0.]] + >>> ot.sinkhorn(a,b,M,1) + array([[ 0.36552929, 0.13447071], + [ 0.13447071, 0.36552929]]) + +More details about the algorithms used are given in the following note. + +.. note:: + The main function to solve entropic regularized OT is :any:`ot.sinkhorn`. + This function is a wrapper and the parameter :code:`method` help you select + the actual algorithm used to solve the problem: + + + :code:`method='sinkhorn'` calls :any:`ot.bregman.sinkhorn_knopp` the + classic algorithm [2]_. + + :code:`method='sinkhorn_stabilized'` calls :any:`ot.bregman.sinkhorn_stabilized` the + log stabilized version of the algorithm [9]_. + + :code:`method='sinkhorn_epsilon_scaling'` calls + :any:`ot.bregman.sinkhorn_epsilon_scaling` the epsilon scaling version + of the algorithm [9]_. + + :code:`method='greenkhorn'` calls :any:`ot.bregman.greenkhorn` the + greedy sinkhorn verison of the algorithm [22]_. + + In addition to all those variants of sinkhorn, we have another + implementation solving the problem in the smooth dual or semi-dual in + :any:`ot.smooth`. This solver uses the :any:`scipy.optimize.minimize` + function to solve the smooth problem with :code:`L-BFGS-B` algorithm. Tu use + this solver, use functions :any:`ot.smooth.smooth_ot_dual` or + :any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='kl'` to + choose entropic/Kullbach Leibler regularization. + + +Recently [23]_ introduced the sinkhorn divergence that build from entropic +regularization to compute fast and differentiable geometric divergence between +empirical distributions. Note that we provide a function that compute directly +(with no need to pre compute the :code:`M` matrix) +the sinkhorn divergence for empirical distributions in +:any:`ot.bregman.empirical_sinkhorn_divergence`. Similarly one can compute the +OT matrix and loss for empirical distributions with respectively +:any:`ot.bregman.empirical_sinkhorn` and :any:`ot.bregman.empirical_sinkhorn2`. + + +Finally note that we also provide in :any:`ot.stochastic` several implementation +of stochastic solvers for entropic regularized OT [18]_ [19]_. Those pure Python +implementations are not optimized for speed but provide a roust implementation +of algorithms in [18]_ [19]_. + +.. hint:: + Examples of use for :any:`ot.sinkhorn` are available in : + + - :any:`auto_examples/plot_OT_2D_samples` + - :any:`auto_examples/plot_OT_1D` + - :any:`auto_examples/plot_OT_1D_smooth` + - :any:`auto_examples/plot_stochastic` + + +Other regularization +^^^^^^^^^^^^^^^^^^^^ + +While entropic OT is the most common and favored in practice, there exist other +kind of regularization. We provide in POT two specific solvers for other +regularization terms, namely quadratic regularization and group lasso +regularization. But we also provide in :any:`ot.optim` two generic solvers that allows solving any +smooth regularization in practice. + +Quadratic regularization +"""""""""""""""""""""""" + +The first general regularization term we can solve is the quadratic +regularization of the form + +.. math:: + \Omega(\gamma)=\sum_{i,j} \gamma_{i,j}^2 + +this regularization term has a similar effect to entropic regularization in +densifying the OT matrix but it keeps some sort of sparsity that is lost with +entropic regularization as soon as :math:`\lambda>0` [17]_. This problem can be +solved with POT using solvers from :any:`ot.smooth`, more specifically +functions :any:`ot.smooth.smooth_ot_dual` or +:any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='l2'` to +choose the quadratic regularization. + +.. hint:: + Examples of quadratic regularization are available in : + + - :any:`auto_examples/plot_OT_1D_smooth` + - :any:`auto_examples/plot_optim_OTreg` + + + +Group Lasso regularization +"""""""""""""""""""""""""" + +Another regularization that has been used in recent years [5]_ is the group lasso +regularization + +.. math:: + \Omega(\gamma)=\sum_{j,G\in\mathcal{G}} \|\gamma_{G,j}\|_q^p + +where :math:`\mathcal{G}` contains non overlapping groups of lines in the OT +matrix. This regularization proposed in [5]_ will promote sparsity at the group level and for +instance will force target samples to get mass from a small number of groups. +Note that the exact OT solution is already sparse so this regularization does +not make sens if it is not combined with entropic regularization. Depending on +the choice of :code:`p` and :code:`q`, the problem can be solved with different +approaches. When :code:`q=1` and :code:`p<1` the problem is non convex but can +be solved using an efficient majoration minimization approach with +:any:`ot.sinkhorn_lpl1_mm`. When :code:`q=2` and :code:`p=1` we recover the +convex group lasso and we provide a solver using generalized conditional +gradient algorithm [7]_ in function +:any:`ot.da.sinkhorn_l1l2_gl`. + +.. hint:: + Examples of group Lasso regularization are available in : + + - :any:`auto_examples/plot_otda_classes` + - :any:`auto_examples/plot_otda_d2` + + +Generic solvers +""""""""""""""" + +Finally we propose in POT generic solvers that can be used to solve any +regularization as long as you can provide a function computing the +regularization and a function computing its gradient (or sub-gradient). + +In order to solve + +.. math:: + \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + \lambda\Omega(\gamma) + + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 + +you can use function :any:`ot.optim.cg` that will use a conditional gradient as +proposed in [6]_ . You need to provide the regularization function as parameter +``f`` and its gradient as parameter ``df``. Note that the conditional gradient relies on +iterative solving of a linearization of the problem using the exact +:any:`ot.emd` so it can be slow in practice. But, being an interior point +algorithm, it always returns a +transport matrix that does not violates the marginals. + +Another generic solver is proposed to solve the problem + +.. math:: + \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j}+ \lambda_e\Omega_e(\gamma) + \lambda\Omega(\gamma) + + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 + +where :math:`\Omega_e` is the entropic regularization. In this case we use a +generalized conditional gradient [7]_ implemented in :any:`ot.optim.gcg` that +does not linearize the entropic term but +relies on :any:`ot.sinkhorn` for its iterations. + +.. hint:: + An example of generic solvers are available in : + + - :any:`auto_examples/plot_optim_OTreg` + + +Wasserstein Barycenters +----------------------- + +A Wasserstein barycenter is a distribution that minimize its Wasserstein +distance with respect to other distributions [16]_. It corresponds to minimizing the +following problem by searching a distribution :math:`\mu` such that + +.. math:: + \min_\mu \quad \sum_{k} w_kW(\mu,\mu_k) + + +In practice we model a distribution with a finite number of support position: + +.. math:: + \mu=\sum_{i=1}^n a_i\delta_{x_i} + +where :math:`a` is an histogram on the simplex and the :math:`\{x_i\}` are the +position of the support. We can clearly see here that optimizing :math:`\mu` can +be done by searching for optimal weights :math:`a` or optimal support +:math:`\{x_i\}` (optimizing both is also an option). +We provide in POT solvers to estimate a discrete +Wasserstein barycenter in both cases. + +Barycenters with fixed support +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +When optimizing a barycenter with a fixed support, the optimization problem can +be expressed as + +.. math:: + \min_a \quad \sum_{k} w_k W(a,b_k) + +where :math:`b_k` are also weights in the simplex. In the non-regularized case, +the problem above is a classical linear program. In this case we propose a +solver :any:`ot.lp.barycenter` that rely on generic LP solvers. By default the +function uses :any:`scipy.optimize.linprog`, but more efficient LP solvers from +cvxopt can be also used by changing parameter :code:`solver`. Note that this problem +requires to solve a very large linear program and can be very slow in +practice. + +Similarly to the OT problem, OT barycenters can be computed in the regularized +case. When using entropic regularization is used, the problem can be solved with a +generalization of the sinkhorn algorithm based on bregman projections [3]_. This +algorithm is provided in function :any:`ot.bregman.barycenter` also available as +:any:`ot.barycenter`. In this case, the algorithm scales better to large +distributions and rely only on matrix multiplications that can be performed in +parallel. + +In addition to the speedup brought by regularization, one can also greatly +accelerate the estimation of Wasserstein barycenter when the support has a +separable structure [21]_. In the case of 2D images for instance one can replace +the matrix vector production in the Bregman projections by convolution +operators. We provide an implementation of this algorithm in function +:any:`ot.bregman.convolutional_barycenter2d`. + +.. hint:: + Examples of Wasserstein (:any:`ot.lp.barycenter`) and regularized Wasserstein + barycenter (:any:`ot.bregman.barycenter`) computation are available in : + + - :any:`auto_examples/plot_barycenter_1D` + - :any:`auto_examples/plot_barycenter_lp_vs_entropic` + + An example of convolutional barycenter + (:any:`ot.bregman.convolutional_barycenter2d`) computation + for 2D images is available + in : + + - :any:`auto_examples/plot_convolutional_barycenter` + + + +Barycenters with free support +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Estimating the Wasserstein barycenter with free support but fixed weights +corresponds to solving the following optimization problem: + +.. math:: + \min_{\{x_i\}} \quad \sum_{k} w_kW(\mu,\mu_k) + + s.t. \quad \mu=\sum_{i=1}^n a_i\delta_{x_i} + +We provide a solver based on [20]_ in +:any:`ot.lp.free_support_barycenter`. This function minimize the problem and +return a locally optimal support :math:`\{x_i\}` for uniform or given weights +:math:`a`. + + .. hint:: + + An example of the free support barycenter estimation is available + in : + + - :any:`auto_examples/plot_free_support_barycenter` + + + + +Monge mapping and Domain adaptation +----------------------------------- + +The original transport problem investigated by Gaspard Monge was seeking for a +mapping function that maps (or transports) between a source and target +distribution but that minimizes the transport loss. The existence and uniqueness of this +optimal mapping is still an open problem in the general case but has been proven +for smooth distributions by Brenier in his eponym `theorem +<https://who.rocq.inria.fr/Jean-David.Benamou/demiheure.pdf>`__. We provide in +:any:`ot.da` several solvers for smooth Monge mapping estimation and domain +adaptation from discrete distributions. + +Monge Mapping estimation +^^^^^^^^^^^^^^^^^^^^^^^^ + +We now discuss several approaches that are implemented in POT to estimate or +approximate a Monge mapping from finite distributions. + +First note that when the source and target distributions are supposed to be Gaussian +distributions, there exists a close form solution for the mapping and its an +affine function [14]_ of the form :math:`T(x)=Ax+b` . In this case we provide the function +:any:`ot.da.OT_mapping_linear` that return the operator :math:`A` and vector +:math:`b`. Note that if the number of samples is too small there is a parameter +:code:`reg` that provide a regularization for the covariance matrix estimation. + +For a more general mapping estimation we also provide the barycentric mapping +proposed in [6]_ . It is implemented in the class :any:`ot.da.EMDTransport` and +other transport based classes in :any:`ot.da` . Those classes are discussed more +in the following but follow an interface similar to sklearn classes. Finally a +method proposed in [8]_ that estimates a continuous mapping approximating the +barycentric mapping is provided in :any:`ot.da.joint_OT_mapping_linear` for +linear mapping and :any:`ot.da.joint_OT_mapping_kernel` for non linear mapping. + + .. hint:: + + An example of the linear Monge mapping estimation is available + in : + + - :any:`auto_examples/plot_otda_linear_mapping` + +Domain adaptation classes +^^^^^^^^^^^^^^^^^^^^^^^^^ + +The use of OT for domain adaptation (OTDA) has been first proposed in [5]_ that also +introduced the group Lasso regularization. The main idea of OTDA is to estimate +a mapping of the samples between source and target distributions which allows to +transport labeled source samples onto the target distribution with no labels. + +We provide several classes based on :any:`ot.da.BaseTransport` that provide +several OT and mapping estimations. The interface of those classes is similar to +classifiers in sklearn toolbox. At initialization, several parameters such as + regularization parameter value can be set. Then one needs to estimate the +mapping with function :any:`ot.da.BaseTransport.fit`. Finally one can map the +samples from source to target with :any:`ot.da.BaseTransport.transform` and +from target to source with :any:`ot.da.BaseTransport.inverse_transform`. + +Here is +an example for class :any:`ot.da.EMDTransport` : + +.. code:: + + ot_emd = ot.da.EMDTransport() + ot_emd.fit(Xs=Xs, Xt=Xt) + + Mapped_Xs= ot_emd.transform(Xs=Xs) + +A list of the provided implementation is given in the following note. + +.. note:: + + Here is a list of the OT mapping classes inheriting from + :any:`ot.da.BaseTransport` + + * :any:`ot.da.EMDTransport` : Barycentric mapping with EMD transport + * :any:`ot.da.SinkhornTransport` : Barycentric mapping with Sinkhorn transport + * :any:`ot.da.SinkhornL1l2Transport` : Barycentric mapping with Sinkhorn + + group Lasso regularization [5]_ + * :any:`ot.da.SinkhornLpl1Transport` : Barycentric mapping with Sinkhorn + + non convex group Lasso regularization [5]_ + * :any:`ot.da.LinearTransport` : Linear mapping estimation between Gaussians + [14]_ + * :any:`ot.da.MappingTransport` : Nonlinear mapping estimation [8]_ + +.. hint:: + + Example of the use of OTDA classes are available in : + + - :any:`auto_examples/plot_otda_color_images` + - :any:`auto_examples/plot_otda_mapping` + - :any:`auto_examples/plot_otda_mapping_colors_images` + - :any:`auto_examples/plot_otda_semi_supervised` + +Other applications +------------------ + +We discuss in the following several OT related problems and tools that has been +proposed in the OT and machine learning community. + +Wasserstein Discriminant Analysis +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Wasserstein Discriminant Analysis [11]_ is a generalization of `Fisher Linear Discriminant +Analysis <https://en.wikipedia.org/wiki/Linear_discriminant_analysis>`__ that +allows discrimination between classes that are not linearly separable. It +consist in finding a linear projector optimizing the following criterion + +.. math:: + P = \text{arg}\min_P \frac{\sum_i OT_e(\mu_i\#P,\mu_i\#P)}{\sum_{i,j\neq i} + OT_e(\mu_i\#P,\mu_j\#P)} + +where :math:`\#` is the push-forward operator, :math:`OT_e` is the entropic OT +loss and :math:`\mu_i` is the +distribution of samples from class :math:`i`. :math:`P` is also constrained to +be in the Stiefel manifold. WDA can be solved in POT using function +:any:`ot.dr.wda`. It requires to have installed :code:`pymanopt` and +:code:`autograd` for manifold optimization and automatic differentiation +respectively. Note that we also provide the Fisher discriminant estimator in +:any:`ot.dr.fda` for easy comparison. + +.. warning:: + Note that due to the hard dependency on :code:`pymanopt` and + :code:`autograd`, :any:`ot.dr` is not imported by default. If you want to + use it you have to specifically import it with :code:`import ot.dr` . + +.. hint:: + + An example of the use of WDA is available in : + + - :any:`auto_examples/plot_WDA` + + +Unbalanced optimal transport +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Unbalanced OT is a relaxation of the entropy regularized OT problem where the violation of +the constraint on the marginals is added to the objective of the optimization +problem. The unbalanced OT metric between two unbalanced histograms a and b is defined as [25]_ [10]_: + +.. math:: + W_u(a, b) = \min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + reg\cdot\Omega(\gamma) + reg_m KL(\gamma 1, a) + reg_m KL(\gamma^T 1, b) + + s.t. \quad \gamma\geq 0 + + +where KL is the Kullback-Leibler divergence. This formulation allows for +computing approximate mapping between distributions that do not have the same +amount of mass. Interestingly the problem can be solved with a generalization of +the Bregman projections algorithm [10]_. We provide a solver for unbalanced OT +in :any:`ot.unbalanced`. Computing the optimal transport +plan or the transport cost is similar to the balanced case. The Sinkhorn-Knopp +algorithm is implemented in :any:`ot.sinkhorn_unbalanced` and :any:`ot.sinkhorn_unbalanced2` +that return respectively the OT matrix and the value of the +linear term. + +.. note:: + The main function to solve entropic regularized UOT is :any:`ot.sinkhorn_unbalanced`. + This function is a wrapper and the parameter :code:`method` helps you select + the actual algorithm used to solve the problem: + + + :code:`method='sinkhorn'` calls :any:`ot.unbalanced.sinkhorn_knopp_unbalanced` + the generalized Sinkhorn algorithm [25]_ [10]_. + + :code:`method='sinkhorn_stabilized'` calls :any:`ot.unbalanced.sinkhorn_stabilized_unbalanced` + the log stabilized version of the algorithm [10]_. + + +.. hint:: + + Examples of the use of :any:`ot.sinkhorn_unbalanced` are available in : + + - :any:`auto_examples/plot_UOT_1D` + + +Unbalanced Barycenters +^^^^^^^^^^^^^^^^^^^^^^ + +As with balanced distributions, we can define a barycenter of a set of +histograms with different masses as a Fréchet Mean: + + .. math:: + \min_{\mu} \quad \sum_{k} w_kW_u(\mu,\mu_k) + +Where :math:`W_u` is the unbalanced Wasserstein metric defined above. This problem +can also be solved using generalized version of Sinkhorn's algorithm and it is +implemented the main function :any:`ot.barycenter_unbalanced`. + + +.. note:: + The main function to compute UOT barycenters is :any:`ot.barycenter_unbalanced`. + This function is a wrapper and the parameter :code:`method` help you select + the actual algorithm used to solve the problem: + + + :code:`method='sinkhorn'` calls :any:`ot.unbalanced.barycenter_unbalanced_sinkhorn_unbalanced` + the generalized Sinkhorn algorithm [10]_. + + :code:`method='sinkhorn_stabilized'` calls :any:`ot.unbalanced.barycenter_unbalanced_stabilized` + the log stabilized version of the algorithm [10]_. + + +.. hint:: + + Examples of the use of :any:`ot.barycenter_unbalanced` are available in : + + - :any:`auto_examples/plot_UOT_barycenter_1D` + + +Gromov-Wasserstein +^^^^^^^^^^^^^^^^^^ + +Gromov Wasserstein (GW) is a generalization of OT to distributions that do not lie in +the same space [13]_. In this case one cannot compute distance between samples +from the two distributions. [13]_ proposed instead to realign the metric spaces +by computing a transport between distance matrices. The Gromow Wasserstein +alignement between two distributions can be expressed as the one minimizing: + +.. math:: + GW = \min_\gamma \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*\gamma_{i,j}*\gamma_{k,l} + + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 + +where ::math:`C1` is the distance matrix between samples in the source +distribution and :math:`C2` the one between samples in the target, +:math:`L(C1_{i,k},C2_{j,l})` is a measure of similarity between +:math:`C1_{i,k}` and :math:`C2_{j,l}` often chosen as +:math:`L(C1_{i,k},C2_{j,l})=\|C1_{i,k}-C2_{j,l}\|^2`. The optimization problem +above is a non-convex quadratic program but we provide a solver that finds a +local minimum using conditional gradient in :any:`ot.gromov.gromov_wasserstein`. +There also exists an entropic regularized variant of GW that has been proposed in +[12]_ and we provide an implementation of their algorithm in +:any:`ot.gromov.entropic_gromov_wasserstein`. + +Note that similarly to Wasserstein distance GW allows for the definition of GW +barycenters that can be expressed as + +.. math:: + \min_{C\geq 0} \quad \sum_{k} w_k GW(C,Ck) + +where :math:`Ck` is the distance matrix between samples in distribution +:math:`k`. Note that interestingly the barycenter is defined as a symmetric +positive matrix. We provide a block coordinate optimization procedure in +:any:`ot.gromov.gromov_barycenters` and +:any:`ot.gromov.entropic_gromov_barycenters` for non-regularized and regularized +barycenters respectively. + +Finally note that recently a fusion between Wasserstein and GW, coined Fused +Gromov-Wasserstein (FGW) has been proposed +in [24]_. It allows to compute a similarity between objects that are only partly in +the same space. As such it can be used to measure similarity between labeled +graphs for instance and also provide computable barycenters. +The implementations of FGW and FGW barycenter is provided in functions +:any:`ot.gromov.fused_gromov_wasserstein` and :any:`ot.gromov.fgw_barycenters`. + +.. hint:: + + Examples of computation of GW, regularized G and FGW are available in : + + - :any:`auto_examples/plot_gromov` + - :any:`auto_examples/plot_fgw` + + Examples of GW, regularized GW and FGW barycenters are available in : + + - :any:`auto_examples/plot_gromov_barycenter` + - :any:`auto_examples/plot_barycenter_fgw` + + +GPU acceleration +^^^^^^^^^^^^^^^^ + +We provide several implementation of our OT solvers in :any:`ot.gpu`. Those +implementations use the :code:`cupy` toolbox that obviously need to be installed. + + +.. note:: + + Several implementations of POT functions (mainly those relying on linear + algebra) have been implemented in :any:`ot.gpu`. Here is a short list on the + main entries: + + - :any:`ot.gpu.dist` : computation of distance matrix + - :any:`ot.gpu.sinkhorn` : computation of sinkhorn + - :any:`ot.gpu.sinkhorn_lpl1_mm` : computation of sinkhorn + group lasso + +Note that while the :any:`ot.gpu` module has been designed to be compatible with +POT, calling its function with :any:`numpy` arrays will incur a large overhead due to +the memory copy of the array on GPU prior to computation and conversion of the +array after computation. To avoid this overhead, we provide functions +:any:`ot.gpu.to_gpu` and :any:`ot.gpu.to_np` that perform the conversion +explicitly. + + +.. warning:: + Note that due to the hard dependency on :code:`cupy`, :any:`ot.gpu` is not + imported by default. If you want to + use it you have to specifically import it with :code:`import ot.gpu` . + + +FAQ +--- + + + +1. **How to solve a discrete optimal transport problem ?** + + The solver for discrete OT is the function :py:mod:`ot.emd` that returns + the OT transport matrix. If you want to solve a regularized OT you can + use :py:mod:`ot.sinkhorn`. + + + Here is a simple use case: + + .. code:: python + + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + T=ot.emd(a,b,M) # exact linear program + T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT + + More detailed examples can be seen on this example: + :doc:`auto_examples/plot_OT_2D_samples` + + +2. **pip install POT fails with error : ImportError: No module named Cython.Build** + + As discussed shortly in the README file. POT requires to have :code:`numpy` + and :code:`cython` installed to build. This corner case is not yet handled + by :code:`pip` and for now you need to install both library prior to + installing POT. + + Note that this problem do not occur when using conda-forge since the packages + there are pre-compiled. + + See `Issue #59 <https://github.com/rflamary/POT/issues/59>`__ for more + details. + +3. **Why is Sinkhorn slower than EMD ?** + + This might come from the choice of the regularization term. The speed of + convergence of sinkhorn depends directly on this term [22]_ and when the + regularization gets very small the problem try and approximate the exact OT + which leads to slow convergence in addition to numerical problems. In other + words, for large regularization sinkhorn will be very fast to converge, for + small regularization (when you need an OT matrix close to the true OT), it + might be quicker to use the EMD solver. + + Also note that the numpy implementation of the sinkhorn can use parallel + computation depending on the configuration of your system but very important + speedup can be obtained by using a GPU implementation since all operations + are matrix/vector products. + +4. **Using GPU fails with error: module 'ot' has no attribute 'gpu'** + + In order to limit import time and hard dependencies in POT. we do not import + some sub-modules automatically with :code:`import ot`. In order to use the + acceleration in :any:`ot.gpu` you need first to import is with + :code:`import ot.gpu`. + + See `Issue #85 <https://github.com/rflamary/POT/issues/85>`__ and :any:`ot.gpu` + for more details. + + +References +---------- + +.. [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, + December). `Displacement nterpolation using Lagrangian mass transport + <https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf>`__. + In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. + +.. [2] Cuturi, M. (2013). `Sinkhorn distances: Lightspeed computation of + optimal transport <https://arxiv.org/pdf/1306.0895.pdf>`__. In Advances + in Neural Information Processing Systems (pp. 2292-2300). + +.. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. + (2015). `Iterative Bregman projections for regularized transportation + problems <https://arxiv.org/pdf/1412.5154.pdf>`__. SIAM Journal on + Scientific Computing, 37(2), A1111-A1138. + +.. [4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, + `Supervised planetary unmixing with optimal + transport <https://hal.archives-ouvertes.fr/hal-01377236/document>`__, + Whorkshop on Hyperspectral Image and Signal Processing : Evolution in + Remote Sensing (WHISPERS), 2016. + +.. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, `Optimal Transport + for Domain Adaptation <https://arxiv.org/pdf/1507.00504.pdf>`__, in IEEE + Transactions on Pattern Analysis and Machine Intelligence , vol.PP, + no.99, pp.1-1 + +.. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + `Regularized discrete optimal + transport <https://arxiv.org/pdf/1307.5551.pdf>`__. SIAM Journal on + Imaging Sciences, 7(3), 1853-1882. + +.. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). `Generalized + conditional gradient: analysis of convergence and + applications <https://arxiv.org/pdf/1510.06567.pdf>`__. arXiv preprint + arXiv:1510.06567. + +.. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard (2016), `Mapping + estimation for discrete optimal + transport <http://remi.flamary.com/biblio/perrot2016mapping.pdf>`__, + Neural Information Processing Systems (NIPS). + +.. [9] Schmitzer, B. (2016). `Stabilized Sparse Scaling Algorithms for + Entropy Regularized Transport + Problems <https://arxiv.org/pdf/1610.06519.pdf>`__. arXiv preprint + arXiv:1610.06519. + +.. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + `Scaling algorithms for unbalanced transport + problems <https://arxiv.org/pdf/1607.05816.pdf>`__. arXiv preprint + arXiv:1607.05816. + +.. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). + `Wasserstein Discriminant + Analysis <https://arxiv.org/pdf/1608.08063.pdf>`__. arXiv preprint + arXiv:1608.08063. + +.. [12] Gabriel Peyré, Marco Cuturi, and Justin Solomon (2016), + `Gromov-Wasserstein averaging of kernel and distance + matrices <http://proceedings.mlr.press/v48/peyre16.html>`__ + International Conference on Machine Learning (ICML). + +.. [13] Mémoli, Facundo (2011). `Gromov–Wasserstein distances and the + metric approach to object + matching <https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf>`__. + Foundations of computational mathematics 11.4 : 417-487. + +.. [14] Knott, M. and Smith, C. S. (1984). `On the optimal mapping of + distributions <https://link.springer.com/article/10.1007/BF00934745>`__, + Journal of Optimization Theory and Applications Vol 43. + +.. [15] Peyré, G., & Cuturi, M. (2018). `Computational Optimal + Transport <https://arxiv.org/pdf/1803.00567.pdf>`__ . + +.. [16] Agueh, M., & Carlier, G. (2011). `Barycenters in the Wasserstein + space <https://hal.archives-ouvertes.fr/hal-00637399/document>`__. SIAM + Journal on Mathematical Analysis, 43(2), 904-924. + +.. [17] Blondel, M., Seguy, V., & Rolet, A. (2018). `Smooth and Sparse + Optimal Transport <https://arxiv.org/abs/1710.06276>`__. Proceedings of + the Twenty-First International Conference on Artificial Intelligence and + Statistics (AISTATS). + +.. [18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) `Stochastic + Optimization for Large-scale Optimal + Transport <https://arxiv.org/abs/1605.08527>`__. Advances in Neural + Information Processing Systems (2016). + +.. [19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, + A.& Blondel, M. `Large-scale Optimal Transport and Mapping + Estimation <https://arxiv.org/pdf/1711.02283.pdf>`__. International + Conference on Learning Representation (2018) + +.. [20] Cuturi, M. and Doucet, A. (2014) `Fast Computation of Wasserstein + Barycenters <http://proceedings.mlr.press/v32/cuturi14.html>`__. + International Conference in Machine Learning + +.. [21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., + Nguyen, A. & Guibas, L. (2015). `Convolutional wasserstein distances: + Efficient optimal transportation on geometric + domains <https://dl.acm.org/citation.cfm?id=2766963>`__. ACM + Transactions on Graphics (TOG), 34(4), 66. + +.. [22] J. Altschuler, J.Weed, P. Rigollet, (2017) `Near-linear time + approximation algorithms for optimal transport via Sinkhorn + iteration <https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf>`__, + Advances in Neural Information Processing Systems (NIPS) 31 + +.. [23] Aude, G., Peyré, G., Cuturi, M., `Learning Generative Models with + Sinkhorn Divergences <https://arxiv.org/abs/1706.00292>`__, Proceedings + of the Twenty-First International Conference on Artficial Intelligence + and Statistics, (AISTATS) 21, 2018 + +.. [24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. + (2019). `Optimal Transport for structured data with application on + graphs <http://proceedings.mlr.press/v97/titouan19a.html>`__ Proceedings + of the 36th International Conference on Machine Learning (ICML). + +.. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : + Learning with a Wasserstein Loss, Advances in Neural Information + Processing Systems (NIPS) 2015 diff --git a/docs/source/readme.rst b/docs/source/readme.rst index e7c2bd1..0871779 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -12,9 +12,11 @@ It provides the following solvers: - OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -- Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] - and stabilized version [9][10] and greedy SInkhorn [22] with optional - GPU implementation (requires cudamat). +- Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], + stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU + implementation (requires cupy). +- Sinkhorn divergence [23] and entropic regularization OT from + empirical data. - Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. - Non regularized Wasserstein barycenters [16] with LP solver (only @@ -33,6 +35,7 @@ It provides the following solvers: - Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) - Non regularized free support Wasserstein barycenters [20]. +- Unbalanced OT with KL relaxation distance and barycenter [10, 25]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -67,6 +70,13 @@ modules: Pip installation ^^^^^^^^^^^^^^^^ +Note that due to a limitation of pip, ``cython`` and ``numpy`` need to +be installed prior to installing POT. This can be done easily with + +:: + + pip install numpy cython + You can install the toolbox through PyPI with: :: @@ -115,14 +125,9 @@ below pip install pymanopt autograd -- **ot.gpu** (GPU accelerated OT) depends on cudamat that have to be - installed with: - - :: - - git clone https://github.com/cudamat/cudamat.git - cd cudamat - python setup.py install --user # for user install (no root) +- **ot.gpu** (GPU accelerated OT) depends on cupy that have to be + installed following instructions on `this + page <https://docs-cupy.chainer.org/en/stable/install.html>`__. obviously you need CUDA installed and a compatible GPU. @@ -209,10 +214,13 @@ nbviewer <https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks Acknowledgements ---------------- -The contributors to this library are: +This toolbox has been created and is maintained by - `Rémi Flamary <http://remi.flamary.com/>`__ - `Nicolas Courty <http://people.irisa.fr/Nicolas.Courty/>`__ + +The contributors to this library are + - `Alexandre Gramfort <http://alexandre.gramfort.net/>`__ - `Laetitia Chapel <http://people.irisa.fr/Laetitia.Chapel/>`__ - `Michael Perrot <http://perso.univ-st-etienne.fr/pem82055/>`__ @@ -226,6 +234,9 @@ The contributors to this library are: - `Kilian Fatras <https://kilianfatras.github.io/>`__ - `Alain Rakotomamonjy <https://sites.google.com/site/alainrakotomamonjy/home>`__ +- `Vayer Titouan <https://tvayer.github.io/>`__ +- `Hicham Janati <https://hichamjanati.github.io/>`__ (Unbalanced OT) +- `Romain Tavenard <https://rtavenar.github.io/>`__ (1d Wasserstein) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -366,6 +377,20 @@ approximation algorithms for optimal transport via Sinkhorn iteration <https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf>`__, Advances in Neural Information Processing Systems (NIPS) 31 +[23] Aude, G., Peyré, G., Cuturi, M., `Learning Generative Models with +Sinkhorn Divergences <https://arxiv.org/abs/1706.00292>`__, Proceedings +of the Twenty-First International Conference on Artficial Intelligence +and Statistics, (AISTATS) 21, 2018 + +[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. +(2019). `Optimal Transport for structured data with application on +graphs <http://proceedings.mlr.press/v97/titouan19a.html>`__ Proceedings +of the 36th International Conference on Machine Learning (ICML). + +[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2019). +`Learning with a Wasserstein Loss <http://cbcl.mit.edu/wasserstein/>`__ +Advances in Neural Information Processing Systems (NIPS). + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index bb952a0..63126ba 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -10,6 +10,7 @@ sum of diracs. The OT matrix is plotted with the samples. """ # Author: Remi Flamary <remi.flamary@unice.fr> +# Kilian Fatras <kilian.fatras@irisa.fr> # # License: MIT License @@ -100,3 +101,28 @@ pl.legend(loc=0) pl.title('OT matrix Sinkhorn with samples') pl.show() + + +############################################################################## +# Emprirical Sinkhorn +# ---------------- + +#%% sinkhorn + +# reg term +lambd = 1e-3 + +Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd) + +pl.figure(7) +pl.imshow(Ges, interpolation='nearest') +pl.title('OT matrix empirical sinkhorn') + +pl.figure(8) +ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.legend(loc=0) +pl.title('OT matrix Sinkhorn from samples') + +pl.show() diff --git a/examples/plot_UOT_1D.py b/examples/plot_UOT_1D.py new file mode 100644 index 0000000..2ea8b05 --- /dev/null +++ b/examples/plot_UOT_1D.py @@ -0,0 +1,76 @@ +# -*- coding: utf-8 -*- +""" +=============================== +1D Unbalanced optimal transport +=============================== + +This example illustrates the computation of Unbalanced Optimal transport +using a Kullback-Leibler relaxation. +""" + +# Author: Hicham Janati <hicham.janati@inria.fr> +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +import ot.plot +from ot.datasets import make_1D_gauss as gauss + +############################################################################## +# Generate data +# ------------- + + +#%% parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a = gauss(n, m=20, s=5) # m= mean, s= std +b = gauss(n, m=60, s=10) + +# make distributions unbalanced +b *= 5. + +# loss matrix +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() + + +############################################################################## +# Plot distributions and loss matrix +# ---------------------------------- + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +pl.plot(x, a, 'b', label='Source distribution') +pl.plot(x, b, 'r', label='Target distribution') +pl.legend() + +# plot distributions and loss matrix + +pl.figure(2, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + + +############################################################################## +# Solve Unbalanced Sinkhorn +# -------------- + + +# Sinkhorn + +epsilon = 0.1 # entropy parameter +alpha = 1. # Unbalanced KL relaxation parameter +Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True) + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn') + +pl.show() diff --git a/examples/plot_UOT_barycenter_1D.py b/examples/plot_UOT_barycenter_1D.py new file mode 100644 index 0000000..c8d9d3b --- /dev/null +++ b/examples/plot_UOT_barycenter_1D.py @@ -0,0 +1,164 @@ +# -*- coding: utf-8 -*- +""" +=========================================================== +1D Wasserstein barycenter demo for Unbalanced distributions +=========================================================== + +This example illustrates the computation of regularized Wassersyein Barycenter +as proposed in [10] for Unbalanced inputs. + + +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + +""" + +# Author: Hicham Janati <hicham.janati@inria.fr> +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa +from matplotlib.collections import PolyCollection + +############################################################################## +# Generate data +# ------------- + +# parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) + +# make unbalanced dists +a2 *= 3. + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + +############################################################################## +# Plot data +# --------- + +# plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + +############################################################################## +# Barycenter computation +# ---------------------- + +# non weighted barycenter computation + +weight = 0.5 # 0<=weight<=1 +weights = np.array([1 - weight, weight]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +alpha = 1. + +bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + +############################################################################## +# Barycentric interpolation +# ------------------------- + +# barycenter interpolation + +n_weight = 11 +weight_list = np.linspace(0, 1, n_weight) + + +B_l2 = np.zeros((n, n_weight)) + +B_wass = np.copy(B_l2) + +for i in range(0, n_weight): + weight = weight_list[i] + weights = np.array([1 - weight, weight]) + B_l2[:, i] = A.dot(weights) + B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + + +# plot interpolation + +pl.figure(3) + +cmap = pl.cm.get_cmap('viridis') +verts = [] +zs = weight_list +for i, z in enumerate(zs): + ys = B_l2[:, i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel(r'$\alpha$') +ax.set_ylim3d(0, 1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max() * 1.01) +pl.title('Barycenter interpolation with l2') +pl.tight_layout() + +pl.figure(4) +cmap = pl.cm.get_cmap('viridis') +verts = [] +zs = weight_list +for i, z in enumerate(zs): + ys = B_wass[:, i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel(r'$\alpha$') +ax.set_ylim3d(0, 1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max() * 1.01) +pl.title('Barycenter interpolation with Wasserstein') +pl.tight_layout() + +pl.show() diff --git a/examples/plot_barycenter_fgw.py b/examples/plot_barycenter_fgw.py new file mode 100644 index 0000000..77b0370 --- /dev/null +++ b/examples/plot_barycenter_fgw.py @@ -0,0 +1,184 @@ +# -*- coding: utf-8 -*- +""" +================================= +Plot graphs' barycenter using FGW +================================= + +This example illustrates the computation barycenter of labeled graphs using FGW + +Requires networkx >=2 + +.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + +""" + +# Author: Titouan Vayer <titouan.vayer@irisa.fr> +# +# License: MIT License + +#%% load libraries +import numpy as np +import matplotlib.pyplot as plt +import networkx as nx +import math +from scipy.sparse.csgraph import shortest_path +import matplotlib.colors as mcol +from matplotlib import cm +from ot.gromov import fgw_barycenters +#%% Graph functions + + +def find_thresh(C, inf=0.5, sup=3, step=10): + """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected + Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. + The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix + and the original matrix. + Parameters + ---------- + C : ndarray, shape (n_nodes,n_nodes) + The structure matrix to threshold + inf : float + The beginning of the linesearch + sup : float + The end of the linesearch + step : integer + Number of thresholds tested + """ + dist = [] + search = np.linspace(inf, sup, step) + for thresh in search: + Cprime = sp_to_adjency(C, 0, thresh) + SC = shortest_path(Cprime, method='D') + SC[SC == float('inf')] = 100 + dist.append(np.linalg.norm(SC - C)) + return search[np.argmin(dist)], dist + + +def sp_to_adjency(C, threshinf=0.2, threshsup=1.8): + """ Thresholds the structure matrix in order to compute an adjency matrix. + All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0 + Parameters + ---------- + C : ndarray, shape (n_nodes,n_nodes) + The structure matrix to threshold + threshinf : float + The minimum value of distance from which the new value is set to 1 + threshsup : float + The maximum value of distance from which the new value is set to 1 + Returns + ------- + C : ndarray, shape (n_nodes,n_nodes) + The threshold matrix. Each element is in {0,1} + """ + H = np.zeros_like(C) + np.fill_diagonal(H, np.diagonal(C)) + C = C - H + C = np.minimum(np.maximum(C, threshinf), threshsup) + C[C == threshsup] = 0 + C[C != 0] = 1 + + return C + + +def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None): + """ Create a noisy circular graph + """ + g = nx.Graph() + g.add_nodes_from(list(range(N))) + for i in range(N): + noise = float(np.random.normal(mu, sigma, 1)) + if with_noise: + g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise) + else: + g.add_node(i, attr_name=math.sin(2 * i * math.pi / N)) + g.add_edge(i, i + 1) + if structure_noise: + randomint = np.random.randint(0, p) + if randomint == 0: + if i <= N - 3: + g.add_edge(i, i + 2) + if i == N - 2: + g.add_edge(i, 0) + if i == N - 1: + g.add_edge(i, 1) + g.add_edge(N, 0) + noise = float(np.random.normal(mu, sigma, 1)) + if with_noise: + g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise) + else: + g.add_node(N, attr_name=math.sin(2 * N * math.pi / N)) + return g + + +def graph_colors(nx_graph, vmin=0, vmax=7): + cnorm = mcol.Normalize(vmin=vmin, vmax=vmax) + cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis') + cpick.set_array([]) + val_map = {} + for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items(): + val_map[k] = cpick.to_rgba(v) + colors = [] + for node in nx_graph.nodes(): + colors.append(val_map[node]) + return colors + +############################################################################## +# Generate data +# ------------- + +#%% circular dataset +# We build a dataset of noisy circular graphs. +# Noise is added on the structures by random connections and on the features by gaussian noise. + + +np.random.seed(30) +X0 = [] +for k in range(9): + X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3)) + +############################################################################## +# Plot data +# --------- + +#%% Plot graphs + +plt.figure(figsize=(8, 10)) +for i in range(len(X0)): + plt.subplot(3, 3, i + 1) + g = X0[i] + pos = nx.kamada_kawai_layout(g) + nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100) +plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20) +plt.show() + +############################################################################## +# Barycenter computation +# ---------------------- + +#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph +# Features distances are the euclidean distances +Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0] +ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0] +Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0] +lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel() +sizebary = 15 # we choose a barycenter with 15 nodes + +A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True) + +############################################################################## +# Plot Barycenter +# ------------------------- + +#%% Create the barycenter +bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) +for i, v in enumerate(A.ravel()): + bary.add_node(i, attr_name=v) + +#%% +pos = nx.kamada_kawai_layout(bary) +nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False) +plt.suptitle('Barycenter', fontsize=20) +plt.show() diff --git a/examples/plot_barycenter_lp_vs_entropic.py b/examples/plot_barycenter_lp_vs_entropic.py index b82765e..d7c72d0 100644 --- a/examples/plot_barycenter_lp_vs_entropic.py +++ b/examples/plot_barycenter_lp_vs_entropic.py @@ -102,7 +102,7 @@ pl.tight_layout() problems.append([A, [bary_l2, bary_wass, bary_wass2]]) ############################################################################## -# Dirac Data +# Stair Data # ---------- #%% parameters @@ -168,6 +168,11 @@ pl.legend() pl.title('Barycenters') pl.tight_layout() + +############################################################################## +# Dirac Data +# ---------- + #%% parameters a1 = np.zeros(n) diff --git a/examples/plot_fgw.py b/examples/plot_fgw.py new file mode 100644 index 0000000..43efc94 --- /dev/null +++ b/examples/plot_fgw.py @@ -0,0 +1,173 @@ +# -*- coding: utf-8 -*- +""" +============================== +Plot Fused-gromov-Wasserstein +============================== + +This example illustrates the computation of FGW for 1D measures[18]. + +.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + +""" + +# Author: Titouan Vayer <titouan.vayer@irisa.fr> +# +# License: MIT License + +import matplotlib.pyplot as pl +import numpy as np +import ot +from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein + +############################################################################## +# Generate data +# --------- + +#%% parameters +# We create two 1D random measures +n = 20 # number of points in the first distribution +n2 = 30 # number of points in the second distribution +sig = 1 # std of first distribution +sig2 = 0.1 # std of second distribution + +np.random.seed(0) + +phi = np.arange(n)[:, None] +xs = phi + sig * np.random.randn(n, 1) +ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1) + +phi2 = np.arange(n2)[:, None] +xt = phi2 + sig * np.random.randn(n2, 1) +yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1) +yt = yt[::-1, :] + +p = ot.unif(n) +q = ot.unif(n2) + +############################################################################## +# Plot data +# --------- + +#%% plot the distributions + +pl.close(10) +pl.figure(10, (7, 7)) + +pl.subplot(2, 1, 1) + +pl.scatter(ys, xs, c=phi, s=70) +pl.ylabel('Feature value a', fontsize=20) +pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1) +pl.xticks(()) +pl.yticks(()) +pl.subplot(2, 1, 2) +pl.scatter(yt, xt, c=phi2, s=70) +pl.xlabel('coordinates x/y', fontsize=25) +pl.ylabel('Feature value b', fontsize=20) +pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1) +pl.yticks(()) +pl.tight_layout() +pl.show() + +############################################################################## +# Create structure matrices and across-feature distance matrix +# --------- + +#%% Structure matrices and across-features distance matrix +C1 = ot.dist(xs) +C2 = ot.dist(xt) +M = ot.dist(ys, yt) +w1 = ot.unif(C1.shape[0]) +w2 = ot.unif(C2.shape[0]) +Got = ot.emd([], [], M) + +############################################################################## +# Plot matrices +# --------- + +#%% +cmap = 'Reds' +pl.close(10) +pl.figure(10, (5, 5)) +fs = 15 +l_x = [0, 5, 10, 15] +l_y = [0, 5, 10, 15, 20, 25] +gs = pl.GridSpec(5, 5) + +ax1 = pl.subplot(gs[3:, :2]) + +pl.imshow(C1, cmap=cmap, interpolation='nearest') +pl.title("$C_1$", fontsize=fs) +pl.xlabel("$k$", fontsize=fs) +pl.ylabel("$i$", fontsize=fs) +pl.xticks(l_x) +pl.yticks(l_x) + +ax2 = pl.subplot(gs[:3, 2:]) + +pl.imshow(C2, cmap=cmap, interpolation='nearest') +pl.title("$C_2$", fontsize=fs) +pl.ylabel("$l$", fontsize=fs) +#pl.ylabel("$l$",fontsize=fs) +pl.xticks(()) +pl.yticks(l_y) +ax2.set_aspect('auto') + +ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1) +pl.imshow(M, cmap=cmap, interpolation='nearest') +pl.yticks(l_x) +pl.xticks(l_y) +pl.ylabel("$i$", fontsize=fs) +pl.title("$M_{AB}$", fontsize=fs) +pl.xlabel("$j$", fontsize=fs) +pl.tight_layout() +ax3.set_aspect('auto') +pl.show() + +############################################################################## +# Compute FGW/GW +# --------- + +#%% Computing FGW and GW +alpha = 1e-3 + +ot.tic() +Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True) +ot.toc() + +#%reload_ext WGW +Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True) + +############################################################################## +# Visualize transport matrices +# --------- + +#%% visu OT matrix +cmap = 'Blues' +fs = 15 +pl.figure(2, (13, 5)) +pl.clf() +pl.subplot(1, 3, 1) +pl.imshow(Got, cmap=cmap, interpolation='nearest') +#pl.xlabel("$y$",fontsize=fs) +pl.ylabel("$i$", fontsize=fs) +pl.xticks(()) + +pl.title('Wasserstein ($M$ only)') + +pl.subplot(1, 3, 2) +pl.imshow(Gg, cmap=cmap, interpolation='nearest') +pl.title('Gromov ($C_1,C_2$ only)') +pl.xticks(()) +pl.subplot(1, 3, 3) +pl.imshow(Gwg, cmap=cmap, interpolation='nearest') +pl.title('FGW ($M+C_1,C_2$)') + +pl.xlabel("$j$", fontsize=fs) +pl.ylabel("$i$", fontsize=fs) + +pl.tight_layout() +pl.show() diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py index b6efc59..64b89e4 100644 --- a/examples/plot_free_support_barycenter.py +++ b/examples/plot_free_support_barycenter.py @@ -62,7 +62,7 @@ X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, pl.figure(1) for (x_i, b_i) in zip(measures_locations, measures_weights): color = np.random.randint(low=1, high=10 * N) - pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure') + pl.scatter(x_i[:, 0], x_i[:, 1], s=b_i * 1000, label='input measure') pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') pl.title('Data measures and their barycenter') pl.legend(loc=0) diff --git a/examples/plot_otda_color_images.py b/examples/plot_otda_color_images.py index e77aec0..62383a2 100644 --- a/examples/plot_otda_color_images.py +++ b/examples/plot_otda_color_images.py @@ -4,7 +4,7 @@ OT for image color adaptation ============================= -This example presents a way of transferring colors between two image +This example presents a way of transferring colors between two images with Optimal Transport as introduced in [6] [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). @@ -27,7 +27,7 @@ r = np.random.RandomState(42) def im2mat(I): - """Converts and image to matrix (one pixel per line)""" + """Converts an image to matrix (one pixel per line)""" return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) @@ -115,8 +115,8 @@ ot_sinkhorn.fit(Xs=Xs, Xt=Xt) transp_Xs_emd = ot_emd.transform(Xs=X1) transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) -transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) -transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) +transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2) I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) diff --git a/examples/plot_otda_mapping_colors_images.py b/examples/plot_otda_mapping_colors_images.py index 5f1e844..a20eca8 100644 --- a/examples/plot_otda_mapping_colors_images.py +++ b/examples/plot_otda_mapping_colors_images.py @@ -77,7 +77,7 @@ Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) # SinkhornTransport ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) ot_sinkhorn.fit(Xs=Xs, Xt=Xt) -transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) ot_mapping_linear = ot.da.MappingTransport( diff --git a/examples/plot_screenkhorn_1D.py b/examples/plot_screenkhorn_1D.py new file mode 100644 index 0000000..840ead8 --- /dev/null +++ b/examples/plot_screenkhorn_1D.py @@ -0,0 +1,68 @@ +# -*- coding: utf-8 -*- +""" +=============================== +1D Screened optimal transport +=============================== + +This example illustrates the computation of Screenkhorn: +Screening Sinkhorn Algorithm for Optimal transport. +""" + +# Author: Mokhtar Z. Alaya <mokhtarzahdi.alaya@gmail.com> +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot.plot +from ot.datasets import make_1D_gauss as gauss +from ot.bregman import screenkhorn + +############################################################################## +# Generate data +# ------------- + +#%% parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a = gauss(n, m=20, s=5) # m= mean, s= std +b = gauss(n, m=60, s=10) + +# loss matrix +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() + +############################################################################## +# Plot distributions and loss matrix +# ---------------------------------- + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +pl.plot(x, a, 'b', label='Source distribution') +pl.plot(x, b, 'r', label='Target distribution') +pl.legend() + +# plot distributions and loss matrix + +pl.figure(2, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + +############################################################################## +# Solve Screenkhorn +# ----------------------- + +# Screenkhorn +lambd = 2e-03 # entropy parameter +ns_budget = 30 # budget number of points to be keeped in the source distribution +nt_budget = 30 # budget number of points to be keeped in the target distribution + +G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True) +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn') +pl.show() diff --git a/notebooks/plot_OT_2D_samples.ipynb b/notebooks/plot_OT_2D_samples.ipynb index 96e84a5..cd1b541 100644 --- a/notebooks/plot_OT_2D_samples.ipynb +++ b/notebooks/plot_OT_2D_samples.ipynb @@ -34,6 +34,7 @@ "outputs": [], "source": [ "# Author: Remi Flamary <remi.flamary@unice.fr>\n", + "# Kilian Fatras <kilian.fatras@irisa.fr>\n", "#\n", "# License: MIT License\n", "\n", @@ -108,7 +109,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -118,7 +119,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -169,7 +170,7 @@ }, { "data": { - "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -179,7 +180,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -223,7 +224,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -233,7 +234,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -263,6 +264,82 @@ "\n", "pl.show()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Emprirical Sinkhorn\n", + "----------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: numerical errors at iteration 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/PYTHON/POT/ot/bregman.py:374: RuntimeWarning: divide by zero encountered in true_divide\n", + " v = np.divide(b, KtransposeU)\n", + "/home/rflamary/PYTHON/POT/ot/plot.py:83: RuntimeWarning: invalid value encountered in double_scalars\n", + " if G[i, j] / mx > thr:\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% sinkhorn\n", + "\n", + "# reg term\n", + "lambd = 1e-3\n", + "\n", + "Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd)\n", + "\n", + "pl.figure(7)\n", + "pl.imshow(Ges, interpolation='nearest')\n", + "pl.title('OT matrix empirical sinkhorn')\n", + "\n", + "pl.figure(8)\n", + "ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1])\n", + "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('OT matrix Sinkhorn from samples')\n", + "\n", + "pl.show()" + ] } ], "metadata": { @@ -281,7 +358,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/notebooks/plot_UOT_1D.ipynb b/notebooks/plot_UOT_1D.ipynb new file mode 100644 index 0000000..2354d4f --- /dev/null +++ b/notebooks/plot_UOT_1D.ipynb @@ -0,0 +1,210 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 1D Unbalanced optimal transport\n", + "\n", + "\n", + "This example illustrates the computation of Unbalanced Optimal transport\n", + "using a Kullback-Leibler relaxation.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Hicham Janati <hicham.janati@inria.fr>\n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "import ot.plot\n", + "from ot.datasets import make_1D_gauss as gauss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n", + "\n", + "n = 100 # nb bins\n", + "\n", + "# bin positions\n", + "x = np.arange(n, dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a = gauss(n, m=20, s=5) # m= mean, s= std\n", + "b = gauss(n, m=60, s=10)\n", + "\n", + "# make distributions unbalanced\n", + "b *= 5.\n", + "\n", + "# loss matrix\n", + "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", + "M /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot distributions and loss matrix\n", + "----------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 460.8x216 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 360x360 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "pl.plot(x, a, 'b', label='Source distribution')\n", + "pl.plot(x, b, 'r', label='Target distribution')\n", + "pl.legend()\n", + "\n", + "# plot distributions and loss matrix\n", + "\n", + "pl.figure(2, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Unbalanced Sinkhorn\n", + "--------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Err \n", + "-------------------\n", + " 0|1.838786e+00|\n", + " 10|1.242379e-01|\n", + " 20|2.581314e-03|\n", + " 30|5.674552e-05|\n", + " 40|1.252959e-06|\n", + " 50|2.768136e-08|\n", + " 60|6.116090e-10|\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 360x360 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Sinkhorn\n", + "\n", + "epsilon = 0.1 # entropy parameter\n", + "alpha = 1. # Unbalanced KL relaxation parameter\n", + "Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True)\n", + "\n", + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn')\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_UOT_barycenter_1D.ipynb b/notebooks/plot_UOT_barycenter_1D.ipynb new file mode 100644 index 0000000..43c8105 --- /dev/null +++ b/notebooks/plot_UOT_barycenter_1D.ipynb @@ -0,0 +1,336 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 1D Wasserstein barycenter demo for Unbalanced distributions\n", + "\n", + "\n", + "This example illustrates the computation of regularized Wassersyein Barycenter\n", + "as proposed in [10] for Unbalanced inputs.\n", + "\n", + "\n", + "[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Hicham Janati <hicham.janati@inria.fr>\n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "# necessary for 3d plot even if not used\n", + "from mpl_toolkits.mplot3d import Axes3D # noqa\n", + "from matplotlib.collections import PolyCollection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# parameters\n", + "\n", + "n = 100 # nb bins\n", + "\n", + "# bin positions\n", + "x = np.arange(n, dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", + "a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n", + "\n", + "# make unbalanced dists\n", + "a2 *= 3.\n", + "\n", + "# creating matrix A containing all distributions\n", + "A = np.vstack((a1, a2)).T\n", + "n_distributions = A.shape[1]\n", + "\n", + "# loss matrix + normalization\n", + "M = ot.utils.dist0(n)\n", + "M /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 460.8x216 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation\n", + "----------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: overflow encountered in square\n", + " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: invalid value encountered in double_scalars\n", + " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# non weighted barycenter computation\n", + "\n", + "weight = 0.5 # 0<=weight<=1\n", + "weights = np.array([1 - weight, weight])\n", + "\n", + "# l2bary\n", + "bary_l2 = A.dot(weights)\n", + "\n", + "# wasserstein\n", + "reg = 1e-3\n", + "alpha = 1.\n", + "\n", + "bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.subplot(2, 1, 1)\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "\n", + "pl.subplot(2, 1, 2)\n", + "pl.plot(x, bary_l2, 'r', label='l2')\n", + "pl.plot(x, bary_wass, 'g', label='Wasserstein')\n", + "pl.legend()\n", + "pl.title('Barycenters')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycentric interpolation\n", + "-------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:500: RuntimeWarning: overflow encountered in square\n", + " err = np.sum((u - uprev) ** 2) / np.sum((u) ** 2) + \\\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:500: RuntimeWarning: invalid value encountered in double_scalars\n", + " err = np.sum((u - uprev) ** 2) / np.sum((u) ** 2) + \\\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: overflow encountered in square\n", + " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: invalid value encountered in double_scalars\n", + " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# barycenter interpolation\n", + "\n", + "n_weight = 11\n", + "weight_list = np.linspace(0, 1, n_weight)\n", + "\n", + "\n", + "B_l2 = np.zeros((n, n_weight))\n", + "\n", + "B_wass = np.copy(B_l2)\n", + "\n", + "for i in range(0, n_weight):\n", + " weight = weight_list[i]\n", + " weights = np.array([1 - weight, weight])\n", + " B_l2[:, i] = A.dot(weights)\n", + " B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n", + "\n", + "\n", + "# plot interpolation\n", + "\n", + "pl.figure(3)\n", + "\n", + "cmap = pl.cm.get_cmap('viridis')\n", + "verts = []\n", + "zs = weight_list\n", + "for i, z in enumerate(zs):\n", + " ys = B_l2[:, i]\n", + " verts.append(list(zip(x, ys)))\n", + "\n", + "ax = pl.gcf().gca(projection='3d')\n", + "\n", + "poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\n", + "poly.set_alpha(0.7)\n", + "ax.add_collection3d(poly, zs=zs, zdir='y')\n", + "ax.set_xlabel('x')\n", + "ax.set_xlim3d(0, n)\n", + "ax.set_ylabel(r'$\\alpha$')\n", + "ax.set_ylim3d(0, 1)\n", + "ax.set_zlabel('')\n", + "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", + "pl.title('Barycenter interpolation with l2')\n", + "pl.tight_layout()\n", + "\n", + "pl.figure(4)\n", + "cmap = pl.cm.get_cmap('viridis')\n", + "verts = []\n", + "zs = weight_list\n", + "for i, z in enumerate(zs):\n", + " ys = B_wass[:, i]\n", + " verts.append(list(zip(x, ys)))\n", + "\n", + "ax = pl.gcf().gca(projection='3d')\n", + "\n", + "poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\n", + "poly.set_alpha(0.7)\n", + "ax.add_collection3d(poly, zs=zs, zdir='y')\n", + "ax.set_xlabel('x')\n", + "ax.set_xlim3d(0, n)\n", + "ax.set_ylabel(r'$\\alpha$')\n", + "ax.set_ylim3d(0, 1)\n", + "ax.set_zlabel('')\n", + "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", + "pl.title('Barycenter interpolation with Wasserstein')\n", + "pl.tight_layout()\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_barycenter_fgw.ipynb b/notebooks/plot_barycenter_fgw.ipynb new file mode 100644 index 0000000..8da80a6 --- /dev/null +++ b/notebooks/plot_barycenter_fgw.ipynb @@ -0,0 +1,312 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "=================================\n", + "Plot graphs' barycenter using FGW\n", + "=================================\n", + "\n", + "This example illustrates the computation barycenter of labeled graphs using FGW\n", + "\n", + "Requires networkx >=2\n", + "\n", + ".. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n", + " and Courty Nicolas\n", + " \"Optimal Transport for structured data with application on graphs\"\n", + " International Conference on Machine Learning (ICML). 2019.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Titouan Vayer <titouan.vayer@irisa.fr>\n", + "#\n", + "# License: MIT License\n", + "\n", + "#%% load libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import networkx as nx\n", + "import math\n", + "from scipy.sparse.csgraph import shortest_path\n", + "import matplotlib.colors as mcol\n", + "from matplotlib import cm\n", + "from ot.gromov import fgw_barycenters\n", + "#%% Graph functions\n", + "\n", + "\n", + "def find_thresh(C, inf=0.5, sup=3, step=10):\n", + " \"\"\" Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected\n", + " Tthe threshold is found by a linesearch between values \"inf\" and \"sup\" with \"step\" thresholds tested.\n", + " The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix\n", + " and the original matrix.\n", + " Parameters\n", + " ----------\n", + " C : ndarray, shape (n_nodes,n_nodes)\n", + " The structure matrix to threshold\n", + " inf : float\n", + " The beginning of the linesearch\n", + " sup : float\n", + " The end of the linesearch\n", + " step : integer\n", + " Number of thresholds tested\n", + " \"\"\"\n", + " dist = []\n", + " search = np.linspace(inf, sup, step)\n", + " for thresh in search:\n", + " Cprime = sp_to_adjency(C, 0, thresh)\n", + " SC = shortest_path(Cprime, method='D')\n", + " SC[SC == float('inf')] = 100\n", + " dist.append(np.linalg.norm(SC - C))\n", + " return search[np.argmin(dist)], dist\n", + "\n", + "\n", + "def sp_to_adjency(C, threshinf=0.2, threshsup=1.8):\n", + " \"\"\" Thresholds the structure matrix in order to compute an adjency matrix.\n", + " All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0\n", + " Parameters\n", + " ----------\n", + " C : ndarray, shape (n_nodes,n_nodes)\n", + " The structure matrix to threshold\n", + " threshinf : float\n", + " The minimum value of distance from which the new value is set to 1\n", + " threshsup : float\n", + " The maximum value of distance from which the new value is set to 1\n", + " Returns\n", + " -------\n", + " C : ndarray, shape (n_nodes,n_nodes)\n", + " The threshold matrix. Each element is in {0,1}\n", + " \"\"\"\n", + " H = np.zeros_like(C)\n", + " np.fill_diagonal(H, np.diagonal(C))\n", + " C = C - H\n", + " C = np.minimum(np.maximum(C, threshinf), threshsup)\n", + " C[C == threshsup] = 0\n", + " C[C != 0] = 1\n", + "\n", + " return C\n", + "\n", + "\n", + "def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None):\n", + " \"\"\" Create a noisy circular graph\n", + " \"\"\"\n", + " g = nx.Graph()\n", + " g.add_nodes_from(list(range(N)))\n", + " for i in range(N):\n", + " noise = float(np.random.normal(mu, sigma, 1))\n", + " if with_noise:\n", + " g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)\n", + " else:\n", + " g.add_node(i, attr_name=math.sin(2 * i * math.pi / N))\n", + " g.add_edge(i, i + 1)\n", + " if structure_noise:\n", + " randomint = np.random.randint(0, p)\n", + " if randomint == 0:\n", + " if i <= N - 3:\n", + " g.add_edge(i, i + 2)\n", + " if i == N - 2:\n", + " g.add_edge(i, 0)\n", + " if i == N - 1:\n", + " g.add_edge(i, 1)\n", + " g.add_edge(N, 0)\n", + " noise = float(np.random.normal(mu, sigma, 1))\n", + " if with_noise:\n", + " g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)\n", + " else:\n", + " g.add_node(N, attr_name=math.sin(2 * N * math.pi / N))\n", + " return g\n", + "\n", + "\n", + "def graph_colors(nx_graph, vmin=0, vmax=7):\n", + " cnorm = mcol.Normalize(vmin=vmin, vmax=vmax)\n", + " cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis')\n", + " cpick.set_array([])\n", + " val_map = {}\n", + " for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items():\n", + " val_map[k] = cpick.to_rgba(v)\n", + " colors = []\n", + " for node in nx_graph.nodes():\n", + " colors.append(val_map[node])\n", + " return colors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% circular dataset\n", + "# We build a dataset of noisy circular graphs.\n", + "# Noise is added on the structures by random connections and on the features by gaussian noise.\n", + "\n", + "\n", + "np.random.seed(30)\n", + "X0 = []\n", + "for k in range(9):\n", + " X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 576x720 with 9 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% Plot graphs\n", + "\n", + "plt.figure(figsize=(8, 10))\n", + "for i in range(len(X0)):\n", + " plt.subplot(3, 3, i + 1)\n", + " g = X0[i]\n", + " pos = nx.kamada_kawai_layout(g)\n", + " nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100)\n", + "plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation\n", + "----------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph\n", + "# Features distances are the euclidean distances\n", + "Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0]\n", + "ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]\n", + "Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0]\n", + "lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel()\n", + "sizebary = 15 # we choose a barycenter with 15 nodes\n", + "\n", + "A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Barycenter\n", + "-------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% Create the barycenter\n", + "bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))\n", + "for i, v in enumerate(A.ravel()):\n", + " bary.add_node(i, attr_name=v)\n", + "\n", + "#%%\n", + "pos = nx.kamada_kawai_layout(bary)\n", + "nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False)\n", + "plt.suptitle('Barycenter', fontsize=20)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_fgw.ipynb b/notebooks/plot_fgw.ipynb new file mode 100644 index 0000000..b41f280 --- /dev/null +++ b/notebooks/plot_fgw.ipynb @@ -0,0 +1,359 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Plot Fused-gromov-Wasserstein\n", + "\n", + "\n", + "This example illustrates the computation of FGW for 1D measures[18].\n", + "\n", + ".. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n", + " and Courty Nicolas\n", + " \"Optimal Transport for structured data with application on graphs\"\n", + " International Conference on Machine Learning (ICML). 2019.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Titouan Vayer <titouan.vayer@irisa.fr>\n", + "#\n", + "# License: MIT License\n", + "\n", + "import matplotlib.pyplot as pl\n", + "import numpy as np\n", + "import ot\n", + "from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n", + "# We create two 1D random measures\n", + "n = 20 # number of points in the first distribution\n", + "n2 = 30 # number of points in the second distribution\n", + "sig = 1 # std of first distribution\n", + "sig2 = 0.1 # std of second distribution\n", + "\n", + "np.random.seed(0)\n", + "\n", + "phi = np.arange(n)[:, None]\n", + "xs = phi + sig * np.random.randn(n, 1)\n", + "ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1)\n", + "\n", + "phi2 = np.arange(n2)[:, None]\n", + "xt = phi2 + sig * np.random.randn(n2, 1)\n", + "yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1)\n", + "yt = yt[::-1, :]\n", + "\n", + "p = ot.unif(n)\n", + "q = ot.unif(n2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 504x504 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot the distributions\n", + "\n", + "pl.close(10)\n", + "pl.figure(10, (7, 7))\n", + "\n", + "pl.subplot(2, 1, 1)\n", + "\n", + "pl.scatter(ys, xs, c=phi, s=70)\n", + "pl.ylabel('Feature value a', fontsize=20)\n", + "pl.title('$\\mu=\\sum_i \\delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1)\n", + "pl.xticks(())\n", + "pl.yticks(())\n", + "pl.subplot(2, 1, 2)\n", + "pl.scatter(yt, xt, c=phi2, s=70)\n", + "pl.xlabel('coordinates x/y', fontsize=25)\n", + "pl.ylabel('Feature value b', fontsize=20)\n", + "pl.title('$\\\\nu=\\sum_j \\delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1)\n", + "pl.yticks(())\n", + "pl.tight_layout()\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create structure matrices and across-feature distance matrix\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Structure matrices and across-features distance matrix\n", + "C1 = ot.dist(xs)\n", + "C2 = ot.dist(xt)\n", + "M = ot.dist(ys, yt)\n", + "w1 = ot.unif(C1.shape[0])\n", + "w2 = ot.unif(C2.shape[0])\n", + "Got = ot.emd([], [], M)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot matrices\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 360x360 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%%\n", + "cmap = 'Reds'\n", + "pl.close(10)\n", + "pl.figure(10, (5, 5))\n", + "fs = 15\n", + "l_x = [0, 5, 10, 15]\n", + "l_y = [0, 5, 10, 15, 20, 25]\n", + "gs = pl.GridSpec(5, 5)\n", + "\n", + "ax1 = pl.subplot(gs[3:, :2])\n", + "\n", + "pl.imshow(C1, cmap=cmap, interpolation='nearest')\n", + "pl.title(\"$C_1$\", fontsize=fs)\n", + "pl.xlabel(\"$k$\", fontsize=fs)\n", + "pl.ylabel(\"$i$\", fontsize=fs)\n", + "pl.xticks(l_x)\n", + "pl.yticks(l_x)\n", + "\n", + "ax2 = pl.subplot(gs[:3, 2:])\n", + "\n", + "pl.imshow(C2, cmap=cmap, interpolation='nearest')\n", + "pl.title(\"$C_2$\", fontsize=fs)\n", + "pl.ylabel(\"$l$\", fontsize=fs)\n", + "#pl.ylabel(\"$l$\",fontsize=fs)\n", + "pl.xticks(())\n", + "pl.yticks(l_y)\n", + "ax2.set_aspect('auto')\n", + "\n", + "ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1)\n", + "pl.imshow(M, cmap=cmap, interpolation='nearest')\n", + "pl.yticks(l_x)\n", + "pl.xticks(l_y)\n", + "pl.ylabel(\"$i$\", fontsize=fs)\n", + "pl.title(\"$M_{AB}$\", fontsize=fs)\n", + "pl.xlabel(\"$j$\", fontsize=fs)\n", + "pl.tight_layout()\n", + "ax3.set_aspect('auto')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute FGW/GW\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 0|4.734462e+01|0.000000e+00|0.000000e+00\n", + " 1|2.508258e+01|8.875498e-01|2.226204e+01\n", + " 2|2.189329e+01|1.456747e-01|3.189297e+00\n", + " 3|2.189329e+01|0.000000e+00|0.000000e+00\n", + "Elapsed time : 0.005539894104003906 s\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 0|4.683978e+04|0.000000e+00|0.000000e+00\n", + " 1|3.860061e+04|2.134468e-01|8.239175e+03\n", + " 2|2.182948e+04|7.682787e-01|1.677113e+04\n", + " 3|2.182948e+04|0.000000e+00|0.000000e+00\n" + ] + } + ], + "source": [ + "#%% Computing FGW and GW\n", + "alpha = 1e-3\n", + "\n", + "ot.tic()\n", + "Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True)\n", + "ot.toc()\n", + "\n", + "#%reload_ext WGW\n", + "Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualize transport matrices\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 936x360 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% visu OT matrix\n", + "cmap = 'Blues'\n", + "fs = 15\n", + "pl.figure(2, (13, 5))\n", + "pl.clf()\n", + "pl.subplot(1, 3, 1)\n", + "pl.imshow(Got, cmap=cmap, interpolation='nearest')\n", + "#pl.xlabel(\"$y$\",fontsize=fs)\n", + "pl.ylabel(\"$i$\", fontsize=fs)\n", + "pl.xticks(())\n", + "\n", + "pl.title('Wasserstein ($M$ only)')\n", + "\n", + "pl.subplot(1, 3, 2)\n", + "pl.imshow(Gg, cmap=cmap, interpolation='nearest')\n", + "pl.title('Gromov ($C_1,C_2$ only)')\n", + "pl.xticks(())\n", + "pl.subplot(1, 3, 3)\n", + "pl.imshow(Gwg, cmap=cmap, interpolation='nearest')\n", + "pl.title('FGW ($M+C_1,C_2$)')\n", + "\n", + "pl.xlabel(\"$j$\", fontsize=fs)\n", + "pl.ylabel(\"$i$\", fontsize=fs)\n", + "\n", + "pl.tight_layout()\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_otda_color_images.ipynb b/notebooks/plot_otda_color_images.ipynb index 6499daf..e2bd92b 100644 --- a/notebooks/plot_otda_color_images.ipynb +++ b/notebooks/plot_otda_color_images.ipynb @@ -19,7 +19,7 @@ "# OT for image color adaptation\n", "\n", "\n", - "This example presents a way of transferring colors between two image\n", + "This example presents a way of transferring colors between two images\n", "with Optimal Transport as introduced in [6]\n", "\n", "[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\n", @@ -51,7 +51,7 @@ "\n", "\n", "def im2mat(I):\n", - " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", + " \"\"\"Converts an image to matrix (one pixel per line)\"\"\"\n", " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n", "\n", "\n", @@ -238,8 +238,8 @@ "transp_Xs_emd = ot_emd.transform(Xs=X1)\n", "transp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n", "\n", - "transp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\n", - "transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2)\n", + "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\n", + "transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2)\n", "\n", "I1t = minmax(mat2im(transp_Xs_emd, I1.shape))\n", "I2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n", @@ -266,7 +266,7 @@ "outputs": [ { "data": { - "image/png": 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TM37Z9/kGO+GNzviNMMWy8fsOjE/S8F6/5Li+3Z+T8vJ+Jnc8rw2PDh1fN59yLAOfjHM+kC85ry+/U5/SkHgtNDyTO94cJnQIMcDSjVl9y/zMacfXziCGuqDOkLcHik8sL/nde3s8YhNueOCoTUy85cPzjSP2a68ckPpzLM65Zs4/vzjn35jNWbLi5W7g8aa4P0pvdKWNfWYWG9Kq5+nWSQSOFb7+SHhpWbY8iOXB/MTZwEcOAp84K20avz8WEy92xtfOJnzs8pJ5fZC//5HI37vV8/zNxNAo4ByEyEsL4WDq/MwbgY92pfTQlRYutOPnlxGfGnFwNAIMfGAmWBIIZaL35XO4D+afXxTsZH4n8/dC5u8LBUdVGXygwXDZcD9EWCsyg4D5xmKwPcxtthdAUNHK5dDP2uaO376xomTAzCAXa4Kqrs/RixOkaNlSRn/Uy/LR2jIoWNVwUs6YjMpAAtc7zj8qXtHlDp7QIBvX29qigjOI0zBes5BUMC9KwXYfpq2+Gfui/m1tt/m1dn+p3vHdRzddunN2dXcfqip5VOy2hHLsv3HhqLCJs3aFjX2QqtVtbHOvTkttA5R7WV1962t6wLEXYdX3XAtGysaRlRcS2epLveNj6YCvtnMAjqzDKPI3vujH5ZKNo9rPT3jVeElI23N9M0nken3pfb2c8Vya8lIwnmyWsIy8JMYT3nGt+uh/2fd5srkoR1o1POGJJ+LAL/s+b9T7/Jzs8Yxd1u/G+2xA3ZjFFTI0zLYE4qIKwHfMGzrdyNSzseWxvrRrZmX7p48O+ecnJ3zrwYQmeXUzLPjG1ng1Tdb7Pjk1XhmAIHxof1q4eLny8bbk9COzuP7+yW6zbPz+3gZOk/KBxni2S2xjnPGO+Nr9CTdu9tzOA5Ot5X/vVs8fPI48X2fK/+qy/J+LMYsGZH6pq/3ROd98VNp4ECJ/53bmh64scU+8Psx5ry5ZrV3b6aFQ6GEn8yN2Mv+llfn7QsFJ7pjGcRjbuIFMKr+FIuwyurHKNN/dEZW1T1ermyu7Y1Y6NKWEiKGe8WqFoJ4JNTyDklA18MIJMSsPU6a4Xprs4x5kpbhoBGw8vzsqXiw2qoW54sVa4xiumY2bVFEvR0vIhqxMdT7pyDKqECFmKZweEXDDRDDARddl4XvVNW9HxWrrrR6j9jPFapWLboeSGXAsKy6JwSB44SllT0hQJDtGWZa2uE9oOchGjzFEi1VHYM27GbcrvCZnyPkOpa6QtKuVx8HQ0j4xghcWk8oopvkO992DijdSmaX1owmu3u1cSGe4wIf9kpsmXBmEU1OuJnjT4GoqJmlxOAvCI97zgjV8mAsuXblMRhLhAzlxHuBGaghNebO93k+4kVuutGc8Rc+wajC8vPSBf93tcSyZpxnoujIjXKox8cSLPuGIxEQyS5R5s2S1bMgKj5PoCBxI4qW0z56ktbxfiPHlsQwKn0kTJClP1N8vZOOVsEd0543aEwJ8y8EEpwwAF6nhfQdTDuw2ljIXSegQfva283VzB4VrwbjMTkvL03ubmfGvnV3w1CTwWteSFB6JKw5aZanCy93Ay8C8CTTe8sluhUhEvOcPXWlxgU92wnGdI33FPpwk46OH9ZnSwJE5vThf28LHLpfMRdlrImerzKNty2urFc+9mfnyvc29/9eXsLwMUHly33PVWIQ5LvCUL8hbLmjuclc/yNjJ/E7m74XM3xccnMl3/aTDhny25nzw9haYt8L2tayVHlVGQ4RoMTeMW42/1xyVt7AOOBu3DVR2uTsiTs6jclJukGrYRDbdYbFJd1iU1nyUu6w6m+veKD3u5eG3vHGNjYqK+0ikzus2bCtwIuXa7haY7XPdvY05ZNlQtt1LlNVaKdMtHo1TlKLEHW0dydVJFKuRZuvzOqRKRF5b3ipPZ3RpiQiatyx5dX+1ojid/fyffKCntT/y7d95h7w/nRMvqnFl2FzWpX/uDA57WxFp47EGUd5MZQY3156UA4sq1+Pvi7rP7C2OuUCY4lzWvt9zZ+HwmHW8kCbMmyVDVwaQg2TcTOXlN926I33syHUgAFjW53jCpr1LlAl5vW5eBxxfxTp5KGr+E7rin+V9vk7PeKnf44UY+LJ8QZdaGlvhLtTJIzNznpOWR4fujmvqEBp8/f+fnS/4mnl5A18PKy4HOKHFMqzyijY3PNWW9vzCRebxJqyjdn7+MvPNe6XNr6cSXfN0LBf/fG88026iIV9brZiL8q+7xONReLQtA9Gi7/ngXNbm/fdNlaAbeZ90xU/QirJi4If+0a8+0PIOO5kfl+1k/ksr8/eFgjP97r90h/D7OxCBgTsUAa/E5DECZyTwIhnqA6Mi5Grp0OI3KvurlJDz+nub3b8NrUqA1Qgo584w6vX+Y4idbLaDO4nJ2/uVdaNSUo6TqzVoXDYeJyC4+pqEO24/bqNbHCDJJawbMpJLNBKwNvniqbiGXHFPm2iorbbmMTqsLnPJKLZ1fEeyr4nWI0YlBraivLKDZFQCzp0m0eLOkvV24zUVdQ1i3rjQjJIO4Nbf/88e6Bf+f/ut3+EAN3LgMR24kZt33H5b3l/NE65ZR6jSNQae2haBfl/hLMMiGVd0wEw4z2XbgDNXOM/wemq4Zt1nnW9f4dm+4f2xY0jOQoRFMqZW7t24/6K+6K/owCLbelCZ1rYtkPVLfcRjuuJGKtc7FbiF8V5drJctKbP1Q5xjK9vePVgsUfY9c1avdzbARYCZJ0jGK9UC+wSF9BjpeZ49rqdET8eN2KzdBMeVw3HDwzpqxjLMbMW0yrtawsJAGpTLQdkLm+tpE58l728MwqEl3LREEm7hxnLJY5Py0r/set43LdcQAgwDXB0uUVVOreEgdQiZP/KP/ukDLe+wk/mdzN8bmb8vXFRoHTgBUSkKSbWErJUEERijkFC8WhQK+dVBiqLjWgZdy4brhmsjOjp+RiZLVQ60uE60um6ySBW3MryPDiP1Yvlx3ZBvUSWLo6m4WVSEvkZ0cRdPqJxso5iUh1dwtHB6qnIjIiCKi5dcMjUvkANOaatoEaJUyb9QlcN1s8rxs2jpA0aXW11fHFwlt45oJf/mDXmYvMmVU88tvmX5opB9vebWwUu+Ha2WmZHDk0elUEFq6Dzr424ph0JRgrRchHiVBYqOKurE6qKU6ot/kPGal4ddBG5PMxNJrC4q6bpu0+z12LIhTzpyH/j0snjAn5osOe+NSczMUuZGjtz0wPut53YW5jHRpMwkKq955LhxBpxJ7bbzPnA7l3wZAz2v5YYr2jPK+2PacyNH3hc7HDgT5UgdSAwoN90g97yeWt4XV/xmH5mKgGU+K3ZlnOlaYpGMlUdu5HKqKzpwMwcmZE4l4Cp8QJc8mybrweJGbllKGSwah1OUY8ngmV42L38Lwr47Z2IQ4KAq0Wd1kNj3yHu854lYZqiv+B4zW3FNEy/1ezS24pgMVnvBoMnGrRqleJXEK5dl7n9974I0KC8uI4+EBCHTJlgZfHqpPDXJPIKzqlFC07a+p6q8v0em6zwve03Da7kcv8mJlI3zOOHQVxwPl1XeH3jdBtjJ/E7m743M3xeZjKVaVQrBtVgPkJE8ZYhYieAxLSqHCMHKR0Sw0eKjNVxZdf1dZEPCCnW7vKV6jOtcpViODEQUq5FZLiVEGS0fKcznotwA6kIez0nl4IgiVrc3q/vU9pghoqgaYiW6q2S227QXtohjdR3r5WWbXI83XkPh/tTrE8A21x7RdX8WhaP0hasVha32M7Vvdd3vslZIxn4XEfpQlotq2S8aaqyPPV6Hqt7RlsSWCyqUSC+0KkpWP6McaKQJAYsBCWMyR9tY6B5gNHs9ca+nnQ1reY97fVkenRids8sWEch9IGblmYMLnjm4IGblPfUl10ng2JyJOINljs2JWZFsxKx8IJTZ3I3VZrY8BeYx0VvmIhumA1OE95CZx8SFleOvsrLKyjwmVlnLiz4HrkjitdzwuJVjq8JchUnMDCgRZa5l5nwtlgFirsK1mJmHFVPLnBMwK+vLDFm4cOM0B84JZTppmZVHxMssuhPKi54yC75iic7LdXU4QcvLf0Lmq2wBIkzIHOD0AkGFF33Cy1G5wsBA5JU8QS3zeOgZiFzTxCOVdLoSY2bOzJyPyxxT5yBmLvspMQSut84sOhYyXZXPZ+YDl4MytOUl/enl5pm5Rg8qTNvMC8vM3qR+msy0cc7yhCPLNAgwPFTyDjuZ38n8vZH5+8KCM0b+6Bjysz1LH90+1VKzVliqpik1z4tnWc/4NYNsRVBRjDhYtfDAVsbGcQMAdaKXqJ0kpUXmJbzaR14yMACNWAlh385ho1ZC2U0qcbo0X8fl3Mmvlep2Gq0l4OCytgAlg0ixEo0WK6+mjSL3m/0SxXIjNeeP4yUlTi7KjVMiu7zyhoRRdzFwH715hXycq3Ln1TUkStaiOE1dER+q5ac8GOIlDqoQsDeuMnNAcrk/KgQJhcNEuuNeBjY5jLYTGCYvrjnziFjlDPHZWTIfNIzyPrs0LvYSzWoj70O9EddCYnAlZiVIRpYbwvhNb7ndOU+EgYsh8kToCXkj78mEi77lKKwIDscYr3VlBv1EGIpAA3uiPBo6FgLPdi108H7puekNt8colZx51ZX320CThZk5l+5MzGkTtDX9wY1Vw5NxYJmUSYYpQsvA49azyqMZHxZQlykWB4YcOFJnYOAE40O+ImtihUFY8XpqGJD1oFDgvJymHMcSMnN7aIGeufW8mfYYukQTMlcs8eow5VA7rtuSV1ILfcPKA70Uc/1SjBeHPUSEV/KE5GACp6rc1Mj3yi2u5cSrOoFq/t/3zBmRRbbiCa+tepyBw5g5oTznH5gpTQr0IeHZeSSV9j7WUpN7bnK7XLGORCa40fZ7aytvssUXQ+TuOXYyv5P5eyHz94WCgxgihZ0dPDA6U0reFVnnVfE64xcqMVaVYRgws2JU8epCkpI/JqUyAActtFevx9HsqBlp5M1UrSPkjDt4DRePomQT8jCgoZaGEMME+pxrUkEviQe98nSqu81yrhFAFGuHl6zLAYEhs4zQJHBTchrDynWTu6f+H6rLxiqBeRz8fYv4Ii53KE54WTbmn3GEiJGkMINFpCohNR+QyR37uvkmIstLmYTgpU87HNEAOaGUe+UuaFXU3H3d9kzCEKyWttDqe/IcsJxZtYoNCaplqbSIdbsTXiLafIz6clTvD5H9QtCsiry/7C2PrnqoPnF3IUgmUJToi6GhD3DgA6cEDixze6UcNonr2ZlIwqPSZmcpgcuaXOxqs6APsLLIaVIeiSuueHETXPTlmACPxgWDG+d9CyR+Fz2natwaMvNgnOfMSW7ZN3guC8fAMgmdljIgz3pk35STbDwZO14aIk+EgQRMcG4MM56UJcnh4xZ4vyQWQ8sbPvBkTISsxBoqe1EHq0+JcTUr+5q5qgOxOisXbGT0SGGgW3MxenGOLJNQjsIKz8IzKfFxJkQBLPMKEcy5PbRcr2Z7gNXQ0GvmEakp8XXg2aHlEOeKr/hneY+pwFPNgk8PezwVLnmx3+PpuKj3zBlq208kc4RwhBFJ7NODwHmvHOQVL7aHHOdlkXcvvoE75R0OspeM6xR5H3wrHOUBxk7mdzJ/L2T+vnBRYQpW3EKYMpgxmJXsv6GGDQdFLRBcELQoFymv+TsuYz4W2SQHDAEPymDKVAIBYQ+DUNwvHhQzI5tgZkWxiVoUFjPEFBMhxohq2TZUt4sEK66YGIhqJdRZSx4XFxArLhixqrHW60vueLTyXYqVQ4NVPous/wdVwppfU45d6nSVbVTL/hOsELu2XGiqigZDzNbuuo0rqLrQqqtLTBGXNefFtChaA6U/R1eYa+UIrS1pVvpBipvLtZSwGBSCFbeVRyNHw0xoJ5GmaTAzbBKQJjBxLfkttIbd1z7XYJgZUUqfuxSlMwmkhyAvSOcBFbjWrugwXstTXstTXh+mrCRy6YGVRKaN8x5ZIigHDJwNSrISLeIqXHoAFy414JaZTnq6CK+yx/vyioNh4P1pSaexWBNN2IuJk6ZhP/asaEiqdMG4bpkcYa6JJ2KpfXM9ONdtRU9gZso0DMSgPG3wZm7Zw1iK1bQCyh5G9sKFu+0Nh9bzDr1YAAAgAElEQVTzWm65NGOfls+kwLWwZB6Mi76hTXDRNwScZ+LAM7G8cN90o8nwXJpwSOZNN47NedMDX5FXvJyKm+LSjSlldrxfrZ1XdSBPEnmScHHmoWOuUj8wDx3TDFN3pu481SyZWualPCULvELkiiVMnFsY05BZeHHRdpWt1wu8nApr4oUQaALc1MDtJuAGR5rYj8WS63srpjFzfhC40l5gRFxK1IzvrUh7S/Jshe+tiASWsSTnvBXL8x3vjzf0F4ydzO9k/l7I/H0xHS6mqZIcrlsniSsZeoMX14dX5cWDUioiSHH/iBKyr2tS9e4EKS6ToigrKkqnmdaVQfNdfBCICNGFTmStYY78kULW3UQKJdkQlJNuQsudWt7By0BdyieUdowKQqKQkSUXF5KprsPiU+H9oqIkz8XSA2wP5yNHSby4fKZJOQ+Zada1C2x7+89KiLj1uWNhENZGx7xxHVlVakY34VinSlVL/hwU8oboLAhmiroUi9qQcVPIqWSKzrm4CWufwpg1uaAnEytPyrMXBdWdRgyRxLoA1gMOESFJyX79Sm6J1Qd+0CT2PXEWitnO3bkQ42woOSQcJ0rieOi51Roo3B6MA+uZ5Uw7ALHnIGfeaJVrndOp4po4z5GZdrjDk37B1S5zM5QZ1SKW+3gmhctwiq1Ji+cSCXWmdyKBw60w3adY8hla9mPPyRA5iAPiwnTIHDSZkxwYQmDw0vq5Km2C/ZC4GVpeosweXx8mPCllhnk0+koF5qq8RstRLoPH7/YV/zhM+ZrUs6zbTsLGZTkxR9LG9dFSSKDtlltzSmTQ4ioAINVBoFkxzbCnAx2BKYm5ZV7vjSs28Kk84aqVfa7UyBoRYS8Zh9JzawaPX2b6kbM2WZEXkzJTRcmVMHuCcVAt1HfLezsI0JBdOBxKLq30EMg77GR+J/P3RubvCwXHzDAvhS7NlMadJEZ0GKrro8uJaIonJ1QrA0BNIICkMvDGVOpHqQidgqZaMduMLjuqVpQpHxWOYh1a5kxIxZ0TBDpxWjH6nEpV8UKMWUf4BBeybbg8qNFLUXhSzmuybpZCCiq5bIolJ5GwarXQVC4hICXSK2dMbV2mwsc2ejnnIA7JiQg5CE0u/mfyyAMqSsg6hJ1NJuGYNpXPt+tfQU0L6FQ/X91mXCuglMguMyttGSOlxJEqtH31r6acqpITcDJigcEd1DZB4hsPW3WjKXjJ8ZMpOleu7RQVhqxEMSxs7fiAYt50TFPi0uBIFhx7Lj73AVbtwL4IL3V7PBIXdNm4Gi7W97MdlMtJ4WDt21AslhRT7Auzlse6FSdBMMlcxiJX2ZV96TmrCRvPYyRJT8hl5vSEX/Bpm/Oh4ZSXvOE9ulyHoyogbhxoR0JJ9WW/1yTeFGWWE2epKXwuy5z4hBwymcQhkHTB7X5Ko4nT2PJ4XwrxXbUO14QnLeGlXgaWLsNpbOh6OCJxG6PLwntYchoDV7NxEuE8GViZRS+GwFDf8Y+z4iUt3Iv31VpFGVub9ifVPfBS1/J+6QHH62DrpfgKDQMzc3qEJ+PAishjJGbSc+YNkjPv044XmTAVuO3G05dDcdFKYjHraS9qfE1XM69sie0Y4WjLGam9RFZ7SHvJ4nCFuzNZKsuJMz+dPBTyDjuZ38n8vZH5+2J6YCasKqk3qLIKymBOZ8V9k7W4jDJACLgoWZVUuSQZKXWYRPGmuGWyCY0rQYsLxDJgurbcZBMkGF5rYJgoKUjl8yhRiwumsRLOp8GKK0qLWyfbJtJJTMEaSqXxGkElY4TSWDVb8Vo9VoMRpLjZOisK1nZOnWxCX0m9qoWlb5SIriCKhOJmS1p4PYpUd1g5TrRNhFJf2e7uXtrNxn23iRQrLrEcNhFgHuSOSDSr0WCjZWeMyhIRsEDPJkori4KFau0aXYhCclmHDmYt26qG4qKyQKhuwGi2dlNpjVZDS+HRh8FFpUl4xaeAMtXELRPODF6PAcnGJcbcEsvcQG5IYiw08KZNuQgwoBw4yBBIEaJmbjfKM6sVUwcTZ97DpbecmxNt4EbT4qG86A/TikYyi2AcuhM1815OuQjwpK5YGhDg2J3sSmwSCSXaAK5cxshNPWbhUy6kJTap1pJTZnQc5My5T1jUjPGHzYL36JLr+ZzfiAcsIusXrIgwWOJlJpy6MQ89T/oFh1o4dwehRyJIKLPpXLl2c0vsh6LIX2tWzC0xt8THJ/vsa8JTiSk86VvaBHvizIfCo7joG75cem5oS1Yhi3ErTUlWXviSDR0iksv3ac40DKQUaRkwU14lMJOOfTpu5cibTHlhYqz2yox3Met4IU/X8n5LIoawL85yPnAx65HpEl1NEDLSTWhXRrPcyPvF4fKhkHfYyfxO5u+NzN8XFpyVO9HKACjuNKPeNRKPRBHPlRCs60IGwYsVg3xn8j2TwsNxKZyQXHPkQI0+UrnDtUTdVtf5dEpCuYzTayEQuztJIWSt+XKKpUPGNrojo1LigTxyccgMlKR6QE0sU6xMoo5lr8TeAF5Y8wpr4rNWUyc1xwDZ1+HuawULQb3koOklr113gziWShSUByGn6k5yZ0EiihBd6aW6zqoShDsqRV0rLiTdqi9VI8ME3EvJexFBqtbt7gQv2XOCFFddRmjEGDSXvEGU7JqpUdqhnHMgEwhFYaoFOEMWUGOVBkxrviAe/Bf+aYSjvuNSAuaZ42oB73B6AjEJe0VlpFM4iWVG9N7lBW82U6arng5YYgwWuNZf8Eqc4nLBy5MZ59KwiCViol3Aahq4tloByjwnIHAybWnSgk6MCzvkeHXCRZN5tjlk3ztCWnArCsd94rRvOGvhMA0QM1PPBO+Z5oGh6RnSnCmZQSZ0secCY9opDC0JWOJcCkgcuN6VlPV9YTMU0ntWZlpekvueuVhbTMvj8h4tpvxZTdA2rdvMcuYVmVSOWuIlnfNkf4loRiIssjENA2R4Lu8x0cTT/YI3Dc6kWBtVSibuqSWmqVgNuyAsPVfzOaxMKDZLpxmUVpyplVASd2e/+q+vL5yLMOGkER6/dA5Dz+Vex+QicE06kjp7OXFl1XM7txxpx+0Mx3HBgoa2L1d8updoF+Nk6LOT0j2I2Mn8TubvhczfFwrOngRWZKJBnwrXA8qs32tIHqJlENeiCLlQksDViKhN5fC6vG6rCpZ9zfVQKwpS4bIUBWUsACkYaHXMpGI9USqRlhpxZU6hXxXlIld/o0nNmQPgxZ1TyjoIJiW7b0mOd0cWnnW9raJh1Tw/ZELVYhMCnkqfpEwKslbm1sU9tURNuWsx3HjphyYLXssbGILapnZVI6BWrDfkEskUvIS3i5ZUgIkSPuiqhKrkjUVIM2kTDw4lbLy6xiSXiLjso3LqZMmIKlFYW6vUnSSJYssU+pQJurHyLM1LGYhqScrKmpv0IOPaoJxmY64D56JrhbWvCSxFhIGGKQnzjHQ9fRBOm5Zp37MIc/b8kinOIsJpbmlyRjTgEZ48P+Nkr6Hpnak5njOLGJn2PUNrLJjR5AUpzxhiB+0SVqDScjUtmQ7OuczoraGTE1aTQAMklF6V7BOuDOcsWsVoQZzb05a9DKvUsB9WHPiS27ZHtPNNenwHj9VtmhSzzC0/4lhuc60vI97LesCenDIdlMjAy03LOSXC8epQQlbfDJFmKBOImQzsMXDmgd81nOIKlx7Zt46ZlEHhPAWuaYnc6YIy9wSiXPcV57nko5pLCdk98p6VGVc6YYhCzDANA5lE7sP4gJOt1HATEQ7zwJvByANME6RFIMuKSVZml6HUaKuTkKUoyxqyfCvtMY0brsTtoEw948uGqS6KvMumztCDjJ3M72QevvQyf18oOB60VAoXIYjXXDNe89bopoRCdVOV0gTVYmCluKZWC4f4mjmy5uJI0Dt8cTH5liLEeuAu5QtKErwuKJZKyLOTC3lWS6h4TmldtNKqFWWb95KBqFaz82ZEtPByvFoh7nIxFgK1bqU8CHXgBxXDx1pOoeTlKZwcR4LWOlC5hIWP2YhFoZZgAMBKFuFEJmYBM4acSJWYrTUqTVQIOW9qQlGub1SokpZjlDpUgigM1UKUUi5RXLlY2fBiZzOF5BlRK1YxhyCCZeiEsnys52Ult86YAXnMam1Ssn9mFFlXD35wkb1GuTm0GO4DZzHQDo6rcS5T5r7ghh0BMMSBq8syozu3OY0uOGVeDzbQ5UijC06C0S4iZ7FF+xJ2exrgPavbvBEOuLQWSWCScVqenUz54GrB45cX/NLBo3xwcclq2rNsIawiV+U2J/EQbMnRYuDmLLK/GuhyYhGVsTzuYurMPHG8WnIQnG6YcjJ3wtIZmgmHl3fOyBYxEsXL4CILUprxyqQc66jrYCivpTemM64tVpxMW4IvuGimPLZactlrCUclE0PmJM1RlpzWoqyJBjKcyoorQwZL3FZhaUYaFNXMLJcSfweeORNDU5U3Fw57GEw4tTJj2c8B8UAbV1zkSEAZekMkI2N5EVcWWZmFgZn3dB5pNbH0iEtm2mVWsaGlq++JYuVcubJiwqEu6WSPZVcGkL2h5yIo7XD2borilww7md/J/L2Q+fuCg6OqBC0GMSofpbVAtFDcSWO4M8WaY0EJIRQeiekdfBIbw7lDwE3LB+itKFCxhocXi4CQKoclVb6LmzJoIQSL6jocPG/VrbImluUiNdy7rHcrnJ+xGjk18R6ASVhzb8QqT8eUFDeZmXXMaGwlgqwRQ9TR2oYx5HuM1ApqDFK5NaHyccY2B8MsFu6SQOuKYfQxMIiXPmArU3PFYIXLM1jhAiWpQflSeEDrfDUi5Eq0huqqq9wcVQobqbY3hECs54lW1nuAoKBRiDEwCcpeE9izhlmMNNGYScDMmKhiJgQFHoI8OKsIUwaaWkvHKL/nOCc2Y25ntEWVZz9dciWdIyEjITO3UxoZOOKE/bTgeNkztzMaGfhEc5VFLArpIjjztOAKt7kILVe44FOTPX5zuseVfM5vtlOeyEsWUfhMM+eZ1ZKpr8hDg61aOnPaQdlPCy7yAVnKQDIfEr/ZzrgV9/hEc5X9lAldiZa4NZ3QpTJ3jcsJ2VqaZcOlzbm0Obf29vi12TUimUd8geeWZauoGNmMxxcLLnXCZZwjpszywKKNHC5WtBI5HnpOMbxVXp9Oud0ozRDo5zBMWlwnzBwWTeLxfMmRZz41OeIsOOcxlpdwEA7H6rvArWhMbeBWNIaQWYWSeDIkOOzLByAkZ+ktl1oyyeYItxsrk7LYc5w7TJwLNTxAG3qyZBrvmeSMB4i5YxGUliV7fslclsQcObBLLvKMA79gz5YcxXPMhP0Mi+bBl3fYyfxO5u+NzN8XT4/X+lK4ElQo5BZh8IFAiTaSar3IjPWaxkE2E7REV60tPxR3SmslzHiIMHGp0UyyDs2OYyI8ddox1K0mtovBqkXHUaluIKkuMGrodN0mZujEmCTh3DJtVrYTKbsX3o4BeSTr9qlm9xU6LS4owwuvqF5jH0o9Kijntgy9OYON7q8SSp1xbIu7UmLBtAZZGtYIKTlILm1A1tmT2wSdZZRiCTO1UkTTM60GejJDtdhsLEwlTBxK/alMSdalKE07FtCspR4wXEqI/opM0FAj04oJ06rVZxAnmFKDzlAZQJ0JwpBSiZoT1iHrDzJcnaUCbuDGwpSUW87iBUec4aJcWsu+d2gcSHm6JofftMhx7jFZodrzGbnKE34THyJf7aeYrng97nOUOyz2dHmKxo4Tafia4XVW0rBqhK/IpwC03vGSXOUK59yatIgLh3rGG7rPKsJNLbkvzmzKuTXckglfs7rFa03LNyxe5+fn1/jIxcm64jvVunizmXC1v+Tl5oCJdDy+PGWxjFwNr/Gr8VG+Jp8wt1P2V0LyzHzoeGF2yCOrMmufiLPfZV5qZvz6/IgPrhbcEji2cy5zi6FojgztgoOuJw4l2/d5mPJq3Oex4U0WUbnKgt6VRgMLDTy5uOD5Scv+CjSumA1UWYRHc+ZWKMnGxMGbYkqXzljW6slRVyQ3ZtoRh4bj5pLVEBmiM+t7SMXifCwXvBFmzGTFTd3jOJ0DsF9TRwziaBAmukQlMI9l1jolMaTExZgM5CGQd9jJ/E7m743M3xcWHKnWiTYWLk1JUqeYxTVHZrue0xj2vOaEOJgpolbqF+lWpI8qjSpqQhOKRSOYgTit6jrZn1dFxsaIpjX/p0RbiYPEgGQnUi1IUiw2EoyJGH0QWjE06tqSJKF8mqpsje0G1hab1kqywByKxcNNMVMmWhILhlCsP10cr8cwU7wtFq5oRU9dW61qH041oFZSiLuUmh/F01csQo0rOW7aOdaoGpMaKtSaVZXMrCVNt4Ri9ZKgTM2YNkLTKDGAi+JWrTZmNAGCCbSxHEtKuzZWOQqZfEzFbIVsN0jECfQYk7bkwYnm7D8EM9rBpySfci0vMV2hrkTpycxJTMg+Wct79gm3Yi1K6I7lIouWheRTHtNL3Nu19Sz5hCN6TJWY20Lm1imIc5idhcwYdErrHU0WBpswka5o0IXUxokcl8RlHHGlW3LNz5jYikc4Y54TJ2HGkXc8P9nnvcOS07hXXMlmnIQZtyYtV/KwlvNlrZ+jccBF+Uh6g1nueSMe4arcbKdkpjySelBlj4FLa/mN6SPs5yUfGV5jbqdcTpSlRPbtAoB5uOATzdXC4xDjyItL9/cvXmMRlWurJV0UWonM8sDTywUnbcscZXEQ6FSZWZHxmUNwOEpwRlsmQIOhy4hbmWhkN64gXM9LDlPiKJxzrnuE2DFnYL+55KA9Yxo7FpOWeeroZMKjeVHc5FXecxC6cVbsxpCVlbfk1NKlhkMTRBJzTzyi98Ur+gvGTuZ3Mn8vZP6+eXpEhI58Rwz8ZxWTrHyXiYa1y2YMeV4XcayDc66du71fL86g0FdSWxeEada1JSSE8lCtQ8VrBmORks1YRGiaZhO9NEZrjZagSuAt5Q+Kayr6RlGKMVa3mjGZTNbXZmb0tYSCmdGwaXMOmwimVmyjfNx1bWbGoNCKrdu91E27QgjrPtlu/9hn4zbbx8/tRhEZ3Xpt2zKRwLTZ9FHhIG0Uo1FhHHQT6ZVSIsZYCIDNnfdLVdmzeEdboyYaK5/xnowy8jBgNlxy6oHpsPHVx1pvLHpH9Fz/d1zvBxp6GnpEhLl0QCilP3pocE7DXt0n0w6Jdki8rlPesD1uS6nv84Id8bu6G2hyLDf1OBA8ED0zS0LIDbgzS0LrGZFS+HD8DNFJ1hOzsvSGpTck62lqZOMT/Ql4LXHSRuZkZp4INUXCShoGnfLp6QxNyqBTnlqera/1NOyxjIVv9mWrN+miMBmU5FMsGxo6Um451DNet30+0r/OTMqs/8XmgFRleZrgPLRkAl2e0uUpKUOXp3hu0ez0zbwEKdRgg89MriCuTDSxUkM0MDFhrsZj9IgpTQ0uOA0tliIhOyE3uBcehgxG0owMMBHhYMhcyrRaioFsaAocDgEZynMS0oRJ6DnQJQc1emavlql5WOQddjK/k/kvvczfF9PhkuNGMLzUn9oaGMvHauRTqeXkXhL3uRfXTAIsGORSJNNqiPKovblXknI9RgyxuJz6BFHXxcMGoJR+KFFAljfKVTmO11lDLJFOCILXkGmnCUWpyTkz4ESzkhwvF3J04a84WYUhZ0IIJS8PFAuQFPdaNtaDvQjE6obChFjrVQ2UDM4aAyD0norGLSXEG6CJsSRYymPklhDDhksk4iWJsQqhmlsnozvOS2i7atHk1RWp7RvcUXFmgHuqSlCJrQLH3CGUaKqsApUzNCToRSEVArmI0nsGcWIp416tWk6DEQVSSuRc3GdB8lZh0gcXDc4q7KE4TVoiYjTSg68QGhqFi2wsZcJVOafzzIXsl4RYnrnJPlMdCEPmLEzYH5ZMcybXIrXuTpTMFb/kwidMk3M6U+arjiUTOho6gQufwFDcB8/GY47SCtyxyYoBCIvI+QxsUfJW7PnAvHP+f/beLNayJDvP+9aKiD2c4U55c6rMrLmb1WyqRZE0J1EiTUkkRROGLRA0DcmTQMvygwFDD4YsySD8Sj/agC3DNmzA0GBIEAwDBkyQEmVRImWSTTYpdqvZXVVdU855xzPsISKWH2Lfm1Ui9SAQUmUVcgOJzMq6eU7sff6zY+21/qHJGSeZK7kM63sqnriKvdixdjXePGs8B2nN2hXPo1NtqGOic3OuxBU3ER6pp02JwT8tbjEj5AqXI4MPLDKMLnDuZuzlLc5qnBj3/ZyDYSghsLkFgxvpnLWr2dSglI3sSkpISd9FvWOp55gqMuaycpeLI7nLHNgZ3md2DbxlhJLTFnMmu8gLdkZPoPOeYLB1DsHIUwBikzds1YMZ5mHY1vQCkqZGQfR0TokusaBYQoxSUUnGaSLVUI/yqcM7PMf8c8x/PJh/NgocX6i4KZY/Z3NF128OKe7cZAE/ZX5k9YW6O/FpmOTgGaX5UIjmRR0YRBjtaTimIWVDDoVEW4uj8yVaIGLFcI5CSI6TPHycnjSqyZMHPhTboHIpI6/EEaXwaJJIcV72ZbRVpOqGz0UBBYWMK9O5VBM3SKzIAb0JwaWSlG46fYlLUVAB4oQYI0mMRqdiSpSc0tR+fepdc5F7BRTH5KmwueDyIBBE6S2jGZyUckwEqomQPNp0DrEY9F28ppBJpqgUTg5TByqlVHyBpuLJ+en8stEbBINq6nSZCpUqnlRCOtXhJnNEiRGJmWg2Ddk+2Uet5ebQ5xofEpscqKTHx4D5AUFYL4zlJoEZR7rLrm1BoLUNZ27BLPXcc3sc2IZtCKhlTidx6s14wj23S0vPaBUjQj30bK3mceW50a95qzmgCiuG7PHRsW+JO/Gc98M+0PNk2AUHB31Ephvn2cyAkfN+Ds0aemVmiVh32FBztoC8bVAcO2nD6cyhg0GGroGucjRdIokni/CCneKzEl1mKzN2Yse+PWLrAjapKutR6IPxgp0CRo/wxO1yc9xgGDG1zPOW87plI3VxCo8tx8GzP8ZC5zMYnBH9RNC3HkRYJuPUlwehWczI1Nndy0PB+6SMTOIIqeB9YYaQWPmaeUwF72MZrwySGM1TpxESZP/UNmGILTUDrRlEpdPAsjlnxuYS78uTkSjGduFoBv3U4B2eY/455j8ezD8TBU6euDTelzGLt0zRU2dcLrwUdzEKMiVI+X2gXEw3dWeeZiTBoFx6yZgIOvm0ONUPBVvmEi3gKEWHK+x+MSNO+VZl8xe8TGMVDJHivJgvNnhVNOXSgZj4O5UpIxnvixX2hQrci5TuRpoIz6LT+kvFDbkIhXJPYheL4H2PqJEiJC3nXowPp5gLFSTZ1A0q60UKYThPzjpKuaQpG+qKP8Gle/JU+EXA4S7P2Wmeul+lKIxaIhlEDKHkypRMLVfk5AZ5yqYax7GM/HIuzlJSij8zw7wwnz6b0aAWITgjqGDiSRPJqssJnyCZFlPEZ2ei+vs6lEiWUCzfm4bDzSlk2DpPlRxRlb08YvUAW8/NfEIr8JbbpxNhf+jKjV8H1qLsD1veD3ssKZvI1tc02rHNDTMdSpxGdtRui5jyuJmxtC0yehpA6NlWiScyK5/PMGeHhIhQ65ZOWmg2JKtwfaBxHX1XNpbr+Zy3hn3m1rOh5NpA4szNoIdGOswbTSf0qogzdocBpp4mklFRDtMj7oYXwWA/nyAuYZZJTjnTObv5HEOoFfZtoGako4wyz0M7df2MnCJC5PpYNqi1C8zTCLkiMExEdWGUmhMHi9hPNvKOui5/LllxwklT8L7oRsR9CO8RdvOABb3Ee2cjtXncOIUIiFGNYEGxmAm7heC6Xe8wn59z9fyUsCkqxpQSkDjaC/gMKQOacIR/pbj8l3k8x/xzzH8cmH8mCpzgi8GdiqEU4q3lC8JxiVfwFFO8mFIpZJJRqyfmjF1IwA1izohTai1FRMSwbJgTavH0WgzyFCGKUmnxjRmn9tyFK7L66TW1zBpTSlM4puGmoLZR7ENdESU4pU+xjLy8UlH8ZpxIUS7BpV+O957RcvHLiRm8oFLqgT5l/uK3v8Z/8P07/Df/cMP/8avv8me/peXvf+kb/OQf/gKo8NO/eBdMyNkoMyBKHD0gkyOmiJTuiS9zzSoLznHJEyoq7ol4bNCnWIosy4gUk0FnRmbi33ARY6EYGewp7wgxkpSxXIyRqg6X10Um8MepwHTGVIZC6zJNLVgqhW7OhUgtptjkoROclSwre0YA+/s8mgDbwVPPOtJYI3PY9A1kIQU4czW7Q895mMFsw5BbjjK8bGcc58BpmGOq3Mjr8t/VkqVm5uPIoI5BlI1Xrvdr3qv32O+3tESesOBVjjmh5qgKzMdMZZFBlCoWj4/WFWUGFkE8s2yIy7SdcLeasWAD2dFoxw0G3tZ9JBe38P1e2YjQWMZcR5PL02dtECTTqXDDBpzC1sGerTih5X5b8V/MPfv/yV/jg7/6Y/z9u1t+ZHHOw7MVb9zZZ2jv8d+/ebNgQiIz1iBQU8YF1aXhZCm8TYz7bpeb6YzaRgYJhaeheSrW4epwwql6ejejSpvyNDo4Kh85q5Q6ZWpxzLfFJ6T1XdkkRl9u0CqcN8bORsmWqHLxmsIEb5BUibXhxjLWTSc7ACwPzjmIPebkd+F9cWasl4Hd846zXYda+lTgHZ5j/jnmPx7MPxPfn0uTPUoHRsTD1CVQfJE3WyZlwztXuCE+T92DizlmUR+pFnl5ME/yZVxTVYFoGctlxJScFUM/YDCKLhlQK6ZzUUpBlSdjJFPBucAgRogXHBSo1JVNWyjjKCmFk28CMZZQt2rqGDFJv4MIWQXNEELA5Yw2Ss6Qrcc3S/7tO8Z/9kM30Lbip//1hhfdGd/16i4/9QMvU7mKs9WGv/nL53zVdoprcfKIllFWxlC7mEsnxJcuTkUZYRXFWYmiMHTqipURnPd++ns/JYdHcEVYXnlpNtMAACAASURBVDg2JdNryBPxF0CKY7Qh1DIZHupFxMbUIbISBREmJZlJxtTjbcSp0Eeh1EsT7yhfrBni9PSh4hFnRPvkky7rMWJtz3xt5PnAI9vHqlKUXs2ZLcLVWIh352HGtTzy2AWeUFQIziIpVzysZlhS1i5yMGSetA0xG8upk7h2LQdpYNMoTS/MZOAtP2d0UygeG2QUNrVnFoUxJZo80LsKcY4jN0M7YeMMs4oDtqwrZZZg45STEVp6pHHkHjZaFQ8R16KW6Z2yiFtWrqXJHS4E3DhS6cAiZtYzaGLNn0/vsPO9ju53/jC3fuKL/OTfusnmmze8sbciHp5Q3Zvxx75yyq81txmqQO5bOkklP4eM5NLZiy7ik+NJaJmR2EhLaxvmeaSynsFqois3/MfVkoqBhawYJRCyw1xHYatl1IxmyAie1dxYUbNYA2q0fiw39m0N2BRw+2G8l25sKyOjeEwyrjIYYbkaONYdYgpPDTtzcVc/5IgkcLbXoBjz84717qeji/Mc888x/3Fg/pno+TstIyBVJThXTP+0SNlwGY+R1BV5shaZ8oWZnxdFJ6WPWOmctKFCHCzUoY1Hp9fy4alBoA+Kd0I1hTpWMhFlPySVrnyRqfvJ06UyQZzgVaidKwTgybAvuJL9RHB4pMjWBbgMjXSXCqPCri/nVQzyekYx/sq37/PH9zp+6rteQNvSOQrzlj/3Jz7L69fmzLxj0QYOD3b4b/+9b0PV06ijriblk4PgoarBuyn525e4BaZr5iZjPtPy80WaWdZRuWlMpyWKQbUUQ6qKk/IeBEdVhcKdCUqcOl3OTQZQ3lOFQJiucxWKcaBOY0EvxXAQJuVccogUrtXcKzu1Z3fm2W0cdeOofTkX00DKIM9GTf77OursGcaGwSljqtnNW17tT9hNPb1Ero+nfH1xODmKbgE4TCO5gavjgHlPQ6QeE30Nyyoz1o4vbI5xtXJt3DI34wYbWvXs5WIMdiOvOciOVjuuj0NZR+WZm1GjtK4C19DiqFIhTErIzLNwK28RgYUYQ1V+P64Dm9pzo++KHYLApmloJUEQGnHseLhhGyBzo9+ACbX0vDnb5d8/vMcfie/y4s05Y90ioWP7+FXGHzTmrz+Cqz08vgUvDXz2xxqy1exuoWHgahoRn9lxK67olqWeIziOmpq9uAWXcfK02N+4luQNnxTUCDKwn3pCEuYpMvpIFY06QRUFh8NNMQAHuWexFaLPnM5gk2qShDKKriJejCoYVRVp2g2hTrR+QIDKDwRXnrqDH3mcrmIi7LojbrjH3KqOuL73iNv1E6ra2F/17Jx3pHXD+U7N/OST79wNzzH/HPMfD+afjd3CFQ6H4skkejUWybGdzOPQPI2CJgfiiVcCxR33YrSDFLJylsLZ6KEEd5ZAcdQyeRq/gIAPOCuVq/qpi0Lhi2Ss8E8mvo1NPgzZSeEITdwfM0PUle6Qm8wIzQCPE9CcLiMQQNBqSsRuPEiFDef85W+/zY9/91VOz7f8xPc1zOq6vG7wJe4heBaLYlOeUiIo7PoW9YLkovpSAxEHk6mS95lkxdhvLcIuQvaUHC8r8RGlq2TIFA0huRgPXnBzPDCaFROoidBtEwFagsPHTPAVMUZMJw6OSAk05YK8XK6DTWOzlHKJmJgcmGdokS+a0gOBXMJNKVlWwXk0lFHbKIGWT76qpPOOHQaCOJZ55KiqaLaGNVBHYd00XB+3XOjushSC/I3NwIIOGeH+vAIRWkbEhFRH3gwLFgw8nnuUERkzXb2ZTCaFx4sak57GjAOLnLgdKqsZqzV9GKmGGbEqT9F+aGlSoG+2SAUPJIBFrq1HHs0CPs5YNmuurSP3lwHygBO4thl5uKi4cd4DIwtWxYbfwdHyCrdO3+TfvWakf/Mf4e7OeePfuEt6eAcJkIaXUfcQsznj+YtAeQIbP7hGNRaTsBxLQT4ILPPIEA7YRkdr51zNPVf7nl+e3+H7Vu+TBXo3MlSO5ZCAzDALLLtI1zgkCY0WG/nGRiocvY6I+cLJS+CqgS7VeM20As0gDCqIH8kmmMvFvA6KVQI1RhE8IBCIjOpRjcRgHOhIxcDjvZFT4HA9w3cjmUySBq8d6pXr+YxhqJl9OqKonmP+OeY/Fsw/EwWOBIdLECnE25CV6B01JUG81xKJoAY+Q+SpP07RSgmqRaKcUSqM0S4yjiYuCpnRHMESySA6ncjMJcRRMgSdZoRiBJgymSaJ+CRH1JzAVUjMMIW6VWKgSsqldefVyLkUD+Yck7seABmHTsz1n/nXHN906zXuXNvBO7h1Yx9F2G63aIzFz6byZVRmgCu5TKvVFryx44w+GHks88yi3HLEXAq+Wh3JIt4i3eSvk80VGTdCEFdGgjaVi1oIZUqpq+Kk5LrI4Cppt1MBlItkXihdM5elJLdPxaKbqkKRhPqKlPMl78dpITuH7Ar/SSP+wvMIoU+RnCiSQe+xZIxBqXMJA/2kH6PusshnnLsKE5gNwpN2h11ZYd5IzEAq1KB2x/TjQfmHbmDta8g1N6acnkwpqh8udrjSFVLf8XKHw9MtD3b3eOH8hGTwYFlzbT1ytNglKTzx0AwjQwhcW49oqkh6ynEoWUBWaSGd57aQ57Ngzng8X3NzJUDPPVGOd3ZZxJ61ixxuRx7v7uIMVMr61uyjk8/Ffzn7NdjZhR95C+eU1Fwhne5RXX8fr0q6X4iQ+C2h94x1IjcQlndhdYPr8ZTRhWIKhzA6YTA4qeY80Iob/cigHVfSCb+z2OV2d8Z9f8iL2yPAkxQWnSFWEbZFaTg4oYqOwSWGKlGNHq9KlMwYIuNEezRA/IiNNRYG6qh0KmxjRbASgBh9Bk3YMKeRgdFlsgmVH0g5s9MFTJSjOhDWuwCcIww7a3KCq11HthaxLb2b4esNqXsmmuy/7+M55p9j/uPA/DNR4NTiGLwhKaNOcaFIjFUmu/9pg48Y2ZdIgwuzPm+KWSFABRSsFEOkkaxPPRKyE6psKA6VwovRXLg9ph4FhmkDD0wckAxpKnB0Ep1n51ASeEpRERzOMkM2QiXkXBjlFzlSHmPA4aZiS5MRJ/XXLx1VfNtn5xxtOq4tG9Q50jAWuXk1uT7mDJPHj5mRY2KxbPBqnDhhL3rMCxtLVKm4QVYiiC9RFD2Otg6lSMpQ54Spw0xIWrhIWR3V1BW0qcDDirweilKrsN7LORQpfOIiwsqJFNXCJHN3zpWx2OTj06eEZKPyhd+j5QfRKV09qCBuKqLGWNRWPqNajBGNxMIcg+SnOV+f4GMxRlZNW0LrVMihhbiGcY6KUKlR+RP6cY+N7CDVCMkhEjBJrBqlq+ZcWfccz2YcbI1rqxMeL3cu3+PRXuD6+SlZPdumwTnP8aLm2uqE+zu7CLCuC79q3c7ovdIO7lKi2YzlBt37QD0Zs0lSsjasF5mND1zbnOPihrPWMU8VXRO4c3rK3d1d1otya9k/2zBOvlZvnV9l+UdnXH13B145Y5QK33dgRn5wexrpbknxGmN4VHyuOmGUF6hef8I7/+jzvLTaEsOMdTB2xw7RxJ3hPiLCbIw8qPe4mR3ZOgLwQjxl9AXvqJFN6LRlnsoYxOXi9VRlJamQPDQp03tHbZOfVM5E57DUogq1VWwmuweA4BzFTkVZuoHT0NHFQO1H/Biooieb0UnEZKSKRoXQqxDnxxye7IAvvIqoDqlhcRIYuiUfyXz5BB/PMf8c8x8H5p+JAidj1K5sclEUbHLenTbISfpDmKTFcTKCg0IA9jHjvEfyBanIcOqnUVRJCM/TmMQuiLCuEH6TusvuSGUT+dYmNZcr3Q03FVNJJxl0eWOyljaqGAQvhfjlHT5nsgPLnpLzWpK4QwY8eKnYnVXcPzvnG8cLrs2VTVNj6w19jNRVKLI9MwgB3Y5kgXEcqeuaHDNNo/y9f+eQH/6bZ1SqLM1hZWZGnpTiUIC6lczchM4VE8DibzNxcaYRVLrIB52SxXX60tvkfeOcQyfFVNKJu39R0Aioi1iccsOIU7enkMXroJDBqSsyecswScb9RWcrl0wx8ReS80LYtpSLRJ48qc0/+STjwWf2dYUgPAnXCwk7LOhcD1rTN4nUzZBKUALbZqTpAwaYVixiZo4gruGgL6DP1YL55NJad56+iWzmk+t2HhCnHGwz650a1TLTVyseRuYMZ4pWRmvQdMXHqGsjWTyuOKNhGgkIdefRsAVtWe9EFluhn0UsO/p6zpUu0rWR66dnaD1iqWKRa54s3uLV+xXbfYcbjebwTbjyEI6vX+I9DVeZpcdkEdyNuwxHN3GdYv11/uxP/i3+l7/xH3ElntBmQ6rMTucYXEtIwtZBmwc2lXBz6HgYGuax3NRl8kkwjJC3jBPeQ4Y0pRw7IIogROapEOIHp3SVY4TSQTZjdI7ZsCVpXeymiIyVgSknplS9MAuJPkKdB0Y3vY9XWivryJZpM3SrPUbJ1Oaw2QY/rTLppwfv8BzzzzH/8WD+mShwnCuhlZU6vEDK0wkW+g0y5RRptuJ26EogpGrxtpEpw0pccTUuidtPCyIovJFgQsIwp9TTxuwxtmQ8oE5J4p56xOSy+UcUjxWWuUB20wgGxyBGZTrJ6DKVKpKnmAVXCF85p4moK4xqNKY0HmqNPFlteXQWebAeubXbUHtHIpIrw6P4oay5HwaC86SUGFPidNPzpQ9g3jakOKAoyTIpgg+ujJByBu+Z5YwFqCkE7ZwzJqCTUkxE8HhGKTJ5N3XLJm5yMeszJVrh22s2dIrH0JwmmXmAEPECXgsL3kkZnUFxgFDKe06CeVSLjH6MGZmcj4MvRRJceP0Unx1ScbiUT/6EilYHjtwNXhiPWNZnPAoTv0orXDZMHd0MNGfqXsF5+iZPeE9IfcGjKnhPo8dXEbXydR7aMqr0E94lKE0nDG3xOWpzR9sH+sboh4CvInnwECIIdPNi7d30AcJIdr64elOK+rEBEU83S9hYMcwjJp6sRu+g7iCLwxrP3XafzxyfMPdbdh9f4f2X5ywe3Gf3eEZathwv/yAHX/i5wvNCae6VNfvr7zN88AKqgnvhLnzgePLVH8aJ0lpCEc5CheccQoPvM6IDPgVC17BhxmKA0GQ247woGqNidUeKFUtdcZ4XWBhYpMy5lsJ/1zLruMu8Hjifbh+zbiQ3gRQr2ryiiRknARhorcNLXSh9flNuzq7gfQEQNggZZw6bD9gq0FMX7pvAXjKiFK8nv56h2YGLJFciCj4lPn/PMf8c8x8L5p+JAke8I7iMy4UDcpkJRUJdiTPIOZc4hunsS85aIf0KJcIhYYSLZOqJW5Nz8ZrRiyIJcDkXErEXnJWsjcu1IIQLIvP0+xSbNkmgi+Z/1Iw3oRa9dEhWLtogpcByzk2Uuck8D8prqXI2JK7uz3l4HjlJjnvDSMrw8pU56y5yvonUjWPpjXZW4aLi2hoqT2XG1ariKycnBBJuakdaBleVUQ8qOK+leHDTmE2suCLL0zRxkZJsm0WwXEZ0WcDrBWcIsJJ8bnEKN/UU6TYR790UjQGeEg3BRXBmnooTMpUTnILE8vRQSNyZ4DzBQbTMYAmdGP8pZcQxrakEgWazy1HhJ/lIrad3ghuKZ1HYTNerdlRDpp9GitUQ2dQeRoq8fxgZmwpFqPuRvg6l6BdDhlCctcNItYLcMCXLg8Wq2B8kT9ONDHXNxlFeVwSNVVHSpapEggAgrDUX64SRkgQ/ZcW4fmSswtMolDHgqgixIvQjGajXcMySegXv1wdc1/u80TquvdvzQK+jtTDLH7Bfv4P+2utYBuZb3NUPSE9ugyhjuIa5JTx6mda/h7yv3ErnjE0imrDsQajJ2wvbgsBKBHdBGnWRFAMhKaE5B0+58VdrVqJgayLCiUK1LRtuiTRMbMaWuRxxJrt0UpEiHNgJjgrVjHMjbUqYOdBpvptL8KHqFqeekBISA12VyfNtUUV6RyDSy8CYWiSPBDXGXIH2JAmoRdBqskt4mtv0ST6eY/455j8OzD8TBY73JRogUjogkVy6LeKBjBchS3E0FhFc5hKU1bQhM2UzjRU0uYyTXJ5GXTzlh2QMr74ojKRstEEKxyQ4z2CpGBddrq5kMUWFSjymhR+0cDU55+m9y88/dQbOl+u76DiIlM5OdpPJHp7TYeBoOzBm4/xsYCfNuLnTlFFYTGziyNhA6Huu7y0RLTlOpEQ/jvzmkzOyzDG6ouoSR0Swy+ys4m1zIWuyyY/mYp1QPIj89P/K2ifDPWzyyAGnHohUakBizCW5PEjxZKjlKek7SSqha56SfyIlX8zMGKNcjrxIFwVkKgVRKtyeUUuRpWqXAZs5l8LTzOjlk8/B2WjDkoGsRqczQjNyNR7zRA6hztQSyzVqBS99wbsYNBBIbFR5uLPg+vmWD3Yabp/33FvW7PcbTtyc3dmmJCNXM3bHDWehYmdYc1bNUY2cVzW7/UiVMn1QLA8XpTmC0XbCtjEWUXmwbNntR5apGGf2tTG2SjPkkjMG1H4kJwNJyKSAKGOCC7xH5GyHMz2j0YzPA9vjEb9a0B80LOOa6BNpNWOTXuGQ3yI/uI0LG3JcoHofd+MDHp4dcu5eo5a3OfFXMG/0o7/E7kvpuDhkT186Sw4z2FY95PKYMnMjpOojeMeNbMJTvM/DhrorD0OHdsrojFh5/BgQ37HIhaMmTuktErKwoKdjRsOGpP4SqxYiMTfoeUadMLabgveNI7gtoxV3XC9bqDI6RqLVuNxjtGz8p8MH5znmn2P+48D8s1HgYKVzo8XsrQaQqYgRo8yXBG/F5tlJkRt/OGcJMkmnOAAFL9NGilFPAZUX7c2LTCcoIxOD4lNjRjOFeObEU0KrZHziMnVb1LAi88GJQMrMfCTlBlNHlYTOznFJGOqakHN50vCKTx2mLYmeN/uag6ajMRgAM+Fs0zMPynrMbNOIZOONF69j85JdK8mgDlQZ/tL3fit/4efe5jh5sARTzMWFysCsBJhdFAeY4lRQP3WUrLDqBT4iAdeUYTrXiy+AmidV5WcaDK/l3CsXLkMxHVCpR6c8khQNURiiTMVZuUE4y8g04spZiJlLvpNJLpkmKCkXyXgpGROoI3wKZlSH6QkmMFYF79c2a6BGgrG7XXHaLtkdN+RYYwJtThzNA/ur8mSTQuL2dsXRbMbNTogB9hc7+O2Ww27LgRWbgWspcazKYbdFxHG17wDl6qRG2TfPo/1DwuP7PKobXt+Wa/v41j7VwwcsfUXuBoxE8JFePcGUSkZu5zOGYUY3O2C+6RjrR1Tdgq+9+AYvv/N1tuqg2aXZ3KULt0jVPZ64in16vGUSUM1HunHAvXPA2UtH7K02yP5bjNdu0M9uAuDOlbhzlf6o585rf4B7X4PtZoc9t8IETtrZJd4f2A7Xu/Pfhfdjv8etdPJRvPMU7ytfcSVtWbWOEC8kyDBMLuazKMzGDicZr5mZQJc8bU40jiIgUE8zRkQDnUUktQTtiDQE3SI6cUM6oaMBNVwURpfxsSJSY7EnWA8oUSpwRviUzKieY/455j8OzD8TBc6FEZwiJM0lsVohpCn3SSheNVoUOyJCDmWDjBguJrJoiXOYWpTZBO/KaMsm8z6zNJFoiymfN/lQe7Iclx0ZX2a+AIZSSSZpLt0coxgCTvyg//gzC15yPXF/yTcvEz/5D57wd37gBXYWM37r/in/+S+uWLiK//D1hr/95siQja16XpCRHRKz1rPXBE43HW89zMwah68c+3XAUqbrR6p66maYoUPxkvncLeHqcI9t2CGrI+VMzsULyDAKT9pdDuBK3pQBdhmXkAV8LLETxT47YZWi+WIkBDElknP4KQ3Wa6YRBxMVebBieNhnI+WihKtMkWBoBu9zGUrplPl1sb5J5F+k6hOPKksxTBRwVhyuyzv5Kdz0kz+iIrZo8sX1ei8TvbBezFierzCnLMZ1MZGsehoK+fFaGpCZkHMkmrGdL2iA2o0IENf3cXVisVrx/q032D+7h1li5j0HRx3vvPgKL737NkcHzeUyBkZUIV27wRURjnYvnvCU+SLzZLnPzvk9TmfXicCNd99he9jwE8u38fo+6+r72b31s/ztf/w5fvw77mIvrfmO3/kGf/fRdzJT5bXX7tJ/ecMwfsBRVbOMHdIesYh7LMzRn3Ysuxnrw4dEuc7WP2a2NeRgw/DwpQnvGT1xmGtpXnqLW79xj3vhdXrnsREOzssGaC5iAj0eieXB5GRZEzGUyENbFK8OgRfX5x/B+3x6WFr0GcGxdYkPlle51h+Xj4stPofC/TDogSuu56HVhXC/d5/do+tYNbLMGQkj895jplTWTXiP5Zo7GCyDVZgaVQx0UxBlnWtGqfEyIOaIVuE4+5ePx38Vx3PMP8f8x4D5Z6LAqRWyQprSsNUmrooTkhQvFtESdJkvR1JFKeSsFDtVhkEu0qxLgSNkcA41T5Sx8FIAMHwqSd4OStK4XqR1lygFM8NTukOaE0nLOAznkAiiJdjSqfHz37jPf/cnP4cPsKgd//ePLFi0Nb4KfM/LDTu/1vGF2cD1xcCtmfDBxrFMibkb2GYhDZHaKRlhE5V5Ul7fnfP6rUM2my2n28ihdmjwSB1AyvhpM6xAMhbKNcvicVIiIsQcWCJNZK+LYNIql1ZqzAkxRUlkd0GbMZIVAneeZORikzRbMnkKzExMOVyZy8/CMoxqhKwTI76AKylINqKVeIqsGc2OLWlqmTpEMs4JIRudK/L2i7EWMBkuCu3kk/RJP2YIqzoxCMz6RGtGe14m4RsnzKNhzl/iHSvz7sd7Vzg8PWW9u8u1k1OiZY729zHg8Pi44F2F5bCl6c6pzDh+4UWOdnZ56b2v4hSungw8WczYW284Xcx5+YOvklU4XczZP1txvLOgXa3ZzGe88v5XeXD1Di+/9w2GKiLBODyP/Hb3iG/7qScsj36B/PAmP/7nfp7xt/8gdrbL7lWYN2d8rj/hij7gfl6AZm5tHzNLENM+R9UxVe+xWcaFiCTlir4Lt16GK+8w3v0CjX2D3EKMr0x4N9qbXwOZMVQT3n3h25lAM9b0OuCj4FQY/Ei7dTQpIiI8WQTElIP0iHUNF/qDh+kad+IJ2YQ354dcsycsN/DK2WPOZ0oTe8aQ8XGGpi2mxfL/cTKia/Cpoz7dI/kBzcK5lky9jQTavGFTQTs4TuvCT0s54CQiOTGXkdO6praiGoz5gntQOHJN8+hynZ/04znmn2P+48D8M2Gy8NpsoKqLiqpSwfuJu+Gh8h7xijpDHDDJu4NTvJYYgUqVFAR1RpjyqNQZ3ju8d2gwxCuVKCF4VIVQFbKsVIIPJbG1nqIgnBjBCSJG5RWplDqUOIGajA8ljFNrxWvN5/f3+O17jxm2G7Ry1F7Z9h1nZ2c8Pu2RUPF9tw/YJsdByJy3iaFx3JMZu3tL7g6BowTHecTU8Z2vH3Bjt0FJ1HXFQaucbjtOz84hlzwm8Y552/IzP/Ya2Y2oVyqfqIISvKIuo17wThAHoUz5cAomuUReqJVcKkfJx0IIrvwbH6AWw/ny5yoIlUJQY+7LWMqJ4hCcE+rK0zrDh0xdKa3YlC828aGcEBGw0jWaWyCJopoJVUH0qDCvPU1Q2uCZe0+rJUKi0kJevih6PsnHjepdlu0pc/HMRVHtMaeYh+CWPDk8vMT70f4eznlSWLK3Gog646CD1XzJ0Z3b7PcwHzJHd27TLw7oFwfs9CtWt+4wtvvsn26Zn5wz7Ozz5PCAzc4eB92a4B0z8xwdHHB86za2d4CI0VpgdecOMyqe3LnDtaNjTq4tWd+6zfrFO+TqkFe5if4/N3EnPVo5eHKIu/Yu1atfxPf7nF15jdm1m6zqA/ZnmXf3Z+RZw7HMqds5J2e32ewqm6qoZ+bf8gT33e+hJMaHt7F0hH7+N6he/fVLvJvdoHv4Kp//M2/S+4HoZ3hdMfeOmXNQlYeEVGeGOhGSw/uBWCVMMvvdmr1uhcYGYs1RuIIiXM/njPXIo8WCF+P7BNsytgP4iiZtaNKM5bhkJie0CGodzgmN9yzklMb3qGvZN8NcZjEV97RnrJnBOGcrLftdYhBP0Ei3/wCAjWRm1QrvVzQ5cCUndiVTz0+oZyefGrzDc8w/x/zHg/lnooOz42q+PSS+2Dks66XR3Uimmhx0bVL1hKylszONj9QVMz4H+IthjBmVfHSY4a3obypxpCmPKSKlq+McwUritrfydzkXGTMquFxIt1VVkTDImToLoqDqadvIXrtgPm9JsVTPF/yV33xwijjjf3znmB+96pmHmh+TDR+EOUbkahP43CsVey7yqw96vnw88kcHxbxRpxJJcUG2tZyxnPFtTe56JCXquubO0HG2mDMkKSqpf+b65ok/BJDclDAOU5ekSNgNmSIVSmcsWQnNrLzHS2acPImcFKF3qCrEIiqFpDzkSGNFai7TGNGpMsRIyp7OMki6dITOEglTbETMxcxLBDYjk4y8cHrUl1lzshKAKp+CDs68m/P5SvmKz8RxW9rIs8Cgc65sT2mP15d4v7I9IDYOsUIYRM/IUlOh9LnmbL9m5/SELtesd596nF85ehdkh/XuweV3ZffkGNST5lfBzkEde4NC7BhJpPlVKhEOnmwYVRAahp0GdhdcebjC9Izzg9fIgyMdvYI7+CrpYcSLoNeegBnd2weIM35pWPCD94V1XfFH5J9wzKtUB8bjq8IL1R5V+xXc/Ve5N87ZOVqyzF9DXngA712/xDs5I+4BunOInW4Qt8Guwzed3+XeCwd0/S6Sd8mA608AaFBygqHKCEJyMhHXHdXosZxQERabjKZlwXsX2HMbtFJymrMw41Qybd55ahnBAqkSswxN3rBVcLEhFh0EKxoqd8aZBbb9FaJC47ZsXRmPnItRDal8r49eIElis3fM/PgFhMzJ/vvsnu6jKsxWMzYO5hGG8CkYyfIc888x//FgXp6FYTa/0AAAIABJREFUJ4Qf/R9+2cy4HHnkqTQREbL6S56xpVjCL5GJS/L0ECvR7UKRIWZ7uhmqJDLCX/p8w4vzil8/Gfirv9MhVnKrXEr0HkL0pKnz4KaxCHkiJ0uRnFdoGaWZTGMbeFV7/sQNx7e9uqQWR8JTeagrTz9uWdZz/vT/9R4/8eqMQ83Esef6lZpfv7/ldFBe2q1RiyybhiZkPnNtl02fMQaceBZtoI8wq0v3ajafF3m8QX++5u7xiv/zbeHvPjpHUyHtZhJDlknFpMWJWARsYr986HM3s0tlt7MiDRe14jVEOc80FVfOOWzyBSoy8Im4dmGxmJ4qtpKUIs9hkzJLIKcp8+si36o45Njll6rkgYnl4kOkxWixmP8V5dvP/lc//Imucn7xT/+pj+DdtacAxG6Hkyu32dY1bd+zs3nI2eyQmW7Z5PYjr7Gt52Qf0DgiYiRXXV6/K9t7HFUH/JHrv8D1xcDDd/8Q/+94FTFIoebw/B73d679C+NdUk8KgW/9xj/m5itHLL/r68hql6Ptd3Lofpt4fcCvBthp+M2//llmn3Nc746pxxVuecx4dJV1t8f6Vs3e+j4ufp55+0W4ncmbGTKCVJn8HW8iv/IF0isf4EXoHr9OkiUqK9qDr5O/vMfxvVf4UtxBk/DY77PYfEDMu9T9Xfr6Vrn5/3PwLileRqd8GO9VMlJaE9SRCB/Fe+4gG7VkRuQS7xe2FUUOe8yQZjS6ok8zUGO7PCp4lziZWYxgASNyodUs29LI4mSPze6Ka0fzEjgz4f0Lf+fvfaLxDs8x/xzzHw/mn4kOTukJJMS5kkkUjdZl1lPoop/ETI1Ttrl0Xj5TZWKMvE87fZBThhKTz4E8nb95PD/9RuT2YU1be37oYMbPfe1Ndg+v8qXjHpynAahATKmmjRkMU0PFkW1k5txEeJ7MeLX0Gh5Iw9dXifROx7wqf3dzUXGwbNlbznlwuuK//p7rnK9XtMFT45kvW/747h7vPjolSsXBfMlXPjjh1auBFAfapuXB8ZprezPGmCBntqMjqcF6zWw+Z7vZcN71jBnu7BmLh561T2gCwbHQzBYgG8GXTKtJJHiZb5WluBJjF9ylch0dSpaIOcFT0tqzCZUrirILQ8ULCrOoEXNxmSYX4jgxkZDpE04EEUxcCdKbOma1OqJN1HApxlcyLbMI3TKVCNFA8lOO1Sf5GK7s0j4+pr+2j2jD8qSn3sKJh4WuufHgHlAK5Pb4PqvDPT578EXy11revf45PD2z1BNjjZeeSE1OWy4CaPes5o/t/XXy9xyhJ9e48dI/5Fv/txdo/8ABv/34NjHMONyuADivFhz0K1bVDKyQv5d9z9FszrXunCywrmaYlfgPtcj62mfozt7m+Ms3mVdC9bAh+0OO4w2u3PkN7P2GG982wnAf9oRQ3wVuc3b4Mtfe/wrz1XdyeqVDH3yZYRHYrR6T5DX8m4/hZkD/yQvk/fdwD2fkb36PBugev15cYO86+gq21Q5XtzV3g3CQjrF6RmM9az9H8imnN19mefIOF3ivNiuCzhjzpuQEXuDdzdG4wiVBpME8UA+o9cQu0PoTGjLjuAQHIayoAUsNMQc6K3gPCmPeo3IngKNyG1SEetUyagnDPZ1v2dssiWZs5tBuKiTOWC9PAcd2/wwBgqs4r9bMtrNPBd7hOeafY/7jwfwz0cH5U//Tr1i0DGQqdYwIXhJNzryqgmniUTL6oPwhtgTX8P91DW9UZ7xcwRfHmg/GmjNpqFLHRpUGu/xAo8J315l/68XA9b0F0Qsnqy05BXbqkQd95n/9p4633YgNVoz+clFzJUr76MJDpxxlw1YTKoTASGuJJEqjxmcr40c/e5WXbjScbgbun2443W6JMbJoZxwunj6ZpJx5eJ7o+zW7s6oUW2I0swWr1YpZU02mhkbO0Dae2juEzOkkoXx4uuFn3vGEIZKm8ViKkweBZcZJ8h5jLFL8LCTS5EQshZxteSqALmITyvou/IPEMnLRacEIKFlK92VM8dLv56ITc+GQfNEqvnTfNkP/mTlrzOnyRgVPuzt+yt+C0sFzuRgC/vxf/pOf6Cfaf/AX/lOLlmkfP6RpKk52DqjGLzGv4cqDfaqw5TS1jK3nxfEtGtnnG/kGy8WXuZZG3u1fYyV7fOXgNV7dPuR3mj0+c/LeJd7fPrjND86+yIH+U3jZw9CwPWto33qB9AO/wPr9z3H3S9/Eb73w0r8w3l8dzvHrI9y4JXhPf+su1x/N8J8Rdj//Dnzgib9xG3/wLjFGzg922ZOnvhYpZ46ycHBywubKHJxndmJwVXEfeNJB/Aje2TuDb7mHkJFffAOAbv2Qn+3+DK+sji7xfo6wsMyZG3nk9tmJHeMIM7/BsrDNNSLC3pMHnOw1qGXavhBZx3FF1RbFhxhUMf4uvNe5/RDe5XfhfTAIGMM0/r7Ae6PHDLb/Ebx7efJ74v1sdsbOZL5mAovtnPPqnO/9a7/6icY7PMf8c8x/PJh/Jgqcn/yff8kwJU8Xyk8jqjYnaslk81zxGRFllY2ZDBwqfBA9r8uIr0bmwUOOiGsYNx11XZOqOQ+88bIYY4TvfvkAFyKIcLIeSMNIzInDvTnvPDxlsICrHP/7O4kTmxg9U0r3ZTK5fZSXLTqNYSRNni3wYpv585/dJUnPjb1djjrj/fuPWSxrTlaRboyoKrd3GxaNQ7Xi3UcnjFpY64sQaGeBYRio6wrM2I4jjddLl+fiR5gZxxGVQPCOv/IrkZEVdhHbIIqlMvAr5oJltKc5k6R0mi7AOqYCfAVq5/CSMZQxZZIZo9mkbjIaVUynp4FUuDxMGV4Xv5heTcmla5MjYTJW5GI9003GJsVEsqdruvh1GSVhwkjJ/vrZv/hDn+gb/pd+6ic+gvd6CsGTlNC4IpvnoM3Ea0Z6MoedDzhIkdPVHQ7Ce1gw6jzg/Anj+mW8rsjjnFTNWe3UePeIxckC/cLZpQ9GPu3R4YyE4m7skk/fZrv5Vtp8ytcffJ6jagcBatfybrXk2vYhAI376JhgyF3xyUiR1JRZ+8HO29y5/oAkPf7OyPbe5+D93yTuX2F5NLJyOzTDCelqSzh4iB6/RH/yPqftC2jacvDOVey1Uzp6WqnBjLQ2dJbRdhpXdxUmGZu/x3r7Bgt3xm9+/fsZxiMq35Isc+SrS7wHOpLViPS/J979qih4FMiLOYsnJwXvecL7cstsGMq50v6eeE/S4TRd4j2Pe/hwwpgDKsM/F+/b+eTt8iG8d1JcX5ebOWf/P3tvFmNZlp3nfXs6wx0jIiMip67Mqu6qnkmRINykSEuWZIgGCFgw5OmBL7bhJ1v2iycZBmwYgp8MP0qyQBgwoAeDsEHCgi2ApihaltCkRJEt9lRd3dWVWTlnzHc8wx6WH/aJyCoVW4JhdldlIw+QiERkRsSNc797zrpr/ev/6zVWFKN2zKpa84u/8u2Xmnd4xfwr5j8e5j8RIyqjFNZEEg5lhCAahyZYjU8Kh3AilqCg17BIBq9hYgNPgyJ2JYveIqbgi7EFW5Oix7UXvFEKytbUyvPsQpiOKpqupQmaPgbGteV41WCUZqdWnK9bvjLx/HZ7gEo59fXSM8eQ06/xQu8iGocWcCIkq1DJYIzhUW/4R8+3fHFH4UOka1saH7lTlXzh1gG9h997+30mlcOniEqB2aigbRu00wR6tFKMR7lw6X3EBs3BzpTVekvjE32KOAVFUXBtXNMFz1/+Kc27ixFvjBL//TfWBFMRLh2JlSYkcHl+hBo+b8kdFYxFhZTXziWS0IgkrFYoyeK34kognGdIItmzWQ3uw0h2HEZe/EyBnCWjskkjAkFFKmDIzCMkUMogKpFkiJSQhDFQqmzyl5LmhfPOy30UqSc50NRE40mmopREXyZMrNBKWIsinEPSFpZvkMRTpyWbZofYKB6+1iBmxusXW9B7iF1TFN9hr16xbt8gTN6lWGxhbonHDtNPibEmjDvkeIVWJaV9ROoMtyffY23/DCq1iCTubp+TqmrgPVKendPVBaGe4KTA+EgsDSppjDFcbL5IWpTcnf0uMYwpL54hXUW5d0acvYHxYL8f0HsnqPEKFlvSbI/9JxGtK9Ltb6HVDUaffh85vY5qa4LVFG8u4P0xPjp63TEeP0dv7jB+8yHx2nNuhznx+esc6q/zzqMD5NqtXFCLoJTjVGuscngMVuWb1ORkgdaa5cEcFRLu7ALX9i94V4puf447BsUYBTRXTuAK59or3rVUSOLFRd9t8Vi0ycZpH+Rdd5ZQpCvezeIQ2T2+4h0y7/14TTHwvq5XV267L/vxivlXzH8czH8iOji/+rf+njw7b4jFlCSa91JWlM8V3NRLNnHEM9Fs9WAGh6HGs0UTlR4M/3KFabslt6ipbJdFZM0KawvKwtBs17xxMKc00MdAZR1JJKd3K03TNaANe5MpKxS/8SSCNaxiCQZ86nAq8G/tOdbNlt/a1mzF8obr+OXPjfhr73hWCf7SXcOi6xlpw2xUcrHdcrzsORgZvnjnAO0cj0/POV22RErmFXQRlCScyVtT89rQBTicjwgpsNhG4vBUKa3ZbLaIVoysYWds0cqAsdQmB38+O+v5b75+jiRFL/FKTHelu5H89zB4GDglxBhz+zPm6r8YPCly6CVDJya3LCO5owPQiwyOyfnQwwNVSogoVIqgcvclO2kKJil6JbgEnhfC5HxkAXP+Hgrlc5xDIs9+/8///Jde6ne08T/6ijTNgra8hWA4udEgDw+Y3tiy0z5lvb7O2o9QVXXFe+q3qKKkH+1+iHd38j7T6Bi7C/xupNi8T2r2KKaQwjEzuUM/e4obLYjNDkkECoNSGlecsdneZmzhIvw0xydbsAY/uX3Fe9WecrMoSeYbPL54izS7wfWdd7hWn3H/+5/BT/f5nP5DlDtnMa6oSgPLI6Tdo5o+gC/OwDl4tIJzz1F6nd1rD2iWd1CSKGcP8atPMa4jsi1Rnz+DgyP4vbeQPm/RSBmIxQPkyV3sSOAXvo1WBn9yiDM6u55/d4dv3P9pqvWSZVlSRRDfX/GeBIwI2zrfTMd9Rxg474r3saubOGdp60dMT/cQgdW1c9zFIZe8j7wHYGMNrug+wvty94LJxQ7L2dkV71YCooT6ZMb62pLxyZTNXhbYfpD3qHIX0yZDSv5DvP/Zv/Hdl5p3eMX8K+Y/HuY/EQXOX/mf/668OYrMR5ZzHzjvx7m7EHtGNjJ2ikKV/IOuJEhiLBGRyFIVGB/wruAvTFtGY4dPQvCJJ9vE6TKPqib0rEnMbcFZ02aNicqr00ZnI8G3bs55uu5ZLBt2JwXXd6Y8PD0nGEutS3aKxO9spvzifjcka8M7xxu+xpyfNEt+8vac7z8/ZV6PmZWOL7+1z3bT0frEg6MVd/bGiNLcnFU0Elg1HW2T6H1uLy7bnsrmQEprLbuTisoqpnVJWVh8gGcXKzqfIVm3HZXVHFybs18W9DGw6hpWXc+nrx+w2W75X/7gnN9pFE6W9KoEKSAqeh3QEYLKQuN+0N5kIXHEJXVltnQ5d72cmaaU8qbUYIyotUZiJJFXufUHvgZyx0VEiB/U3Ci5jMfKRY8SJA5F1GC6qAYhW34MgkguAJVS/Np/8nJvUZ3+u39SdifHyLQhTta0T38mWyPYU6xXmMN7NJu7nKw+Q5BEfbhCJLI+2mGkEmuxfGHva4T5ArW6RtIB1Vxjde6ZmDnoFX3dU/sRm66B6BmNGmIzoh87iiAUuz3x4hp9WlExQu2vobnAh1sYXxN2H3Ix+jIH5nfzWHa9T7equNe/xZ36XdpPKabtfWL3OpW0+H+xQ58F1PPryMkStTNBlMZ87oR4eARPNN17n6Y+rkmAtud0xRajwWxvof6Fd1DNFHYs8fktzM0j0sOSvsk3PLs+hcM1zl0n+r2ctrzs8asxtryOunaf7W+/xj2+hMz+Eeb568TqgHq1pCscqvek1Gbh4+gZAFV3l7a6z3hz5wfybjcbOtVRpJLO9FSppFPtP5N3Rcf6sLv63B/Fu43+Q7yXp5MfyPtP/6+/+1LzDq+Yf8X8x8P8J2JEtW8865hYn3uOW81evWZvBEeh5PkmsjM1nGjLXdXhU0cKEcGxcDV9AZO24cwFTpueQkeKWYVLAdU0bIPC1oG4CrwvLZuQ9S+SFKVzWCMUqeN4XVCpxNooFuuOTRsJPnCqHF+5LlRW8YvlmqQUYDEpcOtajVstGZvE6cUCFRXnixWNK7lx7NidTbl3dMyzszWf2i1ptpFtBa6a8/B0QQwwLqD1PVE0F1ufK+Ai0fjEW586oNCJTRNYtx19F2h7j7KWQoNPER8CS50Inaf1CYLn9759n09/6pCiaLjeWbYe/tykwJiGbywTnzIdf7+YcmPb0yTNxgwp4AJRG5IStKg8WhpmqJ6Ek7yFqGweT2Hy2rfSgkZnQbKAHsiO5BV1BehBGGckYQVkqN6TKEwyRJ3ns6jsbjlH6NH0BSivsQIJQ5Lw8YH6x3SMpue0pkBfGNYnt5nUT7F7D2ibz7L2HaPlG/SHe+yrR6hNh38aiLVlpa6xuXPC7N2CcLqPXpeovcd0+nXKfsOYYxadoppHyuWChV1yEYSCEacbRaFgFFcYf4FUGnPzCPPsNfq4QR2XCDssisDOnd/HJccBf5+kFBqLGj2hKb7I3acPqPfuU55NScefphw/huYu7sF94k82pMdr5OgN3N1H8NzCSiH6New/UZShpx+fU3aOIBrlFaAQfU77zTn6px328T76xhPSSUDFFdVS093wuGdvkOiJsxnmZ/8JfG8HNnMcT+HpM+JnIxx2TNcl8uSQT7HAtM94Hi03zYLv7f0Me6cL1tuOubmLCh3Q4bY3CUbhQo9XFiserxyb+n12z/ZBQUWBKIXfW1GdWMpkETSd6VECrh86mWVCRIOU1Eclig5zFfcypC9bh+oFKSqa3fUV7/3eEeOTPdY3VlTP5z9WvMMr5l8x//Ew/4kocBqfeH7eIUnxxRszAh0P1orxJGJtwGjNZ1LDRgsuCqvOE2zk8xJYhcj5eJc/3BquyxqJiWm3xqIxVcHFtmFaWcQVWOkwyREVbH2PEiEZYba7y/m6pSgKemWICkKfMKagMI6L9ZbJuMJ7j2iFTh5nDLXp2TOREHseby17TvIKtIk0TUNMoHA8W7Ssu0Abep4vNBfPv4/tCpbBk5IdVCW5qt0ZVZxvcvT8e/cf8fk7N1h1Dc8WDRIUdWlxQyK5rcY0ixXnQdA2b0kZY7DW8u7jI26aiNMlCxe5PossF4Gd6BnPav58s+FR7KjMiO+HSD9U0qlyeDF41WOGwscYg44KPZTb2UMioMQhaVhk0DlQ81KsDHnrSWmP6MF7ISXUsF0gw2+tU8RkGTRKZSH0nygDP3dnl6fPDb+xWJFUBAVFinxCkP3/dfSrxKoHSZrru4FoNKtHr2F2AoWPmJFh9t6KMO4IVlOGE1pd8br/Hs2zJavyp3jWjZimFn3vFsUsEENHCPsEt8Fi6PsZql5QxhFRCWETUcWWbltSj9+kf7bFqIroAnSaXiJOFZhwjaKx+LSD9hHRCkmaaBST6ddgtg+xo9vcpb7xDursEG6/A88d/Vdfp95EVs0Cc2xQ5YawsahHF9DdorcVKd3ED0ZeZTpGnBD7jpEfwzcfk74YkEWPPLRIKDGuoojncOdd2L6JWR/Db0yR/RZ1EvDXQfsp5v9ZMVo6bjyNxNlzVN2DO6Z+cBsbpnx58TUW5QkTv8/JKiJmBICelBQxsjl8Snl8EwEKArp5HX/7YX7Czm8jOw+Yn9xE/Da/9lxJuLklPRldPa9Fowi7G+yyJMw6dJPQXd4QSdUWgHa8wIxy/hpd5v0t03O4H4lB8bW1ph9nAzeTYLTZ/RFR+cM9XjH/ivmPg/lPxt1CC/uzMfeerfj6oyWjUlONYLQ2lBFkG9kgJPH4OKyk9QE7KXhytOQ1U3J9rnh8Cmss69M1k2nN9drwhdsTdqqCoBLvPFkRaHh8EUDlXKdZoThdrDmcFtRa40ykC2C1xiqhdh3vrRXzvielRN+Fq3lwUTmW0ZB0zWa14ZlOHLqSz+1qTjeCrDcYZ/nCzQmnFw1N3zO7PsJ4aExkpqCPPePS4JwjxoRJgXlZkVTHugl87f5TmjZwOKuRJFitaXvBJ0FWKwBC8MQG+hCx1rLtPW0IhGh4klrmIXD/ZE3lFK3v+c6TDqfgpC+RUcS6MXfMljOv2PZb9LbnxFp0EV8kj+vBbFFyRhdKEOnAgBZNokclsIOLcUoJUTnc1CRBSSRh0JJzvXTKVXpQOeHdJPhC1eI3jqJ2HG17vrn1TCXSqKwryi2iH4N3tFqodhWnD0secUI5ctTjDtvOMb3AVki2gdZT621Omu8DYWI5fh7Y27/HzrxgsZ3gAXP2iFBNmO0ucPMCNzlH7iT8e3eQ6pSjJwegdnD1iirBol8wrRx0FkpBjKcIGjWKjMwJZ6d3qG1BSonKn+O3NZ1xuO2ctZuQ2rtw1NH4XWo/wpUP0OevI6FE4pLJnZ7YONJijJt48BBMpJh9F1kdYMYLQpjh6g2xHeG1QUaPkeWM9N3nmLMDUp2QJLBzhrr/BmHyDJGnRMAVG9IZRLukuP8anepoNjeYLOYsKpi2idCO0WlGGD3l1LcUumP19C6y14KasTu7h1/u0sXn6KWm7RXx4BF6eZO0+zxvegy8c+0BKglsV2AgvLaGB2PK++Ugss+8p3pLuajZvnaKPS/RpSW6DdooUsxahrKr6coGk+BusSUe7WCujYhLz6MuMVvvcH79mKIzoIR2cv7xcfrHebxi/hXzHwPzn4gCJyD84b1nWG0w0fDajV1O256LtqFveqK1rNpA8BCSZ1oYbh3MWHeeN3YqdueO1XLF3d0K7YR7z0uM0jzYRp53W27vBDrfcmt3TmcKjtYbprf2mBpBNRsuFoHRTPHgqOGgdllslRLiCkwbCa6k61qmCp5uenbmU3pruGMCxmhKA394FOlTYqU71rFlf2SYjsacHy/R2tLHBkIWaZVlQQg9K59DJjsPhUm03rNoAjEpzts8z+wSVAijwqGU0C0TTRdZ957GhytB3qrraTtPP9z/R6UhRZhXjlnteLqBp4tAsiM2LnIQI3aiGTUd7cUZbV1yY1zydNXwtJ5SlQZtBlGxymp6RR4hiUSUUcQIOeoBUnohHkYuzRYFUsrW4NqgUhpW7wWGdXtDxCrFTgyETnMcEjergp1K0aXAxoJJEU0eZZ51L3+BY0cXnB5NMGK4oXvSeE4nDX1asCGi+ortpkM2BzibII3Yu6YJTcPrOx41KognS6bFHPOl91g8HOFax3kqUI9nzMrrUN+HgxVudYCzFeWXxtiLXWz7jJPVGj1y9ItEHXKL2bsxZmXopxeE8YS0MozaFU+wVLOCflRxfR0YSUdp4GHq0U/nbAs4uDcHeka3vk9/fAOJe3gXmKQT/H0H5TVM6tFNhe+FLtzB6CX9coTrI6bZZ91lcWW82GPkA8Vejdp7gpyXBPOcZjlmUj1FRPDvv0lftRT+jCbmi+G4EKKsGbs9XFOzCp51W2DqL7HWJ0zORoSbgVG7Qp2/k79GnxHO4Pg1zXhxGz85QZ8FOL6WeR9ccmUQ6cdaaA8eUZ/cprnzOGvQ0uDgrcAS6dMGo4BrHeksG46mJFf2Eu3OkvJiwrS1hGnDhYx5y1wQJ+fIyV3We8coFQlV5OZGWHc9Pw7HK+ZfMf9xMP+JEBn/D//T/yExeXzShAgecEnRa83+qMAazdZHjIZV51k1iRhaAobpdM5+pSlt5OHJmr3JlPvHSwqricrQJeHWONtPF1rxaNVTKMPh4YwqRY42ntU6MJ8Ynm56tCooDeyNHWWZAyEnJjBKmrPQoT2cJE09neDOzvFEHq97ok+4wSSptIrdMnAwn/O9o3Ni0ogyxBhxyrD2fvDO0dzcG5GizzDFSBeh90KIhiQ9CWFkC1wBu3VBExLr1jOZzwg+sW469iYFJrWI13QSca6mLkBrg48BnyxRaW5PLEpFMJaTxYaoaxrfY43CVyWNBZM03apB+YjZnVyZT72mFQ+0oBpDV3vKcOlhc7kSrq58ay4NnT7oh5A/IXmUdTXXyqbfkhKh6VAxMXaOelRws0j8xFzYovj6sy3/8mcP+K1vrzjtOv7aX/43XmrRZfsf7EunFSk5fDfGm4Dzjt4lJqmiUIp+tETWNV0RaC4KAqdIf4NxVVPtrZkk4eg8UMwNZ08nmPop+Bt04tjfOcIUgjUNJ88PMeUZ44M501ZYLEo2nWZaRhbqHGluo4tAPVeMY95K5NqWvdOSszKiPTSxR93exT15hCeyXhQf4j3JlHJ2n117gAknnGzmH+JdinjFuzGW0KUr3qOUGNVd8V6PFdJZumLBrJ7iW822GTPaNdAIPjWUdYk3T3GLXfrynELtQ9VSbA4J8+fIxQ6xNsxsg1UJXRVc9Ev05jpb3WGNotNzursnmKThO5PM+86M/rVnuAfXucaax69vMN/egTcXuCeHdAdnmffUoHSdP55VyM7g6XKR/VMuee/na4rFBL+z+gjv06MdVExESsY2MT94xO7uERHF40e73PzcCZs/+BTP2zmf+9+//lLzDq+Yf8X8x8P8J6LA+a//5t8V73vmVvHW3LINgd9/vOGwHlHaiCkcr09Lbuw4SC1ejzhuVry33uXNumcREu/2gRL3oW2dq5su2W8lO/K++LmisrD1cqRiek/QGooit+dUjnAPIZDsAPaqoYwJZwNTbUGE2hmOY+TQWvqoaRWs2sCEnmgLEvnG/+mdCW+fbOi3a+q6Zh0UdWnYmzsuvMEIeIHRoFlpg0eVJTEo/HaFLhwzhKfrwLw0dKln0yaujRyNQIiGz1wr6CPUKbAMiUUv7NWW91aWIq24O615vO5o+0SP0AfPZDJB0fFkk3ABPrenafVXwdtXAAAgAElEQVSY05iDOKOC1AY+M7EcVomvbi3S9ySTz1tsA9ZqtC2JsUMPvkFt0igVcaIoJNCKptQwIeEkMXLCJhk2UShU5P3jyGhkmZc5DuOgElRMnLeRnUK4ty2oUuCv/pcvd4Hz5L/4KeG5xcxWHB4+RZYVD4/GjIsDbDijqAviDKbXHoFd4cMcR+D4yZ+iGj+hDpajcYX08f8z767K7qbJg33o8Tf/Kd6tELp0xbt5r0fcwHvc4qPCjC3bVDE2HbbXrAtP20zY1e+yMvsYmZNSYgfDxSrQdiv0PBH9DF1ZJnsNCymYnFX0h2BixAhwlOANRwwK+f6WeMNTtp7VckxVWdi0dHZDVe6QtokQDbs3OsQqbC+0qWPbzZmw5fR8DztasucCy7UjJGiVQZsjCm4Tqy3btoSw5ebhOWH9Omf7DXYJfldj3h+zc3DMjhHuLV+j3z0i6JzN03ceazXGlOhTS9hbMz65TkOD31viRDE92WOxd8Ls/JDCbFFuwbhoac8P6eotRVdxsanQZUNVNWiB2XiDS4GT412uXTvm+eI20mo++7de/gLnFfOvmP84mP9EFDh/5W/+HdkrDXOnOe+3HNYjHiw7xg6ulQVIpCgKVAw5XdoK3z8KvLY3YdN1eKVYR8t50zCpSlZRchYSUKWET4owuAddFjSaBKIJErDicCrRqiFnSquh05C/JpFv2AARReob+o2nGjn6bc+sKgkp62d8Am0txy2MtDC2QmnBKWFaKHqxbJqG6XTK1BmC71hGhbM1vt/iBZoAqCzurSVw/8LjwoZb8zFbVXC67tBa57Xu3lOVWRMkIRsV3tofUQqgNedbj9KR2INxObp+0fQ4W7NOicJYrNN8qopcSMFUArtjw5N1RLmCo3VLMAbfC9cKeK2Gb24TQWAneGal4mFXAAklns/NHe9uLXM6bo2EPzgHI4lAPp8p5fanSZ59m4jKsAm5iLw+q7h3fsbNsmItBZbEGOGsT0xKx6KPlMCv/Fcvd4Fz+u//rFT7LWZZotwCm+acmyXOOCZthbr9LuvtbWb1IzbtLUqzJBzt0F9zFKcOPfV01tEdafRBYK30Fe+1GPoYrnh3rkZZoW9aqqoihITVJvOeNFqnzLsYlB6iOqLCDi6mEUVcXSAPHeZTHnlk0XsdbjVGKsEnKHrFUnlKX2ImgnYXjLxCjyxiFO3JFrc7pehqtG5o63TFuyLhz6agBH8A5dmaxcUYZU6ZjRyS5lxEj+5V5l0HpFrjmikSFIvesnsdNBegNf6kRumIShpRmjC+IPURxTV8U2F0xE5bZmqLlwOoThgXlkU7wXnDsoHuZkt6rpiJ5lq94f7MIWeacUy4as1q8RqQwHRcH5/zbDpl+iyyM17ydgGzFSwmwmSls/V+qjDJU9lzojIQHdGXTMY1m3iODg3CCEtCmpzjY9SYnoYS+Myvfeel5h1eMf+K+Y+H+U9EgfOf/Y9/WxYKTAgYCUzLesgJMTQW6pBY46joKY3NhnMoCp3wSiMxa3OcsdmlVwtWG7z3OYMJQ5SA1QYVAwmhLivaFPBeUCmbBWqXTfZEYjbDC4IohZZsegRgVPZ0UUqhYsLHgI6RbcyuwCYN20LGsonZ7M5Ioo8GQ8/htCKlQGUUFRFvCpoAje8plEN0dqQUibmdmTR1AZugMSmwRSiBqijZeI+TvNXVKQs6h1mGJChJWKXpY0KLpifhfE/SCtG5lSraoFHc3S15vGxRqaMyJR7NTpXYtIllH1mnbBhVDzbr2xRBO65PLMorHrdbnBiUEkoD6z6v4lttWPc9tTHUKJZtnzVH4vDe0/c9gmZUGsbW0AXPXm1pPXjf4Qc+QgioS39LEX79v/vll/qC/+Df+xlpUoUJAVUcUfu9K979pMetLS01li1MCsou855GiTja4k4m+NhR2AKUwV9bUZxPskHjfoM+n9PsXHyI99FsTtNsUEfjK97D4QZbTq54T+0WfbYPhy3yfLCr3z8himDP9rHSsK07iouElwKTAlJEdC8oKWhT+YL3pElmy3TX4jY9amKY6nss9D7B75D6jqKzOcxWl4hEQvCoVJDmCXyPJIPvHbroqaWi7XM3MBUtsSsz73VHbCcoSRgRYnJAIogG2WDEIcoSU7ji/WAWOF9bgl5TFBppHW43EJpISAv6eECSDZXNWyBNrPCl58BWqNWYhQkgHeg1tjsgmkDULca09N0cazrwU4gtxiiCrujwLOslgmavHTMKIOWKQhq6fkxR+CveXe8+xPtnfv2bLzXv8Ir5V8x/PMx/Igqc//hv/KYAJDwFNrdRyM65moBSL8ybo+QA9jBEQlkUaE1MCqMFUYMAasg3SimhtaVTQhnz39PlknICUTlSAMBrsFFhrcVLyi3EFPFKsiuyUjlNPEre6kn5o0aB5O2qSjxaFegkNCnRh0Tl8uPV2qIkZ0gBGDWMvVJupfrkGZsCnwRPoMJQYNlo8DHgROjJxoRaRazKOpjaOLrhedyE/HsLoJNkp2fyqGnr80ctGi9gNEQZAi2T4Jyji4JIPuchDpkoQEwgMeVKG5VFwvIikyQO5k0SA3IZ2qk0Fk2MMZ9ziZjBgygO6+TOqKtiUiMooyi0IYV4peHpQ8RYjY65m/Zr/+2//VJf8O/98i8IgOiESwpGOSNGlCG1mjh7kXfm+oDqaprZ4PqJou5nH+F9W2an0JQS436HZYq4as243/nn8h6TRjuueF+nyEjrj/De+YgoqI1B9DFdUTBZBoyu6AshbQIpaKyLtHOh3JYogW5vCWTe3dmYlKCfRky3pQozfBJSucZ1YwosXVHTc/wh3lNncNoSY2BkyyveW/9izq+TkIxDEVEIXTTZ4ZuEtw1cS3AG3c6a6tkh3G4IJ4puZ0F5MWM7y1uJCrBbg8SEayd0ozWjzTzzbgdhqCR09Ig2V7wr61Dx0vRBiDbfCLWyeP2DeXc92dururx2LUlqSsmGhPClX33npeYdXjH/ivmPh/lPxBbVb/z2t7DKIiZilSUNK2jWCFZptC4yeNJmN14gDCdYVIZUW4tVHkMeQVltiFpTqkRpSyIQxeNlEHqloeAgoHU+Db1ssaogGg0hFx993pDGRE9RFLQKTBR60VgUSQSDgM56kVLnVG6vItqAJDN0fxI9iSoaeoRK5yIqSY6lQGWHYK1tDoobhMetBdeaPB/WYfCCyVBogV5sLloIWGVRkogSshlfEFrsC0dJgWgUMSUsio68HRVTLk5ijLm9SB55qcu4BMkisjhcg0LM7ypIQtJcOUOnIRDz0ugJeLEyLnnerVJ2Tw5XxbpcxTIkBD0Uezq+CGrTKj+WXBz+cBj8UR7vn20xW/kA72bgPWKV4B/mYllGzcD7Cv10sHBXYOQUHyJu0hMiH+E9rJdEIFVjzornxBgxW00Y6Q/xrnRDTA7j7Ud4t1WPGEfq9A/gXaFix9kokFKDtukF773AJtGzzLyfCUoMWkfien3FO1VENdn4S6qAUkuaTuHa5cB7pKwDH+S9Wzu0Th/iPY08dBrKRLNxH+HdFo7YBsIyZN7PHFoviN+NNLqgPClYK40+G+fzMvAeJEO6Od/PGgMdWSTN3G0hFWgNa22uQnYh/VO864H3+AHe0x/Jex0EvxFcTHg9oYoJJTWi4Es/ZB5/FMcr5l8xn//3j5b5T0SB8+D0eXa01XmcciledaYgxHhVfaMiNtmsRh82dYZmD05l7YxKglLkm3HUFCqirKMwDtEdKllUzIGTYbhjhuF7BRVJMhjmDVEEykBMGqNyIeIk4bQhSB7DOAMGoTR5Jlyo7EWjTO4ipZSIkje4ipAI1qLwKGVxxmLpsU5RKIWtodKRQgvOaoqioI6edFAOXjQGiYYA9F2ii56ND6xbxbLbsuyg7wI9kEQTfSIFYSMvohgAwgehzFHjV7ELJgmevHoe8VeivUsxH4C2BrpAGhjPYOf0cStqCGaDGOPVz7lMBA8S0B94LCmlj1iFZ61OuCpwLh+fQV39zJf5+OpKSKK43TtMmQWSj3u4U5Y87CK33MB7M/qBvI87Qfr6Q7z3rmTStfjCY8SiCoWcdi947zLvYvM3CSpfvJfGMB3MvJSBh26X15YBpRJOAjppxPRIUle8TxCU1RQ+YTvBFx0uOFKtSOtEoRUSHNoKJQGlCjqzxdmOUXIUWmEFOrOm0IK2Xea98FTTxcB7QiIsixvETUkXPXYsnEmk7SydjfRdYLM2OW+ogxR6juopkMeqAF2vWNuKe4/0R3j/uek5X13M0S4QvPsBvAtfMUv+dlMDERHL6xPPvY3h5+vNHwvvv3J2wL+z95y/fnb96vH9xWnHr68r/uIPlcYfzfGK+VfMw4+e+U/EiMr+6b8kSqlsAMdVPMUQEJl1MbmDk5+AnG3B1c7+ZRK1UbkLkav1NOzp57ajEQjKUwxOulHp7NEikajsi5upyjEEl5qbS9FxEkGnDIhS+UkstMGpy3cTQmENKglOqyz6QkjRgI5IyuWsVkJhh8ekFJXLYmJrYFQ4LELhNIXJqbWKMIiizdWLse8DXUx0wbDeelZ9TxM0jQ+0PhJQdBEk5fCyXDCkj3Q/Lrsnybzo1kC6ilVI8kJorUi5dTt0UZRS6Cj5POmhm0PWIV0WjFdfmyRriz6wTn75860ook7omB+HScPjktwVy/EOJieKD7+L/52//lKXOf/pF74sSim+G/RHeH8eNP/KXj5Pbzfmj+T9S7OOb60r/vzI85try5fnPTeCGjZG5Ip33yV2hjO1ri2kRGog1eojvD8KL3i/USge+8z77SKz+tAH7haGceeveJexRSWh8vIR3oPLrzPXR6jlivedpqOXgDWga6EQKJwmrrZ/JO+pbKjtHluJbEikC0dIio0bs9YJWQcCipVzSFI8Dj+Y9986rz/C+w3zgvcn4QXvN01irRQTkR/I+zu94QuF5+0+/67VMEr/Z/H+r086vrqGPzmO/M7G8POT/M73eauveL9VwNMOGp1fQ3/1nW+81LzDK+ZfMf/xMP+J6OAoPRjW+axPET2cNH2pyO4G1bvGKCEpkx0fySFd6EvjoQxxiN0Q3ih4YFRViNZYpUgEQkoknw3jklYQ++ymiyIoQZJClEaT32lIGm7WSuWU2+AxJmtJwvB9e9FY77Eq4ZXBhsEwSSWUKJL22QRPK3xKjIq8gk7SJO0hGkgxP+EKFBpnIQRIEtESSKLz16DxIWbxrWYYUUVyuxCS9xgMgYAkSENxlVKf4SPrZ3LRmCDoHDJFBjIpRUpDy3hYwMxp4UOoQoooEqIUKf/Y3H4VTUgvjPiyUWBA0ChRWZ/D8MIZfpYohcQhtSQJaWiVKpUNA/Onc0SGhIi2L/21ntLltm3fJlCJpa4AeKPwPGlL/uF5z0oX3LUtt1TkXe8Yu+HiJJHfP7JAy3dF+HwVWa6Et/0LL4o/t6uZCIyUQgQWJB4u8vMyKjXrVQISt3TisWSOv9NZvlBFIvCgyxfMQ5Nby12KXDeatPVstGamAlE05UXAqkQ7E6qQI0d8GXHJkrSn9BZ0QnUwic0Vu6XVxBjyzUcLomzOK6sapHWMpiOsCFE0q27Nerkh1iUpknlHE1wPUaMU6M5gbMK3wrc2JV8YZ5PMby7sh3i/oT3v9JrbWhgPDYNNgrESvrcSpNBUEmiVZdUItyawBp6shNKGoauQL+wqdZTaUPrITw25aktRzHXiXEAlxY5KV6aXu0O8yrNOsaPg7a1iR0fu9wPv5jJ1EO73ghjFKETaHwPe4RXzr5j/eJj/hBQ4iSgJpTWDpHVYg86CJKXyhg6o7JCoFaSIMYaUsr4F0WgjKJXQMZcmxubRjpeQdTqDA2MiooyDS1O64YlPIoikfGMeBLT5Rit5DqsNMSXUkJBttMImASVUxmBNLsjMUO2iFFZrQoy5eEqBvmsZVSWNN5QiJK0pAHGJLngwGmnBFo6m8ZRVQVEUeB9QKrDe9jhbYAh0fU+KmqbxdKLYNG1uyyYhhZ6g1FAjhGFGmvVJSuXfLcYXo6sU0/DC4OrvaehYMTSLL79bSv7KzE+jEYk5nJOYfYNI2AQppMvhbi5sNEiIRKOu3sJFeKH1UZJfmECMfrg45DpHSZ5hp5ffyJhbLvHtreZUaxjC5b5SR+5WikPXo5Tmi6MW0HxrY/j5PeHpWnhjnLi/NXwawYlwezw8Z8pwF7gzzvPxCxLT5FkNWwmtEm44kzuWUZgOr/qnMcd+HEw0n9f+Q7w3PWymhg15g++6FmYCNggYgykicQd8Shg9vO1SijJAVIkyGFrVYYLgnGGjHEXvSGlFAdSxx27A144UI/XI0WrLaKTZrjfUdY1SAcOYalKwjZ7xKpKi56icgjdwEUAZTpWQVpHHYnmr6Ale8X+dazqvkELzr847fvPCkFKkAE47zUl6wbsmUhjNYUocY6jEg4WzLvNnUfxCkd1Vv99Hdozw+UnkO9vE50eR/+2s5E/VPSrmc7erFSAoDXsq8g2vqAygYBuEfZN5f9s77poM9Ne2avi6fLxeer6d8o3yx+F4xfwr5uFHz7z+5/+XH/6Rb5AJTURSi1a5SNAq/9vlRlT+mAi+GfQt+cadRBCVcrdAJIuRdSTELUKXuwta6GNLxKOtQnRP1CFvNGkZRiCXQZH5idI2z8yUUVhrEEmkEIY2oNCnSK8jvXi2YcO23bLergkpYq1GiLTtliCBLvaE2FOWJX3whL7nfLWkl57Ot/R9Dvu01hIlETUU4xqNIkZPlJDF0TFydnFO03U4Z2l8Q1FatIaqdBRFQYFCW5NXtVEYnTs/WuV3KYT8orGo/I5iMPTL46DBLEsNo8GhRaNjvHouLoXcGoVWCWM1oiVvOhmFsprgFJqsm9FG5fMpYO2gPbL5j5aEUoLWQ2dH53FYbqFGYswXoRQ8lwX/y34cR2G/CPzSxLNjOn6ugNcrOCgTbYiMXObwpBPGReJXnwptiGw7qG3mvVeKbTeIvwnMXOBRiOxIRyEBtHDaJYwWDnWiN4HWJHoNaGFlFI2/PKHCtFTcLIRJqZhWmk+PhVUfOV4GxkW+eB0rYWkivQr0IbG2DXJqsMus2BIiYR2QJhI2HrPKHMaNEJPQNVsaU9MpS3A166kmuUiURG+F2jg0inpcXfHeO4U/WVCuAt24pCsqquRRyWOdRleJg5jDZm9a4bZO3LLCL+5EKpeolOEb5xC94raGFCwHg35ORLilhCPJI4kTZfmFoue6jvxr055DFThUgT89aq94f6sMHBSJ4144KBJnXvHZmecf9CUjJ+wZ2HcRpWFXC9dr+BMVfLrOf46T5W3vaHS+oNcIisSuVpSh5Xn0iO74va3isz8OivrheMX8K+Y/DuY/ER0cGcYYAMblrksIPYY8w4yiEIQ4bBYRAqoocxFEHuEY44hxWEOzuU3nfZ8V3JLXtZ1zqBQg5J+mtCImTwiDrbYxKKUJsc8jG8ndIWdrfMhPuC0skAsN5xwg2KEKdabIJlKpp6xKYh9Yb9bMqxmmdBDy5sBsNMZ7T1mMcqFGJKJo2pZt7BiVBcvlgul0mnUpkl80XddhrGXvYJ+nxydUpaWuS47XHU3T0YmgTYGpHKr3GCBIJMV0JQITArawFCqPm4i5k2N1nrOmlLCAl5hXFTVANk68LADtUK2nlLtuYYhe0EmIKWGGAkldrl2mRFDZmyiEkLeoLruTKo+xMgdp0F1drt/n5yPP2RTJ5A7Qy360PvC14Pi5KvEXZpp3tsL7a9h6uG7heKWxVeCoyUXfVBKHNnFYaQ6S8DAI18aKTQtTpVFFPm/rTjjpcqft20F4fQpRR5qomQIVgbWCez2cbiM/MwfQ3OsiG68YO+GsCXxxbnnoE1uv+PQsvwa+22h+dhzRCM1Bfmd37agmzXtSZ1BThe7govKUu5E61RAyd+ZAYOEwo9ztSxKISlEvAlihBuLG014bUSj9Id4L0bibe2zO1igllGnLGXuwjfiyRzUGGSVcbzAK3o+KZpt4FOC6FsbO89aO4pdMx9Nk+Gzb8dDDTxSZOR8SbwL/96bkXxq1PEUzKYRTr3kScmv+rSICwknQXHPC39uUkIQ/M+44DppbJnK7bjkocjfgaaP57U3Bf3jY8l6jqBHea/I14vUycq/V3O8colpUKkF1iO54s9Z0IfEZhOOo+J4VnreGH4fjFfOvmP84mP9EdHCUUjg7qK5DzCOLmGe1cegcxBiR6NGiMYXBIvR9S+ibvOrXtQTfITGPgUII2f1YKSpbUxfZWAljcydDDYJhhMJZysKRUhy6F/nfyrLM63YSqKoiPw4fhrwlwXtPksiq2WIkIdqzabb0oeP84gLvPVVVsmm2PH/6jO12y6bZcnp+SiKP5QqnUMagTC6sjDGMphOqqiClgNWQgmcyGeG7lqK0rC/OGJUFo6piPKmYT8YoLRS2JITAtluRUk/XbEiSO07KDMUGQuh72r6na/N5Yhi59X2fz3PfQ+8J3pNCIIVADB46n0dPPq+0p5Tn1S4IheRCRktCp0jqu6GjFolE3FC0xugJISA2+/DA0O4VGXQ/MnSfIErusIkCZfN6f1IvfxsnRMW/Oc1ds5NeuOYi34vCuk98dZs7lUeN4dte2C/gy5Vw3Sj+8Qn86kl+B/v2iebvrICoePtEs27gJ4aL/pv/L3tvHqvZed/3fZ71LO9yt9mHy5CUSIkSJVIUqSWypciyIxveYtd2LcNImwJtWqdG4sRAiyatUzQOkrhtYATN0qBO5ThuDSUOnCb1FslabJJSJWohJYrkcJv9ru96lmftH+dq1P5VoDFATjAHGMzgYnAH9+DznnnO7/ddCslDI/AqMQmJURDUQdzk/SET+NBG5BUPDd/+LLyjyLx1C15uBe8shzfra0tBleFtVeCp9bC6feIFw3SpSTqxXg2H2f56YK1bRlZh9i2zKz2+dzjnCDccVMNb67SOWDs4DqMGGSJup6I7UZK0uMl7lCs6EbCFRuzPiFZhS0m/o6n0eliD9vr4sx+okuf5Rca5QQR/3sADVeb5xvLKPPMHc/iVy5pPzoaXkpwzV5rEnhesY6JLga80mb02Db9cRubAB8uW/V5y4OVN3r+j6PmxrY79INmSkZzh2VbxSg/XO/i6S/zIpOVTc81n14knWsXLAerjuIh7ywA5s+slQmTuKRQyWz7VSwqfuezg/VXg+V4x/3eAd7jN/G3mXx/m3xAuquJDP5VTGiY5tqjofIc1I5JKJB8HTQvgw3EGjPAYXQ43nwxJDiuq4/OaUBIjhl4nmQZFvlASYxTRHyvNj9OJrdXDykYajFGkOPxbzjmiyMiQyEahpblpiZMi4WJE+EBRFIgYEHKok3BuWEMVKLLJJJ8xSqDzYEvfqMe03RqOvUbWaiZVTQqBurRIMpXRxL6nHlmWyyVaa4rCkKRChgS6IISILQr25w0uwaINdCHQ50xIklXXQZRkpYnRH09w1OBCigEph5WbIKGkGVZ9KQ076xTQapiCfUt4LY+zcVJKKDF8WNCK7ANKyCGTJwaUyAhljg+rHoQip0CMgyvqW2NS0vH3kGLI/zl2TXyrxZbj3fi3AgFlBqEVxET4wsdv6dn933vv3fmlpUKnxCMbBZ856nl4UrEnI0frzJs3h7/3z65nklbcLx3vHFtebDOQiUEgVcYfe+a90jxcJ77cSMoUeaASGCMppx63sAPvUtBpz040+KJH9fL/xfs8Zna9wLnEuYmgF4oyZRoyEwkvOlBt5r6poEwCb3pKY+i8Z1KAaQbeMRnVf5t3sw0dPfVKIQAlMxtyODhXAiSZfssyOWiJG5rg3BC0Wcpv854kQUqskIS9Hm8sB2GKM55l6bH7BZdyhijRUvJykxFpiBQQGf7N3HDOBkZm4P1OrXiugVoP3WtfcpIfGCd8ipwuBdf7gfcrKJ5fGT4ydTzdZnYqwX6TeLSS9DHzTK9QIvNwlQk5UWXJKmdGwBdcgGx4d5n5Yi9B9BQ9dMayFxPv04HLDi4dTznJmfeawGfWBTDw/oFxz1O94qlLt35Vw23mbzP/ejD/xlhRxcGtExO0bTu0saYOrzIGhZDFsdC0JSuNEUMPU11N6bqOZIciNZMSQfaQI8716GpE6Htsddx4GhNKCVbrOWW5ScYTukggI2VEIum7iDagdEEKGZQYXFL96maOQASsNHTGggiouiSrTDV4gyjFUJOwbByIwGR6gtV8gY+Zvm9JWnLHeIuIpywHzYxTw6FKWCgBa4fDTRKJV69c5q67LoDo2NnZ4ehwiXeJdXCs1x27s4atkzv0ncNWE/q2Q+VMF1qiHyz0pIRQChkzRmRwS5KtiEkihB90TORB96QFMQVUSmRboGI4DgJMiJSIvcOUQ/FdjmEQZSMwIuGdQ0o/ONqMxaeOQiiSGt60QnDDJzAcW8GVRB0ftqQSBJ8H55YcEo2FGNxtKDlY/d8AB/J/2+vlhWYvCJ5sDL+5hJ+aaF5Y9zzt4IOlIPSWDLytdHRJ8fioJGt46AFHvF4RVeDSUjJRgmu9RxP48txxfrPkG0eJd04AIsEL7LjnD68E3r095qDJqBz5ypHhoRFMKsc3jwz3VKCsQLhMaQbev7yfbvIO8Ohm5Gml0UWHiYb2VGS0a8BoRr0ny4zzkmA7bDUhrhtStrTXBbIw1NIhK4tJHab3eCvwmSG3hECzXaLahiQSq31HPlMwxuNlg5cTRoeOIKGl4nAmkOd7wh6UZcX8dM/pSwVfagOfbg1dihz0ghMlfHfp+Z6JJ/SOUht+r6k4ZTx7GLKXTLNnFSVfbB2PF4J5lJwrhuBL1QWekfAbR56f2jDkmGm9xxQGJQTvKTz/YibZC5H3mkBZlFzzkXePBSeC5IKJ/MYMEJEiGciZLgTebiJPRs1by0zuM2/Vic93kgtbinkOPDoawtpU1txX3vorWbjN/G3mXx/m3xATHPWhn87fOmkJIchS4LoOUSqMkfQAACAASURBVNgh1lsJkg+EmDCFIcXjzBVVknHD6E4W6GzwohumDDkihaG2BV0YhKrGDJOKEHvwmcCQZmytHVZacrB7p5TQhcWHRFkUtO0Sa8tvB+WFYXWkbY2WieQ6NAalQBhFJcFIw7xbobXmaLHEGoMkoyX4dUu1tYFJQz1DqTWjccWkrIdDAgnXNGQ8o3KMspLlYk1ZKEbjTVJKTIqKo1XDeuU5XK+wynLYOPoYqIsRSx/xMULKtG3PaDxGa41zjnS8JjLllJA8yfVIYwcxrx/uQ+96sAUyRXLwZKNQqKGrS0m8cxhrIQW01nStQ+lj7U3+1j0SRKWIXT90xBy7okL0iON8oyy4ue6SSiCQCAkhRfJxtUTyAZS8WTDqn/jHt/Qb7c8/+Ka8dRyUdWoy8P5Lr0l+YGMoE72wIXh+FvmtRcHPnIlcbSJf7gTvm1oa73naZX50w1BqzVeXPYLMnWViC8t2BQcu42NmvBnxQdD5xN5R5kAIRFS884ygWwo2S9jrBbsBLkzhYi94Z5H5o/3EO05IRnH4VB4Zz1hHwiZomTAHmlJklILsFWx3VLOamAKRzH6MWCkoi0jZClzjKKclgoRIAqkTi3OKN11vMCKRRpa4hoynrCTBZuJcYsqeblIPLc3ZEGYdjRccUaKyYN0JPAolFW1KLJCQMv/9VcV/fS6ipeJqStRJcnGVuHOqeKnNfGUZedfYkHPmS6vIn9qQ/P0bmRO1YZo69pOgl4rvryKVGv78a3uSnzqZsNlz91jy69cSj44FhciMteKwTUwKQZsSv74v+Ylpvsn7xT7wfFB8bzWsEp5dBV4TireaTOMzbxtJLrrEM2vNw3Xky8vhQP9wPaRx/a8Xb/0Jzm3mbzP/ejD/hpjgqDyk5w4OnkyOgsIWBNcjtCYljbZDx0dwPUImRPAYZXCxp1QWkeNgK08gjCFFjalqXIyUdUXuPVEKTJYYXaKqYeXUBc/gWU60QVLIMCRkditEHKY7Vgpy7MjZIDJYEl3IxP6QnjisqaxktlyjtUZNSvYXM05sbLFYLNisayqjaYMju8AyOVTXsLG1jUiWMicqYwl9Qxcck/GUw/WM2pZMJoq2XXNyOmZcFkhV0HUNruspraJVPXWhydKwVRbMVz3rrqGuK+argMgBlSMuO/xqTRYSHz1KSFy/QAmBIRJ8PF5JDYnEUmZk6hB5OHA4n8gy4joPZogG932HkAqfVogssWZMDD0+HAu2c6LoI13OpOjQCNyxzimFSPIZ1JBenZFD91Q+7iJLGWJAosl6cGSlHEnh1veJb4rEZ1rBQ2VmM3leXmh+/o7MxVngrong5YXm7WPLm6vM5+aZDZl42AQetJqnesdPnNBMUkLpjveMJX2VSI1hXCVar6k3gdATpaBUmXFXsX1+0LMtVkMMQyE0z6wyby8zlsyz+5G9APedKHjnJOO7RDtyiAwjEsu5ZrQeyl63UdgqcWkd2NKZ0aFiHtbY05J0yXCmFBgriV4STGaZIyrB9lgSfGbLS87sdwSV6IOmWDtmXjJB4jYiqu3ImyUVllFSrIm0ROK2Qe476ui5eqYmSEn1GoSYsFaAy5QIHtael4Iih8QyZJ5qI+8t4PIqcNLAn5xkln4wLDxeJhzweB05pxylUbgQ+OdrySWZ+Opa0qnICRN4bRH5RlKUh5F7deYtVcG13vP0LHJvIRE5cZLM+6rEaz7xSJX4xExxr830XeJftJlzo8jbbObiWrFQgQ2leMVFnm00D2nHKSEQWvORkef315rvqv3/B023xnWb+dvMvx7MvyEmOMWHfioPFmiHFBJaRzICKUp0aYnRY0yBc+74rV+ijMIi6WM8Dt4TeB8RWiBTpipHhOMwona5YmO8iRiUzHTNmul0g5wTITi2N0+wd3RIjPm4B2vodjK6ICZP0znoe4QMqGJEcBFjBEVREGOmtgZlNCYPmpx1s6S0Bb13rHuHDIG6rlmvl4zHY9AGkTybo8ngGOvXGGMojCY6T9ss2dgcMZvNADh/8jSua+hcoLIFu8sZm9MdlKnpU6DvIqvgyUFQjSZc3d9HKIV3Q6rzar1CGoOUhtj35OOU5HzsEkMaSEPFRBRDto2QQ90DPkIOIAXquHcq9z1CFyTnyIUmOX8scHaDUFsZlFLHmqXhsJJCQFuLUJocv31IGUTkw4EohhbSADtIcnKDSDBIpM6QFD525C9+4pZ+o/177707vzhTfDbDh4Xn6tryqk28QyrOFpmDkHnXtuLzh5FTClZCcl+t2EKxTpmvtT0PTOFrR8Pb0VsmnjNiyrpuAfjFFxR/43xBmESQkrQI1JVEVInOQ70hWc6GPCjVGtYKrHXUXhOD5FNH0ATHloy8ZVLwiTn82DQx3c7EmJlogfAD79JoRPBIK8ge9kSi7hxjW7JqGiajih6Q0tGeLTg3y8SmoxQGzBrpDE3IjOo1y/WwSt7Z8uS2x8oKnxwHXcWkcihZ0otEG2paIHWSvCNYXjGEUSB3klxEPv5qJhrDu0t4dR25IRTvrweh6lO94ChqPlp77hkpXkqJSRScKBPPNoYyDA3NUsFpndgNcGkdOF8YXusiV5VCO89jteEVF3mxhwsW7i0kL/WJsYIuwdMrwUcnkdIaOvftB/b1JHjJSd5UwrXgIQlKJdgQgt2UKEXmTUimJrGKms+tMn947flbmne4zfxt5l8f5t8QB5zqAz+Zsx7EpzElYuhg7RCTKVlIdOgRpYEksEWF9z0hJPANSYyReQWmHoL/YosVFSk3yGI6OJ2iZzQe06xapAJb1OScKIqC3geUEPQkDBKf5WBPl7D2PblrMbZCFSUydISUMRny8crEWoN3HUkqTm1vsbe3hyRSGIN3LSkO4q9yVA8uIu8xRrMxmWKyIMpAcI7T29u8evkSm1sbjK3l1M42h4eHCALT6SaVUtw4OgApmO0fUNSbdCExm80wZowe1axj4GC+oipHNF1HVpLYO8q6RoZEHyJKC3IcGnJLUdBGj9GS3g2plYMtPaGUIvjhd/jWQeTbtRmhb4af3SdE7G+KgYda2gqlFEoN90bIQIoRFb/llBKkrMEoRI5oW91ceXnnBgedVEiZUXJwr/H/2I33T/76Lf3A/9W33ZGbkSGJyItzzWdTws8175omHJKzynFhc7DGny5Kdr3juZniKCa+0o14Z7lmS0lOKRjjqUuDCA3TaoMvLD0qRh7brPnsUc/9G55pVVBGoNS0IWJV4uWV4Y460RzzPtKRz15T/P4i87GdzN1TyTh5ms5QywQ20WsojYZFph1FTtqC9XqNJGJSRUorXFKIJKg3LamPxK7DlAV1JdHBk4wkC8UOa3ZnUG1YNnKCk5K8v0IQiJs1Y59p1+0QXbACM7Z0IXEw11DVCC3wUfL8uueMmPLEqkMg+a254r88DyZmPrvKbOuEP46wf29V8PuryAc2M7++mzmlBS8nzaO2p5CSa05w6tihuhshIjkjM1uV5Mm543u34Ldnivu150oz2GnnMjLLmvuLzP0jxROLwHkTeHKteUinwfUXFEcIvMkUWXCHhYsuc58VXHSZRSuZlpn7dOCMNixToo+Ck8dJvn/luYu3NO9wm/nbzL8+zL8hbOJdiEOpZoZkDNbW1CdPIEjQzwl4Us/wH6D3uGaNMRZTb4LoSaocVOjeo1RBkFBUE0JwFIVBG0PwHlsa5PGkQOZE33aQIqv5AaHt8cGRfUc7P6DpWlRmsC+rTPYdTTN8LYmIdy0xOuarOX3b4NsV3rcolVFGD222UlMUBaPNKVVVMJ1OqaqKvu85ODqkF4HJZIOtnR1W3jOZTPAusHu4y8XLr7LoG+rpBoeLJa/s7+OVZDLeYuvMnRSjMVtbW5y54zzjUUXnA9tlxYa2KDkE6uEC0+mUvm1JEtRxBLaQBTlD1BlZKJyUaCMxVlFIjdA1iJJiNB3cUyGgdYk2Q/aN71ZIKXEpDjZuZbDFGF1WYC3klugWuPXR0KLRBoSPZFNAMUHVm9hRfZxxo/BtCykTYxjSkoVAKTBZIKMndh3ZBVLoib57XVn947h+cTbi+cVxwZ2W/Idbib/0QOKkipxIPU8FydN7lmll+eZR4n/bVTy2pfieE4Y+Oz7fFpzWgl+dG+xYsNvBiWnJfm5577bg7k1Y0/PoabDKIJY9MgfyMlOFzP/4WmZv5Wld5pWDyN9+PvLFq1DlhMiZRciEJvFfvaJRItFIz67rSCny6cs9l/2K5bqlnSzwhUCMBW3pUaZgVFk2pwVWJqpaUmxU9MuGxazFaclYeia6ozUVZSmIPey3mcX1SC8L+s2KPOtZLsArSZiOMFs1PgmMtpzYKhkT8dJQ6cQ9ukBttrxzpNntM//tWwJfnDmShG092E13ym/zPlbwxYXkfVXmwzvwvRuZsa3YKQs+eMqwXUqe6TMPjC33G888Cz499xRC8OwKtok8HxRvGQtCkXg+aKQOvOg9XzuIFCIzcYkHgbkSLKThXC14e50oyWymzGstLDuFTHD62C14v85sKkWXE9dWkjoFVinwYvf6v4D+cVy3mb/N/OvB/BtigqM+8BM5tT2iLDG6JB6n+U7MsBIJsiT4JbnvIfQkYZFGkrzHlCW+DyhpkFqhpUIZTWVqAj1N05DalumJTWa7+9h6MkwjpKAY16hjZ1PvHUoWg6i16YmlRpsSmSI6ZeS4Yrno2Nme0swWBFtQiEi7bkg5MK4LqqJAKcVsPWc8HjM1Bc55qqrk8PAQiWA0GSP0MAnBBXzXD7KTFNFaYYEYHJvbG1y7do03velepFas52tObm0jokcVBeu2o2sDB/Mlq5Aoypr92QxjhiycwJBk7H1PYERZliy7BTJ4XAAVI1FEctsjzCA+M1WBb9coIUGb476QIZwwxziEHx4nHOecMUIRRSKFQYEf+54cOmQxHsRmUmDCkEBqrKVv1xilh14p71E4vItkEYc+hng8/pWWmIYG3+Fn8GDUYIkUGv/0rb2i+i/eeiE/20hOWbh/YrjcG0JOfGSawTgOtWW9clxfDhHsn5GGDyjH56LiP9gJ/PLlio+OIic13DNV5CKzYRRtysybwAtHgcfvMPzWK5EPbEturAXbY8/2pqYg0+wF/q91RmvLlhDcaIcH4TsmltpEbBLkzczff9HyF+8PrA4Ty1IyFpEvXEv8zlrxixc6RseryOvtjBP1hNIIotCY3ONnkU4bqjoPa2MiIkpSljd5L1LPSHSs2prJpGE+t5w8D40Be+QpRiXJZGxWLHJg1EiOVpI5Ams0i1ZihaDrJWEaCF6jvGORavSZwOqSplaej+8r7iJSy8jLrUTpRI/kA1Xmn88F76s8WipWETYtuAi/uzI3e9O+f+rY85LzKuNV5tVecLfJ7LbwbAd3jsQQxS8E96vMF4LkfVPJJ2eBP1HAs0FQhczDxvHpXnE5SkIUiBT5SNlxXZU82xrephOPFJ5PO8Fh1qy94h0m8L9cfumW5h1uM3+b+deH+TfEBCeFgB5VWCVhPSc2+5RR0DQdvo/E1RE2eAgeaSt0Elipqcqa2PSU5bcmOMcamOWKw8UBTd9gTIkoSxazNWU1ggKsBkRCu54QI753mGJE8g2GDluAEYHYLSisIsgEfY+1iWY1J4kA/RJBYjSuKIoCYwyL2RH5uF21wDBft+iyYLVYQsr4GGnblkIMWQcie46aOW69pLaWxXwORlHUI5QyFJMJ0SeeeeY5nHOsj+asnGd3/4CQFct1y+Z0ymKxoG0ayrJk1XmabjiwoSQ6G/r2gNXsAOXWONdTqEwIHWSHKiy6tNjSYpRC12PKYgQhIqXCkofOLq0HW3cIxK4nuRafIzkFUgzknIZiUW0HbY73xBDoRCTngBCeuiiQRg2VHEqRzAhT1ZhqA1FOwU7BjFF2hFaKot5ClyNkPaHQBXVV8+9CX8Mng+KBWvDYVqBcdHzer7i3hH956PnmAbx8reV07rgaFRj4sEq8fST5C+PIU3uWn78z8tap4ONziy4En7wU+cSrkXkfqArFhU3B169m/lQdKceJN41XIBJ1E0iN5LKDB09U5OS4r25532bHmyc9L63W1FmzkAG7DnzsTMd87VnmQGwbylbznVsFf/nOiBpLLh01N3k3SbAkIESiC4OzI/kel8CmiAwJ7Tv2Dxvy0ZJtsaJdRBo5ZmQEShlEpdFeMH/Rk50mNR30iW7RErKiWQnGRSQdRjyGSksWITJv1ixbqLNAdhXfOGz42rMZHdd8aj/yZ3YCl2PEqMAZk3isEnxHmbh/rPmeTfjOrZIUBVsGHpaedci8px761y5YxzMtPNNmrkSBTomvNgqf4JkgOV9Joh8e5k82il9daXoym9rx03Xg1CjzHcYxU/BELnj/OPMDU7irzhTSsLIlby41H5k6Hj+hOFEAVvODE89f3Go5Vf+7YRO/zfxt5l8P5t8QExzznh/OQiiKwhCDoO1X2NEIt14MbhptqMqSFCK964epTVaUWUGp8P2SUtf40JOkoaxGNxOQQxj0HzvbJ1l7x6bVrPoekzNJGChqlBakfk2MhoyjzWmo8ZYGq6Dthq6TnPMwXZBgkCQlIPRDNk/O3HfPHexevYaUiq5rcc5jJDQ+UlaG+d4Ro81NjNWs12tObG2SfOBw/zp3nD9DqRUOaJcLkJq6rhlbS5AwrkvmszXkSFGVJKkYjbd59colxnXFjWu7aFMxGo3Z3d/D6ILetWRjhzTi3kM5JpGoygl9Chg0Rkm6fo2xw6TGhzxMa1LGWIXIJT60CJmOW9yHhGliQqRI1hptiqHGIiV6t0aZahBPd2ukMgwSmoAUEaRFkxFSIqXFuQ6hFcE5RIwIBUkqRAB9LBIfEozl0O+gJPlrv31Lv9H+1fvvzE4KHraRGAT/cGn5yXOO39w1LD1Ym/lz2555I/mnS82PTSN/d1HxZyaR05uZ52aORzQcBnglGR45IzGdYikD3zgCnQMfuNty5AVnU2LRGcykQbYVszMBpQV6T6CbAjdquLg0zJvEvZWmHnu+cj3z1mqIViit5SBKzkpBthG/7gkTg3aw8VCNfblBSoWYtSx7zxjJqylzaiL4318V/OjJxLjQXFwG7jtfEVt47saax+8YYYTDAaFpQWpMYThR9yxyRa0aokt0vqZUgSQVRsLVZClxLPYipTLkseLabqQqJftrR1dJrrSSG73CSMVnO81fu1PyxXniZKW5p4B/vef4kzuJNmX+6EihU+QaiveXmZHVfGYReIsOrFH8zsogRGKjTDyQEl9wmu/fiGwCdwvP7/aKNxtJmxJ/uBJcKASzILji4LuKhuuy5F0qUKpIRvNELzltI5+cG3IWfKRY82Sq2JGZR+Wwfn3OSZ6JJbMU2BKaL+3e+hOc28zfZv71YP4NYRPPSJQWOKFIMmJHEyojyfWE7DuyKgnVGL+eMykLvB2jAKUUPgWMqFi3S8ajMaumOQ4lyszXM0xZI/rEfH6dEAWHRUWWApnT0G/llhy1jlE9ou+PkOPpMKloWoQKuMKAkdBrcu7QOiPz0BZLyqxDYmoFI5OYzxdUkwn7u3tkK7nr/FmkMBw1M9ZNR7WzRdesGE9PszPdROmhSPPO028DILVLzkx3eLlfkVKiLjVp1XAU1ly6ASNbMb92hWldcUiAesQjd7+Z+XKFkIl7zp7khZcvs7m5Se89kRLnHOiKXFjy+hCkofUelCFpWDUOsiL0AWKkrEooarJbEzBI1oAkBo+QQ/pyjgqI5DQkUkbXkx14GyELUgxYmSErklKABGlQ0eOzwhuFjBAbR1VbupCRKVGOR8QoCK5nbAWLPOTuqBhQwuCNRyb3+oH6x3R1Ai4omNWaizPFT54LnDOC79v2HKwhCM0rasI/XgX+5tacVbnNXx0BFbRB8g6R+QcrxX96suMT+5oPBkfSmSd2BdrAygmu7a642GrctkUXPdvLTFA96lLD/3A45md34OuzOdNUQ87spJ6Ly0BaazZGmZA1bW4pC8WWi6SRonSRzzeG927BdhngUoe0moP9jmADd26PKLRGpEw67Pi+OzpemRsePF3y4EZGCk8aCT58XwZWjDMQEtcNpKw5UXvoe9adYr+TqGrE/MaK81XkVSlYioI/cbpl7QxKeE5sO169kjl1dopcenotubLW7CWLFpGn1xlJ4NeuC150mv+kmPN3rxesesVLIbPbS/7ClmM8sSwXPS9mw5gVUPK7a42pMlu1ZzMK2pQ5isOb+9pH/nBRECaZKgq+0Cp+ZupZx5LP93DBZtZScdZYPrs26ApGqeCwlXznRs8ftoa7beDHtiJXnOXCUvKjkwWfaEacsZFHdeYOlfijteH95tbXnMFt5m8z//owr37hF37hj+2b/f+9/uY/+cQv9L1DuiHZNwVPbTVCjxhVNb1L6H5wNKntU7jgMYUdEltkJPUBLQzOd8gMPstBr7NYM6nGpLJgVBckXZP6npyOnTpIMIYsBDEEpnVNDv64E0qTM4zKGp0MMWeq0QiXBaUIhADO9WxtbxObloWaIEPH1atXB7u4khx2a2Z7NzBlyaQeE32Db3qW3Zzl/JBswbUNCViuVixdx2wxp+06ck5477m2t8v73vMe3HpNmz1nT+1gy4oTJ0+x/41XyIBPktW8wxeG06dO40Ogi2CPS0dTCIyqAodA1xMmkzExJsp6TJEzsigZGQN5KCDtfAQhiaHHmtHgbJIaVVYIbcluRSENEYnUkhwjQlmMHAIYZY4QOlLqsTITo0Mnh1SCmBIkKItyKEqVEuszQSeiD4TgEAiCiogohmLOrgOl0RlSDvw3f+6n/9rrS+y/3XX48V/6hU+tFKfaxP1bHc1Kc3KkqfOEu2vDy13ibO8QZO6+s+Yrh3BqG1KvMYVnscy8XcHFTiGywDkFTUI3PY9sGjammrMTy0hXXFlELq08J1QiiwJjFW+ygatLyTvOKOoQeH4ZuWMsOPSCD+4YJgXc6DV37ShWK8t50bN0imUbeOgOjZ459ouKMgh++8WO80XDCW255hpmsxZbWWyhqJPg2irwwpGn61qK0mBcpJMS19fMnGDlDaHvEQQaX3B5kbnvvgIcIOGukxEnS3a2Sz77zchdlaK3BfP9CKMxO6cqPIpVyoyqgm2d2AieR04UPBky3z2GH9wqWIfAQyctj/ue957UvLfKvBOPkIK/tWs5ayKfDoL31JavNbAXDB8dJy4owUtt5mOl5+tJ8qE68Jm14VyZ+Wjp+WqSvMNEyhTYFJ4fnzb865Xhz45WaCn4ZlDsecWHdkDKTC8EY2AtEhed4gUHhYJCZW50BffLxK/ONbWSvN04XvPwIz/3l25p3uE287eZf32Yf0OsqIrv+Mns2jVqa8IIi4yZ1WxOlIGyLshJIVFEmbBKg0hYU9N1HSJ1uK7DZ0X2DmmP26m1vfn9R8aQgyMpg5aSLkSKqkKHwNp7tJbEGPFtQ1WNECmRyxEhDcFIynuEMkynU1azQ1YxY+TgLtra2qAwJUdHR5zc2GCpE93RitIkqvEmsxs3uPuOO5mv1igjUb5jY7rJfD5nY3NKJLO3u4+pC0TbcPrsGQ7mM+676/ywEouRly9dYjmbkVLm3je/ictXrnHi3B20iyEp2VQTmq7DtY5clexdfg1tC2Lf4KNCFCO01ghtaGd76NGUHDKxnWOmO4T1Elso+mCwRgw5PdIgU8D1LdIWhOQpVUHoenwOSF2ijCH4brBvR4gM6dFlWdC2LTL7QbSmSoySxNCRUiBlBSQIEaUG0XJkSKu2IhHRZBkQSYMIZO+HOg0kQijCl//VLT2y/+/edl/+vXXkT59R3JUMWvc8eSNyKcP3bXtmrWFKpisEG4Ul5hU1W7TZY7oFB17xmbbguV7yHVXga1HQq2/fkv9s6pAusFCSbZF4wlke2QiMOvhcr3m4Dsw7wa/MLT+7GSlywFvD0x3cMUrsdAJdZE6MSw7nDb+0Lvlp7fm8V/z75xWlkLy453jzuYJ9EQgHPSdyRG2OuHaj4Z6zmnXSKCOx68CGgbkTbBSJrpCsD1rCqKJoV+xMDUfOcPpUf5P3/d2MX/U0uuTuHcXekWOyWbFuFcYISmWZ4YlJIaXhlWszKi2IUfDqCo6KgnsU6JHmf76S+MgksYiCJ5aCH96WPN1Efnjs+QeHI/70VkeHRSG4U/Q81wsKo5kReERmnvWKJ3rBQ0ZRmcwqBDYErKPmKeBchh8/KfnVvcy7pONS0nQIvmsjcbGJXA+SWdRsiCHq4BEVqHXiYlZcbywfGjUsvCGbgPCakfXcaCK/6UY8qDytUnzi1Vdvad7hNvO3mX99mH9jHHAe/75srKWux7R9IBtDPz8kZshJQmhBSWxVIWVJyo6UIHgPrgNdAQGtFMoMCcWSzNbGDjcWh0OfVRqU7TpJzKRm5RxGKFK3IhdDhg5REVyHkok+C2RwVNXoZtWDUgqrLMoqmqYhlprKeYTO5CAoj7/OqMD6jMuDy2psLePphFFRMl/NCaXFklntHnDnXXcTciQ5T589vXeUIXHHPXdydX+XQgnm+3NObG0yHY1pVmvmbct7HnqY5155hZwzB23Pdlly8fIVNnRJXxY0i0N0NaHr3E2tkEolpjJ0IWKFQmnwOSOlwPkE6zUUNbrQg5BOKgoh8CER0nEjeWhRZotKe5q+Ge6HEkMnmCwwStI2c6TWSFVBHDQ5WkgCAu97jJBEIZFh6AZLSuDaBnSBaVtcaBFlPRR5GoMSCa0qXB7Co/wX/+Ut/cD/R28/lSdGsDGpOZo50pZl90bHNa/4ZFOwKSMLkfmzGz0Zi1Y9L3jDkyuDTZlZUhiV+PFxT63gYmd4a9VxfnvKX7/q+aBK7GQoTWBTQrWp+cyB4F1FIvjIeiQZCY3Omisrz5TM35kXPF56PlpHvuYEGzpT2sw5W2Ft4OWlI9uCc7Eb6lRy5oSCgybip4GyK3HS87lDxYfryGi7ptKe/JMI8gAAIABJREFU2CXWpUFlWOz3XDip6BVoX7IWniZ7tpqenTOSg3WFpiMuIkVdsGkjWbQcrGruvrNgMVuSc+Zyt8H5YsFXrxWcEIGVKXlt6ThZGL66TDytKt4SW67mgh8aB36nUzymYMdGvhkE53Xk15uC6wvBxEg+vNmzowSzqHi78lzNmk91hnPHtSnnpOWRDc8zi8hbVKCoNZcX8FK2PGY6/vqR4bE6sSUNF1RDISSbIjHD8FSveJ/2PJsVE+CCSASZ+P1WEYXhgyz4tXnB3RPBq43injrxLtmzoUqMHtax//FzV29p3uE287eZf32Yf0MccLY/9LEMkSCH3JMgJK5pUdJSFobFYpeMRqBIWjGuR0NvUhpKGUupWfbtkHbctRSjMSmEoTagiyAFG+OCebNCx6FVOzEcWNx6BaYCIZAiDBkxEYrJiOx7YkzIssJGR0qJddtjZSLqkpEds1hdQ3hBNgqhC4qi4O47zzE7OkJaw/XLl8jSUEjYOXue0HR0XcPZs6dZz2eYwuJcz2Q8xvuenc0d9o6ucXqyg1uuue5nVEKzOZly9bVLXHjgzRwezHjplZep6zFlMWYtEtvjKW3bsl71CKvZPnkGlwR7r72M2DwJPlJJz8pHpExYWdKtFmBKyvEI1yyx1tK3HbnrEGWNSkOvV8oJKfRgDdclXmRIg3NKSob0YTVokqKQCB9R5YjglwghEcUYI8Alj9WasFoPZZrW0vee5Hu0sVgp6XMi9i3KlIPGynty6I9FyZqIIX/11p7g/B+P3ZGDEPSyRztN0HBtCVWWnJoInrzRs5KaqRB0UvKdU49T8ibvEyF4ail5cJz50qHiXZuDg23eRn75aMQplfnZUz3/ZpE5n4eOnKOkuLcKfPy6oisKWjIftI6xVLwQFf/eiUBoM8+0mvNbkbPCIzrB3zqq+I821rzYVbx7U/A/7UYqL/EWHrWJB0aBB84XLNcSVQv+1TcaviEKPjZuuOvUhK7PpKZh60xNXPQwseRVz2gsaVvB5six6CwndULRctVpTIycmkiuX+3YPK+YLyo+d8XxjipSlJpdDWfKKaFbc2UtsTpxantMwPDxF1Y8eqrilVXig+WKv9GM+E7tuU9KfmWmOW0FP3wm8+n9xEenjv/zSPP1leRtI3hQNFyOmuei4awYTAUfGgs+7RQvOUUg8+HCQRq6lAiRJ2LJyku+eyvxqdXwlHp4lHmbDHw8aH7aJg7XjnUWvH0k+c1G88xa8SNVy4NjeHYNT3eGD1SOs6XgD5aSr3tJmxU/VHu+mTS/eenWn+DcZv42868H82+IA079oY9l4Rx96JEx4oXEGElGoGIiWs2krId2befR0wmhaTDGMCpHrENAxR473qRZzMGW+OURRmmkNYNtWUswBdpF+uioioKmaRjVGyirWK1WIBST7SlxuaSPGW2HaVA/O0RIQzW1FOU23WqJNxILiOCIfUfXtzCdIISgSEPpZK01ejpBhB7pIvOu48Spk8znc+q6RuVE4xtqazDG4PqMiz2L1ZLN0iKtoSwMOitmu7vce++9KCF49eCQsxvbtLHnYN0SXURaS9s0XLhwN1/52rNsTU8QcmZUapb9UCtxeH0XdKauN2n7htKIoXVdFfjkSV0PqqIeaXJStG6JRKCUJno/dEUhUEpShkSoR/TNHLRho56wdnE4PLoeqdLNGgbhI0kMtREpBcgZUxaEticLDz4j8uCYEnloP1dFSegjupIE50GUw7TOaPLX/+CWfuD/00fuzDIlvrhUnNaO32orfnLScDUZ7lOeBZq3jiW/ty8RvufcKcVrR4r7y8AdY811D6PkkDtjDq52MJX8kyuKH68c4yqSg8CTeClb3ing6V7w+DTwy3sFf37DMRpL/tmB4pSKPHaXxd5YcM0XTE1iFjW/vCt5zHp+6ISnnNTMDyNfTpl3jyK6TxylzD/a07x/W3JeJ06LREFiwwrE1pjkHaXz7K8iJ+6acnijYbJtsL3kSLRsY9FG0fiMCi2XF5G76kA2CltU4CNxvuT0qRolBFeazEkrwASuNwZ8pi9K8rLh3Jma//xr8HNnEnhPOSk5nCfsiYJfe8HzXJT8la3A77Sa76k9z7SZe0zmea/5g5VEoPnLZ9csessnWniEHqRF5sgnvWGdM99fBu43EScNn1gKpJb8+Y2Oz3WWk0rwTCM4IxIkz+dCyb2p57ownJGB60nzmhf83E7D39mvGZvAYR/5wXKIO7js4Q4jOGMD/3Be8jPbHb8xM8yoeFCseUVYvrR76ZbmHW4zf5v514f5N8QBxz760eyDR2mNSOr/Zu+9o367zvrOz7PLKb/y1tulqy4sNyFsy3ZcCNhCLoEYJngIzZRxCIsFIbAWM2SyMnEyYUGAhMCkUIYYhkDAMGMgGI8NLrGxJeOK5SZZsmSVq3vf+/ZfO2Xv/cwf55W4KLbkYMu3zPmsde76vWefs/c++35/+zy/XZ6H5COaCpZtJGQ5gqMlkQBSgjZics9o9RBNXZOqCuscEcE5RwgVKRkGvjNiloaeKrSEpGRhStUmTLmEtjUaE5qPCNUMsozSZcSotAoruTCZTRmMlphP90muRNs5yXhIDaGqcVlGaGfQNlhX4rOCI4eOkOKUNsKsqsmNkjDUixlqDXUbOuNsWHbeehFWRgO2JpsMyiX2d7eptQsFIbEhA1YOrbEQw4pzGOeZhoZmXqP1goQwqZT5xgZX3XQjoYlsnnqQRd3gxiudd2DoInXHSALEWjQGQj1HkyI+xziPNHOS70avVCymaSAv0NQi1nbtWz8yd1yByx+N8t0sFgxyS20tsWmgnmGKFbz31PNFF26hLMl9RlN1YSvI827LuTGoBmgifjikracQtfNA5Swus4RFC9ToXbdd1B3+z1x/VN+yKHhu3qKtshhYtmvHq0f7BGMRHHMnnFWwlSUG4fA4sLa8xHwu1PMpg7EhIpihUE0q2lSySs7edMqho45m3jLTlqMpcKY1kBukMYTGMB/mPLAXWBslTuYlMSp3TxI3rQnbuzVrawXb2xXTgYFpy/3JY0m8cWJ5ZQGLWHN74/gf8prLB57LLxshi4qFwqSpWY3KVC3zKqDWcG+lnCwTy6sZawFSjAxLz2wxwRcrtJM9NoPDGMMwtpQ2kq8usxDDkWxOMJ69WtE6YZqahHB25njLWfi2mwpUMs48OOU/nLH8zXXDCdNNZQqGQhKfXORcXbRUKtw1jbytLsgzeFmmHDcVH9GS1AQ+GDL+tq04nWXENnCZTZzILR+YK6rKZSYwE8flNuC98JbdjO89VHFvFN48ybgiNqzkjq8aBd6wWbChyq2DlmcP4c4Z/OZ0gGQwBi7PIg+2QqyF71wNvGlukACj1LX3q5crfnc7Y9+13LV5+qLWO/Sa7zV/fjR/YRg4T3uJyqjAakvTtEhW4hJEk1Dvu9hI4ggIEgMxJVLsnM25YkxoGkxRYrQLIinWkTnH/tYmbrRKmG0jVhmvHWUx2SW0dP5WQoDUQHAgDZSrECLLKyssmgWq3fblVgU8ULeQZ6TdfbKVJQKgISIoxw6tY51DQ6RWZbq9ycrKGlZhY/NhyuVljhRDHtrdRHE452hCzdLhVXRvSrk6YnJ2l0PlEI01h06eoJkvMJllOp+xtrxC3QSGecZOqnnwngc5evIy9je3KcdLVIuIATa3t6nmc2yegfM48dSLRbdAul4wLAbUi4qQIs4YkukCa+r+Pt4NWGhNlpcHoSYMcuBJWEiocaQQMabzUmzF0s5n4B0Yj7eGZIRYdaNrbRsxBjIjiDVUTeh2YYUGTSCpRiP44RBDoqkWnUfkeY0UOSYq0QBikdiA7TqEcMc7LuoO/19ddVyXR5GhEf5iO1GWnqt84HQSMutYtTVDtWwloZDIZ2uPtpGPVfCcZeWPJxnPWjJcScu6E1qTWMkd/+t9jletJ/5gW3hVPud5lxXcvdtwx8xxMlPeX3v2UiTVhqtcw6YvWSThxy+LbMwaTkXLNa7l7VXBkSxgF5FYOt62Zfj7hxvuTo7UGgTl5VfbR/XeBsPG2T2Ori3hbOCOhypOLlmuLRN37UVIBhkIsypy7NgItzXDr40I27uMvEdUGay3FMlRJ9hvHcuDBanJyPLAnh+yc++C0WUFYadGypxFyDDAw2fn/MpZy63LgV3JuN4F3jUx/I/rgXfNcl48avjszHGqTVw/gLvqjKvzitmic479R4uCb1luuHumXDcAZ5Q7544lGnbV8c4m5yV5S9LIdaXw77c8xhrmCD+6UnFX7flILdw6qPidScnz84avHAZyo/zGbsmzXMuHgkeT4SRz3lU7vn8tspwpt+3AUS+crWA9g/taw4ei4VpjOWlr1ixUSfiFhy5+A6fXfK/586H5C8LAyZ/7clUMKXXus6kSxlryQUFd1xix0NYsrSyzV3dbvA0C0lLanCY1VNMJxpeoCt5bmraCagfMGDscY2hIGIw6jHfU+1OK5SEpWkJUBmZBMhlqcpyBpp6DzTBWWFQLvC26iOCTXRJCquYcOnyURdUZC0srq3iTc+rUpzi8coyl8ZiNnV28M8S64uSxE+xVE5ZX1yE2lGXJfG+P/cWC61YOc7beJ7Y1wRhmOztgIcfgrWMmsZtuw9LGxHx/h2Jpicw4skHBrG7ADhjnORubW6jzbG+cgqolG5WUy4cJITDb3cF6jxoHaiiLLhhm0yhIoMyHBCfEugFnSIs5YDsj0I3JstSFYIiGNrbdiJoeOP2zDiGSYkRcF4ZCumU53T+2Cx2hxiJak6KAKuIcue/89SQg893ivEVdYUID3pJS2w2hRoMYJdzxtou6w//FG4+oBOHe2jHLLe/bM3ytr7lpPfGBvQyrhjVd8MyjhnfueWgMpY2s2ch1eWJfEj93puCWIvKm2vMjK3M+PLXstZF7G8/NhxzXZXPuakquJLLmhP9ry/NdhxqmreH2JuObhvu0agneUqbIbkjU6lkqIq/fKXm+TzxnRblnL/Bg6/mjieE/XNlytonU0XDk0IAsCf/LvRU/vR7xY2VzLzAyjrk2XLY2YBotw2W6NW1mgWkbtuZjTropiywynZcEYzDziiDtgePJv9Q7KRISzFql9IncFpApdfCE0jEOLZPNlmpoed19lhu15ZmjyBWrBW0b+dWHPV9bLthVx2mT8cpxzYOV8p92h6z5wN8fN2wZ4b4ZtKXnTduGscAwNqwNMv7WeI5DidFzdyX8l8YxAJZUmIrwHFfzJ4uMWwYN9ywc1/qKP21KrpSEOsNTXMu7oucVZsHv153vlVsGLc8fKG+dGO5Mnu9brrEKv7mX82yZMxWYqXJbGPKNvkaM8lP3X/wGTq/5XvPnQ/MXhIEjN321ogq+wCpo7F6kmXPENhBjS9SIEUWHJVobBnlOjBER7aaI1ODKcTdqgJDCLjF0z7a8eoTZfEKZ5VRtgy2GXRwoUQois9mMPDM0VYUOD1NIookJsRk2L9B6QZuNcWEXa8fEZkKI4PKMtJiT4gL8EOICXxRYhMxaVlfX2NvZRa2jqmcMbcn2/sMcu/wamqZheTSirubsTieIWI6sH2acWubScGi8ypntTY6Ml7nq6GXsNQse+Mx9zEU5efwYd9//AJXPOby8ikndL4bPPHCKcjRkVi3wS0uMk2VrdwO0W+OT2Yz0SHyvekFKIKKkNmDLAbFuSUbJfY6xSttEUhMIEunChAegi1NlsgxrPK22mHnoFgRXCyQTtFmQDVdp6orcG6gqWleSNOBdToyBFALGWjAR0xpCs8B4S0pCYR2tdZim7rbp25oqZWgIOBNoP/Hei7rDv/nkSUWV5+Ytx0XZbAzRCDePAtuV4XQLp0NiIMr6wPCWecmPLS/YBzIVTjURj3JZYfnwwnPMJaq24QMH0X6/50TigzuGp48jd9fKiTwjxZY7mpxnZTVv2YUXH4p8atsRs5xn5Q2fqC1BHZevBHQBd2vB04oZNBln28j/u8i5ZdSys0i8rxUWxlKifO/6glEQVgsYrZdMNqYY43hwAZdnibftCLdeVeAmFeXqEm21z0MTJTNw9PiIo2GT3ZCz6g3blXBiGHGlo5HE7kOBynrWl2tOb8LmYMDlpSHXGSTD7Q9lHMoNZ+vIeEVZ1SEf2pgxbS1fNVQKoyQPtVimTWCnNQf9RTf9fN9MSEZ55sDis5ZJYzk9S7wjFUwSFKJkAtYZnlkErjSGezXx2bnlxS7yG/uO5w9b7phG/s6a4zenlh9YnbOzMJxuhQ9ozt8dNXymMry/9hy3LcsucVKVX943vLKI/FnI+Y5BzRxhgJBSYH3c8jMbKzSp5TXDln96/8ZFrXfoNd9r/vxo/oIwcNyNX60mKmFQorN9xA8AMNIidgzSEFoFn1HMp1REBuuXU1UV3uyR7AqhnqF1hcsdRbGGSqKpF7Rti5GEiCGFBXZwCJNlxKrBDQaU3lEtplR72926lBC66RgVKJbJo9IYRWY1DJdJ9Rl8VkC+hJUKoxnz+ZSsHEPYpSgKMDltFJQWjYmEkDtDmgfKIjAJXbRvaRvaRct4fZXx2hKpDbSpJTaRdrpPrZFlLVnM9/HjAXUMpKDgYPXYMXb2FjTTOWUGhVtja++hbmu2tzRBWXE5+9U+QLeIuTFYpwTxeANOLVW9323JLpYxCilUiCY0ebTaxzlPEoV6Qco8DoMvSqrJHO9yDIngux1WKbQYn+GloVpERMBlOe18hs0NUS3YEist6rrpxDZEmE0wWY62DWo8RjsDV13spqSmCRsjZlSSAdOPvPWi7vB/6upD6oF7reOtU+WWro/miixgrafRyDsWA6wXXqFT3r2wvPpyxx/vOl6xtM1uM+RjC2FWB05kwk3L3cLs+xZw3xyWSAyMAQlMyhFPyRvu2vPcsKacdHBmHvmFTcPX2pqNBPNomaLcZwf8vcGM1y9Kiplywyhn3uzzwqXIjim4PqspkvD2PbhhmDEo55wEppmQFo5auh8nMyccFmWvFq4tZpxKHk2OoU98ZgeedRjccqd3JBCrhkWAWiNHazgTE+tZYs966oXBl5HV5SVOz2ac2rE8Na9x2YAP7gYyERoHb5mX/MPxhDfvdY35wuXEr2+PePlwzusXJa8ZVBx1wjv2EsdKR/Ieo/BghCOq3B08d84S3zGcc2dynEgtH5SSr8sqri7gZzYGfOvyAkNi4B3vbTyhDrjC87XFlJ88s8QVPnHrqOY/bzn+xrDh9rYEHLfkU1rvuS5ruKvO+C/7iZcPlEGKfCiWPJWaoVOUyNAa/mA341rb8uxR5HAhfO+nLn4Dp9d8r/nzofkLwsApnn2rSjagbVtS25KVBcYYFtUEGwO4ghRB47yLZu1LnBkicUolCosWLGC6Z3HOEZKytDKgmgspzBE8xjti7HbraKxBLJnLCSl2W8brGVmWEaJCaIihQk2Bcx6Tj1keZMznc6zLSGGftoEE3YJbEaSe05Q5JmWMBo5hUdCmxO7uLi4GyvEy+9vbxMWMpeOHaaqWYVGiDpqQyFQQJxxaW8Fmls9+9rNcd+XVPLR5htVswL0P3M+JE5cTrWXoPZPZnLWlFbY2tji9t0vuPH55GXcQwfvqo0d46Mw2lA5pI/PJNvW8QpzBZyVtXaHNFFt2EdaNMRixhGaOuBHmwHAhNSRfYp1BZxXZ0ONFmOxVmEGJikNDgLjARmG0vERd11SzXYwvUI0YK+T5MlUKWDHE0OC9J7UBtTmxmeBstzW8queIJogwLBzTAE6VsJggxRLpExf3FNUvP/2Ilng+XgkP1olbV8Ag/Pk0csJE9sVzfyPcGbvo7V9XRI6ZjNLP+VDluH1qOWzhK7LOZ8QRI/xpyPjxY1POLDwPzITSKUdz4d76IJ5uG7FOuSo3zFJiYA0f2088Y6w8FIQsRm6fWk6bjJeVDWtZxg3jxNY84q2iBDaCY6elG7204EPkT9KQG2m4+UjLunOoGP7slPIVw4ZVb3jHlvBwE/nWY7AdhBM5qIvsNY5lSYhLFKOcrEzcv9HytMOWh3cTpRHefX/k+Sdzguki14emYTAoqXdrfuKU5TvXA3ZkKGtlP2Y85bDw8NkGlhzFVNkIgX921vNSH7hpGW7fFe5pEq8cw7sXliudcqWHX50pVoe8ZjTjoeQxGvmUKbjRR+pZ5LmrgSWrvO7hkluHLadMxp21YyW1fF3e8pTlyE4DP7FpeJmHKcpT8sTJwvPRhXI8F+6uhK8aBTZnymrh+egkctzD5QPhN7czbvI1b5x7fvJ4wz8+M+R7ywkfbwyfDAPev3nfRa136DXfa/78aP7CMHBufrkmMZ2hAIhCkwK5zwgIWeZYaGCIYzGfY4sBsrdHU1WwsoxTxTihaQIIZK7Aekeee9q2JYSE0GBMRjtb4IZDqsWsW0TsCyR1jvycsYjzaGoYjEpim6jrBW0wqNBdV0/JihHZYExsG8Qo2gRsJjRBCSGw7D07dY11jrIoqOoaqSva1EBQsrVl4v6c5UMr7DeBgsBiMmcwHjDZ32Jw+Ci+gay00EZGkrO0vooUhvVsyP1nz7C1O0Eksr8/x6piTMZoacgsKt5n1BpYbO9jbbfVnRBYWV8hJqFpFrQpkZLBiMFLIuBxzhGh2wElAsnhbSBowloP1kJT403LfGcPsgp0BEYwxtEtxZNulEli5026CUAisxl1mkI2gFSBH2CMwcVEMkKazsBZ1HoUxSaIkrARkjYYNyTzBrWexYcu7hGcX3vGYa2T4XRrOeYC0yTc3VieN2y5I5Y8p6x5f214Xpb4yJ7hihIWc+Wzc+WTZcFLzIKVEfzuTgEC3zpqKYzhiE/skKhbixAoRLh3BicGjtumyp3R8ZUZ5HT+Lg75xGFvmZvANU7Zc7BVKf9+e8gxB0/VmjfXiR9YghPLUC0ShYGYLKVtuau2bM2FW8aBP5h6Dhvh6cuJO2eKbyOnk2OjhhcdVu7bT7zseOS/7hY8N5/z5/sZz1tq+O1tx6tOwLAGP2hx0ZCpY21VkcKAtuzse2bTgEjkPVs5V/pE5uH4QDjdCGMn7CG8+TSMjPDRynKdj3z78YbdNqNKiY/uCx9MJTfYluuzwDwIx0tlJzk+OAVEmON42ajijrnlqjxxNmSspQVXLyl/dEbZMMrlYnl3EL4xT4/q/e5aOHkQsu4zlXLSCVd45Y0LuCaDNaM0xjBU5ZBVrE18aiqMnGEnwZlkuNFFPt0arnLC2TbyzIFwxTCxr4a/94mLfwSn13yv+fOh+QvCwBk9+xadA7rYxRQrB7ubEjbPUZOjqUIjlKNlHDCbbVEWK93W5PkearMuIvV8FzGOgGMwyplPa4y1pGof8RkS9roAkDHiRocIdQU2w2eGtmkY5iVVWwGdsUUzw1qLMY66Ct0LXhVSBJuwxRoGJSj4cohtZiRbYOKCetFiigwRBVEGKHvNDKoGnECK2GxI3J+wfOwYGKVtWw6vHGM62SIb5mxtbXHZkWNYYzBtJFQNdjzi3o0HCXtTTl5/PbMzWywfXkdmNcl7zuzvUYWIcSXjYU492WJUjNjc3ESLMaSEKweEuj4IpSCY0IIExA0JVQO5wYSGFDrDUVXJXE7dzrqFwRJRGRwsybGYvCShlFZI0u26SjFCCiCdb6NkBwidoeiNQbHdgmXnCSGgrsA2M3Q+AWPQcggxAJBlGXFeEU1EyIh3/dlF3eH/zjOO6LvmlrxtaZzno3VnyN66HNnRgkpb7mw9/9PhhjJEbtuFrxxneJ84NescYh72wqlZQ2aFP66HfN+hil/aLHhuFvj0NHJVKRzVCRtasNHCV4wMvz/LeI4L3DhM/MdpwY8sVdwxgwJhnpQEHMkSq2Xkp8/kXGegVLhDla8vlMJ7lm3g/tZz0zhQtpFkM7yJvHcTLhsrIsqSwDWm4bbK8vaJ5+YysBngqTm8Y9/yo5cFMMoekePDFeJ8F1Ma3ndGeOHxAisNLlpC1SDDjHecDrx9z/GPb/DM9hcsDwW7UJL33D+vef9+wcgJz18OnAot11nh/3zQMcyEj9eOl67Ab+1nvNTPsQLrKEkiQ+t430x5wGV8lQ3MK2VQdHp/WhZ546QLKnutjbw3ZbzIwE5Sri8sv18V/PMjc/aicsee4Y8bi5XEMVGe41ve1Ga82Ck7QXjBMDyq95AivzYbcsMg8QKp+MQ8gjEc9obtbnCZl65EPrht2TOJ3WD43Y0zF7Xeodd8r/nzo/kLwsDJbv46bdMMmUbQFkMXKbzxGTRTMjemaSZgbDe80xoYjsAPsGlOUS4RGiW5RLuIlLmlqmdo3SDOoc6QuQxNgjctLl+ibitSW6HWEduIz6DRnKHLqREyUZpQkxnXReQ2By/kvQ3aVONGY9Rk3fxx00Dd4IuM4XDI7nQOoYYA2dKIpo1YA2m+ix+vY1znTybPzKM+fZqmQlUpvWPWKCuuIKYF1d6EpWNHeejBz1IWA0bDIbN5QyIy2ziFLC+jwSB1xcqhw2yfPQtZCVYwzYKUDFKMMFYhJmLTLcgWn+PzAW3VokawvgvPoG0DJMR2noO1rbF5SRNSt/URsN53621Cg5YDNFQkFQgLTKhIWneGIGO6sOEWrHRrbx7xQK10W/zbGkgQK0yyiM+ICIQWbN7lk5V4Im3qZiEvdgPnX95wWDfaxH5lOWqbTu9q+KNU8PRUcWMp/P6+gLFc4yKfqQ2LsmDNG56lM569JEwD7GL5rb2cH1yd8tGp4w8X8FKfqK3lhjKgSbi6rDFZxnwOlUuEultoeNN6zZ9Nh7ykSPxF8NzoW85KYs3APRMhiOWYjzw8a7mtsrx4rMyi4coC/uO0YLdpeO048oxV+KenB1zZVnw0CT+6nvitacFLXc2ZpuXGJcfQQ+mFlbzFRSV5SwqREAxFFtisM45kgtPARqUcWhrwvvunXL8uHPKGrWBJRH77vpZnLVnOhJwUWl65nviZ0zn3GXimwBUusJnAiuMpeWA3Cu+eoBdMAAAgAElEQVSdK6hhw1m+f5z42Nzxztbw8iKAsQxS5GxUxk64KlM2GuXqEfze3oDtVjDALUXnh+SwBDYxTGLiT5sBKhXfki3489qyIokRnrujcJ1RsMJlvkFVuT8IqnCZ9zxQCQ8oFESuyCPHjeFUMLQp4sSDKu+MOT8wXPDeymGUS8LA6TXfa/58aP6CMHDkmS9SUoX1OTEuwI4oypImgoSKiIE2ARHjHGWW0+zPaPMApoAQ8LGibSIMl7FFhhFHu9iC0K3hIVRglvAyJbkMkSF55kAsqalo27abyok14gpGKytMd/dIYcpo6RiRQFVVIA5REC8MBiNSiN36ntwTm5bCQktLMoZqZ4+VlVVCCDTtgqhKaiIhtVx78jJGgzGnNk5DlpO7HJ3v04qyv7lJCkq9t8PRK6+k2p8yrfcYrR99dHQkpi7kxGAwYDZvsE5ZXz3E/qIh8569nbM4yYkaUOsJTQNYrD9wud3MUFNirDLIVwhEmqbBKjSzPYajJZoEViNVdbabispyaFqMaUjkmDyHpkHzIRpanHOkZkEKLV3I8GXQFkIDIXb+doJ2Dvwyh88yFAgayIwjhG4dUIwBmw8JVY2YhC9K2lBhNBEj6Kcu8l1UJy7TF7uaI07ZSMq745B/sFLxwTanrGruS56HKlg44SWu4SvH8PA0MXHKA7Hgo63wrXbK7889UhR8zSCw6oSdumISDO9OOV9taubRck3ZEOniil1bKKJCi/DwPPDpyjIUaAS++njiXQ9bnAZesGKpVPnI1NJ2A5BcP4gcWRFkDslYbOEIdeBQitQuMs0ND24kvnLNQEpsRSWqcnpquKsWvvsaw8oocmojQJZjbIlZ7BMl8tDEcc8EPjiv+bErhI0a/u9ty7eeNMzniSYKGxUkZ7mxbPnI1LG+FLmuNOxWjoFVPrwHV+TK/XPFOcN7Jo6/SJbXjht2QrdWrlLhsixywwD2VXj/1HOtS3xgT3nVMbh3plxRJF6/DZeL5dOt5bQq3zKq+Z15wdflkSZ1u2nOto6XDGvuroWUEkLiPhnRtg2nJfEi0/mAapKgUfHO8LSi8wV+29zwwmHik5XhKVnkodZyLFPeNzOsOeUZA7hzkVi2if+nyrhr4+GLWu/Qa77X/PnR/AVh4LibXqwpCqqJsixZtN3LMobAYFASIqhAM5si1iJYSIHRaERbBxazCYwLcr9MnG0SxeJEaOu6C76Zj3C5Jatr5m0iPxhBmU03GRQl81ZhPsWujLFuxDjPqdtAq4lYz9E2oPUuIkL0I2grxGdYawnzKW71MsL0NEfXDrG1X5EXjtn+dhfXav8U5AUyPIxKS4Gn2t2CImN9/QhNtUAyx3A4ZLK7R72oWF1dZbE3oaoqhqvLtAdrYrwKa0vL7MWa3GeQhK3pHofWT7C9s8OhMmNra4s5wnA8Yra5iVEh+QIA4xwSI3Y4IjolthlU+923GSBGjM/JJbGYTMHn5EVGvZh1hoocTNFlUPhlVJWmWqAqCE0Xn8o0kAZ4Vdp8iEkBa0znnLGNQMLlOaGdg2aQGox0nj4lbKPlic6hotdu5EcCPhuRUiC2C1xW0N71/ou6w/+pa9f1ocYyScrL1g2/uO25tazZq+DmNWURLHMPv7rh+Vpb0xzETvuGQw0PV5Z/sem5duD4xmFkd1Gz1RqOF8qf7VtuHiZOm4KnDFqWQuQ9c89zRy0j4He2hFcfqfl3W2NONjUnlww3DAyXW2XLJVIt3LNITBYwSS0iwvsYcqNpyEhclcN9s8RwdYzO5nzT0cifbxuuHkTevWN4sPVIswN5wck8Z+gCVwyUNzwkHMuF7zkR2E+QxcRgZNmplHs2hZuPKGcq5WN7ws1HLfNZBBGcF054WGjAWoEk3NkK1w8LTk2Uk1nD/VPLG3cs33a04eceslybKe+JGQDfVEQkJQ4NDBMM75yXaFVxTd6Ni09a4VimfPVy4E1bBi+GW9YibzpruT/FR/V+dQGvWOk8u75916IqHHM1syCcIvFwnfG8THl363lBEVkxsJvgA3MDJF46grfNI9c7Q0vgkBg+HAzH2i2G5Rr3NI4rsgqixWWRNS3YT5G7Ajw/h5988OxFrXfoNd9r/vxo/oIwcOSGmxSbQ6gxzpLqCMMxkkAXExAHuUVciTFCnE4hL3BisZoTMjCZZ5Dl7O/vs7y0yv5shxQNYlp0VpMvDTDFEm27R5jMuoKNxfqSIiuZz+cUPqPRllGRMZnuYIwj1J3/AcIuJAuDVbwvcdmAxXwP6z35aIwLiUi3G2u2uUO5voJNDXuzCi+JdjFhtHYUcQKt4ocluxsbjIdDjDfsbG5RFAW5MwRVlpeXOfXpe7j6uuvx3vPpe+4myzxNXeOzjGgNsWlZP3QIJbG9dZZyuEIKkbpuMVlO4TMkRRbzbawpaK2F6R54D3jECcaWxGrSBS0tl6gm24CANuCXutGTaoE4i7OCthXiRrQxIArOeUKMGCySl9hU04QGXdTYPEfo4luFZBBt0brzSJwNSkSEejLpRngyAeMghc6PkXPQ0C12jjVoizWWKA699yMXdYf/XcfXdZ4Mh0zLyFveN4NFWfIcGj6+iCCOIy5xjYehFT49hztdxjfnDZkkzkrGkVw4WShv34ZbDhs+ttvwR4sBX+MrztbK85aVpaFhp4q8fbPbVfJZ63hV2XJDIfzSbs53LtfcXyvPX255744lN3CmVjZaS8sckiWagheOE4czy8/vOL4+a3nK0URZJxo1SJ74wCnDcw4bxqnln2wN+eZ8xsdn8E1HeVTvg6Fw28PwvHHEF8qbHnY8bS1w1MOiMRwbJX72M/CPrnZYq/zWvcqVNvKWKuP5RcvQwB/OPP/8iooQhZ/ZyPmWIwFfGd66axl75WvWQFrltklgCcPHW0cjDVXKWES4yisnMuHhtuXe1vIDRxKvP2MBYUVqxj7jqQPhw/vCB4zwbWUiasOS8fzyzPFKr1yeJT5ZG/aT4cY8MnTCxyulaROHvWPkaprkeMM8428Xga024sXw9JFwPI+8eUt4oBWuyBvuazOu8g3rWM4eTP8iwgMBlmzDy+2U2+OYXz+zc1HrHXrN95o/P5q/IAycnp6enp6enp4vJeZ8V6Cnp6enp6en50tNb+D09PT09PT0XHL0Bk5PT09PT0/PJUdv4PT09PT09PRccvQGTk9PT09PT88lR2/g9PT09PT09Fxy9AZOT09PT09PzyVHb+D09PT09PT0XHL0Bk5PT09PT0/PJUdv4PT09PT09PRccvQGTk9PT09PT88lR2/g9PT09PT09Fxy9AZOT09PT09PzyVHb+D09PT09PT0XHL0Bk5PT09PT0/PJUdv4PT09PT09PRccvQGTk9PT09PT88lR2/g9PT09PT09Fxy9AZOT09PT09PzyVHb+D09PT09PT0XHL0Bk5PT09PT0/PJUdv4PT09PT09PRccvQGTk9PT09PT88lR2/g9PT09PT09Fxy9AZOT09PT09PzyVHb+D09PT09PT0XHL0Bk5PT88ljYh8jYg8eL7r0dPzRJwPrYrIe0Tkq56kvH9IRP7lk5H3F0Jv4HwRiMh9InLL+a7H4yEizxeRPxGRbRE5KyK/KyLHz3e9er48XMoaFZFfE5Hw5dSziLxTRF775Srv/0/0Wv3S8oVoVUS+AZio6oefpGr8CvDtInLkScr/cekNnEufVeCXgauAK4EJ8PrzWaGensfw361RERkCfwfYA77jSa5fT88jXGpa/X7gNz5fooi4LyZzVa2ANwOv+WLy+WIq0B9/zQO4D7jl4PN3A+8Bfg7YBT4DvODg/APABvBd59z7t4APA/sH6a97TN6vAT4LbAH/5DFlGeDHgXsO0t8ArH2BdX4WncV+3tuvP57841LV6EHZDwA/DHzsMWkl8GvADvAJ4MeAB89Jf6Rek4P0bzon7ZE2+rd0L6RPAS89SPsJIAIVMAX+7fn+/72Ujl6rX16tAhmwAC4/59zrgN8D/tNBW772idrn8dr2IP3bgXecF02db1FfzMfn+EIG4HsAC/wL4H7g3wE5cOuBSEcH138N8MwD8dwInAG+8SDtaQeifNGBCH8WaM8p64eB24HLD/L+JeA/f4F1/ofA7ee77frjy3NcqhoF3gb8NHD04JmefU7aTwHvBtaAk8DH+KsvjVcDJw6e61uAGXD8MW30I4A/SN97pEMH3gm89nz/v16KR6/VL69WgacDs8ece91B23zjQZnl47XPE7XtwTXPArbPi6bOt6gv5uNzfCE/fU7aMwEFjp5zbgu46fPk9W+Anzv4/L+d+wUDBkBzTlmf5MBSP/j7+IGo3BPU90ZgG3jx+W67/vjyHJeiRoErgPRIPYG3AD9/TvpngJef8/f3cc5L43Pk9xHgVee00SlAzkn/c+A7Dz4/7kujP3qtPuaaC1arwAuB04859zrgXY8593nb54na9uDc9UA8H5rq1+B8aTlzzucFgKo+9twIQESeJyLvOFiotkc3F3ro4LoTdEOaHOQxp/syP8KVwBtFZFdEdukEGOl+IXxOROQ6urnQH1bVd/81n6/n4udS0Oh3Ap9U1Y8c/P2bwLeJiP9cdaMbPj+3nNeIyEfOqdszznkugIf0oGc+5/4Tj1OfnieHXqtPrlZ3gPHnOP/AY/5+vPZ5orbloIy9L7BOX1J6A+f88VvAHwInVXUZ+EVADtIephsOBEBESmD9nHsfAF6hqivnHIWqPvS5ChKRK4E/Bf53Vf28C8p6eh7DharR1wDXiMhpETkN/Gu6Tv+V59Tt5DnXX/GYcn4F+EFgXVVX6KYF5JzrLxMRecz9pw4+n/sy6blw6LX6l/d/oVq9uytGLnvM+cfe93jt80RtC/BU4C+eoC5PCr2Bc/4Y081LViLyXODbzkn7PeAbROQFIpLRDRueK+JfBH7i4AuAiBwWkVd9rkIOxPt2ukVmv/gkPEfPpcsFp1ER+RvAtcBzgZsOjmfQveAe2anxBuAficiqiFwO/NA5WQzpOvCzB/l9z8H953IE+Aci4kXk1XQd9B8fpJ0Brnm8OvacF3qt/ndqVVUbOkPtbz7ec/D47fNEbctB/m9+gjKeFHoD5/zxA8A/F5EJ3TzmGx5JUNWP0wn9t+ks5CndroH64JKfp/u18taD+28Hnvd5ynktnchfJyLTR44n4Xl6Lj0uRI1+F/AHqnqHqp5+5Dgo7+tFZA34Z3RD9fcCb+WcbbCq+gngXwG30b0Ankm3E+Vc3ke3bmCTbjfKN6vqI8PuPw98s4jsiMgvfJ469nz56bX619PqL9FNoz0en7d9nqhtRaSgG6369Sco40lB/ur0Xc+FiIiM6LZKXq+q957v+vT0PJZLRaMi8t10CzNfdL7r0vPk0Gv1v8nnPcAP6pfA2d9j21ZEfohu2vB//mLz/uvwRTnx6XnyOPAw+Ta64b6fBe6g22XQ03NB0Gu052Kh1+rnR1Vf+MXc/3htq6r/xxdbvy+GforqwuVVdIvFTtENQf5d7Yfbei4seo32XCz0Wn3yuGDbtp+i6unp6enp6bnk6Edwenp6enp6ei45Lrg1ON/8nFeoqmDFYHKPGI8vC2JoSCnhYmIkiSiGViGlgFHDQBrGJtGKEGOOtZZKlJjAqmVhEioJkkUwZLbFRkNMiTYmaqOIGpIqpQiiYBU0zwmiWCI2Kpm3aIhkEqkwJFvSkEgoaGSgFieBgTZUjSPzkWQ8ahqUkqGNIEqqAwlDFVu8EVqFOgUKyVgoDIwFaWhFGQHOeGZJUI2oGEQENQqSIwqqkWQsiUiM0KRI7h0SAevAeTIjWAOIkIJS5DnadmWIVcgGSF1jNWLFQISI0rRznDg0BkQEtEUxJISAJdmcTCPNQZuROVoDtErUQEQwpqtzUmERE1WoSVZIkmO8x7oDW1sFNIAqEhISasoQ+ZX/+obHbj28JJCX/htFBcRgc48Yiy0LNIQDvQdEIRmHiqDNHKMG1RYVi/EGTQabZyRVpE0k5xBAJSEhIBjEClEEaQOhaVAjj+rdinYaEkEGAywWTQFJAZPlxKYGsSQCNvtLvedJQARUoWmQqBhribmSouCyAucsiBIncxIGbebgMkCJTYXNhmhs8EVGaCJIQlQQX6IaIQVUDBjTpWUFoiAxYI0j0pKioiEiRYYERWxG9I7c8ajeQ1RGowEhJkJdIVZx4yWoGlQUKxnEQERZTOY4hEZTp9mmRjF452g1YUyGBWKKKILPDW2MaLKQIiotudhH9d5IIi4O9K6ewqT/Ru+Vy5CQoKnJ1DD7g+/u9d7rvdf7F8kFZ+CMraOOAQNkUWmlJcwTYsAn8AJJhbkoSbR7EZOoWoNKjs0UJ0IjIFhySdQpYRRsjBgRWgISDG1oSQJRlDwZnLPEGBEnWG3xNsOiiAl4V5DaitgGGlECQobSaE1qw0HdE6qKlUiFpzGKxoA1Bgk5Ig1z6xgq4DyelqExNBpJYhF1rAispYZoALXk4lBAiBjnCVGIBLzJurqbhJoEqaSWlhQ83ibUGrxYkkkoCQ8Yq8QQUSytE2gbIEGWE0jYpAydBzwxBlptMUlRkxFEEOuQ1H2/vSSsGrwx1KKYpBTiiIaugzFK8ooNlkYESRGPo5KEFRgbR0BoHIhEUIt6i4pAsoQQSKZFbU5t43lS45OPM4KmzqV4bBpwljRPiDHQRvAZKQWiEURb0oHeJQmCQTkwdkVBDJiukzfWkZoGsZaoYGOCuiIKGBFS0u4F09SYrCC2C3w5IBkDRFwxgHpBqCuiKNZYHA5NgWyxAOgMd1WMjah4ING2C4wbIpLQuiG5EquCG5aEJmDdiNg0BGtxZYG1HomRGBXjLVk2ONB7IrqSNF0QCWRFSRJFjEFNwqeCSgO2yfE2UbsExuEO9J7R6T00CcWCN8xnCyCRr4xpFi2Q8MUAgKataao5JFCxJBvxYtAkmMyDUVDwxoNJmCAYb1GjWDVkY089WZCka6cmGYxYjDRYgegsmTNEupd3FvWv6N2HQIotjc0J9Hrv9d7r/Uuityct578mEcWKI4hgiVSNx2eJlJQQD16EeUapgRgMrYnkGJZ9ZJ4iUSzReCoElcggJazporXhc+YxYtRQayQ6T5YSmSgRRTWSWcWIQaXAAsGAcwVNWxMV1AAxodZTKfgYyA5GNmYxYa1jrkqDcMglJFpSK0Rf4SK4NjB1JdbCujiapsEYw5IqhzIIMZEb+/+x9y5Lli1Het7n7hGx1r5kZlWdC4DGhd1qso0zjfQGmstMD6MH0GNopKneQSONNJNoJjNSYpPdDTbQAM6lqjL3Za0V4e4arGyImhMHx8oqxmWWZbn/jO3h/41IAYHVO6iQuW9ODgmpSh8LxSrEwkhl6JWSUEWINlO7QxU6E8Mgs1LSqUUpZpzViGC/IExIqfTeGftvChKsVmI4ZoVqBXxDx4bqtG+SrJF0DgSRBQg8BC07iFM6ocAYdNknd5HGQQbRGjUdRJDWyDK//tjYP2cU1PaLzj/JxywAGUmG4EUxAu3xuoUbuDs5nHqoqAc69pekSMWk79eClf3LYQRpgmeAgMeGzQfCY9+q9TvZGuS+nSQ38ECKIhWsnggRRBQpbX/1ZRIK2gORYJgiW0d64AUYC2aVsTqiidZG6Un2TurrVvEyiOOBosrhzZn1wwtSC00Vm2Zi62g7oxkA9OWOloJ7oN2hCI2Gvzyj7UjEggND96vLRdBTo3bQKoQoOVWKJH1Laiu0Q6O0I7Gt0CpiSj0J2zYYr1EomTAdZ7b7xuE0MZVKD8fXRFXJdEqrdN8fM6rBWFY8k3IsRCjSCiyDNQSTTgSYFkbAsRqk4wLlaSLL/JqGFsh3V/4Z70og/ukOOJ/x/hnvPyTef3QDTpRCOqgmmxYi4O6GyZ1HqZTcEBfWAmaFMwONjY5SatLEWcX2YWJszOzT9laEKZ9prqw6Iea0DBYRHKWpkJmIARhWBAuQCNjuiAfaFHMnNVjTaQK4cipAH7gG4cKC8BDOx0iOGK3ceXAh6cAEojQGlxEMZH+dCKjDoYANYZF9GzRbkjQ0B5HJscA9A1MFNh6kElWJsVNeayp+uyGtsMXAZEN7o8lCpTHEaSqMUSlF6d2JTfZ1cA42bdQU+lSYIwmpFBINJ5OdGrRCFQVLLBsDQxPWdNCkE1gOLJLonYZQXmkqr4Iys7HR7EgSkMowZYyBpIEMZhVuQMXp8ulKxbIUwgEJTI1QJwJEk1YqozvZA5pgZngKjG2/7EtBc9+YRQi5XjFPpBa8CNw/IqFIq3gpWAYjHSg0a2QmYQ3EKFoJIKOTa0e3DoeKhYFBpL9SBUARdBnIJHgfmPhOZ+aCppGxkqUi3olppkkhBJbbndG3/UslA9mEdj6SK3gOkkGZCiqNokEIHKVwpyN2wJfBdDyRRYjRIdlp2Q8L9jDjOCKCbJ2OMJkyvJPR6NuNOjW25U7mjvd+u9MO877dPRqaR2arEEloQdyJscD5QJWCmDBPlUj9I8Whmfvqfl2RTEYmCAQFVaGbcDjMxO1Ke/eGXO5EV+r0n+M9sTcH4sMLFUdq+3NC8k96PuP9M95/SLz/6AacYwAS3D3Q0Zms0AVI5WZQ65GqxoSzVuHujS4NlaSqcM1CrUrLK5GDVSse++bHrTBZoeSN6IZNjZMpYcnWHTXFFDKcHImoM/J1e2CD7AYuiCSH7BAdJGimNDNWhJngbhvhSqHiOtjcGFU5ohTg7AulNO4EkxghHQ1loFw8EQRRmLRykpklb+iAkIbTOZDUEoQa7oMpkk2Uj5q0vtKl0MdKAYobTTuuCjHQSekXR7WDCeciLJpoOXEZJ8wGKftwlyglO9GdAIbqTgnGYHHBTBFTSsKCojkoCBZChuO+Um0i5Y6n4VQSQQwO2sjYqFFYa9kvEhlYBr0YugaHhK00JD5dp1+JAAmG75ctUtAi9BGYCfV0IiyxhKXCNJShglgjzNmyUJvC2HbtkjWGD6pWok5YVny8IEOR04lJ605brhtSCqZKhMHo+1rakwRCQBanxCDcKQmDlRyB1Yo/zMjSsamSW0A4KoWUzkjFxJCqf3y11elEv18ppzNxvSIUshXWpaOqBEmbjzycjiz9xnZZqWVi8w2jcGxKHI2ld07tkS2u3Me2axxqZVyvAHgWjIBmbCFwVK7ffoDWKG1jnirLunJ89wYTIyUoZdd1SAxGJv16B+4gQqrgtzsjknI8UCTRcsB9IxCK6L7hvT8z3DnVwsb+90wm5TBRZ0GPZ/q6UgP0UPD/DO/LuxNlDaRMaDqo/dnw+Kc+n/H+Ge8/JN5/dAPOrQmzN+ZcyGxUghpOlIlQQTDSjKsH95FUd8yMA4L6Rs1Orhsv8cQkycWTt2b0TD6sSUmnxYFDczKCVZISdedVPRiiHFswUNQnPJwlBpKJBXRNcEN9wwVmLfTNGXX/kLcysL4PYCXvmBibFArOrEpNIcpAKMwFpA+KJMmKSkW0oBKog/nCSxa213WoCEyuvDXj4nuqkpqBTKgFbRtMpdA1EA8WNUyFEOEuAAlrR2thUWWNJFcwMR6q07YPMBWCMxsrYm1f62Yg6RgJmRiJ6UyzjfQEkmrzLpCj4LZxEOgt6S5AZSIYETQSJ3AmQgyXIHLD+mAWY0lFSVKFrVYyCqGf7so+jkeqK7leSFfUgugbc9vxruF4a8QI2IJ1u2NVdy58TUqujNtGLUeSSrhjtRARxFj3VyYFmSoZgeeG1gOuA419vd+OlRSluOHbDV9jFzFqsolQUDx8/xJohRgDlg6h5FipY9exMRbCCoYAAysVVUMVlNg5+duCHRvj5YZmQ82wqsQm6G3lw7LhY0MOJ+ZJGXd4++6R/rKRh8rpblgrcHhge//MVCougXrSQ8AmANLG/sp+3ijneRf23xeut5VhyvF64/58p0yCtAfu14/Mj2fifocMiNj1ArlrYqfjESWJkcS4QjPydifnA+u20s4zY7lj04TelWJBZFBKQTwRrVgz+rjvtHQOpmlmpP8R73NT1k1J/XQp2c94/4z3HxLvP7oBZ+mFVZ2/mk/0teNyw+1IK4FqZWgirDR1LgjXSLJvfBTbV3QY3gWVO1sIB+582ArXg9Lugahzt+QoO6XV+0Z2oZSK5EoluG+6q/xtH3Se6kZfC6JOAVIGURKxHfBeZ7wHIUEZiWaCJE0NQTBL6q7zJ013flkWiipjJ6Ao0wylQmyYKHMrvPPkG9kBM6rwLjpmzohdVOSyb0HSlEMqzMJjCC+pLFo4pRIyCDc+0JmoNFPcA2F3jWWpjHSumUiplIRSr/gmSC5k5r7aTaGyU2NVk6YrnUKxJHEqjk0TawwWn7iF09MoWkCEKk6tsEkhNsf0RgKujXkTvmvJqQ+aOGVz7tqwTIg7PT9disqXjVCnnk9w60TeUWvQGloaCkzZGSlYCbInsXbME0phROJp5FjIAJGObwM/VspFyDJeX15tF2muF8QFaxOy3khVtpcFrFEOwkARXUEKKUljd09AYGbItkA74aOT3nd6MUBtFz2mGGlCTVASqYas2/5vFARBpfLw9dfoeSJuC3GceDpVaggfl9eLuiZnFezLI7E6PCgFJU6NNKelcvj6YX9g9M56uXOi0ftCqnF7uVDmmXKed+2cCC6KlgLpXJYFqbthwS3QAv1+I3Mf4jMMNSjzhIljs0EKrQkhu47k9MUTa4CzMHZDDCmFdhKKlV1mII1+uxGxELEP6rULfdy5j0TVkTXgWPFlo4Rz75/x/hnvn/H+X+L86AYcESEo/HoMWmnM8oa3rDzahntnuNJrMNbkMR2lQRG26OgrVXV32LYND7gU5ZQL56twtzOzdkqFPnYb+RsJtCxEyj51mnAO4aZBVbB50PoRaZB9Y43OkN2JpD046IxkR223rlcpaO4K+SkDVWVkcEAgdzt4xuugE0kzMHmlYGRfXwpCRDBNE28kCIzuQq+NksmjCacMlrhhoegAACAASURBVBFsPuO60UrBfHAHNI2pTkQPHGFFmbNQy4ZrJUZwlEFVpacSqSgBODUruXZMBEvBBYYoCUwGkwoKdKu7kI6BjMK1BCWdkc5xbHSCuRRi9P0zNSFCOfHMrRgmyroFfnPWufK0Ct1A1cgIGCutO7eimH66mgQRoaQwNkeKYfIGjYGJMrZ158dF0PX+erHq7jwZA1NDtALrLtAMwIzGRrsFQwqS9Y/C+CiK2ITHjbjfKdaweaKI0EdHRWmtQjvg4cjLlS0WwsZupV03rBxgLBSCLAplgu70phQPmlWCjtYjgaARDBLNAVHRqfLPn2asK+VY6CLcb4PzF295O3eCYF2TIgkivPtSuIexXDasDNYuzPOEritj2UCF6fERdSc7rJ6005lwh2LIGJgIWhRLCCqSjgR7RMPlut/WUUA7KeziSNtf+0ohRGm1sKVQJfAcWBr4BlvgvTO9OTFu2+7yKU6g2LSRJky1cv34TL8LdgKxQsjY8V6D8bIQwJpG+4TDVz/j/TPef0i8/+gGnL+Jj7yPnZN7kIVDGURpXKNzEyPGRuqRh9YRT34pnaV3uhQyB5dN0FcqaQOm3hlTQVbnzEZkUgfYJOD7F75kJQuoGPe+T++nhOfpzCEWpAZlrIglua2oNpLEFZ5TmaRRCjQZWAQUaKXydlqIq7BYMjUjRucghdRdQBcxdhW8KEXhAGQsNK/cphMfbeWXs9M6jCl4DuNGgB2YGdgMyxDOfoQCS6/0FEZClQQLjiStOktXYNqHMxMSxSSYNPHSd4F1VLwowsQBYU1DZKekWq6IznRVzJwDxksKizTm2XmfM63f0aLUrLTc6MAijZr7y+hGo4gi4ty6Iih2FGRqlJGsETwvdzbdnRJNBw+Z9HH/s+HxT32mdcUFjPFKT97IeqSvFwSh9w09PxDSsK1jxwP+/HHPJeorBNT0ncZTo7mzGohDkihORCWaIOsL1CNKI5sihwP95YYKlNagKGTsQsLLHVfB1oGakQBN6RlUKWR93TbG2PH+cOJwbuTz4OYrj49H7pcXWj1QSkEfjuTlTohiquSpcqqN0e+0VBxjyY2//GUjL8JQuPWNyxXaYeJcob9tfPOHzts3Cux5VB8SigTOjncrlQdxFjtQ6m4dvmaipogP5qmSFTKVdKPMlQJoKcS6kFJIhAiotVJMQJWDNpbhFFG0Du5bIWKAKe3hjPnG6EH4oEwNmyp4gBgiwf35Qooxv1HkMBEpLNc7y8uNTQsH74BwbPuG81M9n/H+Ge8/JN5/dAPOvx9GRzjmHg60OHxpjg6hFqjFCF9RSxrOnMFBkiIrgXLT4CbB1WEDMCU2J9NIv7GIMvJA9gWNA3ftHKpQNthk0KyBOKETbXREKynsavx0qJXivr80wjiWPXshRUkKjzUoadxV2PpMr4aaY+E0m2iWiCTQSSuEOTaMe0IXYW4zJvCUiTPz7+7JeU7+ZRG+7sGLJs9irHng2Fa+NOPj5iypHMw5tw6j8o9daAYloOBIExaUOXeRtCN0KRQSy8Jiu0g7ZOVUJ2QkzdfXASx3KoskRVly5qIQ3pGeLKoMSWJzSoW7rwxPsiduwaoGQ/Ac3DSZsmASZASXVGLtvJjTxJjV2O4bd5zMoBos/um+aO8mkErBASH3FAukJ9mMcjgRPSgIkYkO310XEgTK1lfC9sgEEyfDKSMZIWhulGJ0C3y70aIRfSNqIddgWz5Q25HIAdMEvm9Ete6xBJEDtQrekUgkgApeCoVXevFhQmh4DOKe9NI41IIFPDz9hHp0VBRI4vFAGuhQ1ky6CPV4ZqpGUaccC3/7dytPXx741cF4Oky8PCWXW7B6IJPx1ZeV61jY3i+c3k3M72ZA+e0/XjBgCkCSetgHeOqEKjhCREHMKLWyeadNhT42zucTMmCrDTHF0ymmDCApRDjPdEQcGZ1tBFjh9uGF8nDm+vIdMYI+oIiw9EBernga2gzrjjXBRzJ6J56D0A5aMavkZWXNG+i0B9r1Pxsc/+TnM94/4/2HxPuPbsB5R3BLdpoC4VF3W3SWaVfPU3hDYD54NGij89ySN+ZkOi9bYRr7uq87zGUFqXhxlhQ2BOf1C1g3hlSWNGoNPIMkd0W+OVPC4sGtd2QI54g9+RiookQGHkZRiEgwYSkFw/C+ca3KY3Y84aMLG8mvxHmwPTCpS3IywQs8WNKHUJsypIIF/zbPPHjnQxf+MJyvK8x1X43OJblF5VAEyT3heUkBP3Ix5VGuqAshEFnQrJxVkbEioqy+m9ZDjQHUEGYTWiSy3BER3AfpBS2GuxBaGCEsmeiyMsWd1JkeyeQ31uWG351ih93aWCs1Bt0dotDZmAdIDO6er6GgQTHjrVa87/byJYPZhOHCixj3fVT9JE8ZjlmSEYQFNWD4ik4HysOESWMyI7fO8fEt5s6337zn4d0ZGcb7bz+yLs7MfuFE7SAVs9iF2iORV13X0H++fBWaoew5ImWqpAaK4ays39+RIeQIMvYcDMQQAlHZzSfdoRodxaaCv1yhFY4F+hJ8HJ283HmjJ968fSB1d8oczoVclYPlvgpXqOcDWPC739+YJHj/O+fb2fjiy5mjJSKNuSSLd44cueuEfgHb+xW8cNOFNw+Cen3Fe0PdoQm6wYbRfWNbAm0V3walGq1URIO+LIgIfRmECG2u9EhUYYyFIUZeN/p9o55mMh1/vnB9XtAPz9TjCUypkyCDnSqQgmega2ek058dEdjSMXPUCtO2a00KnWIzmcHiRh2XPysm/5TnM94/4/2HxPuPbsA558ZRjFtxxBWKs3LmHp3vlsNrQJKwphJywMceECeRlM3RHHx9bHxtGycdZFZmhBd2krEM3xMgpaCaVAu0JEML10wilRThNoQH6zCMTLhpEL4H6W0oWya9TKg4akZFmS1oaTTpbKVyjMEohZM4RzXu6fwhlW+H8q4KcwYfs/O2CGdJYgo+sKdI9njgb2yl1UJq8n02fq+Vea6wbRwkkXLk9+vCQ61UHRS/k2w8+QRSuUnQu1G1U2yQPnNqyrRcOU2FG4pr5ZiDLQu3cLZMIoNIo1pDcUyTWzpjDRI49fGaE5TAHTWBZQXppE6kBwXHZXvVHFVmXbHdGIHKbidXEpVCHwGxQBZeAi4i9AHuQRuv69hP9DQR+tpJE1p2oiaUE2NcWC+J5p0rgm433n9QfOy/i8tlfQ1X3BOuwSkZ+GhUg0RIBS0QYajabrcvBSmFWnS/tG3PyPBrJ5vB2POO0hzcESBM0c2JuQFBtkpJQVvDrNHGYDuc91duwPHrJ04RLNeFl9vK9TZ48+UT1eDl+ytv3jzweJwoTfj9x92uKzLx1c+FWQTvwb3rnk7uRvdkk8IkhW9YMU3qMHhzIh1OzCCd6/cLY3VKEawo4cL5q8bldxfePD3gfaekrRSIzn11fDWCQFKZj5UgUNmp2duls/Yb9KQdZswU3TYcZVud9EE9HcjIPafEjBR2l00Lqg8YgcgecS/sad9jdDQ6CxXpC5KDMfawuyKJf8IanM94/4z3HxLvP7oB599H42TBWwqmzohkKsE3/ZGflgtQ+CctzK6EJ6tC+j6J9gbphd8ug2+lMrLxy3LlV1W4dfgqld4qw3bb95JK4AQVd6ep0kkiK6Ty4htjDMyTkkkXo+xScS6Zu4ZEYMQedvTSj2wEWYxzbrQyURx+LYmm8lDgZ1H4oga/CSESzBpI8r4n773x17Pwnk6XG6U0RJKCc1JFVTA3ru2B57Hx8wwalWVb+ZATELRwigpVlBMDsSBSXumeO+AcDjM+kpJwiN2enQJn0/3n5WDdOiMc58A9Yk+FlsLBQV5tmUvc2brgIUwKJ5mJCKw6ipGZXF4DrXbnloLs+TqesGZCDEils/df/b4nQ0FE9/BAEXr8mUH5JzybNdISc9jcKUPQKXE5U3yACL0o0g5IX3fZwCvehZV0CDFWUTCj1s7Yk7fQqIgZcgTxIAUiOpKF2AZZKhmOlgPkwrh12O6kgL7iO1FQiGnPyAgU607WwpawLjeuCtp3F53WwodvvkM9mU6Vp6c3PDwe+PDtR2KesFJBkm8uG9s9+Vd/9cRvXlbKNCh9f1zI1Dj62G3DbvQcfCR4MwtLF6arcPeNsd5xs93dJ8ZkE/MR7DHwDx05Hwl3nr5+Yrs6Xg5UgdNJ6Vmp58Z2Bx3G/TbI7MQmdA2SzjRXWn1EVOnrxr3fWJZt7x2aC4fTad8IlKSWA9md+7LgteG3Fdj1aom+hqL9f3bc6IkVZ1vWPe37tR/JUTI+XQ3OZ7x/xvsPifcf3YBzxJmysAaICmfdeJeDd4f3XGLi7zZh3hZWHHxX12/sqb/5mhjpwJo7UP8DZ7YIWk1SCldPclRqdSIGRz1Cd5y912lkgA4Wb2wYJffwPrdguGHiTJpYGKXe2XJCU1h1MHLjgNJjwwkkk7RkW9trlYPzXpN3ogxPWlEYyn+8CZMFpRi/vu+WuSzw0w7PfmeqE2sVYONl3Glt5lCN39TCohMaVzzgF7b3RSl1N25X44uycerOM4LIgZOtFIerOdur6Pcgg68lKCJ8P5STFt5L8Dsq4YHGHqm9rQubCMsaiDbCGqrGTHDU5DY2jk2IEfzeCwdN5txFe4Vki2D47lCYNGkBf1/O9NjIrJxF+UV1ahhXdQ7p5B6j88me8AUpgEHJiogz10JVp8vEWO/ouuC6u9BKGlOV/x/e94gjAxWu60ZrBYqQ0naacU0oA5PE9IHsYw9WzAHdGTGwFLaxr+ctZa8XFJDX1TooQuw1HewhbeK6c/f3lSy5z6oZyIDUZHm+slyuPF/O9HvH1t1m+0//8D2lCtPTmf/933wEoNbCw7szy/VOOz2gr+GOy7oytyPtqfKBjeNW+Ob6Hu3wcH4tIjzAnIVt6zy8axxa5bkODhloaRSH9WCsXSmiNA1O54L25OUwMx+S3/x24XaHxTciKqhyuVzJbWNdNqxNgFAPjSyNUzWeb3dOh5l+X9huK1YNi4HZRDk13IN+76gURIQ6lGFGsodpdgnkeGTak0Gwse7VAu3TBfxnvH/G+w+J9x/dgPPXBm73nUKaJuZ2ZFjFQnlvzjQKSw2sG0M6rewet20MZE/Z3lMlbe8G0Z5864bqrmZHNlSFn3nlXRmQzyh7WNK2JbeSyFZo0rmZY2KAYuFgzoZBQqnChTOFjRDIKLgn10wOBqIFFwUxWits2WkykQ7vXfcU3zRMoB6ELHBZnHe64AOEGcvOlz34+23hFwItDE4n3n+8cy9GEdh8o5XdmfTrvhemPUzKoSjKgX9ajD9cP9LqxGwrX8dAmvIwHRFtvI/Kbyw4eedNbqzW8TTeToVpbLxH+T52S+ZQUCkcZqdKIgPA+SaUWzdmGayboFr4ed2baSczrj743nldSRbCkksvbAizr7zJPcjrEJ2QDqWiaczFSBnM8emucJoLoYGlkKeJcjzC8chsjeXlI+oHUoLddBB7MZ0XPDaOBW5DMDFGbpQ0kMo2XtuIs1MtcHFKTKQmo3/A7LD/8Jvva/01d9cKsHvm9u6ZiFf9QgRa96RYfNnX+FGJsYIb6YGWilhF5mkvOoyNsAdk21hf3RYZFbVBe5io08R6v3J+mLm/rJTDTLystFC++7vf8vTFmZKFaVY+fnyPLhNF4HcvG4dzRUT4/sPe49OkoVPDz4Xv/uB884d/YjpNTIfK0WbKQ+FpnpnKykcP8l54Hy+cyok1Br4qP//6zMtz8L7OfHh/oQfE6FhrHFSpx+kV7/DxdsNve6HselnRVpmfCg1DzgcuH2/ctxXr216Qq0IJWAzAmeT15d4Tz45Y3Qshm+1VA/3T3eB8xvtnvP+QeP/RDTjftyPFki+bcJwKqHE4n7j0Tvl+3YPk1o0aRpVAY1BQap3YiN0VhPBKFnHNvQNjj8dWRlFw+PtS+ElWXA6cTFBzDjbowxiWCBXRiTUHNQVKxdT3PJrSqBocXtu97zfHTJiKI6ocpwpjL0Pb1kHZjQOs2WnS2JETRBi1TKyxQQhZKy/tABPctsEf+t5ei8F/KMahFiYRHqdgyU7kLpPLFC63haqVqzjf3VZaPXPbLsw52LaKz7t9/B+8UKTT2krmykHghZlFkxqNGzO1wTstuF15qoW0jXkIU19ZYvc99D64kWwcKXJHbWJEMuPQB5sIlcJNB1cKT1LQUrlFp3eolrxFWEUZWXhrG2+sc8gkZPAS+7r6xYU+/+hg+l/s+LHtIsnDxPGLt9QJDm9PLNeN8iHo484YTgkDCTScIUGmsoSRbIwIpn/mxVVpKJv7nrvEfmm7Dao0bHogStt1ANuGjmSIUyyhnmAMJAPKEY1OpqDTfplPVRlRGe+fkepYFLQ16kMlfKBTo9+W3f7bGt7vlHJEdI9e2C0CB4ave5O9NXKemOeJ5+8uiAiEIFW5ZeFUC6KF02l3f6TvBby9K7fnC+U1Hyz+0Xl5e+bleaGSrFsg1z0IrqWQOjicznvB4NzYhjNwSnzAe6c8Tkz1hObCw7tHJA7UGDyJkb7ryq73jbEuqBhVQV8dPVIr/cMLMhU6DXlNRJ8PR2w+sW231+JbZS4zks7IwlSVWYI2f0FOyfXDBaSx5Z6F8qmez3j/jPcfEu8/um+OX/z0xKyFNHiqM12Cl+hsHaa48843vhWj6z5eViucWfnge4eUamFm8GCwhDCZsJqwhTJn8Fcy9pTd8cwf/IiM5GZGqr3qPpwiQrMVVaFmpeSAELRW6utUKkVZtjs27nxZlO+lUqoxtYK5s/pK8VdHkQRVArRSRXAXpMxUNjJWphz0UZAwooCEMxclLPAUfEnatuL3jd6gS3LcOpdimFUiVuY6MzCWceHIiXHfGBkshx3gZEIIF1V829XuXhpfGHzwhcGgjoDSmHvy3pwzhWdZOGTQM3FRShEig/pa2aDxkRHClRW60FuhUYFBK8IhjOLJUpw78KhCr8awfe08qdBHZZXOxWb+cexbovO0l6G6VS7/3HD+CZ6v/vWvOJYGKjx98UgQfPh43bnrbSGXG62WP+I9Faw7FKgx8N20CroHjBURhu7OwTI6Nva07JKDjNjdg1xf8S77lwhG+kJ67BqGsfs2y8Ppj/9PkT351JdnVApIw+fGdJgIkrwv9OWKzSdSAgbU6YAGgGCHE4xg0w3Rvf1Zwsh17/U5nGfShOgw7jf6hyvvTWmHQl+2na/PYD7OsC2cHna8X9+/cGgTtw+d9ETPM7mtZOx430TIdWO7fU9W43hr3Lck5IZ1gXKgrRvP8sxpmrm87MJ+X3MXw8+FyL31umgl+kA2Z/MbsgXxWNDDTMbg8DBhW3D3GyGBd6cej/vfHpCZmDWyK6kbeTjz8XZFtqQ9HpEscFtY8tPd4HzG+2e8/5B4/9ENOOXLtzxIYUTnJYyvZeX5alw/vudtrPxjnunSiRw8jg1P4WLJI8k3JogqWY1LGpt25lGJsewcYKn8XwiVCU2YMigWnGyvZbiIkSgzIGXnOUcTVCemw4y7E6OzbCvaV548+HYUrrBXw68boQtnnDe1ciuViI0milll7c7jofCWG99ppTNxkhsvkbSeTMV5swye03majOdsXM14V+48pZJl8L47NRsvqfwrK2yy8VXpfBeN6Bf6JJye/4nHUvhftzd4LqQ2bPi+yYogPOgI3W/8Rnb7ZCvws9kpsnLhyNvsHOXC5EmNYJPK3Qx68IhzKskqcE3lEsaJgjfFUph1Qa3RCNyTU0umSL6nUNTZXFlzTzd+r42bwI0jF4Xv2S+XsymtJyvJLT7dAef09Mi7Q2M7VO6L82iNjIVv/+Pvya2T5UCX/VI8RcdTGALqg0UVrNBVKNn2JNSxN/4GySh1pxVD8KxIdkKEqo3QXatmlF17UCYKdR9QH89MxzPbuuA+8MsLvg1qbni+pqqNFb/euF0Ui6Qdj4xa8HXZdQDHCV82Hr54Qy2wbIMeyaEJ94/3va5jnlAp3O93Hp5OuAo9k+PpzMEKoySXDy8Um1iWO//iVz/hdrtzPh8ZXrnerxx+8kh8+8y7n77l7/7+G9aXQVNhDGGMde8f2gYhBuudaz2gCFmU+TwjdUZ65zgfsBlKKCU2Ri17c/OlMx+Np58+sY5O74O+OFbf7D6R3IWVmDFbYeudg0wUMdbecQMZwjoSVAkb5IeFEcrmu35QEnpP9H5FJYlPuGzzM94/4/2HxPuPbsBROfEP1/e8/+5bPnzTsdjL027mFAfNK0/FWGUwysSWQAGTyrTednuaCOfTgeVlpXHhrSXfeCHGxpdAVKeOJDWYVfZqhKp8RacpVFd+ycaTJFPcuCA8bY2PffCmCi9WuYyV/1SfmEUppXCqxqkpNEV6p6YwEa9DTGceKz9vwv/5cuHpWPl1CtvLC//6WMi84TL4Np/4siSrF74kaOsV16SPwj+UwEYweedwaCxW+Lch5Kj8rSvrslENNi9Y+5pY7zxwYbSGfXzPl+J0m7jEwMfCKo0ildjuuFRSr/zTpfJfv3V+JTcuceCNGj+pK8cSSGxcAr7ViWcpXAJACREep8ZSlAMQWnlIJUSZQ1hyr7W4+0rv8D1Gq0J6YFL5WjZ+UQoPdeNSDjxvyq+z8isJHltn7Z2TfroanC+PT/zm+2/5/f/xG/y7F7QPRt1tlhqgKUgqJZO77ommaoKgaF//iHd7esv64RtqD0Q6Uz0SOLkmOYG+RgkpAxdFpkrpA62N7PD1T544LI4P53Jf+MXP3/C7v/vAz/7qZ1w+HPjut7/nqk+Yd3RqtNOB4/mMnQq5dNRgbDtdajiahS//8gv+73/zt/zqr3/J9x9Xlm/e8y//5r/iOq1kqWwePM4Gfebd+cyHjzeuL890jIt0cCFeVr74F2esJr/+zXdoON+8T3QZbKpUdjfN+3/4PdIdmw8s332PilLaCR8bcX8mbEKqwcvH/RHknZcPhTc//Yq3X5xZunLUyldfVGp5QsK5rVc+nie2noweGJVV4PGrM0Gi7GFzx6dGiKJZ6C/XncL1lf6srC+D6UH3F3EzVA88/sWR06RslqzXR95fPvLufKT+7B3rrTNNf1ZI/knPZ7x/xvsPiXfJH1nmwv/43/53+e1tIUi2MZhEuYTiCKQRueLRUAY3SY4Bh2qIB39Z7rznwE2ML46dX4rzsR9pEvzmfuXb6chfhHPWwWoH3JO/fmv8JJzn+8qhNooFlzX5vcHHMYEo4sEvWvANxjWcqRi9d8RmvjIh+516KjyS/GW7Yhz4bR98MR/w9cpxBL9J45AzU9n4364Ca/B4PvDtZSUF3ojyB5koJajbYNHK1pSJwNsJKbt9umWlD+i9M7bOT1gJGi+sbOvEX+R3/OPpif8m3nOQma/PRvWNf7ckP+kLzw9f8//c7ixb0tjoNMwHJ618dOGaO633tXW6Drw88ZWufDUXnjCqBd+uC3c58uLJR504amJz8G6duI3k2yn4KxEuFK5WmLxT9Ebvyt0mNiY+jMJ3AkWUNOWjBr/sE7+d9hRTzcEXNDyvfG3G//C//E+fpDDh4b//n3O9fNjX3qujRWgir3hXIgJH0RxoKfjY7aTig0Lsa+9qaDGmudHvic4T95dviVaYfO8+s2nCPfmL/5e9N/m1dd3Ou35v+RWzXOVee+9z9znn1r62I2xHthsIIRpJRBqgQExkSyEYYWSJThpIGLogQeRGOiiAcAc5kTAYp0GwIXFkikYiw7Ud3xvf+p5qV6uca875VW81aMzjA1EiOgfbR0dr/AFbS9q/9a7xjTGe5/nKm9Si2d9umM/XiMv0m55+HBij4jBPD6xO1+yHwNT31POWsNuj6zmr0yXx7o7Zk2O8Eh4vKoxrudzf8/h0TbfZUyl4dTvQtHMqB1/9R99D+o71owu2V5cHWW61OIzVrRDHgLOOVNnDQWLdHEblU6BeLRh3HTkEyhSwKHA1sb9HJQX9Dk6WtBnmqyWPnx3hCnzre5fUCZo3H/HOt7+H6qfDPYexmDJRVH3IKzIKjMIqBWnCHD9iWWmWT09ZaktVO56/uEY5S7+dyNZhG0NTQ+0b+ptErycenTWE0bJNUOUItTCNCadA4en7gSFOKK3RWqNzoG6O2ZjhI96dXiJhYrFwfO+v/ZkH3h94f+D9Y9YnrsH5+X/pXxdXBsqUD/cwGZQujJIP1tPKUBshpcCgYSWZBZqTleeqaFI6HPE+NpnKFJQUxmh5vziKDfwL80SfDbep5q1KMWhYS8dtNgdZOJljLbxKlm8FwaeMNw2hbBHdsNCHrwzXFLSqWZFp9MgRNaOZKOKYcuFIF3bW8a2doLuJe+dprGeImWOTqeWwN34dM7faHQIvI+AOB11KKYxklioyGkcpEK1BZyE5SyvmEPOgK5wkFnnAieJFKpznwGpK+DpzXRp+dJHBJl6OBtdHss20WgGWQAKpuBsiy7njdRiRYrHVnK/vA3MDt8mQDFw4WBbhuC4cu8jaam5CyxbhpanR5pB02yhP1cAyJh7NG/Yy8n21YDModjlzJxaKIRtBG/C2ItSGKghoAxwmNqUUjC6cKfhP/tZ//ql88P1f+CVRw0hOCS0ZxozSBwVaRqPVhxJZrdBaYyWSo6I+XxGGAUVNKYmq8ShAyUEN0o2Bogs/9APP2PWRsc88enbEVGBlhdubnqgOfiHro4qr646rF6+QUaiP14x3V+hmSeVrsmRmyxq7XFBZhXeKs2bOkAaygmFSHM8UCcM3vv6CfHdPqjz1bMY49FTWUBkh+5ru5RXi1YH3LFAfWFBageRDjIkylPJhQB8ZVTdUxlBEUbUOpQy677DWcXt3R5UydsqYZU0oih/+wWeISry+mdg9f45zFl8tqFee7es97cJz/eKKs8++yXvvv4MOQv30KffvvwfOHn42W7DKYLNidXHC0VFL29bsgjAMkS4mtLGMfUe7WNE0BiOGp09WbMvE/e3EbogM9/vDDVwxSo7jkwAAIABJREFUuDKiDajj1f8n75WpefGLf/aB9wfeH3j/mPWJa3D+43/5L8lWWUKKxCGyTDuSQJfgWatoUiFI4tTBPmvu0NQGjG7YmsgSh9WFWkHnyyHTSdcsU+BK11TzmkoZXNiwUnC3T4hzfK/LXOjEF+c937mrUDpxVsOpMrzMiceLiuttjzMtp1XFLmaMHdhMHmMT18ZxUa95MdzzhrW8bxVhNIQSCFj0CCEPnDWGSiz7nOiDYtKKTjIaQeEPB3CM2GpO4xVTDMxCIRiNsQdrbpsTb9iMywFVe45ih3OR21BzmkeO24joyO3uhN8aHfc582UbyDbhkuHeFnJ2nBTHrgSczbRBcCax1R4RwxiEva3IktkJJBRGwcIq3qwyLiusU7yOjh7FkTf0H67rRj9DEVDKgKl4y05slEOlnldopr2jy5noKswk3KmE0prBCo1rsFV9sFqPkSwBmyL/xa/+15/KB//k3/rvJaaEhMjYddiuP9xzoTF1jU0wlYHWe8J0CPoT66hnK6Zxg5+doIvQNpbQWmpnKNrQAvsus3o8x2MZ08hRXfPi/SuaxZx3v/OC1dzx9ttHfP2rH4BOvPHsCcfzBa+3W56+ueI7/9dzFk+OeeNszaafcDpxu8kYm+iM5cn5mueX15wv59xsR0rJTFMhxUTsMqG/4+TiAu81w7an78ZDox56RDLGNmgsOe1xJ+fMZzV9v0dPiqzB1hXeGqZh4PR8hQyJalUxR6grzeXdyKrK/OijGtGRr77w/P53nxO7npnV6OowKe3SiHUtq8Uxw90NUhm05EOIorf0XTgYlTUH3iVnMAcfLmMaji8WOGPxxnJ715NiYn5xTBwzfu7xzpNJOKVQuub8wjL0hqIK/fWW3VDY91tU3SCbwGgOvGeTmS9bqBcYpSH0ZAmUIXL/X/3UA+8PvD/w/jHrE9fg/PW/+DMyxYhNBRMLY0mEvmNZZXaloo2wsvfYtiWEisFYtOawy1WKc5sozQyVI0opjuuKfn7ETiJ6F3k9bjnJibupsPIZ8UIZLdrWQGGMiZupIlrhx9KWtBD2ccHnfGJdBbYIuwx2KmRjKGKwlSaMBm0VSY9MasFKTRx7zU03IrqmuMTtNGdUEcbIsdfcl0QI+tAIkHCNw+dCYyxKC7oE5qVQE/GVQgXHZKBjooyJ1k880jWVGTmfBV5Pa/7gOvMKyzupxcbCo9bwKsK1ZCoMISuSylRkHqlIYzxDjpw7ResKS3NYhR2LYVNgzIqX0TAWiy6Ri8aiZWTlHLspoOqawdZU1pGNhxzZauEta7nHYXTF+3mLGRRFCVUoXGW4kZo7lznOml0I2MZxpiwbCp3S5DFybhObqTA4x9/8jb/5qXzw3/z3/55M+w6yIo8Toe+Jm2t0ZSlUmDGCnZifPUISZMwhM6ZkJMLyeIY/XpDShNeGk6Ma6hkxRsb7yKu7a2bacvnymvWqOWzSlcO0Dg10r+/ZffgVfSqK5umKCcNnzhY8eWq5ukxsxxEZCmOJFDEsly1jOqwXYkoY07CqYDl3XL7eU6zBe7jeWqJM5C6wWNX0fUe6mxA3AxL18cH5urEVSgsCtDpTK4WdeVRwFDPRh0joJiRmfuKzLa0VPj9r+MYY+M1/dMfdJnC/78ih8OjRKdfbLSWMKO0oOVKkoHTBRsEtVsTdluXFCbV1nK5bkoZF3bDZ9kxRcXV1jSiD7HecffYJsYvMjmZ0m3ua5QKpG3zb4heavO3Z5cyz4zWdBhsjzzc9aigoEmkydP2ecRKKGjCqZgwdTe2ZzZd0wwQlkvqIbRzj2KOUJf13f+WB9wfeH3j/mPWJOzI+qVtKW9Bjz26aWIwDy6VhTJp5VYiVQakTjBZOZpqlPmj8mzlobdDiMUwkM3yoGNLUt1uOJTEVocVxbgP3KhNSw20fUeqgDOriwe/lR2YDS5e5yZrLUnFiJj6wS26yxhNQookzx4TFloQxkZNjiEFYVoL2ELYjrTXUC8HaRG0Cou9ROqHrzF3SXEfNrC2cNYIlMfORBsXzZAilRufIdlI0OmGLwjWRcQwYZXiZ4P2xpV94rjvL7aZCUubMZDqj+IqHz55MXA0eI4XjAkoHGmfYBEXUABVb5TCu5n2bOZJCVoohBC5pkTTgfeGzTUbrzO/u4S5DzhUvMRxXlkYbjDFMpiKLYSeKO7FcYvBGc5ECR8XxgfL0dJxbuJsUokZOpoPo89QoTtLAJitmxiGl4KQwV5kfqA8XRp/WOjrz+KMZQz+w3QtOR9rjt5niSAJUFqyb4Vtws4aFFUIRVrMG9CECxFBIWSFJUDmTtjtsCFQCx7rieGFY2yP2g+F+u8V4YWFr9rc7xBneenTEyXHNq+t7tkOg8RV3A1y/E/F6QommXTdMU2bpDN5nTpolKQeO1w1iK65e7nmrzTz5whKRyJHOmMnQlwpDxdc2gZ2f4ZZzfuhJoVINKy+steEb3ch9O6PdD3z7teLUT5y5CCvL9dVIkYYP3rllN+3x8y/x4vkt+/4VkjKr2YJcIk8+c8FnHi14dTswEokdKJ1xzRHj5d0hwNZrRBv86pgxaiiZO20Yb3dsGkW365h5xcVnTtAa3v1ux+Z+JA6BfcqsT5eHXCJjsTkR7ww3g8IKfD/ucEBbV6y155ZE1/UsmobcTWgliDkkNc9tg/ee/n6HUYaQCl4rDHB68Qj3hw5rn8J64P2B9z9O3j9xDU7RBe0rfBZWFLJeMoSRWCJV0VgUnQRe7gVrNVrBTGneroW5P2RqaFWoCngskQj6cHk/k8KSyHWXCNnQmIm1TixUpjXCTUnciSZZj7WWtxaBN0tmphMLfc39mBFjQTtQkZQGGlc4TF+WaCNEDHG4pfUwxILWGhHF/U5oTaDWiWIKjTLMMViJhCmAbnmVhTHCeVs4r0YgcVZnbvae++woRRGpuU+KRa04VTASuZg3vFEKnWiOreFzKiMpsSnC6AsnPvBMamxb2PURszSMRXE7Zrb2YAAYs+FFUTwyoENC53tqbXFSGMVhsuEzPlEQrBMihTErktGUrIkKRl2YUFzogW4QBtH8wTQRiqaxmUcidCbxto+sdMTGxLlv2BjFFCxLvcdGQ5kv+GDMtNPECwsj9Z80ln9k5ZPDaE99atEURj8jjR15UlirsVVFd3vL5TtbTNugFTS+Rj53znrhUQgpRyRrvDukCFuxqFlFFRLtGdxfd2x3HdW8QcXExcWKduZhdKTNgLQe4xU/9sOPyMpw5gvLXNjsdmzXLVkUKIUeLSdekUNivoK4P0R4DHnk9Ei4toeVcBHF770S3l4NzOxhdP25leIfB9A+I11k8o73UuG37jI//qjiTSWwsLzRJl7uDN+4cbDLhFQxDBNPPv8Ib56QVeELX3jEGAJDhpOZQ/IpGmEXIzKznNNw8fk3OX8E7723x37unITm6tWeWEEahTxF7nYdKwMlJHY373L09ByDR3uHQXF6fkLBUJ/NyTkz9ULWiRBhbA7Hm1ZBO1P0+4E+C5cvXxEnRdVolvMVw66nrg3z45rcDZxfnDGJIubA9nJES407q7i6uUUNEzdjQH2KZeIPvD/w/sfJ+yduRfVLP/XTghRScbyQgE3CWAo6JVqtcMWyk8hTYwhlImMIqrBLgtaWKmeOnKJ18CoUGpu4qA/BjXNRFDXhRNE4hcsgJLzVZAVKKfYSqZQi54p73dDPKhYa+u0NGoOPkTMyR00mmZalFnpdgxWGweH1QIiZscCRTRQJHBtBRNDKkDVsJ2HuYT13DKKYpEIJjAmKKGoMEkd0Y3nZaTplqZUQkqFLBVGGpA19LExZCFZhi2UzJe69oQnCOAlNFThzNW8RuSyZaZpYOMPcDkhyGMm8FzKSa5Z14Hu9572sedwatjEQUkVRmmXp+VJryVJ4MSbOfYUzBWcsUTuuikaUZTAH3x+H0KtAG2FeNTxTidoI78fIVVnSx4lOEtvcUNnMroCPQlAF5Sxt2jEqy79oE9fK4sXz87/+K5/Kkf2X/6PfFKSQk3C/2ZKSJYc9kjTKWipv6e47ji6OSXcbMoaiM0MX0K1Hj5Hlo2Pa2nL9+hbjDI+enKIFvCuHkFVRzOcVecoIifOz2Ue8X24GZr5BysQmCWoxYy1wdbtHY8hxZKktX3kMxtRUksl1C1Z4sdcszcAdQgmOp7UQp44nviLmhDeerOGyH1hXjn/tB4/5P77fMxaPEnge+o94N6IoLvKNrSPVBRUcymS22/4wPjee3X0gDhFxGVssd/ueUAw6FaZdh185zo4XnNZzXu92TLuOdjVnbhO5gJHMu8+3GG2ZLRzvf3BH7DvmZyeMdxtEOcRocuj57BfeIsWRV+++5tGzpxhvMMaCqdiNA0YZilKEMOKcZt8HnMBsseD4aMGqznxw3REmzXZ3zzgERDLOGCROJAGFxmoh9xOl1jy7OKeL4I3nnb/x5x94f+D9gfePWZ+4BueXf/bnZJYDz9MhkPIo3GCMRibHUEXoQZGw1iIxkJPCEOgEFkqhaw9Gk5SnmIqOhA8HGfSbbo9brqjVcHBTdOqg53eePAYmIlYbquYwMYjdwGAUs2JIGtI4sGwEqZaEuiVNGaMFhWXMhpyFzdhTW8cUErssaMk0Xlgp4dRnjBfiZKAouqLoizBzjm0RPJkkhRQLc2upBboSycoT9YQMlqygoLjtobYGZ+GqWIYUcHLI+giqweXMfQzkoghO8cNamLvE7+0UT8zAY5e5ngwrL1yPmqAMWxTXxeIlY1Shn2oUPQGDMoZWJQyekcjaCKcoYha0SRQnhEkxqywnJdBWwm1W9PWCLlfsp0wwmtU48hKhzsIrbVhbz1oLOSWWSvO88YwDTMZQrGMKmVIK/83/8sufygf/J/+z3xZXR17eDthByASsMZgCuzGRc8ZQsFVNGidyL0jumfqRpm1oThdgNFobxDimGCAEZrOWk4VjsW5pqxHEYo1BlYhyFWWcGB24XHD+MMgNUTNIYimKpGHfaZ6uI5OpUSLEqEmeA+9dxCrP9TjiXEWaAmPioHysCnOteDw3H/GeSmaXNcOUDuqMKX3EOwKVc8wo7DWkAcRHph0f/mGy3F/tcOsZzsLuJjCOExgYu4KtHDEm9nc7FJmihbeePsY1wrf/4CXLhebJ6TGXVx2rk4qbyx2Kiv24J06H3CCtFDGASv3B9dYatBSMbQipxxvP8aMzxvstyh9Cd/f7gfXjc9okLE4aru922MUx/TQR9xPoTNwHhq4/uNGbw6S3rhvC0NO4liGNpJTBFLJykCI5FaZf/TcfeH/g/YH3j1mfuAbnP/3pf0dcOayV6g9lgj/IQK0i7+w75soQRaNrzyIG1q3CGEX0c0opfKtLvJoMJo7MfculhgUBpytiMZSqpa04HMWGTNYJUyyt7pmFgSSKqGtqBxWZuTfUFpzzYA4SbRkytQSMMezF44whCqACU8wE5SmlEKeJuV+Dz4S2olIGS6EmIRmkFHxJqCK8joUyJbqsSCXDlNmqxFva0uqByRjOZOIb3QxcoTIWHSZa74glMyS48CMmC5dBUSnD3RSwBS6T5yZNPC4dF5Xlda65UYoLq3hkAnBI00UiJjsezYQpTrzbt3QCJgsbrRmK5lwlggHrIEbNygg6KW6lEIqjT8LgC3W29NYyugbnLPt8yBUb9h3aCKaMqOnDZN8ScapirEbWqmEyhy0g48Fe/X4K/PXf+h8/lQ/+W7/wP4vLGrSCWmNQfPZiTq0jX/vaNctFwyiCm/uDRH9Vs24zvXYH3t8duNsN5CGyXM/ZdgE0zBYOKYp20WKNYa5gzPoj3pUfcMaQRKHFM9OQXGE28zQ6fMS7ThkVCjgwxpAmMGiyVqDC4d+cErlYcgblPa0OhLbC2OqfybtRcNVN5EkzqJox7kh4UgycNDXzeqTXji96xW+/KuAK1np8iXhtiCUT0ZzMCrMivLsZaGczrl/3pJzY7WG73+CnyOM3Vmx62OXMuqk5WxoMmkgmjQdPqx9/UvEBkd/52oCkRE6aaIVpP7JqPcGAsYaiDjcHhsLtzRZlZtxf3qCtwmlHbP1BojuvCcOANpbt1fawuk4C/QgSKGPE+ZpeB9p2jTbq8P9fDnLpcL9j/Ds//8D7A+8PvH/M+sTd4KzjwIRihufUZm7FEL3w3XxKPrpgKh2mBEKueMfsOJ80j1YztiWzFs2RzjxaaV7FhpwMra3p7TkuTaxN5A2TsGXPPkGla4wWkinsJ8WlXqGcJpkKJPO4TgiFUYQ6TTQZXOpQxjOomq2uaI1m92GPuBZD6yd0KOxiQFcVpe5ptCXEnspZOmVQQGXzoXtVDfcWZi6hfM1ZGdFF8DmQY0QkcNUVdrlBS+Qr1Q7SjvVqxuX9xDDBsWsQB102FJUxGrQ3rJRmM4GoxImpWJmJ9wbH4CxC5iYFTpzi2AjeRAqKmy7x7lDRVIXoElUqnJrIl+vMVWxpK8V+qhnVnly1XI0KcTO2KTPFiagEmx3BapYcUnarLrH0EdUNzNLAhTheZ0FVlqsRFt7xTnEcF826sXywT1QFTOy4K/7DvN9PZ1XKMolibuF4XdPthVAmru8siydPMCbTmEzcBJ6PO+6S4rFbHqzkrWGxNpydH3N5E7ExoBcVeTZDdROLuWW+nGFlT2cUrViMdlTJss0TU6fBGaIYJskcVYZKBEkWoyZq1fDnVgllKv7OVjFO0Lj/N++a2k+kpmEXA9VQ8O1IJQd7hErKP5P3OwXVwqAazamaCLbFKYUJgkji5aTp9orvzTI/caqYpoHPrWu+tx15XSwrqxFVuBPFbdBoZdA1LBeWy5vDSH+5WrNoMtd3I6M1mCxs7/ecro84PrWcG09B8fvv7PntlwnjNbrVSCecLTRfeqr55s2M8/OGy+vCELYk47i/CVSLGnzL9tUNmYJoj641cziEB/aJRmv6V7f4fsPjZ8+4224xjxZsXtwyPz1lOyXmquLofMX1O5e4VU3Y75CsQT/w/sD7A+//f9QnboLziz/9M3ImwkwSR7XhXoSvZse8VKz1wArHKIGSDZUuzCtNVzTfMcdkhJlOzJSwlgE9ZJxOzCVhiDzRhV3t6ZLlMitAUyuFsgalLWuV2CrPZ3xPVYSNMkQ3x/kZuYy8to5FEVZKsxfFIJpUEk4bRikfdYtLmxnFsCyZ7IVKGS7MoVn4HRxLHA2RkiIhZYYQSXmAIHjrIOdD+rmCYRgYgmYn+WAEqA2ZjFNQKcPKJBqBpStMRjM3wjQpIDApw5kRxilijWITFd/fdXjveTxf0qSe5yMMAntTU1RhmgyOQwOUc6AYxTRNuMpzKRZbIMZMXQSfM2am2HXCF/MNt2aOJoH2nOSeFANPZoVV2/C9zvH10cDMsZs0kzqko5s+85PtjquQCKUlSo8xhkplzlHsLGyi5t/7+3/vU/lF+/n/4H+SmfOYecPp0tNNiXc/2GKdUNWW+WpBGTKhm6h0oV06ctD02ZMRpmlktnTUShGswovG6YLTlvM6sbOKfjgk3n+YCfwR78cIWyecLjVVEYZ8OBj/Q97HEjCuwZuKnBM2jXQlMdeGLpaPPo/qqsKliURN9oKxFRcm8bNvPOWvffubJL3GmUP6c5km+qAoqkAQLIXBO2ZToCi4U1AGGEMg7Kd/gnezWHDcCN5Zlv7Ae6sUMQrTCDSZz1vLBymwKsL7e/jmt17gj1Z88e0TdJd4fdfTjYfjyVg8cehQxSFkZBhgZrh/tWN9uuB2P2AL5DGBVuiS8KcL9i82LNLE5B0hRdr5HL8f2fd7PvPFZzx6MuN7373j6uUV5uj4ENpoHFVTEzYjbz874fL5S3S1YNrdYJoKVYTT5ZxOGfrdls2v/bsPvD/w/sD7x6xPXIPzN/7iT8l5ZQ5wpIpL73kVFbWaUEFRuYpYBt62CW2EoA5Ovu/mmkkdOvR5yVQ5EgUqEVqnSA5SMYixODIrqzkymmAt90UjYlCmgCROtCIaR6UL3gb67LjRy4PuX1mOKGgSnRVUsWRlSFnQSqhLobaG+z7QuMxSOYIp7GIBdVBzPVHCTCLRFq6Sp2EimxnLHLmLI5XSeJWoVMbFQh8iOhViDuhYOGqEUgpJWe5KQygTgzjutOdIMnOBlUukYY9zlikL+3K4FepyZsgwSEUICVdZXofIubKMElibiq1EajKvgyWHSF0KuQy86S2X/QCp4q1VpI8G8harK7bdyCs0T51nIx7qwwj3SllSSgiGpDVWK1wZqbWi9RXjkLnVhkDBFhiComIAa6krS5eEZ5XlF37jb38qH/y3f+E35PHpmiFFrCj6zchNCDgyKSqaqmFKHSfzNdrIYeeO537oD2nCzuGUUOxhp15ZobEaU2uGGGjbFknga8OJduADQ2/+Cd7njSUaR60zykRisYyDsEmaptKc1AZNYpfCP8V76w21NdzcBtomY+sVFXs2w//D+6xqqbQQbUENA8kYWhSjbhmnHZXSKDKu0rhY6BIE7UjjgHGWMyeMCqRkbsaaoiNhNzIWR20KtW+YzRQlDThvmZLQDwatEtsh0G8CGcV+NzFbVty83nJ0NCeFkdlixtiPZGvZ3w2ErkMnIfR7nrz9mFffegdSxdMvnTOlyPDykmax5tW7LxE/0LgVURxuOUeITKNA7BDnETTKKsoYUd4yW6wYdjtEhJIKCkHyRKGglIZqDiHSHh9z/7f+jQfeH3h/4P1j1iduRTWvLJupcB0NprLEEEjaovDcO+HCRC6sph/2nLUW3SfGknhcJ3Q4hG7Goln7zJAzUjyXksm9R9eOdZrQWvMyKe6sY16Et1xgoQeGYrk3DbU16By4LTWEwlPtqMuGd2JL0JnJZ4bi0MlgVcJKxCvHM5VYuz2TQKwtxliuJVNwaFfYivBEg02BJu15k4nPl0wWeLm9Y4vjsS5UunAzZUbjyRKorAYNtTGIN+yVxjJybjJn+Z7Bzih1Zt7dcKsDEubsotBqxT4lUipU1vBy3zFva9JkULnHK4P0maMpMaV7mplh7AKVy+hYuBBFlILExAsqrmRgjvBc7Xk1WIwU9mpOrS132tMYzSAj64XHifBBhi/MPE0piNE0znM/ZdANL2NCouJkBm9oS6MjnWsoU+I+Q5AKKxMrVVjE8CeN5R9Zna6W3N7suN/saectY0qUQWFrx5QDRieOFnPu9jvOjxriBNN4z7yx5GBpzyz5NlKfzhhvR+zCcHPb43qhXi1J3UTjLONQeOFgnRTHJxXej5RgmNLBx6jkg/cFg2Zde0w9Me0yIUBvI7u9RmuPVYmqhdo7aj9jrfdMknl0VmHMoYEu+TDyv5ng7bVGSsQ44WdOOER0CPzyKyh3G9qlodKJmx7qLrJfOFytcUmoZx4BbpWmKj2fbxs+VyVepApZZM5GzbWPhH7ivi/MGs0+Jsb7RN0ovv/+lqOLFSofcu1c5Ri2I343cXV9zeLRCZv7PcplwrhBiaIukW7XkxFef/cdFBC54+V7hdo7uqQwWoG3eHtECJHzr7yNksDle/ecf+4xRhesdcyahu1uBC1cfnBNToXV6ZJ6PqeuDGI1XReZtntSBkomDhNq7P9kofwjrAfeH3j/4+T9EzfB+av/yl8RK2BVxuiDEVwyiTfTyOnMorQnqszX7gJOFZ64g4pq7iuCGIIcdqALZ7keBo7cjFF2VMZSaVgYzX3RXBfNujbsywjJoZ1hZj23WhFtxYxDY/DGTNOj2IyGpctk0R9mhmgChYQmKoNVBaUMVmn6D29GGiIzLEs7sFENWRVc0SyNYCWR8YwlgRjmMnEx3TOkiZxglgYWtqC0Ieo5sYz4bHg/J7aDcOLAlMzS13wwJSZds48DAxVHZiDuMqM1iBxGtZIyBsEJFIS7FNlRU6lMmyOL4bCX/d5Q2MWM2JrHvmelHa3ViLUwRkqVsBqmQTM4i9HCynleDYkuC31peLMV3nLwnW3Pi7jlC8ZhZi3308ibyznX3Ug2jqY2+OK5IRHGxCM1YYxj5yr+z7uOKQuiZiTr+C9/89c+lV+0Rz/7qyKSESVY43FVDTHhKsUbz07gw8njt7/6bZRWnF6sycCqmpElHTJsgNnZjMuXG07Ojuk3d1gzwy8N68bRjcLtds9iWRFEMENGO0PVWHaTRqtIbS1j1Dw5gZQHdhvDbJHJsUVVCZ00UzrwLlMPzQylDCoKuXQAaO1YtRV2poh9YIzQWljPLIP2tDl9xLtoQ2MzZRzp9OH37VmlUNpwNRWSythS8UHfsxvh2Aq2MSyV5eWQGLNld9tRJLNeW+6vI6HIR7yXlD46ZDRKsbncELXFVwqdFL5EjNd88K0XKJUR7aGMHD9+RN0ucF4zbntMq/Hasb8PFA/V3NIuFtx9cE236Si64uKtcx4fL/jW177D7f07nPgLFs8uuH7xnB/60R/g+995jaoV6+MTVIGuTAx3E0sHdtkQleKbv/tNjA4YqdB1w/BrP/fA+wPvD7x/zPrENTi/8K/+ZcnKoaXn1ChOtfBs5rjVA9tQU2lHMh13o0PpjvNoqUvieoS6FpQIAUVKhvuskaZmTCNf8I5t2WDrIz5rEy47niy2tNbyay9rXlVH/Ji+ZqFrbpzQ0vBeaanlBqwmx5reKt5sJ6R4Vj7xnbDgRQalHMZmlllQzqNRzE1ho+Vg9FcsSkAp8KWAAYdFkRAOGR0nUjBZWKU9takYJbDb7RjdjHk3MKJY+8I67QlJsVWeMWV2uaD1yCNp6Mp0kFmmwo1aE0JgbieM9oxYTm2HCZmrtKDYiZITsxyozSGdfEwOZpH5qJiqRN233KcRN2/5dpf4c7ORf3BXeHwxx6eKuR5pMOxNwafMraqZ4h2NesR7445Je3bFkMRRvEJZR60LMSgmnSjZED68p9KVomiFKZZVqRnKDuszdUzcieUXf/1/+FQ++Kt/+1fEVTX5vmf5aMmsrnn8eE1Pz+4msnA1oU5cXweU7pjLHKG1/rd7AAAgAElEQVSw3Q3MZh4lwgTkPhLHTLWq2NyPfPaLp1x9cM3RkwvOZuCy4yuPM6Zo/vbvbxhzw5Onlrmq2Oc9c+O4iy06bhBrKLGiGOF83SHF01q43ByUdH/Ie04Zlyc0CudqBrp/inftZ+Scab0mhYL1MGbhyUJhspCLYP2MlHr2AyQTIFYUJtYFmllmUp77HqZc2N9OJJd443TFMGTGfUeMgVg8/V1PPTc431Akspw50j6xl0zIUKYRpsxsVRE2A0EyyltMEbQqSKrZPH/N6s0L3n/vPX7yn/sC//B/+12e/fN/ikob5k5oMAcLgzGwC5lxe097/IQPvv9dRBxDErTRYDR1VYE2SI6EOFLyYXLwh7wbBVI0R6s1m80tVEDSSIzs/9u//MD7A+8PvH/M+sQ1OP/hX/hpWeXDH02LYmaEEy3cY5hCPiRqU7gVj4jwJom37IhxgdvesJk8xQcaJj43azhyE6+D5WowlJTp2hVHsufbsSJpmImwQ4N2HLnMj6w0Y4r0dY0fDwdZKhf2xnOZLee+oCRQZbg1Na+CJxpL0cKpg3WlMSXQl4bee8wwYowhaE2tD7vVFkWjA7Xy2NzxlIIvHvKWRgZ6b/E5EMcZnZt4vXdsY4AMIQm1jHir6JSDXJBiOXOZo2qkC3AkMEbh/awoaeTLq5rf7xNV0hQjOAWLotgTmfkZ/XRLH4TXHDNNdxRTESbPl1eKP3ve8Y8vK77WWWI9sMiO2maU86yM8Do6Mhn0BGPFRmDUipPK8pkGYup5UzJD0/APb4TLLAQ0KReMGJ5VigBc5kDKFX3O2JyZMFRJyFJQSvilf/Drn8oH/+jnfk3mVc0wTRiraRrDoq7YJk0Ze5RWaArdCCKCbxqePapxrePqgy37LqF0RJfCl758TmUN26B59XxLTglmNVVJXN/tyUWoG0vfZ2xTsawVX3q6IqAZfMQFjSqOSR1SnndJsagUSgKiDndbd1cDi3lN0YJuNEe6gNZMQRHKRLENtggldNh2Tpp2+HpJowON8wSXmGt34N0dmutBR2YYuqGmcxO725G+g7GfCDGhpeBaf/DPyAWKZn3UsKgz/QgLZ+j7zMttTx4mvvT5U7727oY6Z7K2KJdpTcN+t2O5WtJtNuw3O3qpGK6e4ypHDp6nX/4Mf+lHjvi73+z45jdeQx1pjaeqLG41o2patvseQSjdHhUd+6Eni+Lk0RHHZ2v22x1vLD16WfE7X32f3a7HlXLg3Rma9ZIYMmW/IZmKEAdsKof07KyAREGR/u5ffeD9gfcH3j9mfeJucKytyGYkAntZsW461tNIEuHbuqaKkBGiBZUVXxf4ZmpRO8/aZH7iaM+qLJn5yCzds9SK3+jPuBo1Wh2Mo6xrcKon6yOSTZxZQaHIRfjqzrC0NSH3MC0Y9YQumhIKS1t4ORlksuyMRpTQMlEzIDhSL9wlxdoJoxootz2Di6yDRyRS1w0lDmgsSMB0I3Md+U7MVMsK2x7jpWVHIpUTou4pYYbIPSkEvugNz+bX3AfF770WkMSfOprxPCnaXNh2AirzzTxjDKDxeMm83xmO9QTeIDGjckXneqYp8arf82dORt5sa26G93nVnCB+z24854VEvnXn+A6Jl1bz43nP51pPVbe8f33D3s+ZR0G0ELVwNiZGGzhLmiMs793ueLfU/K/aEYJCmYmCRtkABTzw3dBi9IQRSKUwrxyiNHNJIAWjEuZTLJttZw0xgdGQvcHXFfWiZtqNvLrfY40nSUEkoRL0+3v2V5pYFJXTfPlLj1kfLbEy8Zm6MCs9v/LNyO3Le5Q9jOY1gioj9dE5KQlHpy1WCblEvvGy5+hoxv56xFcNXXeDLkIRjW8PHwYyRWI2iBrQXhhCh2kd5U64W9Y0GianiXtF6DYsVyuirmiKIgRLFAGj2MfA2iq+8+qW1dMFs6zwIkwpcx0dQ9xTckXUlrtXd3zpB0754fqe91PN//733yVL4k//6bf5/nWEfuB196HHyADjkACLA955PjCrDagGu+3QtqbvBqbN/v9m785ifc+yw65/9/Sb/vOZ77lj3apbU1dXu7tp23HHsTN0ujFOCyPHOEJGsYKElEgRWEYmEB6QkMgLYCIS5YGHACZgkhAnEXY8Yne7q6u7q8fqGm/deTjzf/7/pj3x8C8uQuCnUpxS6ayX83R1zrnnc/ZZe++11+L+4ZSf+dFdns2HHE8dX1/ugHaUTcLpasGXHlTcfzzFhZpBK3jpE3tsX0h57ffuYkceHSJSeGwQdK1gVi1RUWHcBm9/6RUCkrs6QYb14MSApNUeIshGMj+2ECwqAhGyXkFUgdA26121iKjwkcxtgHPv597/eL1/6E5wfvHf+ivRighOYlVAy4gIJW1MKXQkiZEyJsTosDaADIgokVEgRIOLCVFbTIBOiGxExUkmcVGj6kBtJEImSFnTzzKM9HRVy8x3QElyFVFKcVG30DpIJLdLzYt5zUVdMNJTjE75/TPFIe+PgJCSKAWls+RREGKkCobNpmUlPRtakahIt3AkvuUoFgSvKF2LC5H5sqQQCUms6EpFmiratmXLaK6EA5bCsKgTlg6M9wTZsE3KHVbsJwmJ8Eg6HFaBgOWCMKxEBcKQJpo8WqRWdKSnQ0tlG0w2YBIUIeZMYkstMjJKYvTcnBqSJLATI9e6AqWWTMrASvWYtZoOEh/mDGPASolWGVI5nJXosK7tsR4KWXFGwmaIbKg5PQOnjcSoDI/CesdZvS4qvj5ICSFwt8z4WtmA7jELjtw3JCryd7/6Bx/JVX/jr/2z6K1He0vrIiIz+FWNkJGsW6C9poqOECyxbJ54DzohuvVur9EKE9ZX8P28QysEIXraqiUaSaIhCslw0Ee830TRWgNKksqAFLA9WE86JpEcHFVc2DdspQWjbo3RKd+53TJdlGvv/e7a+3RG3ukQYqSc1qQGvPMMel2MgmyUkPiWsxqCV1TzJS5ETk9mdLrpevxKJ0OnBXVTMRomfDxdcrfOmC8C42VD4jzKeIZ5j9uPHnP5yhap1kQMB0dzwLM3GLByy7X3TkoaIjLXPLepyV1NU1bITp9DDGUlWDQVs6jpqIirK+6+cYLMc4b9lB94YYs8THnrKOKEZjatyfsZcblCAcoosqKLDxbvBK6uKYMAKyjywGRl6WrJdlfxwobk1QeBbkcRo2ZZ1xzeOaNuZjz3yReI0XPvwYzxwSOiydYTph2I2NL83i+dez/3fu79A8aHLsH5q1/8uffLhCU+RiQglWeAZT+FsYU5iioEdLIeChZjxPuAEOvhlkIIahP41wvBp/UpZ/kW/+jhgkUokCJByQDCIVHI4OmkijRZ91nwwjDAIYxhP5Wc4hlKxdJLuirQSsO4alDCk7hIIgOidRREgtLUraV2FhMcF40kU4LWz9kInhALvrIIjLxlu6sxWrJJw0mj2cgN7yxbUhkxnZxuHXlcO/IYWUZB6kv6MhBUXBcph5aoDDWQqZQiWmwiKZxDZIFeBBs8reyQmBYtCubBM65T5m3DQIBU7v2iY0GqFP3EYXxLjBGjJIjA92uJm3jyJOHp3HJvFTgWBd5J5s5Si4C1im7i0NFjG8XttmKhRhgl6bWCRSwxMYJISUygEY4EgxeQIAnSMxCai1jmSUq0NWVUVKVjKSJaSv7LV3//I7ng9/6dX4nRJIBEtC3BGKTypFLT2+1RTjzNYkHrA/logLM1MUaoanyikFIipcTGmk89f5XPbMDDJOPXf/sWIoBJE7RW2LLB5B1k8HS7GTqThCiwwLCfI4xh1AuMK8mWik+8++AZe01sGqQxpMFD8AgNQWmClaxWUwiwvztAx4jHMUokPgq++p0TCh3Y3R1S9Ao2ssDBScP2bsr33xuTyoTiQk5eOQ6OSgSRxkViXdNNJCpRdHJN21QkWY4X0M0LIi15YrggGxbZug+JDR6fDSniAh8yTpTi6NRxNC3ZLjIQgegtWiaMNlIu6ZrgHQhJJjWIwO8+Ljn5zjE713f47NWM3397yjwIBJ7x4zFVuSCJCp9rdPSEVaDKD9F+D6JENQqr3x+bYiGmAhfDE+9GgI0S/X6rftntszg7gSwhLhusXntvfvcXz72fez/3/gHjQ5fg/Ec/9XPR+kiDgBjQEvASJyFVLc96xTOdhqWEb1YprRS079fjKCWeJDwyRNKgCCkolRKUJdgAKkOLFhElRIskEqSgQ6SXGEJsEcpAE9nswDRqsliDTvENyGaJC4I90fBMLrnWaVnULQ3QzwylS3lg4d1FQhlX2KCx1uEtvNipGCEpVMJWEnh9AfsDxRU1x8mMeZVyUM7oCkV3KBiGkrk3hEaQSM/QKIKuSbIe37p9Rn/Q4XIu2OrY9WWji9Qh4XDlSdOUR5VAWsudVUI/b9gCPIL7beBipwtty9yum0AlaYd9a/nN6RFSbdLEyH7jeHYUOLYGFzz3VoJelHjtuZB4zpzipEwYJUtcUOzngjYqLA2biaIwCVndYIqAV4I2dFgul7TKIYKmjilDDcvWU8aEZWOxJqEIFucNj1dLijTh+bTkC1/67kdywd/4d/+3aH0kYiEG1ttCCd4Cko3RJlevZVQIbr97hhQKGx0xRrRJsG31xLuKAp1nmKxDlA22cqi0QIsW0LjaPfHeNRrT1dgQEMqgWstgq2BVenQWECRIESgXS1wjKPKMF/cVP9LrcrecgpLspz2Oibw+WXH3UcN0WSGJNKsKWwUuPjVgNy8oMs3LQ8U/v7Xk2sUBn+lals7xiIw7t0/JspyXLgp60TN2sJSSYePYyzoEXbNhuvy9P7zDjRs7vNSXvLypn3h/3Ajemy1JteJukzM+XXL3YMXmTkJfaDyCw5MZV5/ZoZ41lE0k2JJ8a4urA/jVX//HDLLP4BYHpKLLiz9wldmy5vjxlPH4mFRleBW52N/gpC5ZzUukcOgYGI62CdITbEV/uMnWxRFJU7O1ofBKcFp1ODme0q5WiABlFdm+PGByb0wroZrP196lwrWWsFoSpaC3PefRr/2tc+/n3s+9f8D40CU4f/Nn/724Eyu6usV4TxSBi8ohmoTCNCSFwPiC1xYND2LBsRXrVv4yQWmBEBEI6BhwQRKDQElLDIqARGkPSiPC+lUPvkUTUEaTCc9GonHC0bEtmTdsqTn7oQbZcCmVdDsNUvVxccWtsMsr04D0AiE0ZYDUOxqfYFSJcppCJ/TtihAbugSGYk6a5BxUgo9vCByeRCpKB92OZDyzDDODl5bp2RLRLYhBkuqcW2VAu4bdLMElhnGl6aaew6rmdKmQMjCUFutSlHBM6sgLA8fxytNPYeIMnRBJjeV6p+KkikyWfdKeJzMlDyc5g0QwpiWGHj2xYmEDqdRsppbjquBoVbEgwWSRCwSKxBBjzq1qfXJVdBIsiuBqAFa2AOURwZH49bN1Kx3bKGZEQlvhomXPpFhrGRmDVZqzes5Jk3AnKhqv+UdvvfKRXPCv/sJvRWkkg6GhXq0T70u7Oa5sSTJJUiT0iLz23or5asFs2qKIyFRh0vSJd+kVbV2j9Lp3lEIgE4PQGmkiIghUnuNKiyagi5xMeNLtLq6qyIoUaSRDo9gsapIQ+CGT8icv9TEbGhdX/O2bgW89WD7xvqhWSA/BSzySJDq6mwVh5TDBkmnDiCl7e1t892HJF64PqEPLjjDcb1Zc6/d4d7bkmayLl5Y7x2PCIF97F4ZvjQOxdrx8uUslLIeVxKB4dDLh+PEMoQybuWLVKjSO2fGSG89vMztbIDKJs4pESqJs+TPXUu5MK24+TuhtZmybKa99Z8XW1SHzkxm6N0Qpy+TxhLzoculql7Pjllvv3CHIgNKRfmeb3evbtK3g7hvvIaKjt3sZG2uW8xMABAXCe6Jo0dYQo6cRng4Gi0fFBS5aRNzA25ZunqGygrn7HqK8DNkEbzewX/4b597PvZ97/4DxoUtw/voXfy766FDSsKkCGRbhSmaywKMpncSLGomBaBFCEKNgfa0V8DFByfUfVxUgSoEIgRACUgiIgijgohHsZYJWJjjVUijJqoIyeC5oWKER0jNMJc45RqnhuPLMvSAqQ0wkvm7JjMa1NUmImCQgGkGhPQtvuGHGZCoyX0h2OxJNRKkey3ZK8n6tTSo8MlicSAm05FIincM6aJWkIzyPlwKlNIVoGJqA8pZHVcNsklAlDUu/w7Steamv2N6L3L035UovZ9YsyfMOWmQ4t2R/0FCLgrcOW/a2RjyaKvqq5WjpKYxAFzmPS490gZj018/MM08WAjZA6xukVBRGcLeGpgYjPTMBSYigDUJBMqvZGGTYCPdXDmUkpdWULtAE0ESEhOgiWgSkBGEtTnm0NyQSvGvxSAoV6MjIf/Dtj+YJTu8v/2q0bcSkkiLP0Giadk5AExuHEwLr7B/pvVWSJDgAQhR/pPf+aMDuZkYQCjqaQknOJiW2dYzyDg2WLE/odA3OOfI84fSoYeUqtNaYAJV1dEYDmvEZgQ4mCUTrSTKNa1v2BpqtgeHhQc3Hr/TQREJQTGzFMEnoCAfBkkhN7QNSK3raIJ2jaj2l9Gxqw72zJeWwYLu17CaKnkv45ukZ333zMVXzEIoXODud8/Knn+FPXtL8k9/7DX7iBz/HN97+Fh97/kVkUnDv8Iw/88wGjox/8Nob/NDLT/OtO5Yil9x964jBTkF3o8fB4YL6dELvqSvU8xn9IkdJQdnUzB/PkFKx+9SId9++h1taZCIJvsHb8MR7XEQ2Lm4QXGQxPyWy7pzrY0AinnhvBWgRsJVCCo/Pj1HVHipEhLS4IEmVJzjB8tX/7Nz7ufdz7x8wPnQJzl/9wl+KiWpJnKYi4rUnty1oQ4wRJwwAHo+KAYFCAj6uTzAkYf1SR0lk8BAiQglCCAh4v1+BWB95vv9LI4RYX4UBUQi8BB0VqYhEtW6/nUuBl5rgHUJ6ejbSU44dkTJIG6KsWZae7cSSakEmDKlsOaZPaWsSZ3FS0lUSER0ySkobkYlBxcBprdnPcx6XC5pqQV/lPLQNqSxQpiZvGrwQCGFIo2GnEDyuGqJUJDHSCZYs9UidMWtKfCvY70XGtcBJSZGlaCs4sS1CeDIhqUPKXqpomwlOGhrZQRnJsm7pBEmrLCfvz48qreNSUVBVFV2jOFjW2KSLbxfsyoR5zHhcrrBRsJeXhKpAaIv1gSJaCgO5EUxXFkNCagLeSGZlzbTWDDowrS07BppWctJGDmSBkCmta/ivvvfNj+SCn/7s/xyFCBhh8L7FC/BVS5qvr13F+w8d/XrM3RPvUkQ88ol3KSPrze0f7T0qSQwRJTToAKy9C+GBhEQnRCXQJiEpFEZ4mlIgpKdQApVqNrs5m9saW3nG4xXX9lNGmaZrA1oJ3moymqqik0WqMjAaJHRUREbJu8c1W5s5KgYOjxsubve4dTTn+L0D9i5scf/hEXm/T14EmqMlXgi62z1SmfL0MwXvvDsmSkVwgaEO7A4k3X7Od++sqGcnfP7la3zrqMZJybNbCm0Frz5cEr1kNEyZV57PXC24desOw60dTmwHVSjGpzN6RhBUwt23j9l4eoPp0YIbL1xiMp7RSRNuvvmIYnfE6uAxl69dYVEKHrz9BlpKdDHHN7tIOSb6HokJqO6YntzlaPUuhj5FJtDpdY5Ovk4MhqQoaJoZxkhCY4mVxBZDErFN6w+x/+cvn3s/937u/QPGhy7B+es/8dNRR0UQASEjn1Atp1ERcUQXWETJNorLpsK4EpmsW+apPFLFlLul4k6bEAQoAOnZQpAbR2bg0UpSeUeiNC6w/rcCvPdoo5BREERcf1Trr6mDJhjHptA4Alui4UKWsbA1mRa4AOMqEEODEII0aK51G3bSliYkzMS6+/DDCQyGHZYiJalbEmGZVy1J0Ox1oKsdkZodPKs8J9Sn3H64xyeuS+ZN5LiErWLForQ8tdPndLVENRV5Z5sss6xaqBrHpWHCWdnwaNnQRSM1gGJUeDoiYdZGprEg+pa0STCp47jyZKok5NtEewamR1yBVoFOpmjqlmVUdPoFj4/HyCiZ2RZ0wsU0cGfmGWQeVppTJWnaSGYcSilWNnIUFE9pcKFivxggfEPrHZbAmAItHKWN2DbSCMhkxooakOQi4Re+8dWP5ILf+bf/pxjRT7xfvLDBYtUQXINbWLyETlYw2O6Q1au190Sj+yneBx49XDI5Wzzx7pSkSFPSYl3EPjle0FQrkqKD9w4lDAKIVYnsFP//3ouckGr6hUI4T5LmXLnaY3qyJM0V3sHB6RxfrTcHeaH52LUOTyWelRfcLTWTRcnNb93lqU8+xbxNCG1LolomDydoIdi/uEXRDfRFy/XUsOxoHn3/db79bsHP/8wL3HZw672Kaxsttx5b/vwP7HD/pKQZP2D/qRt0Us/cBk7GJZ+4NOLxrOQb33/I/oXRE++XR4aByThrLW8sJMFFslZwZSPy2p0l/XoGl66sC1izBLuqSLKCS5c0hw8bpouap5/b5bXf/gZRD5jNT0lVwu61y9x9/Sb9DcFiNaauhtg2kBkHvZKm9STtCBEl3izZ3XoO4Rum9TtYSuTqIg4B2QFitksjIFXgxYqEznpH+9X/5Nz7ufdz7x8wPnQJzi/+xE/HGCNSAFGBFGwpyw3jWIkV41bhZI5oa5Yxow4egsCLSIaiUBZhDegWTc5z3TnbIpKmJanusqo0v1tpZjYSw7oiX8V1e2sRPUGAEJFCaFJR0dEJKR6kxcYEEVJqLDoGcmPQUuB8DdajfQNJgQklWyrHqEBfeFLjsNWCe23O0BhevpwzW8yZriIbqaWnPUI6JovI5kBhciBJ8PUKKRIEEtqWR8ewsRtJ05TxxPBo5mmSPgO1okBx8fISQkRWHUgqJtMEMkVCxf2Dil5nQO1SpnXNhgpMvSST8KDJSXy17qwcPYScE+spMoUIHu8jUhkyCbeWgUsF2NazQNGhpaDDg+aUS1lBohVCtTQNbOYwq9ev4GRUjFvHqLOeJNtYKINk0gaKJONeZXDBsYwVOyolSRuyENEWphj+w9c+mic43Z/9lSfeo9ZI5ykGObsbGyzdhOlZRZ73WIzHRK3xrYPK4UVEqRTz/uImoiPp99i/mDPUku1dw66S3JnBq28dUq9aYgApNcFaVK4IlUNqhRCRpN+FpqK3NUB5nng3QtPOl8SOopdmyMzQLEuaqkVUJaY/wLiGzd0hMsLFC4aRs6zGLd+Z1PTzLv/1X7jC//79R5ysavYyza7OENJxMF1xcaNHPw+8tL3Btx6eYBKNQOJXjrdOS3Z3CjY7mpsTxxuPF5xR8MLAkxnDJ/cMhEiuDG1seG/qSaOgmxv++R98hY99/NOsQuSdw5aRsMyiYScPvDWW2KrFnT2C6JFqk5P5hM3eCOuneB+JdMmKhPun77Df26MuHUs5RhDZTF/g0fJ32excoJc8TXR3WYaWqxeu8/jxTWys6ZsOj1Y1e72nECGyCsf4aovFvMQUHdrGooKkzh9hqouEzgEqCmTTI/qM1Vc+mjU4597Pvf9xev/QJTi/8PmfigogepSI6OC50fdsKHhpqKlXJdZHTmvF0lkGiSB6yU7a0tGSvNuQmw5VayFJGC8zfudMcVRnBFnhIygZkRFCiAQtMfH9/9voyJThRu7Y6xiiW7H0CSdlJATNUkRGpiVzLR0lkU3g8o7FWoOJkjyznMw1lbMo37I3TIhRkOv1c29Ey2mdEEXOIKtZlg3dwQ6TsqVZzVErjS5guC24MAIai69r1CChDQGdlgifIHzN3btzrl3aJNgl89keg5Hj9fuR69sCWym+fGfJsxf7dGRK5WfsDzIOJxXzlUMXBeMysCpbWmnoZp7KdVlFx65KuL8MGCKPvCVVglxFtvB4NGeNZZRrStdydVQgEfjaI6Qm1w10+qTLU5YMmTQld+aWS70ES8rpckEWE25Gz7A1pFlJ9HAp0/SU4rCseLMtKIMg4NAIMrnuKfHLb7/2kVzwi7/4P0Qv5HrhkQLqFXs3LrE9SPlT13ssa88Yy4P7jtVsSbebEL3kxX3omZQtZRl1e0wWDWk35Z3S8y++MWYxW+Gr8ol3LzXR1ciiALv+3EKCSlKuXN7i8vWCctzSWs/RyYLgDdZa8o5E1Y5uYajrhh9+qcOKHJkINmPL9+4LGlsiguCT11JiFPQTRRbWSfvxAmLRY6hqFvMV+WDEPCpOHh8zG1s2+wkv7nf5qRd2eXd2xvLU8amnh3zzYM5TGz2ETzhsTvj7v/4b/OU//0VO5xVjK7k+MvzaW6f82LVdxlXFf/tr/5Cf+dxPkac50/mKT+1v8Ts373L26D57T9/ge997wKx5SO1TNrSjjTdY1ffZ3XiWhw+OkFoQ45i2WCJjilERY/eZN1P6RZ+lfZcbz/9pJILmZImQmu5mQW97iLx3i3Fnm7MHdxkfLyhGPaTImNWvw/wy7eCY7nKXKruD9IZc7qM7C5blCT72SEKXMkxRiPU0Zq/wf/jfnHs/937u/QPGhy7B+c9/8qdj6QJexffvZANJFMj3B1iu71cbvBIUPhKAGAwWRyIVq9YxEobdtGZaCfa7ns9spFg9wzeRidjjzjKyshVt1CxsABlJpCLTEm9bBIFUG2JU5HHJhvRsF4Gqipg0YSfRlGVNmlv2cs289eBaep0Ox6dn5Bp2+h2SnsO6IaK2NHZKtytBeE5nK4adEQoDeoUgQ2TVes5Kk6B0gLpEdCtO3sspVY4RcPGy4eZ7EzKtWNgUrTy91HCwECRSoEXNZt7FCMWbK8VWEtCi5u1ZhlCRfnTIFD5zueX4xDBvAwpJ4yRHNuO0WbFdSHJlSKNHpwkPpiWnlcKJwKaSuGAReHpZDxtbEutRWKYq5YEzyHpF9Bmbqed23bKbdGid47iJBBUoJBAlTng6CiYxobQRlGekJH3ZUtUJC1o8KXUIqBj45bc+mkXGo5/7X2LdrgfLxviytgMAACAASURBVBixAgwKyfrbjQhYLvFKkGaGAAQriHWL6uWU8wVFXtAbJsyPJmxfu8jnP7lB7QPCrTg0m9y+OWd+cgZJxvJ0QlCKNNVkvS7lZIo0mk6S4osEOV0y2srZ3N5gOR3T6fd58UKHdx6PuTYMPDXoc1ytEG1ka9jnvbun7HQFFze7pIniM1c3eOW9OaGq2ewLEJ5bj5dsjTbY6ipaX5GmCa3zGGU4WTl6WiKch0Ry8+7JE+8vXevxD37993npuRf55r0TNgvNlQu7fPP1U7Yu9pke3uLTL3yCfh74x2/XfOpKSqwEv/X1A4rUoPKKdLjDX/v0gDdPax4vShSSo9ZwNg/cuvMqF4bP0d3q0ktSZDfl1rfe4OTEEoSnV3QIsaUJt9je/CyhPcOFYxSWyl2gWlV48QBZPYUxgpV+D+pnyPBYEQgqkBAhSqIIWHVGbPfw6TFRerJmF5s/pFhdos0O8aREURKtwn/5vzv3fu793PsHjA9dgvM3vvCTUQdANAhSLqWSTa04iQ4hBM4LSglawEnpUC4ilELgkVLiQyAVCkSg8YEoAooEFRq8TLGiZeAFufLkiaEfQOeGzdRCE1lEz6MaovX0jGLoG2whuZxJZHCEytJ6x1YeSTLPbKnY63gaMmoLqanp6RQVlyRph7TrqZZTtMqZlBknC0+0Gc9dmSCDQSUJZ5MllT3hwnYX0QyRmwmYMaQ9wl1LTGsUIwieZbRktWaxrLizzLm+18daR6FLTseOpUvpb2g60gMB3SxZtJF3ZjlSJowrh1aeQZIzrR0oRUcuUaRoA/iAlAk6FURvuXnqkF7RyQxRtMxbyUmz/lmEAIUSLKLAyEgICTI6rIhYp6m8JaDQyiIRDBNN6Ws6VtPoSKIky7ZFoFkIxVA1KBJSZQixwftI5QKZ1PzS69/7SC74xc/+/RjbiG4qfJazdWmTjV6Xs8lyncyHiJcgpGd6PMWXDdJkSCWeeJdSEIMgRo8sa2KWEXyFVDmxqZFKkWaKfGNEriT5qM/WKGJrmJQN44djQiXp7hR0CbQq8sILW4Q6MJ5WuNryAzuCzEjuzVqe30lpyJiXFT0Jw15O13lER/FjF4Z86eEhRUy5Yz03DxdMmg5ffE7SlxKVJNx+uOB797/EX/jhP41oDS9f32DaNOwOMl79zgmyI8gzTVtFDm1gX0UejAP/4tsP+bM/+ixV07BnNPdOZtxfOq5c7tORnh4FtCWn5Zx/+JUHpNuXOb17j2Cm7F78JON790B3CPJ79NQN8r1d7PEBqtdhdOEC1bLhzTd/hxigr1+k1kfE+YBGPETYbYI+RSeexhmM20QTcF5j0xMyt4ONjoBCJA8xfo8YDCG7jyqTtXe/QzR3EGicLtBUWJuR+gtr7+oEKSq872Bf+dvn3s+9n3v/gPGhm0V1TSqGaUShKHJJ4RSVcRS1YlVVrKSliopWOHqyz35njgsGFyHVgqJxVMYSpKJrUu6Vkkw6hBdcziUjBAtqWpfhhCWowGyxBJsgo6UfBZ8yksFwSRE8iVGoCFYtSducZSewtIYkcfSExHdgVadEIcGV6CBIejWyqjitbsDBPQadPtmgZEvO6O1s8Nb9kgcHA7azit6eoWkH3Bt36XT6JPWETmdGXBVYt0SmgtNHkb2NiknZY7BZIgdDRhsp6njJ2emU02jY1EAWuD3J6Z9G9lLHZsezbA2VKChExbRpGeWRqknxuuTaQJKbyKrqEqXhD09Kek5A6tBWstdJObaQpJqHTc22TAjOo3SKbVpcqoh4hoDTCbN2QiIk17Ie3dhSRokjYqNmgaUNDTsYZtGynabkOKKEmbPsSkf5f19JhZpl7RBSroeK2vZfNct/abG5uUmWK2Tr2NjtU8hIFaFsEhaHY84mFVkv4ssS3dthczMh9rqE+RIz6qFWDbatCFIyvLDDwa1D8mEHWybsXtxio6OYLxZU5PiqIYjA2a0HNFsDVLAUvZwbl3d5bm9FN2qK3KAi6LYmZJ7plqNsJZmC/byPjytmtSCKgG4tGENHgcRyXG3yv37tmP1+D12s2KsF25dG/NMv3+Xd3gWe7mh+/IURrx+1vHV2nc+uBoi25a3DCS54bh+ukB3FK6/d5keeu8rDaLnWz3lxa5cX9y2JgS+99ZAzkfFsx9ApBK8fWN6ezvmRi5owtLjSs4g5G1sFD4++yaBjaLhOffJ1PnblBpeu7HNvskHIenz16/8HutwiHlYkb8/Z2t8hrK7A8JQz9y5meomoalS4iJWPUaJLlFMEQ0R/TNWOMR568kVUcUJabayLKf01bOcObYio+gJOH5P6fejchRCQjcWoFSEAYoRN76JDA22OVwLk4l+xyn95ce793Psfp/cP3QnOP/03fyJ2c3jgBc4q+q2laxpMpvCNIYSAESV1khNrQy01q1jSEYKu1khl6VjHiTIEt6IXI6cuf//0x+ISjW8VdVSYKFi5KbtpQe08XRybKnDqBM4FemlKGhfrJ+U2YIoMT4tsAsNeRoKkq8ZEFbm2k3P7SHJYSZSPeCJ11EjR8ondAdpEetkR+EjdRrLOHlPXMuxG8BWrY4HKHFpGVNIgxEUqf4+kHuHECqkMxhSsqgn10hBsh+0tCZnDpQFdWupaItSQyfSUcpGBaEhywaTtcdYK2rZh1kaq2jLQoI2gbSUuCAba4WVgXAo28hbvFLXskomGTgJl7WlDyoNguFEIDvyKG92UaStoo8VXFSPvOPJdFgI2ZYvRKVmSUdYlQQkeTh2dTLCKEYkCGdiTKZMgmNgW5xyt7AI1EbmeMRYsrTT8rTe+/ZHc0b74H/923BkpDiYtjQ8kZUs3l5h+grPgHKTSQWaoVxClZl4uMUGxvZkjlSXU0CrDcnJKr5dxPF73D2nmLcl2Rlh66kVFmhqmR3e58MzzzMYLcg1bW11OThZUsxVb+7vY5XzdKdY68t0RdrHAL+dc+sSzPJN6+jIQVeTTl/t87eGC145B+ciq8jTegQz8pX/tCkMZ2Mod+MhRVfNnP77L77434ccvb/H60YSzSQTt6YlAXgg+fm2TV28f00VzXLZ0pCYrMk7LBfNjaBPFyxdzWhHYlBnT1nLatASTcngw5q3HDYiGGztD3ls4bp44yrMps9mMVfsuaaJJs+76mrkckQ8mVHZB2aybj0XfEP3HUfljRgmMVxOasEEsN9nc3eHx4jWef+ZHmR0cMPbHuNUjsiiogiFKgwwVJttio/MxThffICgB4wCFx1uPlBpkIHPXWbYBbw5IxYron0XI/8e7E/cgXsW+8jfPvZ97P/f+AeNDl+D84c//uWhbj4mRkTJ85SwgfUT2JUdBsFWnFInlkrFoI+imimMbOCpb5lbT1xqlI5mFQWo4KkukWV+pLF1OYQMysZQhkteOzdSQKEkSPIlyLL1CuAXOgwmeTiHRWtNLPc5lbPYMOtQsF2fIdMDZKhK9wCiIQZCLFa1M0SowLjtcTmecec/A9Nm7JGjiCp0UTB9B0vP0NjIe3F5SVZZrl1KkzwmyJCEFI4iyoZ51kLlgFh+zmWSozhbLwwkQcDFDrioWqsCuPCstmJQTjNjluG0xTuBjZMNUtCHluHUUukMiHBkrLm10OFsJ5nWgUlOu6ZqGfVqpeDSpCQI+tgHRR1prEUqSaEMTV2xnktNlYGx7nDYlc9lnhzkVBmkFhYm41hNSwUmjmDoQJBTK0kSIGOaxoScUxkWEENi4fj46dQGcxMiAEpL/9I03PpIL/uf+zldjtVphYuTiRs5v/8EDQnDkl7rUdUMeMpJEcmGzQ54JNoc59w4dD08OaeYl3dEQpSNCJlwYZNx9OEHptfeGFFVZdAL1ooQmsHNpg8xoJBKdRarSUa8qrHNkYc7ViyO01lzoG4LV7G126XvFG++8ydZT+9w/jrS+olAFMQh28pKzNmHUibx2qvixUcM3332PF557iU9c67IoG/I04fZZxWYv58eeGvErrz7g/uEDPvfxZ+nkhlXZMugqMILGWk4XGiUzzsojbmwOeOniDn/w1kMg4EVBKBseh5a7j5bkvS5f//Y/Y+fa53hw+x5S9Cn9HbZVi0fzYDZmu3gJleSk4Q2+8NnP85VbJZOjM06qL/HUsEs6+BytVLx983fAen74xZeJPjKbHlOkOUlnwNnsPT7/Az/Eb732NVbuWe5PXyG0L9DpvkPdGvCRTi5wrcWnCfVyhxgniHaf2HmArvaIGGx+B11dRsrH62veqBAyEkSLd/GJ9+WX//tz7+fez71/wPjQJTiv/JUvxtpZRJQ8sB6JwzsFokWE9UiGDg6vJEIofGipnEPEhNgGhBOYxNHTisJCMJFlXE8ZN84wUktMIhm3FTtokAZPJDOBDUo2hylaOGCJzAe8c6RprCbYFqzHhpr9ToJnhZEFuRYE3TLIPFm3h2/nxI6gOkzJup5EKKblhOHeRar4iJw92txSzgPDKyM4nhDmAbk5IszmLJeB/m6HUE6J3ZbVqiSbjXiwTBAOxlZysZPTlEdc2E0wsYv0AYzm3gOLlg2N0RytILOebl4wDRYpazJyVq7CRcOyqUhEnxgrSqHJdaRxYHGMZMtmN+Fo4ljSpXEN1kGqA7nQVM6TJY7a5kgRaaIHIA2CMiZ4VaGDJCLoqISUlpVcnwLFqKiDXDf6c4o6eGol8S1oE9dThEOKFxDCuqh85T3/xZvf/0gu+F/4O1+LgXXL+vsHU2xs/z/eExdIhx2EUCxmM5rZAt3v4U4qvA9II9nc6ZLLhGAik8dzRKHR3mCShtFGl+M7x2zvj0Csh8pum5qnuynXdgekzqL0+iXcb04dp0ce62rquUWtTviRl65w69b3ufrM8/QyRWoThsPA0OQE4bk46PH9BzNUEdkwcPeg4Sd/8DKPTudc3OwgjOA33p3xc5+8zs2DKfcnB7x8/TLfvXXMpPH8+LNbvPF4zE6e863jY3KX8t4sUNaO27PID17rcvM7X+NP/YlPsVukT7z/6qtH3Ng0nHrFdx4ekC1mvPz8C7x2WFE0J+xc2OHgwTFlqrj/4Jts5j9I8G8xczk9DatYYb1m09R87JkX+fob30WKpxmXbxNiQKaKrh6was7I8FRiGykitpkDkOhIqJ7G5TcRXhBlJNHbFMoz9w7bTIhRQXsJmR0gm32cOkTZfTynCNMSXIuk9//yrmRD+4d/79z7ufdz7x8wPnQJzu/9+/9GVLQYKcF5gmypY0p0HmMDqYpo0ZIJgUk1tvXMVoLGCmZlyyqsr55c8BTG4FwgSzzGrBsC9hNPP4G2bUlShS1rEuG5sj8gySWPpi0rF9gstjm5e5/NkUWajJ2h4fhsSZaCQ3B23NAdFCxdwmxRM8wMwwKEmLG5scGiTlCuIu0ZhPBE6SjFnKwsOB1HLrwEREnbzqBKSEzC7HBFZ1jg+zXp/gB6S/Db8N4CO3OYRDE5k8S5JWiFqwOrWrHwBS42KBnXBWOJQeoGZwXCOro9jbWeja5jVUKUCbFpGY5SGmc5HjsqL0i1ofKWXEusXb8IyLWnLKEVObW31CIhOMsogdKuO2U57yEavHTEYNZJjw/4EGmjJ0iFjJIYI0EqQnDoqCmFR0eBQ61fSymF9g02JDQiEoPC4vBR8Uvfe/0jueD/ub/7tSferZDIRaTJPdF54jKSDRQEx1Yi2JSas+i5f1pT1y0n742pESigWc0Z7e9QT2tSBf0LXVSa0kkMT295Jqclo+2U2bSiI1I+eW2PH73c539854SJa/jYYJPffOVVPvv0FcIQ/uLVff7J6wf0R4YQA19+5dt8+qUf5MQ3vP7WfZ5+6hLPb3epyzO++NJVfudhSyZa/sSlPkJ4jsctjRQczlbcPLH8/GevQJQ8WJxClZB1NV9585Afur5Lt6vY2+iQpIYLg4Q3353xtaP7XO/v8M3jMeVBRdCK2rW8ebJkrvuEw6Mn3p+6eolBt+Xm8YpOs+CpS5fwseXCMOfd/4u9O4vRLbsO+/7f0xm+seaqe3u4zZ5IShTZFkkNVESLtCQjUpDYQSwlMRDoIfKrYDsTMgAJECQvgQPERhwhSGLASQDbQRTJsmQJoqiJFkVxbPbAntjdd675q2865+xh7TycyzKu6dhBGlYaF7Wf6uVW3a/q9+1vnbXXXuuNe2zu7qJSw8HOJm0SPv+HX2SVNXtbe7x5/w326w261N9wHOrIzfkJxuzTNuesFXTeczDZ5nwxAx72bnQf5Dd+RpJE9kJr9KV3cCjlyWIRIygEG58gmkOyHGD0TWL3BM4ePpiXp0jZEL7w16+8X3m/8v4e1/suwHnrr/7LOasWYxSFEhwaspCLiM8Fr7y54uysYu0EIwFcRQiJygiVVpgy0HU1Op0zsBXTLIz3xqz9gpGumTWZgUmcNxqdFD4nZh0E0Ry4Dq1K6iJA6KhGlroqmTcdbZMxhWUklqVZs11FntybgJyCrfFNha4ibayIYcnAFhS5oVAldmjRdg2VhjohLqMrg9RrtBkT3zYcvgMpBYomsFFCNUrkvQUiCX06ImfNepnJqaULI5oMoQ04HXjiyZrYRlQaAjPuHiuqQWSoRqAvGOwIclESc8YUrn+jxETwGrKlMIEYPJgKyYb5LGKmJbNZR+kKjhcrot0kExGf0GWJTopjn9h0GU+kCYIlYaJBtLAKCm1KkIxRmS5FlC76mTEZPBmtNVEJKRmaqPFIP45DDAWKoPsJw1EZfuHLj2aA81d+5Q+yWmiMV4wL9ZD3hTd8/s1zbr91RiIRzhZUuxuEkFBGMR6XSEwkV9IdvsV09DhuUPA9n9jn6GjFqBpyulgzcI7ZYol4IQfP/ftr2qZle1wx3p1SDiztvOGJHcXG9i7vHq04OV4x3ZpQFrA4W/PkjuJPP7aDLtagKpbeU7kBnQscrQO7RhjlAuUMP3Jjg3mr2N9ImDhAXJ/OVqXisaHjpTsr/u6XbzEXQxEWPDkcszN0fPD6BiKJ1w/PyVlztOw4bwNmbVmYyN3DW1Ri+Ykf/CBdmyidcHye+fob71ANIs8//hwXYcHT22NmZ8LcdDw+rDE6M1tkcB6yRbWGeV4yHRRINrz68i2uPXmdV++8xZMHj/EPvvx5tgYfIxM5X99nc/g4Oilen73C09PnOV6/wVmzoOw82g3IXcM6J8QNQTLOFgSZP+S9iwldlEgIpGTQ6YDO3esDfDHosEcu7wIapQ3db/3NK+9X3q+8v8f1vgtw/vDf+UwemUDnMy39YLVhHTFZY/OKD/ypJ3jzW2ccLw3jUrFXOebZc3gSWCwh0/HM5phB3TDEUpYlraypLBzPPSZaSmWYTD1ZGeZdhJgwuuTCZ1ovWKVJJCozYqNekaJi1WaalMi6JqjERh0I3YjYJrb2A8O6ZVBC02YSllFt8VFxfpK4ezElNgmrl2yOItNhTY4rPBMkCF0rjDZX1HaEy4GiqNDjGfmxFXpVwNz2XZ2ZIKsFMTSsFxGVM0VVoWwghilluc29u6fgMplIkTWYftr6sNDMV5GQM5pEXYI2UCjFYjVmsWxYKM9WPWLVRe6nCT4u6MSybSNaa4Z1yfkqcNy2WF9TDT1WgXYFi2VLlyIx9vNj0AadLSmv0aafI2a1ATRaZXIOeK0gKIwyCP3P8DHTKI2SRJY+vZwU/MJXH9Ejqv/uD/KOSxwt5NL79Y2EyZpJXvDv/+kf5b/5wtf5xpni2pbho2PD/VXka3c7br51gsxf5Sd+/EcYFQV7OTKd1MyaJdtuyGvzWe+9tjxWlHiXWawyxITSHUcBTpuMVZr5KnCw43hmMmAWV6wuDO+eNZT7I9rzcz6wU7KKlvk88tGDbYqi5fv3t/ja/VMSlo9t1ry18hyfJz5/vyOsW9zFCR/78D6Pb22yvrhgPSzwTWR+AY9tJramG+QQKJ3jw7sl28MxyXRcLDvIho2R4eU7FxzNPSfNGpUze2NHCBZrDHo45Oa7J4jSZCKVyUi2GDL7w4r7ywUhZ84OL7ixt402MByXnFzAm+/e5Fsnr/PMU59kdniTO8115u1XWYeax2pPtfUsN67v8c1vvMPd+UtodhiNeu9OBpz4E9K6BQHRqveuNARPdqbv4ZUd3/GeVIdkjcpgHsxOytkCAsY+5N3lzPp3f/HK+5X3K+/vcb3vApzD/+Sn8q3jE3KjmccEpmBDRYpsEBESCZUjg8Jx3GQmTlCiEDQhJupCQfZ4ralixTwkpvUQ7wWlGmJKRG3QGCoLa+8xJGqnKIpMXSl04TFFwpQzNjeugW/J65L1GlaNsMolcRUZDR3eR1JUSPTgKsQkHh+PGNQrkjtDa4Xa2SOfzFFpQNOuCC2QYDKx5LqfyVSqERQJIaB3NwiDN5Hvb+kWLZO7G7CqCXmOK64jb5+hZxOiKjEzISyEEzdgQkPOK7xyXNwThhuW0FzQhglREjkrZl1ka2/KnVNLomQVoaoS2AAJYrY0wTMtC9ZeEOUgrsj0v/9CK0j9cFJRGashZcXQ9E2ttHbo75BSiSZC2ymkNpiU8NCnM0UjKhNVZiId1hRYgWXS+BwQbcnJI2g8mb/05dcfyQ3/P//bX8svdyfMZop21YIpGFSKzbFFRPBn56gc2d2peeOwZG+fS+/t0Tmjgylm1UFhKSXx1il86KkpfrZkVVi643PmeoDG8MRW4vhkjSExHU/YrAVXVWxZjSkSSbf8a09/D/iWozW8dnzIyVpxHwWrFdsDyyIkUlQsusCwKhGT+N7tMZ+6PuB4HtBa8dQT29w8WrBZZ750+5RFBBJ8fGufXHe0a6GuBlAkYpd45to2i27GzsGIbtFQrAcsnafrluzsbPG7X7kDtqNNCrt0XKxa3sbzmCuZtQ02Jj7/4jf54N7j3Lr7EuONp3nj6NXe++yEH/30v8UX/uhtsttkMV8w2BqQujchgXZ7HHW3eGzyEY4X36bkCdbpVTIG/JKq2CHmC0o3pPVLhmXF2nfsls8yj29R2uLSe5dampAIXUSGU0xKxLi+9G6LATEtqeIaM6ixAosOMgFlHDF2KGUQnfGf+1tX3q+8X3l/j+t9F+D80c99NnunaDuPGRrOLyLXRpo1DuccRYbVWcuoBpMjZ+tEqRQ7RWCdIKSMkIkpkbWhdooqghsZdkaGrZHHbQY6sZTBIStPFzIxCjmVhJDALtiabKKs4fSo4ex8zQc+cMDt+3O0LKmLileOYMMkPnBdEcQw2TyD9SaWxLytGGSPK0sQIVqFTFoKV0FVIAcb6MMZi299m3DcMBqNKHZ26FYryiisao2Vc8oNR9rLmAsHSdPdPCHNO5rJJhvPPYVxBn9xyp1vr6iGYwZREAYMs0IPW2xlaUyBDZavvjRnY7LNtWuKbtmAL6gnivVyxvlqjZDxvmAw9FR6yGgYMLrCSQMSma87rFZot0FVW9arltYHBtWAttGcdh2DqqbrOhZtZJ5LggdrDF4pTBZCykQj5NQHOU1naVVEk0m66AFIg9IlyWRCFylUJmP4+T9+9ZHc8H/qb3whZyLzeaDe0tx++5gbzxwQApfez+91jHctOQoXpw2qED600c8i+473i7OGrA07OyXjuGS0v8lzQ/ipZzcZuDHGJ3IJTbfky7cXXDQGnMY3c5wx/MQHD1DW8JvfmPHlW3f5D37yY/xv33gT8cL1rSH/66/+Hjee+T7+zPObBDF8aB9OVkJlDYedYuw7qpEDEUJU7G1qnt7doNADbtzY4t2bZ/zvv/8S79w75+nrU25cHzNbdqhFSdjqiEthf6fiqd0dlqsLSJpff/E10rxla+s6P/tjz2Oc4d2bS/7mb/8K3/fsDzCtLCoVVNoxqoWyMixixIvlF3/5c/zgJ3+Ajx4MOFr33vcmYw5P7/M7L/4hQsYxItklP/z8p9goM8PhAK0CF41w9+a3sVpxsPMMk52S2dGKm8f3eWr/CdY58EevvMhHv/cFXn7lRe4szjhpW0KXGFbDS+/LtqEAWu3QKhDbRNYZkYxpbwAg5tskWyHGYqTt6/UwdL/1P115v/J+5f09rvddgPOHf/GT2SaLaEPIHbUTkoexa3DOoYuaYWkxVcHAeKrhDjAjC/jVCkWktEMaWbOcQ9sUdH7FxqBmWC+oqwYKEG3RbgCpg8KALsA6iBU055DnSNUhxmLrEcQMRqCckGZg6hHJrzGbE1AKmiXM1qCFsBLc9+xDHrM+O8QcHlI++UEYrWERYX4E3Yi8XrFYXFAoRxo9xuD0Hs1qmyo3NCFRxCNcdPA922D2iKsT7P4EH0psWBJTg+4SutTotiDnGmVqFqczBhs7mBreeuddZicaZ5dUwy2GVnF8fMILH9kn5gus2+Gdt1smQ8ub80ydPCeuxKWCqCHSZ34kQ4Uj0KKzRVKksBqJiQIQBY30c6NiFrYHjlFac2/RsUZRFwNyaJiWIySumVGSRbOWRBsNqygA5JiJWYgkrLKgIpVk/tKXv/VIbvg//l/9w6yU64uvQ8v1TcW9O8LWcMVj2wVSbPDEKIEf8omp5fkbjkUckQV+99t3ebI0PH4w5ubRkldOA/c7Ifkz9uuKzVrz9HgABThdcG005TjN2S0GoAuqIkGsuLWa0bRz9qY1ojLTor70XlcjmnXHcDBi1a15dncHlOLdsyXrRQtauLtq+PGPPEUuHK++fY+b90/4sx99HuWEd85a1qsT6EYcrc/40rv3KJTjA3sH3L5/RMoVJZ77y8zs7B4uOn76M8+zMR3yzTtHfOKZPXwouX98gnKWw9WMx4ZTDpcdoc189Pouv/PmHX7shV3wA/7GF17lS1/5fXI84eMf/1fZqxy//sW/z3/4M3+BKJEXdnf521+5zcFozN/7ylsMOOW8fI7cdyUgNqtL74NxRbdYoasRslxSjAd097/O6PoLpJBoVi2SMuHiHZ76vh/GnL3Ey3fegAh1ucNKzrjx5E/QHH6RJu+TRXMUT9BR6EIEQOUGhZBTRNl+5IyRhP/c7Ld7xgAAIABJREFU/3zl/cr7lff3uN53Ac4v/xufzcYqTIwMaouXjNCwaiydErbGhu2xYb9SnM1XSICzVJByxEnkgx++xnQioFcUpkCpjKkmUGRyjqALlAhoBzmDspe3dXJ2BMlk3Z8pkiI6qf5rrSisQ9uMU560mqFTR/YJRUapRJc7Cp1QywXYAbEJ2OE+sAItkAQQFqmjTIliUJCKJebJPeiO4ZunSDFGqwNQiZwSqpmC9uC3ofCs0pKisbiPPQ7HZ6zfuklhWta7NZOdbW599QRXDNnfdsi6Q4/W3H5tzBPPlVBAPMq8fNhx7Gt07hBnCFGjVaJJCUmqvwmloUzCLMb+zNVadOqQ5GhjwhiFwTEykR0USmu869upX1zMmZQjDhdzaq2RrDjvWiyZwjma4DG6IKRIpww2O0LOJPEELJIVRkPIAlhyTPzHLz6aNTgf+cu/lMebA7oUGY1rUmMQGmYnczol7B/s8tTuFh9ygdfCHAlw55xL73/lhSf4yOPboFc8ubGNUplZJ5fed7eGnJx17OyqPni3AcQAEbIj/jO8O+vA9kNiD28u2R3V3F2GS++LdUfhDE2zoio0y5VhZzKlzQ97XzWepCxDVxKT4cMfrVi8k/jVl1/ng9c22HCj/jgzOKJJlAJxbEiN5fjwlI3hkE+8sMPdd+HvfPNrbBUw3qz4oRs3+B8+/xqmSPzMB5/mS3ePubFT88svHvNzP/QkpS747ddP+a03bnHbVywuPFsbQ5p2QTyPl95TjlhVY0PL2foM0Qo32iCtTpHkuGiOMUYxHT9DrYTp5gBdaHTV98h67aUv8uzTn+D1176AViWSFT7cA8mgCpTvyGVBzrE/8vXXse4IuhbRg9573CG5W4ipyV1GvvBozqK68n7l/U/S+/suwHnx5z+VddZkEnW8IA4PuHc4Y2tiGNiC2XJFXTqyrnCmobSKZAy19gzrAeWgo9qwROuwrkS0QimFMgYESBmR/iirDZqYoRVN5zPWPPgda5AAYNF07FWJyidYJ3KMYFouVktG4yHtyjOqxrQTR1lr1MYKfISoCHKGm9Y0xzXOdVipYVSTmnP8/JySlvzcBuZ7d0h7h5jOgnek6RhTnkMS/LzG/V8J9eYAjKELkQikbEhR0ylBRU2DkEUh2RFjJIsmZumP67IhpdS//JiJIYBSDK0lZ+HGaM3uNSFF4ehIo1PmZL7g+s42t9445NhsUlYVqyZyTEJJZiiOJgSUsTQRUlRop0kpYZWmLCImV+jcknPGGYuLglGB4AqsFgbZoKxmrTNG9/9WEgSlSGJY50hSmpjg53/n0ZxF9Rf/x89fep+2HXq34A/+aM5HPzxiWxzfuLPk+mMFWVcMTaS0CifCpisZaPjs8xtMBhXWOZ4Y1v+P3u+fnbNIjrcvjv+53j++t0WpKo7zghwjZRZ+5a17/PRzB/zGW6f8+LM7TMRSbIx5bJp598xjNFwsM1v7htlxQ6EUxpXc2K15+94Fh36BNIrvvbbPxnOJY7Yw0nvfqm5zxgEg+FTTvvQqF/MWjOHLt2c00fRXSRvPqm5RUbOMisYUFB7O1MPeZ97gQ0QAWuHiZAVK8di2kLPwiZ0hP/bBPVIU/uDbZ6QQ+I1v/g4/8+mf4hf/zv/C3IzYu/ZpTo/+mPP1GiWZ0u3QrO+RrSMl0HEfW64IYUBWJ5SDEhefoFU3UWGFLXdwcobCgZ1i3ZqpKkAd0HWn2I1nWZy/BCVY9kliuJC75NahbGD9q3/tyvuV9yvv73G97wKc8//+z+WiGnKy8tA1ff8bqygsfaRdligDXQgYZ7G6v6FjVKQ0GqsiVgnaZBT9XX8k99kaIjknlBYQIceMSpqcLG0A7zVeNFEg50SI4CoNQBbTV37HhDUGY/uJ49ZqlOT+gzxmipxACykkEgVOCxI7Us6UtYYcwS6RQqEPCgjnyNij4wiWGYLrA6RlCV0BsYOugpjIAkocKQueB8dBInhRZG0JSZGj0Eo/iT0rTYiCPJghlR4c/WRxqKxIKiIRtLYkgRw7MoJSiiLBE3tw+3iBzSXVaMJq2bCKmdFgykZeczKfo60lhZZlMmxUY5wO3F15tqqapuuLpjOauRTEGKFwFMrQpoBYjQuBgGWoct/uG0GSxhqDU0KX+1tZf/43v/5Ibvh3v/56Pnh8i9ffXfDS2ey7vH9ka4Iy8JXZko9tDB/yfrCzxeFFg1XC7tb0u70nHvJ+t+suvXfLFd5r3pqd8sVGX3r/4Wn//8pi+EqT/7F3Bf/m43uX3lVhYLUkuQq00KiOk1nH3tbw0vvYFJBjH7gWiuvFNZb6hJaCHW1YhghOWMw1UTfQFRzH+UPeb6/nvfcUkWRYR4MXhTdLQlIsU/GQd588nXKYJKQs3D0O2NIiRhOWEePypffl6eLSu27P+fQnn+TXfu/XsLnkhU/+JK+//G1Wp3c4+FOf4Umz5ktf+02ScpBaZoslzz3xL+F04Gu3vsiHr/8IxxevsVrMiKVjnTcIcU0BjOsbXKzeRushKs3JMsbVltJN6dqbdGbMhtvBtOeEaoLCcPvv/XtX3q+8X3l/j+t9F+Asf/nnsqiKFNeklAihwRhDWbn+g9da0A+axklCIuQUMSmhc+zrYegHRGIdJCGrAnSDjg9uPaVEbCKSPGEdkNAHPtpWWC14H1HJEHMmeo+IYJTDCSQSFkGjUKLQKqNNotSJ0ij0RoOyAYZAC7KsUV6h3AKsIwcDSaEI/QtWkWwTVAmqDmUdEGG4AiwSFHpRwbKARkHroIMc6CvQlaBsRqHIJFQqyREykS70vXUkO1r62CprhbUapzIxeZYLRcwOHzRJR2KnKEcTTpcXICViMuJb5p0FZTDOY80AQ4vONVYrlhcNgwGIsRRti9JDKDpMzmT6gCz4zDoJSjKdUlit6VKkNg7vLdkKXQJHpLQFvotURZ9+boPw737x0azByasuiypJsSGlxL2L++zsbDNQtu/y+cA7OcM/4T0X6dK7wlx654F3Yh8sJyvEJnLrdMW6FSRE/s97J/zAxPKBjW28j9w+P+OLrSJ6T2ELQkiX3kdFgW/CpfeP71eUOnGwPWFiNSKRYjqAFlaxQXmFSHzIe3QPBqaqiM1CziPGhb/07kXxHe9BB1RqCBGOW/nner+5POXxasqr56c02iPZkbwmJiFrRWksTmWWXcvJSsDCeSPMG0/sFNvXN3nx5W9hzQFiMv7uVzhv8qV3t/kR3PIN2PkoVitObv8jdvQIMRZhTrX7wyh/fOk9uJrm6FVm6xWmyoQ2gKpIsmait4hmm2yF9eIUpRdMRk9z1hwzrD3LRohxTvsbj2YNzpX3K+9/kt7fdwHO6pd+NmtVom3A0KAyaN03pMMY+jRGCdaTjUdlA7mPaPs0perfHDFAHBD0irToCCHQrAyxCURxaDpU1hRaYQuH06C0wRYJEDQGZyPdRUQlAZMxztF1GlIkSodFgSgMCpU9WvWFcxIimv4pAEAjkCNZbP/edQGlBKxFBitwAk4hFmyKIH3WiAxI12ePdCKPV6g6w6JGjmrkwmF8IjLGZQ9iiALkfoRFzJbCCNoEtG0QLMqATg5SQLIih4KFhyiZ0GmaWBBCpk1gsKz1Eo9jbCzLdUCjyCx4anzAzAecE47XHcezgiYKTns6PUKFjmwsRjzbpeKsjRhqkhIiBqsThVa0PjNUmvMUSEkRU2KV+uxUp4RsNUZp/qMvv/JIbvgsZrn3HKAtoLx4sMF/t3dC3fdDyg+eWP9J7zaAtwgNIQS+dbji7cO7RHF8tQt8soBnNvYuvRfAkzsTQLg/n3OwPeKdY/+Qdz9vIEXOOy69z9SSrWlJFUpateTm8RKN4cZmBfTeDxuFXzc8uVWRUnnp3bg1OY7BKYZa0eTwkPcQPRZNRNjIBlVn5imyjoHTk0j0ATIMyhLE8Pb6FLJBtGLdJIalUJSKLWUvvQ+to1n33s+l4573rC8ERHMmwqwJnM6FybDg3q3XWFTXOJjW3LlzB41isf4qf/7Tf4E754GNWvOPXnmLd+/eZ9UeM1CZmHfp8jGumuLbCzaHjtmyxbpNkhKssujQYKsp6/UJY7fLwt8nAcYnPIIJDjVMxKhRtiT+1qM5quHK+5X3P0nv77sA585/+qkcRZCYKE1fHKa1JeVIkBJPJMiDoyUTqZRCciLqvo+CUgayRsiobJAEojyKgiwdaIUyGgmgdKRwjkqtmThFQ58OTQ8CEUPGGUEbwfIgU4NDIbhBSfKervEUxmIHQ2gbwIDuwPYNBCVqtElgLBDJKER3GGtJ9QKz2xF3I0a3KKOIjcUelrB2IB00FVEM1sY+MAt93x+1sP2pW+6bJuEtXarJoSNJxpWpLwCzHitFPw089cdvIWfarmQVMlESJjlaJeRgaemneitT9QXXQZGtRmIgJh58nRARprrAOsVKg2kTygZS6zlPkKipSOQEXcwoo9FakxMkYyhCZq77yesi0ErEGIPKGhuhMy2dcqioMDrxV7/yaGZw/su/+4cPeTfKk1Vx6f2kbR7yvlcXzJr2Ie8dxUPeHWu8GlCkFWiFtwUSoFIthXNMy8hBJSxz+V3eKxv50NhhyTRG88zGGIUwtEOS97x6esbT2yMGdoqXBWAQ7/6Z3lsSE6VoxDCtHLV1D3mPHcxpQTpSKB/ynuoLFAWHJ0tyhjIXeAnMUsOqM5BhKS2T0uJUwa6qKZzlqxeHNIveOzZz7j1vnwrt0W1GO9vcPl303i/eufTeRv+Q90YWWDOlSzNSgM1ygnWKLht8u0bZQOfPab0iuzFljIQcUDEjrkJrjaSAmAonHcKAGCdocwzeQ+VQWZMDRLVE2SGqM0CD/71HM4Nz5f3K+5+kd/sv4pu+l3V96vsWuwYoGzKKLMLxWWaxWtB2wz67YhKSM0prJBp89DiBoIWcA05pSmWoy4xRQuEMGMPSe5JXRAM5C3SJaAzzDtAtRmsKlXDakGSN0QalI0lZsEJhAWlh3WCAQaEhr4nLBqP6802yBqXIpkG7Cpwndx5lQVlB1x1BBRyatBR0N0DQ6EFGWyENVmgjpHWN9R5jFCwrrE4QBbqSnBTZCzkp1g3EpBDt+6cDnVjMaryAl5Ighhg1/sHZbdSGEIQoCh9BK0XIClLCiyHGTHYCsb8mHv2DoCb3NUReEkoMZ1qTpCMmS4fHqQG5szQKDCWYSE4QVYa2vwa+FkFSJluhECGIRiXojENEaHJEdCahsUkjIsgjudX3699+duOBd6FQikz/t/g/3n6XOycrLvwUjWGku967Ukgy+KRxyRN0P++lwjMpa+o6M7aJj0wNmDF/fL5i3SZWyf9j77rkftcH4kZrrBJGDhahQ1WOl2aBMieenAwoJHC4Thjbzx17drIFPvLW+TlGCQdVASqDUnQqUNYWR2AZBWVh0A6wdUcbEgXCMi2wRUk3L9CDjC87rImMO+HcCEp678EC2tNdFNTWXHq/x5xbhxEzzLSr2D/R68xcFKehZSkd58s1MQrro7sstSbqfrZaPL/Hmb9gtBzhL25CAi/QpAuyW0KEkECpmpjmZGPAnyM+Qim0bUSvGkQLmpYYh+hlTTQZtXqcOLpLlgLrMqTeeRSwMeOthbBGqUheZ2Jl+u8bE9ZkJCtcx4Nhs/H/X5T/AteV9yvvf5Le33cZnG//F5/ITtVI9mjdojWA7rOYRJwREhmTLZYCbSKSE9Y4lAYRwWbVH3FlS9b9qAIAdEBTksIaYwuyCEpbMB0iIErQWvedGmOCrMAocBV9+2ENJoJOZJVR6UFBsevQZoCPF5jtAltvQjWHQQRnIA9ARbjIML9ATjwsDaw1KloyDuValPHkVKGCJaeACqY/V14XZN2nSxc+0bQjYvR4edD9M/UZrowmpUyXISUIsUCUJmShDZkYFK1kIhBEPegMLeQMPltU5HIeVEqALvr6mRyJKIglKN+PT8CigShCzArQtDlQoum0IpL7mVNRoYyiS/019CZ4Cu1oY8DnxKoNtNlgCkWMEVtVICW1MXQSSDkTc+CVe28/kmHOf/ZLv5tTUBRO/1O9f2a88V3erdWIQFm67/YOlA+aJjYqUWpL23WMnGWdYGA0KXlaoxAlVEn1fycll97roqDxHtAUEiiKkqwyy5Xp+zzlwGZZc9zOGQ022ZtWUM0Ji8lD3l11ytmJZ7U656Qx3D2/QEXLcwdjWh+RlNHGMCjglfsLVDBsDhKnnb/0fuskksRx3jWIySw7x9miufTenNxklktSgtX8sK/by8IqG7og5OxJ0V96d873Jx703gFU9KQE4oY4JURpUdqi108gxa3+92+qS++iOkCDz9hCiNFhXSZ6QPXec+xQ0ZO7M3S5jTT36T9hPCQDhcJ6j4w2EDsFKpRa9xcpwoL8tV+78n7l/cr7e1zvuwDnb/25H8yT2jHQgk+hL9LSQhTBmBLEo5QiSl+EhYl9c7gHx0tK9R+s8p1zTiLqQeV4lgJlAzb3xVsWAYkYXZCzIvXVIeScQGWUGBQJYwwpCWggl2gJfW+YHDGqv6YoQaN0i2YD8rIvHk4FGU8OoK3BGAckEI0xjpwDxvTp0ZgMhS+4OJ9zllrq6Oks+DYwT5m7a0dUidmy6TMvKuOUZtb14w7EOOZRENWyP5iylL5AemJqxCRWGRyOkCNdDJRlQdd1CJl1TtTVmCIrYva0SVNJpiwsFzFhraPtFvisMVozGW/SrBZoZUm24HCxYFBWaG0hJDqtcEYTfQsmU5mKzq9JAkVdEyWinGVjvAnZse4ayqIiiyO25yQ0Igm0YTE/wejMrbO7j+SGv/uv//W88+we0/EGs3vHlGZw6X003aGZn/5/9t7Glqqo/197n8+/hSJR7n+U7vCboGHrxidYzjtEK+LxNyh3P4JSCn/vVZRumTz3KZavf+mf6r3cf4HveK83arp5w2RrSG4gWjAp8/bLv8vp8ggnCf9gRllMLbSaqBJc3EZFjdiAkgKVDLldkKdTWC6gTDC+hsqJvDiFwfUHD9gtoqZomZOJKOPI7YxMBuOgmPRPw3hU7HujKG0geihH5O6MItA/iW4/DWfvgBlAPYaz+1CXUG2iVjOgJA9rmJ+AyejhDjI/6q8tb+6i1yvyZEKu97HpMWJ8G12OIV1D2lf6DwhJ6GSRs9dAZ/Jrv3/l/cr7lff3uN53Ac7P/tCfzbFrGeaIVoImUBnhJAYMiTIPERFKQl/EqjKKgqQfpL5iSZc6Bq4POqJJVAyJBo6i0KWKzs+oyk1yM2c4KDg6fJu6rCiqPcrhitXcMRxXYEByIKXEthFiB5mCmYHiQYW7BMFai00RpRNFGrDULRvG0ej+ltC6jVQILius6ietRhR14ZDUzzOxGpahY1uV1NZwnhJRw9r09XStClwTRzYWiR1VLpjH1F8gkMyFEs5yTe0sej7HDWuWROKDI6P+PnhJxjPKipAjYi33L05ITYfOuh/iZoXKjpjUhi4EFk2LsyXn83NsYajKguwVTiUqq8iFQbIjpMiOGXK4mLFGMyoNVkOMgWFZU5YD1t0ZWhecdIEnXIkUJUFWLFcdGkNHR8igsOwPJ/jU36BrY+Dlu+88khu++cn/Nhcp0UZBKcG4JVEv+r8XCS27iAjoGYWa4s2Sujt4yLvXc2oqJGhStSZ2G5RG0+aE1gVObtFxA9MtUYUjnv4DcrlHVX0cP76DWka0e+rSuzWnSJySpCNTYN2aHLYRJTh1RpIdlDlF6YRudmirM1zcxZlAoECpE0RKXFaYWOLVmoiiUiOyXqJSRYdgzREi++iuIJiO0moaA4We4VVg1OySjSWrOU0aYaS59B7MCpf2CcaSwy0010jFCabbpW82ENBUZDwaBfoEsZa0fgvOTvrbOEqjtUXqJ9CjEmmWsLqNGuyQj271Hc5rB171LeWtg3oIlOBXWHuAnL+FUEKd0KZEwhIzOoBqD+avk4ohLFcwGIObQj6F+Skag4jvLxJg0ZMPIPkcjEH5NfLVX7/yfuX9yvt7XO+7AOdHn/3hHDUMqopCa9a+hQgXRjA5UamMkchajRmaAlskcgzgI2YywK071u0pm/WI1fmMxycT5oWj6zq0cWxgOF+fcRRWhM6zU29ybizBJ7Y2NinWc7bHJYV3fCu1PJVWTAZDzppIqxWZgG8Do7rCAU9tbJKi4s1mTTks4XzF0jn2phPUYsFisSAbmLqKyiVi23F9a8RqueRsrRk6x4nSDJyl0Jo3glBoITRrOjPCEagGByzJjG1Ecov3QswRnTUptiD99PD5ao52I1ZhSawsm65Gt5rBoKBZL9mcbtAI1K4imhqF0CZouzWkyEW3YFpv0+WEbxa4ekBRDiA01MWI6FuKHFkYRa1r1tYRm0DtCnCZLngMJVVVMV8uMNoRiWjR+JxwVmNQeN+hrMGIIolHFw4waN1fX5dkiLEjKYM2UGXNl77124/khu9+5L/O2R2h7eMEMRh1hPFTQnWMyYkoBpOXqPwsSdn+lp+PqHyGGu4Tl5EkL1K6PfzJK5jp9xOrCNLixBFlTG7eQHfvIjFC+RS6rMjSUoxvEJpzqGp0NyWae7A+hfE1dLNCyhqdlrC4j9l8mhA8Wn0PthQSd7Hs063ehsEQo26g/QnK3+0H36uaVHTYdYMa7JPCKWbtETshWANYnNYE1YIrYHZEUW/igcJ9CFEZSGRzSPJCZVa0SaFkfemdi3dR9T56cY80GoHegAx6MELm91HT5yl0R8pP4VxBEzNaZUg3+9q+7g66eBwU0N4jV3s48wSeu+j0OKQOq+/gC03hn0Iqi1p7bFGCy/gU0FL0jSvXS7Ir0TEQtYEQyZXBoJCmwRiLyhD0fQp1gLcWhbr0rkJLxhJLRZU1zed+4cr7lfcr7+9xve8CnJ9+4TO5DZ6xLSnyiigaXZTcb9fcOT6kqgYYVbBuZuxPNtHKslic8qG965w1S+6fnPLZ55/jXBQXacFGqVleWFJK+NKiq4Id1VJQ4LOhS56h0qQQ6LKl1h5jLV0Qgl8yLMaI02zqxJ3QcHQ4o7Y1WnnGw4qLiwsm4zGnyxZbVjQIw7Ik+sAsdvioGRRjnt3eJK7Oeb0VggRKW+L8jIHS7BUlKq1YKE9la05ki+fUirXJLJfCoerYn+6CKE6jgeaM6XhClzu+z4xZFpb1eoktC878kpOVcJEiVYDpsH8NtSsJGWx22Kw5thUjWzOLLbXpOz5bn7gZFcZKn8bMDa4YEEKHypZOWpx2IH3vhlIpusYzrAsaOjYZIBoanyEHrFYIitpYnFEUtsRpQwwrrKsptUBMrJShiIFO+hbqqnSsYgdAXLXY6YjPf/1zj+SGrz7713KRPFmXJHMPEY1hj6Tuwb0XUZv7ZClRFzdRe8/3T7ezN3HTzxLUTbj7EoPH/hWC0uTyLlG20a2mSBlfWqSwED0FBUoyqEQSg+glZa4JeoXOQzwNhCMcTyJOUxAI9pB492VM8QESJzA6wM5eIU6fRl3cRtVPIW7dp667jtzeASpUsUfpnsPLTUQ3KL+EYoe8/DZIja7GZH/S+6q3yfK95HST7Dp0MER/EzZfAFFUaYvYfRM9uIaoOSY9+6BwfY7RY3Jxi7hcgrToziNuga6uIa7f/B0FIQ1RdkpGU+hIzBWiFWWKBPqmnVogxTVFOegD8GwJ+g5GDkAUqbiPSzsE7qPTNXJ5B9M+jmiwAl7dufSu0zWUM/2xCoJOHutqQu4olcZng5LQ3yjJGbEFRnrvtB15MiD+5l++8n7l/cr7e/X2fgtw/swzL+T2QTVUjJEcV0xMDarA2MzKB6YW2i71IwAGFU2GcZHoQt8zR4eEaEVloOiEpsjkJIyqktvzBUZptt2EJA0YS5EVtjT83+y92a9t2XXe9xuzWc1uTnf7W7eKVVRRLIq0bMoKZVqWFUGKWwGOAziBjSAvNgwkgB8CI/9CkKc0D0GQBwNxjCQw0gcIAsOREyW0OopqLJIiVaxSdaxbtzvN7tZasxkjD+vqCvRrxXKhcObjAc4+B3v/9lxjjvmN78vDgUXseZIOnDhjUs++JB4sVyy8Z9KJfrVmv9+ymeZrmM4cW02sHVQvaB24299FxdhPA9q0PBsUfGC3PzC2S7rYsK2JkoVaNvimpZgw7jMqiaPlTaRxNK4l7ye0i5Q8UV1lQUtLz5VLmCZyVY5dj7qCaaW1ijYNoRQuLFCC4acJiRG1SFsOmDgOhw2lO6K1RBMWRBXOQ2E5lFlTpAcO1tD1cb7aMmOSgqkHTXhTorRkqQQFF0ClnwXKlrGc2JMIrqX5A4dREcboiVlBE9pFxlwIAosis2CujaQ80oQW0Up1QJp4+8Pvfio3fP9Tf8vm0xv4lKnDO/jFA5CGGlqYNuADYbya39v1PbARL5GqGXEOG0akaYjE2TLB53kD7m/A1Xfn1vTqDXR4SAxnmCVKu0K2H8DiFjY8pMFRJKL5Er96BW0jPo+UxQ3c8Aw/QW0CVjvMHXDMztNiB2L+8fkE2r9FkRVMNuf67LZY9wrON6h7jE0nVP1dpF3OTO0uQBK++TI+eMwHOGRyDDj5AHUVlx7QuA4LhYkPcCZIeonglUrBVHEhgCqIR1uo9V2iPgDXoPoBaKWOT6C7C+6AsyUu36H0jyCN8+TjsMdcA12L5+V5I46PcNNt1D8EU1y9j8bHOAV1Fe9enQvyMlHDI5At3rXU0sI0zK7rTSFmJaUEzRLkMJ+g9w6hIMuIDntct0D3f6Dz22O/8t9d837N+zXvH3N94gqcr776w1Z0nioOWjhuKykb4iJBK0nmMeVJAke0dF5YBkfvHF2cxcleAoMllm3H7nLDlS3IHk4WS1KqdFF5++Ix76TKeDBWy2PiYsEqLDGf8DujaR1ZJtoyi8NurxuGXLgsCTzss+fMd+xLmn1cYsCcUUJmLWu2ZeAo91wdruiOVqB7fBlp8XxutWAZ19xpEzfyJe1yjUml5RKwu1rEAAAgAElEQVTiS7wUZ0HuKgy8++SCD9wJr0mh6yMLGTlZHfP9i8csbMHDIfH7Q88TDTQ+8ITM1RRomsCzsuV8s0HbNUNVbjh4IJGfOAt4Bh4NcLNvQQLVKnGE7wflJS9Up2ia8AbmOoop3goHhCiQY6CkQovgxVPKPPHUdZFDcQRJ1OJmAbQZLiqmDbU4krc5NkIVp5FUlRoq4hyOBqeJ0QVajKwZ7xx/77u/+6nc8OWrf93EMmYOpqu5fV0nxMXZ26xuqFohrCCs8CjO3aD6ggtriq+IN9oCY2wJuyfgzyge8LfwZYf6Frv4JcgFDgY3XyEcrdH0OrQPkQkEpYZp3qyLkhfHGBmfNmgQpDjQ1yC+hyrgOnAGIdPUV0l8gEwNls5putfJ8jaWLxFbQvuAmI6RplDdOyx4ldRvsOkjFv2XuX1yTFU4Phv49nd+keJPiWVgtbyP5wlvfOkv8Ztf/x9ZxBOejhtciqi2WIyYFKoFkAh8hD19F47uA7Ndf3BnuNCDXs0TtrKGfJcaHxFHyEcH4tRTneJlizdI+QhpB7wVShUsgEnE10IR6NUzppFqho8dTiI5DPjJ4QOoGQTF24oyKbXLs9lnqkhZ4JyiMiDOIX5FznuIPQ1GyXu8c6R/+veveb/m/Zr3j8vbJ63A+ctf+Kp5qVz4QD8WcuNYOEFxULa82qy5QvHes3YdtWaKg75WUhvnijcnvm9KUOgdnDQr9mmkemMzjrRdZJgmFtKgzlNDT00TJ43D5QFrO1Y2EsKKoRxwNTLagUEcx35NGrdoCJz4yN47LE+EWslNR04Do48EXVD8FictC8vsqiMWwYVpzo8qlRLm+Il9nhO1t3qgauao92x0QdXZ7nsla3bDSHCFq8MVTit9UJrQshuu0K7Fzs9h1dAPkdtLx3evtoxdy6q5SWwWhMajtmW933F/eUypI2vXcJkH3hwSnQSOQyW0gfPtgKK0zYqgI1E6St1z1rZk7yhF2YywjC1aKhMTmKNzgcF7SilopzgzsJ6dZvaacWo05ul9ZPCCLweq65mmickq3qB2QixGdJ7WBwoQQ8u3P/h0Fjjxp/4jwz+lNpGwX0G/werp7IvEmzT1c5hUigES8eIogNZMbFtqLjRipOYZdTyhd1CkweVM9QbuCbhbqD0CaZFyRnGeoIoHit8RbUkiEX2HWMHVyBQuED9hh7vgr6i2JIoHH7A6Ed2BZMdUuSTYEtWA8xNOWooWnAoNoM0Tiupsk2Dx+cThTYJFJvf2PN3oAlJfwvz7APj62tx+Dx+i00c4rahUxB+jm7dhsSY+/hBWDSW3+N5RLq5g2cPxD0N/ihOH6jnu8kOak8+RhwtqWCPjIySdo9LhJKPHd+HZe/OJeHUHxnNcOEbrBcgR0nSQL/HZoDlGSwW7Aps369o0+GlHbWa3XQknWDlATqAJxIHvYbFE9h9h3W24ejZ7ZVmGrpknWZyHZgE24Zb3qb/0X1/zfs37Ne8fc33iCpzPvPKjVqaE954udExpINSIOjCXGEtFa6ZfrFi6lr1mEE8ngjgHPpDzhPcelUxSQ8eE6zxNc8y039NKRr3RNz1pHGidobkgq47hauT+KmA+kLaJqfNcXl5y1q7wjXCzEU5Cw3vjOTIYfuFYujWNm4i+8PrqAU0YWWqkyMCq6WhLYq1GnyOHdkcji7kwaCrDZGxrYXfoeStvmfCkXeRRHJiKY8lEtJ4nBplELYE+Zo7FWFWjX7S8lwZqPfBS34Blnu4Dq9Zzu+tQURY4zlYBpj2dX5ENNpoQp9zSFR+Vgb4VMpU8VvZ5ovUBgkdTZbJZJxPEUTEkVzbOqD5Sc8Grx2nkibtiaiJhd6ChYZSO6gWXEuYbYvSEyWiawNN8gH6BbkeCM9y6pcWTc8blluInvAPLI23o+dZH730qN3z5V/+msR/Ae0K3pBx2iHSYA3SA7RZqnk9pi/VzCwJPFCGXDuw2Et99wbuqwW4A30L4PPA9GAt4g/4I9hvwDtENtjiG3W7edHwLm2ewOIbHb81/LyguLFBZw/Qd3JDQbo10L2F1A6ES5MfwVBxQZUcrK8ZwxWq4Qaie/fo7tPmH0VLJq2fUvIc84ccf4mC/OY+iTgvgA6hAHYjxBtUJmp7h0gK6gpliOeEWZ2h6Spd3TKsFVhOMHh8Dsrg/Z/fUTD0+Ilw9RPqXqGJ4Hcge/LQGdmh0mFdk+2R+DfGEsMJyojLinVBrTwgjNhXUG+YjIU0Ua0BW+PqQ2kR4znsOC8wLDCM0HXQ97Kf5/U076I/nz9MZnKwBP/88z/5SBIPDgCxO0d/6R9e8X/N+zfvH5e2TVuD8jS98yfqugeqIZoQAKxMObcdQEn01LnPF4TkEo8FRTfCuAZtIpeDMM4nHa0JjQynKWj3JJa60snYLXB1ZdD2baYv3nt1UkSZwyJBzpvXQqCA10Qbje9tE20XSoBx1sHNh/nLliaucuNEv2Awjq7hEZIDWcbE9EBVC7zmpDX/qtOf3xsyfOO748GrHT798RvGZzzQrNrsD7x4m3nw6kg5XnB0vaF3hxz57g5dWBZkyz86Vo4WR1QFG37SknJEYydPE063nsR6IbonQcHc9EYtw0MrSO7L6OTjUwUSiJT53FgZws5vxc01RrRVqYApKLII9j1rwYiQiOIFsjFbJyZFkJGdPDEu2ueBjwTlH0YyJR6ujjZ6cRoo1ZBeIFKQWto0j4wipkiTSaEW8IxV9rgcy/uF7b346N/w/++9ZbE9e8H7we4IFkFs4LWiu4M9RW0NzeME76Qa+ffqCdy+OSTNit/DuHK+B5BKwx+mrhPCIJDdwPKJqQGrCXEOwiZoSFhSKhwSEEb9/SF2ewu4Ayw58A7XDTY/QcQdHt+YHxOoeTI9wfYduLmcvjEbwskCOP0MZN7j1GVx8n/bkq9TmQx7c/AofXXydtNnD9gP0/DHcfQknB9Z3vsDtsx437Xn0dM96kdnnmfe7R/e4OjxisbjD06uH5L1nW86J4QY+djRNoa2FwSoL58ghYEMG11LcnqALKtML3qs5qkCVhFiCGhCx2XzzOe/ZKaE0L3inq9SDYm6PUyO6u6SakThiTcTSiImHorjFArfbkmVFEyNaB0oZkUXELMCww4cVtU740CH5AnFHaN1QvvG/XfN+zfs17x9zfeKiGr5xZXB5wASGXJCaCKFByBTTWXhsRrs8RXC40KFWiHUke8Vpw5guiC6SpCI50wfhatywjicsAnw3P2Vpxv3btzhxDYMeuLfseDhlXloGlsljtbAMlTEsKHXgweom1RuHOuImoXezT827F5nX28jZyRGd9kxJ+XBvfG6x4nGz4FKh5oHBRb4xBc6rsD30VBd49/2eP6UP+YtfecxWdtzq7vC3fyLx5KJnzcRJf8LVdEkTl2xax6s3esbdnstSCLVlnDzGCONE6I75Ib/hlvQgI7++2xKe3OFxu6VNmVuLY6aSsKxYTOTaMJE5DQ1XBsF5DuPAza4nuh4hsOeA04L3gX11hPo86iEYF6NyC6Nt532C0tN3lVoSizbRGyBQTRldIAClJKr3hCoUVxlo2DiPpIr5hmQTmLEV6KvONu7Ps10+tSsdyOMzcELe7KAmamgQfodiCmWYwwVvvgr7hrRuUSu4wwV5UlwbKc8+oCyPQSq2+Q6lyZTDFhdvIa1Qx+9AqdiDL1JtRSPn1HgP6mNKs4TqiVLIfaJf3GWwp/TNz1C9kVcPqXl27dZO0HEP3RFh/Qbl+AlhcpAfweoltLmHuIDtPkDDAqeGa3tMA7a8hw6e+uw7/MyPvc6v7J6wW3X8rb/xd/jH3/y/+dLpfb74+hf49ttv8dqrr/Mr3/5N/tpX/wS/8+av8+az97i7eJlnA0Q+JG3f4e7xD8Hyks3hFqXd8XC3g2d/jPH0e5TDhtTfwnaVYIX8PHFZ7EB0DVVARKjpQOxXGAtETzCucJrQpuIJaCp4SYQuUopSGaB2iIcoJ0xhItqI+QGpI+QWXzLaHhGcUPd7NEZ8FZKNuLgk9gvY76BfYVJmoakoUjLa3JgTtLP+y6byX9y65v2a9z9C3j9xHZy//sYXzaqhznMSIFmdjYtswEmDIpjAqTm6xnE5Ja4MTmpDXEDnKw0dFit9Y6w08M5F5t5RZNUom1J5uIFt2uEs8t5U+PzNY8ZhgBTwoXDnpOd7F3uWy57t1YHjDrZTIIsjG2yZOBHPrkYWsbIx4fx8x9QIzjka6Ri1gvUsYgu65Y81e5zvqOYxCej0jJN2wSYKD0Lg0S5xMMdjNZpuSbl8wo1S2eKwFh7vDxx1C6oIkwkJ5eWjYx49u+RB23BuA414XAfBApuq6HTg4BouFHJWksHieImMhuARZ5SsxMbja2WtntEZ3nvMV8QWnLUG+UD1nn3KZBM0Zb54esYdHbi/EpI6ch2Yag+SqRLxRUhSOCikqix9oFbDrDI6T+M8wvNQ1Br5fRcQndjngbUvCB7wJPH0pvwv77716TzR/tS/a+hE1/aMWWmcUZ1hdfOCd8HmMTXXImlHESFMC+pSwFe6/ctMy6ezMRkNNh7Ad0jvqNM0F8HThqANeTiHmz8E4xVt7Sky4BY3KMNTbHlEc3VBbQTNERNHCA1FtkgWRJb4xsglwdN3oGln4WC4hbgDtdyARcQNG4I+xbozTN28+e/fgvaU2kai9Oi4o5Q8380f30Ee/RY2TuAbXCvo9gKWJ/jg5xO8Ffz6NerFO/i4oI4fQnNMWAglL8FGuPoI1qdz4E7WOaX67HRuoePnU/k4Qt9DrgQJFDLEFizh/D20i7B7Hx86bDpHTSBX5MbnYdrh+jlgVqdneLlJtRF8hy9CrgMVZSZbqNUQNawJOAKFBk+m1h5pDMmG1g/xcqDaav4fpcVZon7jf77m/Zr3a94/5vrEdXB+4oEjpUDBGDJ49VyNF+Aa7rQ9T+oeVUcpmU0p3Dg94abOD0cRx7N95b1ywf3Y8fvnAmVPZE/tTzkZIkGN6aA8WZ1wVgO3OqWtylMzsImzxnM+7jlrPTe8sVv2rFYr2s1j7p0e8Wtvn7M7HAjHaxYh0WiPXV1xY7kkWeVQI5dlz9nqiK9GxxN23CyVx7agrweWKF1/Qow9t7sV2SbMeWJTef2oo9oF37/4CG4tOYkvcbt1TMMznh1Fjo/XPNlespHMGyen7IswLVcc9huWTU9KlYPvybKncYK3UzZeaDYZWRoldLy12/DakVAkEixiLhGsJ0uACjsRBq+ESfl1PzsbvN6saEypR0IahCl6nkyFt53xmW3GakfjO4IduInnnb5wx1Y4FSw6ljKx8Y4xRJbjSC9GQdgDSZWJiUWdHaMX4kkqmOsYRam1snlhy/7pW/6sgbFjxHBRMIWa34VmRVdfIrWPUHUEU7JeIst7dMNNyvoRQRxql9TwO0g9Q6aJPF2CPYUbb7DYvETwVwwDhKO71HyE625iz2PWTLfIcoFwhe89Lnt0cYNOX2Zsfgtr7lAe/y4yXGLrB4hcku0WXLwF/X3EHcACOr0LR5/FxwaVHT44ktyiGZ+gUnDd55D1HZr8eaq9TQ0RXGVxckTOW+rTr+GObsPJn+O0XbMvv0FZ3SEsT5n2DwlhJLafxZzQyavk8THx6EfQ6YA0p4h/TOUU393F9wG9uEQWRvbH6OZN3NEJxoJgkdJVXF3jO4c3j9gEjaPsCuoOYA2hfQWrBic9sldcB1UN3IY6ZWRYUKUj63sEt0KXIOUMEYf3HqeX1NiirPDTJWjBXETIOCrIFuiorkIRqh4R/W2sOWBpR3WfuG35/7d1zfs173+UvH/iOjj/wVe/bKEal2Ni1QSMhkOuBFfJYuRihBqITnmYJ243LU8PAyUbp8vjeYTQJ54OE2FMXC463BQopvRkYmwgJPZTZPCO1kcUpWZhSkrOA6M06Jigy+D8rEcZoDQezWBtReuez3anvL5WbgWHy4kcFrTiad2Om76hd5VNC0577iwrbZ1Hnvej0fZKqZW1CeujjjQcQCInTeCjq4lu3UDe0xdlJDJV8ApXGnAoqy5Sy8SQK29eep5d7Hm/Om6slC/dPOGlsGFyPaIT6ivZFng8OlQ+HAPPhglpM/cXCx4sFuzqAS0DpVng9on9UnB76FyHtTuSwX7KHIoS7RRBiaY0pZJkAp8gLxCv7LLgXIPaRBGPWKUGRymRLMZCKul5tozKrAuyCt57ssxRnmotxUFSISb4j9/53qfyROt+/t+3UI2SB6IPJPq5bWwVCYYVQyaP6xJ1GnDNERwukaS4xWcRb9R4AcOGut/iV7de8O7YUZseH0ZkXFKaCs0KXwdqbYjTSC4XiO+x7QWuVXAerRWGBK2DzJx/M+6gv4k/OsUkQjngwgkiEdVnxNDja2WMEN0NFtFw/oB3jmGXafpCqZXjEji6fYc6HXAu8MYrb/Brv/HLvPzZf4WLy289jzKBw3iJV9iXmffT4zvshoeMk3A5Znj8HsU87khojl5nGeaJRdWK+krRJR6PTCNp8uR6QfKFrr/JzZN7XG6f4vKGsV3TpAPaOGQCT4e2hWRgwxOqKLHexXmjUnHjgPo9dRrw7S2MhBTQ2ODKhIpDrKJB5twj8WjNSFyAjlQWeMu4DDk0+KD4mqlhhbmCFSUmGL/xD695v+b9mvePuT5xBc5Pf+ZHLKmhMqLVkyxjNGCVqRYIDaQDTpUSPI0TmrxnvVqxTSMhNLTMd45trVgbuNMeY2b4LrFynqVrWQajbz2dOC6udoQ20AShcwkXIn3N+ODQ2EABywXnK2WseGkIkoixIeXKR2OmCcLJesFSCl2n7M0xPL3ieNmy80ouHXecI8bMrZtGr5EuVoSGYWo47CY++HDPk81TvnRjQd9m7txccvXU0a0zm37JiR4433gWC7BGkBhwtoAmYNMT7KAMeUD6I/KUOFPjUNdsp5F9MnwQtESmsse3LapzWuyYIZvgn+vKaq30i4aoyqAGfnaCrgQww88uB6Cz2aL3gups1DcVhw+VsXpQwWqhIpgJxQqqkEomS6TUipqfzQjVURzUrMTYYKZ4ERSoVvjP3nn4qdzw5av/jsUiZHuMSI/kA9ocIdMe0z0sbsDFRzhVrI2YbyFfzBMm037+Poifs2aKQdtC/zqY4bqEekewFjNPdY5OHGk6p/EtEwEXCi5E3DjigyOHE3w9vOA9pxH8itZ2yPKMPAyoHsii9M0p3oyTsGA/QUrv0rQd+wiUjpXriTHzpc9+lvXZK9w/7hAaHm8uefT+Bb/97f+H/ZO3uX//Lste+DNf+jm+8b3fYb1a0C9eog+XvPn0gnW/4Cy23Prcj7MKHqLx7rd+mfNx5MnVOa/e/iHeffYWS/McL1/l3ce/w8XwnHfpqYenNP3JzDs6W0tIxBWlBEfMCWtn3keF6ANYecE7Ol/n/vO8q1YMAVfnNEUVVLdUhKAtVQ84FJ0ukXg05wVZh/kE1mKSIRe0XYHanKitcydZf+P/vOb9mvdr3j8ub5+0Aucf/Bs/buebDXEBd46WOFNydNDskNLRmM3BXVbQ6piGkeOTnvVRR5721GqUPPHRkwuWIbA+8RQVFvSEpdBoxDRQLRN9RKJSHUj2SCzzjWL2GAVniZSNpTOgA7en0TV4JbqK5kIZjRDcLLLyezZPjH5RuHevg41g0c9xBl5pImiuBO8IMlJCJLQO5xLOBPUFcOAcrp0zWOAGTC3UBhRS2uHEGKfK+x8Vnm4K6m/T+ETbtowTmCaWy466vyKRGCePV8V5xXljNxSQhjHPhn5GQA32k9J3gdiATYL6WY8zVket9bmbsaOmirc5ZNTLLAZO2ZgnsRRzlYonqGBmmBWyuFl/ZAYoVR3qBVQoIpjq3P51Zbb0DlCym5N/nfAffvf3P5Ub/s1/+z+xq+130Vj5/IO/+IJ3u/waUjpOXv1pnMDurf8Lu/FjPHrrl3njK3+eWzd7pkOiVuMwHPi1f/b3OdEbPDhyFBXuvvKThKXQn9zENJCePaW7dZMYjX0yJHtW0djCC97XVrm8esat1SlTF2nTiG+WL3g/2B/yvnBG7wq/8du/RRsP/NRXfpa6UWgKdVQaEfroGVKh7+eSuITIzWV8wbuMkX+e99OjJT0t7++3iC+kac7T+ehJ4X/9xj/hg0dvslx8Hi+V26dvsBHl8PRN7t/6Ak/Ov8nF/n2GKdAa+Jhw3rgaFXULrAhOJrRGzBnjONE3CzREXJ5HY5GOWitiihOdJ0+s4E1BIiUITdU5QgCHipJJNNbO1yDPr7rVKk77H+A99M8fClahDpgu8X6iaMQaaLVBxwM4Yfr6/3DN+zXv17x/zPWJK3C+9nd/0i4uLjGt9H3k5ORsjlSwht2UoSbyNLJYN/OIm3dEH5DKXKmOA+uTNY2bH8rjOJImYxEa2q6jaMaJMU0jTlpCCOQykeqs83ARTB21FkopdL7B+4hpxmnGSyCakMcJKRmriveRYIZa5mQliFW8Mxo/X8PkanQh0zIHTprWGXgp1Gr41mBl8x1U22PTAVGZQ8lSQXcrBnOYm7BNw4AjZ6XonDRezeZOCMakgYn5pcQMyjyJlD3U4mbzwAK1Ks45Us54H0E826GCVkIIHEqZjfYcFHWoGWMtPDlkDkOkbQodmT50lByZ9EBuWwqGdw6PEStkrzTqqVWoAi7O5l256jxh2XTze5QrOWeyh6BK1kqqilMhx4b//Htvfyo3/L/6D37VPnr7gv1bv8C9H/0LHN08IriRElr2FyPUxHaTOLsZXvDedQ1SYT8W6vlHrF+6z3GJZHcgTcLlWDhbtbTIC97LJPOYrC/kKmzmNBRWjac6ZT9W6uYZ7foWyy5gUn+A92Ecefbet7Gq3Hjtiy94f2Uxt6idNC94Lzqy8BEfPLe6+AO8P5sKN5YOLx145QsPTvn2B+cveNe45fLKM6WZ920GyYlvv/ctisLT3YFqBniO/ZL3d0/JlnA28+60JdWZo7SfqJoQCqk6olOGbATX4JxyqGBTnm3zteAkoE4o6hBRMiNu2lJ3Dnoj5C3a3KCUBl+3+H75gncVR52ErklMuaVhmqdXmhUSIjVlFGibk/lqNo/zWLQvMGTUjfNkiQnaHGG/+t9f837N+zXvH3N94gqcX/y7f9LGSYhNoAmKVE+uI7tt5ui4pZS5Q1GyQ9QYdpU8bQnecXq0xsxYdv08kpZGnHMslkfshyfUJEjI3Lh5NHdxmCMDcjGmbJTi5oozOkwTUoUuOkSE6WqPM3A6513NcCVWyw6tleiN6B2d8zgZcQK4SsmAOhqtNNEQl2gI+KZiQZFgIAahQihzZXIIsyougxWQqUdLRkrDkD2ZSLXCYQKYnYVLyiiGVqNMbta1BMeUlODnmIneRyZTRlVymQW+mOLEU5NwlSrmlRgjWjwinpwnXJyvtpIUTAUzj/eGoDQW2HmoKRO8MGRFqiPhMOZAtSoBs4rJfLIxM0Q85hqKGVVnrU3B2GXlQyrD1LKVQqtCLoVfOv/wU7nh/5X/8p/8AO+0DWW75+nTyu3bkVKgZuMwFkSN848uefL9rxG840d/5M9iZpzduYNJIdcJ5xxHvmMcdmwvL5CQee31lxlH/UPea8tVqi9499FIWpEqnCwEEWHz3kOcwdXhMTcWd7kcHuE08ce/9Md5kgp3G4jesZRIqgeEdvZXyhWxTKyBPjpqA50op32DBSVM3Qvem2jcaVa8v9m+4L20AzL1nI8jvsIzqyyjY9gP/PY7v8sf8P50ewViKMJwOLA/7OlWS9JuQ4hrtofHrLs7HNI5m6y4Gkiq1Lon+hXVhGm8ms1BwwKTiIinjpfQLSBXQPBSMZuT7lUNfEuVgpQRJKDTME8DVocwAqDicVJmTVkxnChqgdiuqDoiGhCpFGYTUtDZR4SEuojLifqd//ea92veueb9461PnFz/2aNpflDWibRJ5Cq0UsgUOAjNUtkeKssQiEcdZ6vIcGgYr/ZsLg+Ir5S0w8XM3Zs3SFUZpme0oRL7Zk66VtBQKJN/rvEQXFBCEYoqbTacwmjKZjrQ+4Z1tyCjjNOEE6NxgkhgqhUxw9RQLYQm4Z3gKNQSqbVQkxI7h1JQc3SSweb4eEzRmHEugmSoDpxhOc4dmFxnGJxnVJg0M6VEqcbBlGg9Q82Yy4Q2MBWPhufCaIuYGFtVmjZylStWC1ojTfBEA9HAUPZoDLMbtHdgnlGMKhXf9lgxki+UGikoicAwFkTAkSh4cjV0mr2BihSCNXR4soNOjBJmE8FaHCZKphJkzvHyGN4ciLF2xucB6zJJQFTJ+un1Bfne7z5hfXTKfvOEx29/na3sOa4d23DJ517+CxzfPuLRt36BW5/7Oda3Wk4efIaz+zf4/d/+R3zrm/8U8ZXPpS/jYuYzrzwgqSeNB9pQ6e/fIE3TbEZmnqIFlQ4zYRWN3XPeGy90AvvLC55cjPS+4eb9+wxU9KFQxLj16g/jpPJhKogZD7MRcuVeSHgvs/9GVVQLkgPWzBYAlisnfQPmuOUdrBPPRs8X76x56+KKR2lH4x1T8ujiACNspj3Bw3bv0AxvXr5Fqcaz/QVqDZNmzGf6pqVhxeW4RfqIqiLNkifTOWfL+3y0e/yCdwlCcJ519zKH4RwnELsl4ME8SRxVKm5xhhXDh5FSPUUMVyO17BCp5Gmemsml0hwOWBeomnC6QP0cOCCARcjqCDWgbkTdQJkqVJ31I9ohMhBCADzmG+T5Fa5J8y8Xyn+B65r3a97/KHn/xHVw/t7Pv2Y3j1Ys+xWHq4E0jJjP7EU4QV4Y/WVR0uCIJqSU8Ai7yQhV6OIR4hJeFXzi1mLFy18+JbiMxYpppqoxqUCFTEBNyFMFFMMxUAmuJzbGSoy8PTDsBrx2lJq4d3uN5EynikSPHjJX+4Km+dqqi0YpnpQSbdPgpOIMiEIjnsCIb5QjHxnHDaUKySaWixPyCKt+ZHWqIInh8ohUD5h6NCkYjBsAACAASURBVLQInkYaio9c7A7ktJivd2JhnBRfFSEwZscuVw6To8qEcz2HVBAiY5qYyoTzHZPm+RQUoPWZ1WpFHSu1VooFRCDnuaDJNIx57oxFmV24TQOTFswppoFSCogniQI6C4gRqoNaZt5UwDkPVVEnqCrZAoMoVYWshprgDYpz/FcfvP+pPNEu/tzfsdff+CmOXj5i+9ae9x7+Aq217LNx0ngu6+W8CQSlTjbzrgc8QhnAB0X8MWJ5Tl/3mePVGf/Wv/nX0EFplh7TzGGSF7xXIsVXtrv58zEcqRpNDMTGOHaBIe3YvfcRD6c95fwd/vTP/WscJXnB+9V25Lu/900+OH8bq8rx0QmleLZXjzg6vvOCdw0z70syL915ift3XuW9xxecf/jrPNw+5iuf/zPkEdzxCT9yfw2S+O5De8H7ve6YslAaaWi08ovf/CW8rXg0PGUqe7aHC5a+QwjsxoHD5Mm1UnPCdR3TtEfiCXV7SbUdzndInXDuiNx54nCFnb6Ms5l3X2TmfDrgQoO6iE1XuCCoNHMXl4aa9i94d2XEXIO5AsxaMhBwStUMQDD3A7xTC+Z71CZMBfKIuAClEtoF6bf/j2ver3m/5v1jrk9cgfM//eXXLeUWi5ngDR0D2RSiMI3GoEYqwjSNCIGmBrxACRM2ecwZqrMep/ENhT3ZjFg8XgMlHVDpKa2xVsE7h6WCNUBQ+qYnlwPrumJy89ifV4/JHN5ZEJyDNhgd0HYCWYj93IJrK2j1mDsg4ogUutbRRqHxMovIPDRBWC4aJOdZ+LuduHUjMJSRRduwWCux9zCOWGoYB2N3ASOOqykwjBl8P183qaKjMarifcDUIxLZTwcIkWqG6ZzPdZgKQsCcEOb8XPogjNWRUDxGLZDEE6nkAiXOXaW2KsUaCooZtN6TSsULTCXP1TpQXYPH2BtzcJwJIczaoDk6z4ELc/CcKM4aip9/1+kcz4AIqVZGE1ThP33r01ngHP/83zaTiLrKypYMdqBqpfqApMKghkgg6BVCgNGjXilhwqVZPNgFGIYR53tgg+qIqwu8BrRcEl1L7hrIhSBLtOywOAvnXexwdcDpkhI3YM0L3s17JCvOAa2jyZWmFWqNaDPRyMyKVg9hBIPew3rRElzDenXG9vKc6jrOjhbcOznhJJ7ivPGr3/4Gf/VP/+v8xrv/mD/52Z9lfRa5tW643O2x1PB755d8+J2vM+J453IkpyfEpmebDiQNDPuM1WnuMKonSM+4+T5ufUqtRp02+K4h7c8J7gQLASsDhOWsu9MKEnB1glSwRT/n8tSCb8NcuecCfo3phFXBtwt0POAFtCakDABoXCNqqCgubaixI/oOM6W6iukc2igieHFU7ZEwPwj+gPfsAlYSUDAF+/r/fs37Ne/XvH/M9YkrcP6bn3vdpHjM25z0Gh2x8Wgxap0ge6ZqxFCpU0C14MXRdhHXwio42rZh1TuwytX+wOGysli0PDhZMzZK10DWSh4nolf2h8T2UDE8U1VuLCLOQRkHplxnR0gz8r7imojXSqoesUzfCKENSNE5al7nDlMphZuLnpwzvvNkMZwam4PjJFaSKKdLBQLrpuFik5GxY/KZpo4sW8+qz6Rxy6hHbMfAYTS8i8/1LQdUe8Tvie2SKVV8MyvbYT7pRHVEPZCrZ69+Lhy8I1dwE5hsmVgyVphqZTKhsUDOmQlBn+dYWa24+Nyv0pQC+Go4J/P1nHegNueXmGcogvMwaaKap6pgzqjqqAqTg6qKoX9Q7kD1JDGqKgGjYi+yr7w4/ov3Pp1j4uu/9Dd/gHfzkbU75sCWWidcVibVF7ybz3hxNM0CvONWd8bypS9zdtyDVR6++TUeXj7m3uI+P/kzX2EKPX0MJJv48Fvf49Zrr/P973yTt87fxPAMm4kbt0+43z/g+1ff4uJwwbK9CWZcXF2y9D0WBixHBs0sGjhanSFFUWvZDh89n5QT7t64T9lfQOxe8P50u+PesmGTd3z29mtMCF+688P8s++/RdrPgYB1f8H9e6/wxvENfvPNX2b0a7ajkg5bFu2SXVLMjWhp0bDnaHXMxXag7/+Q90V3SnARNz2iKDwZKiJC0zgsw1gDpVzg/TGllFnA7kClxfZX0EREPZoOaNph3eoF7wAuAz4jZnMXtc4BsjiPK5ARjAHBz+HAfkJrh3OVIhmpCjIL6wGoHuoIIkgt4B1mhrOEtkfYb306x8Sveb/m/Y+S909cgfPf/uzrJq4ByWgyulgo7g/HlK06JASYIGvGSUAsEzyYOdoQSP9fe2/TY1mSpOc9Zu5+zv2IyMjMqpruEaebEmdESgIkgqQoaCOBC4GAtNBC0JI7bvQv9EcE6B9oryUXAgVoIRL8EDEz4AyHM83pysrM+Lgf57i7mWnht2s4C656ujoVOs+mAsiMysy47z3X3NzsfatRl5UU4CkzlcRxl/jqvbObleul89KFl3NmWVcg493IBCmDu3NMhrVOyQlXkK54FIhGnsZrsUtBVoV0m8lRQ8WZQznkwN2g7HGH1Tq9GyIwa/BwV/j28cL9PI8XWifo65hlwfgPvgnmsiDmvFzf83hyVk+ceoALa7fbHbCT2VOtUYkxwa9jI+yojTnPgKNzQrtz7s4aCXHBCNbaSHni0oXuNqITmFgcxJyuQb+Fsq29YTKEmUPIqaN5ul2zCj3GfxNC8xXzgvdbcCbDY4fb6nh1w2NEX0QYTdLYsvLb7xOhM66uiOB//dmfvsoH/pv/9u9HpJlwQ3ofNvVFidvDwHugZSYWo6eK9PLn9T7vuNYr0le6JgpCSKLsMr/xo7f8+29+yh9+/l1ezrBcneVyBjIhnUygIdRk3Hmjm8E0k73RdEZdIBqUAoBoY1+gA+/33xBcsQjmUN7dv8UleDe9p3rlTx5/9r3e37x5z08e7vkXf/zPeXf/k7FKKhNK4/PjCcf4O3/773yv9335hv/zd/8vlqvx+PJvgB3n6weqZZp15thzyQ1vRtRleKEAky/cTwfA2R3v8No4tc7FBSQR0bDljJS74RGyVpwg7+/xatCudIGU9wQdlme6zqQIxDs9Ozq9Ga+RZLAFTxNKJuoz5oVkfZiD4qgGhBAeCAvRE5EKvn6E6Q0wQq9ZT9h8D3TEAiLwf/YPNr1vet/0/kvyxQ0ZpyKYLfQ6PoAvp06VRK2JXGIsGa3GPo81uaoGnkEKmox6avTeyZpJEdwxCo1uwqcTTMVGlyXBIScKTskdlWBfMt2N5/MJiwPp7ggLtBaYBSUZroq0UdDoFHTvJBFaCBqJbsrSndYMoSBtHeLRzKRAjEp7WVcymcUq2RLVn5mnAxnj7nhPsU/kvdBtx7I2KkK1oK5C1oz6EIsJtLSQyGRfWQwoM/XieA4e3bG1kxYnzDnkwuqNOSe6OU6wvnROUx/r8VHofWHtnYxSpRDasNqZpoL4sPGuGrSe4HYd6H3kY3US1RhdHRoigrROAlyEUKF2MBUgxuaXOGFCDWURH+7FfjOYEse/rBr8L5aUcauEOT06pVdqT/htNTS5YOuCuiIBlI4D1YfeL5cz9BV0IlmwalDEaFflj/7gE3+cvhuviguH+Y5UMqGBmrIrENFZnj/Qdu/R6e0w1CSN3Jhf6N1gQig759ogF/i8fMub/W/wtHxgCsP9ilD4FJ/RdMViz6TQvCBufH5+RmzPdy9PZEuYf+arr77h7f0d//GP/xrFOn/l/Vv+5eOF7/7kD/nw6eesrbFczhznAFNSQBVY8sLcMievsFwo+7fU1alW+dZW8ssz/bjCcqW8/wq7nKDskL7iEsTnj4QafboDT9TTt2h/JsUB4YjbiagnmO7JXQhdcc0kS8TS8LjgVdE4DedvhwiBvNJvRXquHU8FL8B1wXf72xv2AjjaK9EDnyYoCsszaXqDqxPt5dcqyV8pm943vf+Aev/iOjj/y3/9V+LJg35deZcnqggUQWsil0aYcFmDCB22/j2zimDWiYBqgsqtw0AwTZ29TmTpYJlgwaSMnfxQDiUIc0KUJLCfJ2ptIyelLexDSQmEWyKrZhRjn4U5G2skihjuSgqniHON9H0qbimKaia6MaUGcgBx9kUp2sagbhqCXr2zzxNixt0ED28WHp93fHgeJ4yLKUWU1oPax7XpimDRqU1IkllrQ6cg6Yx6I0KofWVxZbpd+TSDXXF6JFZXrDldYTXB8+iWHW5dIItxrUUEdquxL22kilsXNDmm44Tyi3ZkthiFzM3dOAADwmX8+TI01x08jftvjxiGU33HVSv1FsS59tHx+d++/fZVnmh3f/fvxZoMzp1dKrRIUIQkifB1GEDa0HsXY6eZVSHqOnw/FoMpkUPo4bgaRfckNzDlms9M7HFZ0D6hk+PWQApJQHWH9YoWgb6ACan8md57FBSjpAJpBWaclRIT3Sv7rFRLWP10c5PdU9JEb40pNbJOIM797h7zHUWAMrYFW4d5UsSMv3z/lp/8h/8pf/R7/4R/8eFPEBXOHY4lc740Lmno3Vpg7TrWbPM9/eVP0cOeko+Mw6Bg55/R3dHYkfLYHCyhNC2QdtT1mZL2VAXPSukN8XFq9wyUA9j1e71rF6Ahy4KXjGoj0sOwggDs9szxECxWZvu39B6NNicAYjmjuzeE+Bi6XM+g71BZCJ0JYrwGEcT/8w83vW963/T+S/LFdXByZL6Z98j0wtWUfezoywuPZOI0TvjNJtItr8jdh6txCCIJzT5iA8QwlGi36oSMJrjWmaJ2G5JlzLO4IMlxnVmvTskTqXXakulJ8OrMSUmiHGTlUJTkkLwiy448F7xWYgrIiX3MlH1CtOGmTFR8FvZlR6AQV1pl5EtNM20VLKBaZ2l9pKCzYz8v8PDCab2nd2GO4NorXXecbSGLgDmhmaQdQchzoUaj9M7FhCcLJB3QCJ6rs8YoWtIKGUel0LSOZN/Io6jw4GSZWYJLH8VHaNw2EoJAMHN200ynEr0QBBYXyDNm8N4TSzgWt7RZHe7E1y60MHrAooLXyn0eLVXVDNkotw7PatDFifjiZPoXhsiBY7wnDj+nm6Jpjy4fYUooSqWSdXT2UsyYOdJWpDq23yOHQC0waeMO3QMBXBNaQC8zMQWprcOluxdCnE7H7Yh7Hw/v1okmlAx1DVxH67q0J3KZcTUmOrZO5FwwbYiCZGOXJu4efodz+ze4KW+mxCWv7OffIksf8279keBESj+irZVZKq1f6KaU/MCP3/02+xl++lf/I/7V8yPmlaleebxUGhPt+jxO9cuVOL4lzo+kcmC6/4paF0yEdnnEEsjuN4i1ob5Q+x6k06yRb+8V8ozFGak7kuTh1ZEOY97AF3x5ITSQ60oRHXqvF+LNe7Ku0A7gDbMP2P4dmKFyD7qi9YU+3aGu+Bz0S0euFyLPSJnw63eIZKIcYboD78hS8TkBFc5n0uHh1y3LXxmb3je9/5B6/+I+OX7vCSQvvFgiurPTBTcnbtcdmUSXhQih4EPckWjhJGmoT6zWEVEMwxCWcCYUrZ1DKmNdWzNnd+7amCwPV96Y8TZ1VluQgF0S5hRoUUKDjrOsaWSh+JU79gSVa13IORMNrq0hTVmjMe+PIB21GeNCMciSENvhJREhnNxgMvYpk0UxF/oCXZ3TpztcLxRtZEucu+A20egoibAxDxMOzTMWQbUOFFIOvt4nvjZnZcwAtZQ5XRsnV0xgL2DSeKPKLIKbskyCLU7XhjSjTJkWjeuq7CahmPDkxjrD1RbehHCORKrBUu7xxWmh/K4FSzKuPpxDv3ZnFiOScvCEh3OUxBwjCqKLQWQuqZMpJO3cqyKSec13VN4qi/wcsiN1RXPF2nUM+0VGZKb7Ey0E0QkBUhSiCESF2GG+IqL0+gT7N9SoqCdiqYgWfO3I/A6PTi+ZfmnDdLGslJSIfkZ/cdWoE3mecTMsOdYS6glfnll376A/gRnzdE/FeHpeybrj8emPKNM3RG5crwlz5/H0AdUDwoLrfrxn5ROC8X63IzH0Tn3iDy8/47/sf4vnywvFOteeqC1hWbHeQfaEKHK8QwVsPrL2BV1Ha7yLs3v4BuijI7lvwBvy6RnTwGbFreBpIcsODUXmI14m4sWwtCLNSKKon2AV2B+QxTBW9DAT60c8CpJ2pBqs+5/A8yOUPdYeYQZhxvpKtAaXIBKkfEcsF2x3IPUDttuT1hdE9vguYbpD1YEJfzdh1X69ovwVsul90/sPqfcv7orqf/6b/0k0b5w8wIYPgmmMLCVV1AOi4TmTHMIbwTwKHHWsC03AcRbPHF2JvUM3vDlZx4d72MhScoc94wdsZtwV5dgXjtmHbbeMbR7DEAlyTDhOmYTdrWcXYWOfPwVJ7vBopKIoiW4LIsKuOEdN4EHJMgzxuiEedBLW65gjEmMvnakUjtoIaTy1O9rS6QStC02hmYONdO6Q4QeoGC4Z50rqb3jWK8kK1w5dghY+VrZTonUIHVdrXXQ8NJhYvFN0vGFzSpiPn5WrDUOnaaS5S8nc5cxU6+jGSMct4+5ce8XKRIgztwnBsQJ04+JwouOqzKpET1wF8s38EO9cmrIUcLtlnajwv//8w6ts2af/5n+KxJXuHSzQNH4GxJjhUg+iX4l5h7uT2grpOOa6UkADywKtEVrQFsR9gm5waX9O77qfYTWECzD0nvNM1OdxgpVM7hB5+l7vIRPJHT8emG/vkzDD+wrq7OQ9oRVP89A7C+7GMSn7bERPHA+Fqy+0nobeXbi0hTTtSd44SmU3HykqRFfOvnK+rHQUj4bKRJw+0w/vRgdRQHpDrp+Jw3vs+nPy/Jdp8oQu05iFk8DljFojpreIg0tDxLGYiMt3zPMDtT+R8htsfSHv3gy9Ly9QbnrfT0jMkGbS7h2cnnAB0oqZMK2Vvj4S91+j7QVtO/o8D4+Q9YLoTPgzI1kwI3IctiH9CUlfEe0ztB3sYiRY3/Qev/dPNr1vet/0/kvyxXVwXq4LaxdIMf5yqaOWYeSY0h2UQu1Bccg6sZhxs5Qb4o4ge+FehFRWZFFSDnQyCsM8LlLg1sc9oAdFhDTBb+4c4cDVHdcx4X1ag0zgZDRnptRZJGMeI6MqF0xH2oLGgjvMISz1ShajpERdhSdZmch4Gm1WNCiamBMsBkkcDaGj3AlkhVBhqSsvfZxojBVswj0P624PnOF0XBGChPqeNRrelEbnZt6MGeOut45AtHBjr5lqjbj1u1yg1oqmxGLQREglkVCqKdLg2sfD6SkqoiMLqydBLsKSFJMdJWDvwll8rCiuwpUYwZ6AdqWJkDHEEktkmgRXKeTUMRd2MgaXb4vkr5P6SKsOc0YtcDshOiMMAy7DhgGXORpBeMaloe44mciC1I5EGi3n2ZGXC6oTmg2XoAREcXy9jpgM64QmpiJILpB/RBPDNXAD1iuBIemI5nnEbHjCQiEakiYiC26Vqz/DEugs2HImYiHNB046cXr8RDp8w2N9wruDBintSbtMXMepugPPKCGVnIXQzvOl0S4LHN+Tz5/pd7+JHd8DgVwvSD/h0z1++IrwhJYf433FUZKumNv4UGgV9Fa81ytCJ03fYPUjhNFtHYGB5++QacLqFZ8P6P0b3BsRIwPJe0WT0dcPiDJmOuoFXfbUUmH/o9HC1wOWHNozyB7JQuCgewhFVIh+hiUhcUfEBfQAUx9J2dMdUp+J8ubXLMpfIZveN73/gHr/4gockSBNYzWv1c5jh1U72Qx65a4IqgdUDIvOwfX28RcjpDFuBY40clYahT7F8DGwGVFDzGkiHBzAUc1MVjHJXJ+du3l0fJJ0rigpJWoIzQxSoVXwaMze2edC1KBEJosxMdbH1Rr7DHsVNM20W7dGc0KiUhSyFpqvdEkUGUVIcyMhRHecxNu3e1ZrdBEmFYwjp+sFkyDJhNkogiwSYkCsGMKKUL1iWZgj0cQ4TDP3JSPh3LlwtsJiQRHoVlg0c6wrnhIlKSc3pBtdM4uCeIxtLvERKRGGWUKBozmyC5IZJkL3wJnGQyrD4vpnHRmUHmN4uCcdLVh3sig7NxZGYf+MA4n19XbsR6DgnIgEsVQ0GjIl+nrBrQ3zsfIVkZ1oK8LQuxLjQbwGHoHYC3p8ICITxzu4Nnoferd+QrVjvhvRGjojfgHfwfkZK/dIu5KkD6vHoohlbL2Spz3envEerM3QaSI6qBSITPQO0Qg6OimT3KP5nhaG7L9BU8KakUNgdw+PH3B9IE0FAuhjKHINZ98Tf+l3/jOu/+qf8lF23KfE+s1P6d/9jGydePObWDwT5YjkPdSO2UdC7yEZqT/iIqgckThTj+/IeU8JJ++OxGK0ENL0lihXKAemS8XLAZXxU5XTB9g9oHk44kq9Eqni8QbCiGggM+IBdxP0QNoTlBm1RIQS88N4bWxsGQ57/KF30gQJeHyGN3fw/AyzjyHSywtBgluG22tk0/um9x9S71/cFdX/+Ns/DWkJmTLFKlpGUKMqZITJwMVJJDoVISFhYIl0i7T4hQldicDSyLtYvdOBboJI0KqwzGAuvAkn1MiRKGrkAntzkM7kZcQKzMGu9ZGdpM4ldjzY8L7Zh7HLRkqJ6W5mlpX38wjlnHeCTkY/d/7AheLDj2a/P3JHJedRXPWTstPMdE2c3wSfPzeO+sT03QO/F0YrHemF3SGj3Wk4JxVeqtEsOJdMKkpex3q9iGDi5Mg0XwlJZB1tWouxzaSMwmoVxWL40vzC6GldjZ46pD3NOtXH1tV9FpwghVBy596FS+SxASXDdVoFThpIz3wW4/QLbxt8bBV4IKE3Y8BGjkRVQWPY/kUYkgstlExw6p3/4+PHV9myz//5fxcejJmB9QUOx3FaUvC8+17vvSvKBzR/RW+dIpmu4737C71bLEi5u224VZSn0UJOC9pmtAy9qweWLxAJpIAaiKGxon7EBXwPuhjRryR1Qh+gjSvU7CvWOnG8R453zL5wOI736bwT5v0d1+ePfMfhe70XjqT5ibfHH6P9kcvjysN8oJ32xPvEt99+IMcn9qev+fnlT2mlM/OOdD9RO5RWqeKIVGK9EId7fE7oOkIVNQqejOSFVJ+wvMdVKfX8vd7XMpNiGmGAUomekJuDtj9+Js1Bn7+GvqD9RFCQ6X485ElEvJAsI5G+1zuloAK9nxHfE/MtT+56AnwcIT0gFOZ7qJ+R/EDYGW56Jwz278Zw5u4I9Uz8/j/a9L7pfdP7L8kXV+D8rd/6ndDSuZsm9t3H/hnGQw4mdix25Vky18jDLI7x92+SuJNE9SCnYKUS7ojqeNO4w3SHSQNvFCCrcghBPVNSQkrnvjo5L8yM9ei5C70punNSdM639boXDb5yQfxKjTI8bnwknZSAJIqEkXIwh+DeeavKRYxiikofq4lF6L2SBVa/J9nLeCNOu7GpFdCl0nAeEvzWXLi2hdUzKgHWcU1jG+wWlYCONPGPTbiTILJyaYGkUUwTI+vpFGMVu8dYcRcZK9tmY/hsVaGRyOH0gE4wAVfGfFQSwUSY+4SmRs3Kao0UhQtjU8oManSyZ/pk7E1QzeyTkyKYAy4edJmoODmcJeDsSlLlGkb1zj/8+OlVPvDlv/gfRrbI8R5Z1u/1nqdE9x2lfqTmA5QJrIOdxjeWPch+xGOkgHoF93H37Q4syP7fI9oZvIGMX5MopHhD7AvGCW1OpAU6xK6QV/DqsFfASDFWPkWDHglZPmLcUf4tvVusaLodNGS4vEY0ik30VFHPI3k4MmTF1o9kAS0/oq2fbnp/QFwIBeUMfsXSnuPua/zxX9LzV0S8fK/3JMfxQQWEJSIZvl4oZGyaiHpBEoRO4zTZV5CC20KoouHE/AZpL1DrMDspe1wnxBoQ0K5o2Y1r7HYmvCDTjNtuuLz6QrAi8oCkZczCP17RqeHXAu93pKcL9nAPJMTOpJ7p3eDwFciJFI49X6EIzG/AT2Mz8vf/8ab3Te+b3n9ZvX1pBc5/9dPfjqTK7EbRQFEmqRjwDsV0bEK9s85TGOhI3VYVmjnqgQjco6zSuZIIgbMXniJYA7ImIpyDKjucolDcce2EC0cNjihZOrvIiDaSdZIqDaVEMCVIchOcCEWDbFDSyKqCUaiKDrfhg+pYlXYZvgKdkZFVEr0JoU4WY18S6sZ+0tGF8UZtE5fw0YnqUCSzamOvE5e1s5hyxiiShlfEbQ7pU1eOOkLgUhLMhCpCB/biLK4kEa4xZox6L1wTLN45uxIkEsNLx4Kbj4TS4nbllxhuohFIjJwuk3GvfRVlH44EZDGUoenAWCJx8eBsM1Uq6srinZQS75LiHjwiCJ3mikTwDz5+9zof+H/7v4+UD9jyAikDSvIFA0okPDuWjqR2wmm3oMGb3vtprMkKiB/wshLuN1uEPeOJ3GB+Nwwn8wR2hTKT3TH/7uZNNAJcTRuzF6p31C5o2o/TIGN2q0sFht6TJMpqRM5QxjNEqtBZwa9MsUP3B7okpvNnXIfeZXc38tUCijf0eI9rZ0qFkibWfsE8IefzuML0FSlHzE+Uw4+o50+IH/FcMS1MTekCZCfqFU0jILDlROlBZyZyHf9eUbAZTw28InEHqdP9Mk6cMqNxJUKQuhDTDkkTrC9IB09O7A7oOrYsTacxKGeMAbyebtcQ13F4mSbcLuPrlIm+J/onxg+jIykRd3fj+5cLQifyPHxBfv//3vS+6X3T+y/JFzeDs7ixY2i2CBzoHBCyOCmMtSvX3vi5CsXL+OBFeWpwignRIInzE4Ufy46DGrU7RCdgrN6ZYzif3BBJpJtddInCLiurO8/izD0hKZhqZqaww0nSQQpXOncUNBzcUA+8CwsjcTWFc5RAIpEk0SPQFuN6zBPDUUZpFqSidFcaY/6KGB2nKQUtlMXqEAeFEhnLneQTkSoVyKnwQMJVsHCuXbmaE3TOLVPVmELZCUOo1nimUJKzmlCl0HtwkcbaMu6GhI1C6ZYjlRAmgcUdQYisWpEfqwAACKVJREFUqAgOrBHszSkqlIBGMDGuqiyEIoKEoxSaC6LKnJw7vYJnqgSrKhGOdHhWwz0hcbvF+vXJ8VdPXzGG3sfDOggZLWLDoXfo15EhI4L2Kwmh+jpWSVUJdSQ5yj1RLkhrmFVyWegywXom5DyuumUCW+nhI+aEPZaE5J1kjUUhO0R+M1rK6nSfQSsaOzQcsQVTpyGj9W1D7zkSqcx4S7gEfj0jAlUn3DrJF/y6kA5HvC5Ug10U3Ppws5YrHkp6eSJSwg8PuE1E7uiSYX2kMnyqYCKr4KmhUWjRUXW8VnoytCbGSsEFlguNgpSM2AVPbxECr9+i+Y7cL7eYERs/bw287EghsDwDQmSI+QGi43kmL8utHe/QVuiC0nAyTPMIOpSJ1NvYCMLh+jRM1YqT8nFEtCyC8QieiGB8QJcv7rH8F8em903vP6Dev8B30rheOdEJzTRXHOG7sUPFPeND9eqJcOE6Zuvpkrgwrn8klD90+BmKSR5GdRGsYbeQtwCEjBIeJAViXPk8ekcCJimkGNdIE84sidwTsxZ6dDoTO4G75DQv3IewipOrcYzELiqfiuA+VrEdCEnUgGOZUVuYGMO593fBFEJGkGTjOg0d4XPsyanQJLH2jugtPypNrNfbzAvOxTu0YCcTpOBdylSDrB1Pebh+NvA0oiKqBRKZczgunTVGcZlDgUyl4grBLf3VYXUnAy1uGVE6tqJ26Tbz404LR8g0MezmE1RdMEaibXNI5nzwYdW5D8FtJaUyBI9zDOVNVDoJj8pZXnOJo4g1Ipbb9kEhyYy7EKxjOAAb4wMGlhIWV+AIcR2nIxEiJqAh+Q4vAXqlWwGZgWW0xVUhbqF7TLjErUUMIQ941NHezpWwgqng7EFP5DbTASs7hJWIPaQGYaS2g/6BtjsOt9I0UwHJE0Qn5yPeFpzCVIdFP+VAiWeiPg8X2rvj93rn7uvhUn76gKR7+nJCdu+4rlDKAWmN0BXaFe/vYF5J+R6NTp+dSBnxDmsf661pHrlHcYQ4ofWE+QoybCsj38PzZ3hIuFcQGbYStwI88gTXC9RPMGUkApt26LqMK4S8h77g+98A+zjyUyQRccGWZZx6tYwmQ1tgXbFUuAkeqYlIbZyIaTcH3dfKpvdN7z+c3r+4K6q//pd+EkcRlPGhXyVR6WTJjB5MYoqOpeArkduVlPAUcPaxURQR7ElMKZht2H1fMB5Dsdv/dwzhjuvcogL47evETsbXT9kpi+NpJqkjAjRFtBIa3IWwEwjpvAthlmC9bQlNOCUCd8cyRB9uviklEoaOvioZQemgw4Om1c5OjHfJeVsKHiNaYT8ljimwqixah9BE8RC6KtV9tBoD+m0GZ/Ex0KXiXD2N6x4NwsYcWJPRmVnFsZ4oCVyMe514aQ1PQniiK4CAGMmVS3RmxhXaitN8GCniw98HF+ptYidihJhWMiWM9XZFdvJgRdAkqI8uD8Clj7awE4QrCefkzj9+enyVLfv0N/5uuMCt+kRzwe0M+TgezuWAr89QEgnH6gQiiC5EpFuLK0BnFMX9BH4ErbdfG8WyiIxV0oBgBLDiDdIdooK0K/5wgKcPoF8j6kQpcF1J6YnQILcdHYE08sHGC9IIClkYGybSCelIHMaVrCgSjZFGBlnGPFrOE24Vswr1wiSJyA+IBF6NNE/ILmFVifbtn9N7SUdaMmaP7/VOr8OPKgxPmTAjRfkzvfcVy4GYkJJjekf4dTjmlnesl49joSHfDWt5KcCKxkT0T4TejVw1HO8nsAlcYJ6GUWi/oLuxTeK9Q56h3WYdALmciDlDEiQdiV+YV663GRNifHjj7EK5/sHrvKLa9L7pffDD6P2L6+BMoghKFmVh+LPsVOmqzA6rBtcQ3OGPGYFsgiM6ERJAp8mE0zibI6KMftzwfkkIHSdpooSgImQ6KYJdFo5hPHfjOTnWlKlkNAe1ZSxV7jNEpJEQnsaIs5NZMd6HMLuTtXFbJAQVpN+2rWT86WsPWhJ2BKijkQhbmAL2BWDmieBfL505JaIavQoSnZSGT0RYwd1Ye2NXCsWVzNgA04CHqcBqdE+kJAgCGbIPI8QmI9qixkpypSbnakqZJk7dsSzse6ErpATVg+pClTEjeHKo7uyK4Fo4x7jKaqZci7Mj4xZcSey7c5pATcecTgTHnPjGgms4VYJmHYBdJKoaGkpLDPcj/3fr5f/rOCAkyDvCF5xhRTDyvwreBfZHcq30vIcZaFdiejse9NLHqTVWfHmE3QOsC9gKMTMey7fZATKS90R7Ht873YNVtL+MPLEXR/I75K4ga+B+IrJicYdYpuqClgPSzqALYRmNAE64JkQVECKOeJyIuEfo0DpMHfFMx8kkrC2gfVwb7L6mY/T+QtI90h7x8jX23c9J0wzzHYIRfSXWJ+ywJ64rHYMMUR6Q+R45fSbkgMZKpDz+zWvHc0EPExJ74votpD1hZ1T22DzTL2dkyqQ+06Mhh/14IC8rTDIOnn1BvWBTgulroKNR8Q4+T7BLDAkXOC3w1Q5qQ/Mer2fi7i2lQqMTZrA8AiAtETsdMxGJ8Xr1H16HPxSb3je9/5B6/+I6OBsbGxsbGxsbvyyv2CJ2Y2NjY2Nj4/+vbAXOxsbGxsbGxqtjK3A2NjY2NjY2Xh1bgbOxsbGxsbHx6tgKnI2NjY2NjY1Xx1bgbGxsbGxsbLw6tgJnY2NjY2Nj49WxFTgbGxsbGxsbr46twNnY2NjY2Nh4dWwFzsbGxsbGxsarYytwNjY2NjY2Nl4dW4GzsbGxsbGx8erYCpyNjY2NjY2NV8dW4GxsbGxsbGy8OrYCZ2NjY2NjY+PVsRU4GxsbGxsbG6+OrcDZ2NjY2NjYeHVsBc7GxsbGxsbGq2MrcDY2NjY2NjZeHVuBs7GxsbGxsfHq2AqcjY2NjY2NjVfHVuBsbGxsbGxsvDq2AmdjY2NjY2Pj1fH/AgkmuVz+5kNbAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "<Figure size 576x288 with 6 Axes>" ] @@ -315,21 +315,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.7" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_mapping_colors_images.ipynb b/notebooks/plot_otda_mapping_colors_images.ipynb index 4b19e0c..b66640b 100644 --- a/notebooks/plot_otda_mapping_colors_images.ipynb +++ b/notebooks/plot_otda_mapping_colors_images.ipynb @@ -184,7 +184,7 @@ "# SinkhornTransport\n", "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n", "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n", - "transp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\n", + "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\n", "Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n", "\n", "ot_mapping_linear = ot.da.MappingTransport(\n", @@ -307,7 +307,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 6 Axes>" ] @@ -356,21 +356,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.7" } }, "nbformat": 4, diff --git a/notebooks/plot_stochastic.ipynb b/notebooks/plot_stochastic.ipynb index e784e11..0911c28 100644 --- a/notebooks/plot_stochastic.ipynb +++ b/notebooks/plot_stochastic.ipynb @@ -49,44 +49,20 @@ "source": [ "COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM\n", "############################################################################\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "------------SEMI-DUAL PROBLEM------------\n" - ] - } - ], - "source": [ - "print(\"------------SEMI-DUAL PROBLEM------------\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "DISCRETE CASE\n", - "Sample two discrete measures for the discrete case\n", - "---------------------------------------------\n", + "############################################################################\n", + " DISCRETE CASE:\n", + "\n", + " Sample two discrete measures for the discrete case\n", + " ---------------------------------------------\n", "\n", - "Define 2 discrete measures a and b, the points where are defined the source\n", - "and the target measures and finally the cost matrix c.\n", + " Define 2 discrete measures a and b, the points where are defined the source\n", + " and the target measures and finally the cost matrix c.\n", "\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -120,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -150,7 +126,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "SEMICONTINOUS CASE\n", + "SEMICONTINOUS CASE:\n", + "\n", "Sample one general measure a, one discrete measures b for the semicontinous\n", "case\n", "---------------------------------------------\n", @@ -162,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -198,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -207,15 +184,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "[3.75309361 7.63288278 3.76418767 2.53747778 1.70389504 3.53981297\n", - " 2.67663944] [-2.49164966 -2.25281897 -0.77666675 5.52113539]\n", - "[[2.19699465e-02 1.03185982e-01 1.76983379e-02 2.87611188e-06]\n", - " [1.20688044e-01 1.49823131e-02 1.50635578e-03 5.68043045e-03]\n", - " [3.01194583e-03 7.75764779e-02 6.22686313e-02 8.78225379e-08]\n", - " [2.28707628e-02 3.52120795e-02 8.44977549e-02 2.76545693e-04]\n", - " [1.19721129e-02 1.10087991e-03 1.53333937e-02 1.14450756e-01]\n", - " [2.65247890e-02 1.33140544e-03 2.66861405e-03 1.12332334e-01]\n", - " [3.71512413e-02 2.86513804e-02 7.53932500e-02 1.66127118e-03]]\n" + "[3.88833283 7.64041833 3.93000933 2.68489048 1.42837354 3.25840738\n", + " 2.80033951] [-2.50038759 -2.4083026 -0.96389053 5.87258072]\n", + "[[2.49326139e-02 1.01118047e-01 1.68018025e-02 4.67918477e-06]\n", + " [1.20543018e-01 1.29218840e-02 1.25860644e-03 8.13363473e-03]\n", + " [3.52425849e-03 7.83826265e-02 6.09501106e-02 1.47316769e-07]\n", + " [2.62727985e-02 3.49290291e-02 8.11998888e-02 4.55426386e-04]\n", + " [9.00986942e-03 7.15412954e-04 9.65318348e-03 1.23478677e-01]\n", + " [1.98446848e-02 8.60145164e-04 1.67017745e-03 1.20482135e-01]\n", + " [4.16774129e-02 2.77550575e-02 7.07529364e-02 2.67173611e-03]]\n" ] } ], @@ -240,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -284,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -317,14 +294,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", "text/plain": [ "<Figure size 360x360 with 3 Axes>" ] @@ -350,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -378,45 +355,21 @@ "source": [ "COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM\n", "############################################################################\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "------------DUAL PROBLEM------------\n" - ] - } - ], - "source": [ - "print(\"------------DUAL PROBLEM------------\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "SEMICONTINOUS CASE\n", - "Sample one general measure a, one discrete measures b for the semicontinous\n", - "case\n", - "---------------------------------------------\n", + "############################################################################\n", + " SEMICONTINOUS CASE:\n", "\n", - "Define one general measure a, one discrete measures b, the points where\n", - "are defined the source and the target measures and finally the cost matrix c.\n", + " Sample one general measure a, one discrete measures b for the semicontinous\n", + " case\n", + " ---------------------------------------------\n", + "\n", + " Define one general measure a, one discrete measures b, the points where\n", + " are defined the source and the target measures and finally the cost matrix c.\n", "\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -453,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -462,15 +415,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 1.67648902 5.3770004 1.70385554 0.4276547 -0.77206786 1.0474898\n", - " 0.54202203] [-0.23723788 -0.20259434 1.30855788 8.06179985]\n", - "[[2.62451875e-02 1.00499531e-01 1.78515577e-02 4.57450829e-06]\n", - " [1.20510690e-01 1.21972758e-02 1.27002374e-03 7.55197481e-03]\n", - " [3.65708350e-03 7.67963231e-02 6.38381061e-02 1.41974930e-07]\n", - " [2.64286344e-02 3.31748063e-02 8.24445965e-02 4.25479786e-04]\n", - " [9.59295422e-03 7.19190875e-04 1.03739180e-02 1.22100712e-01]\n", - " [2.09087627e-02 8.55676046e-04 1.77617241e-03 1.17896019e-01]\n", - " [4.18792948e-02 2.63326297e-02 7.17598381e-02 2.49335733e-03]]\n" + "[0.92524245 2.75994495 1.08144666 0.02747421 0.60913832 1.8156535\n", + " 0.11738177] [0.33905828 0.46705197 1.56941919 4.96075241]\n", + "[[2.20327995e-02 9.26244184e-02 1.09321230e-02 9.71212784e-08]\n", + " [1.56579562e-02 1.73985799e-03 1.20373178e-04 2.48153271e-05]\n", + " [3.49227454e-03 8.05110304e-02 4.44694627e-02 3.42874458e-09]\n", + " [3.15181548e-02 4.34346087e-02 7.17227024e-02 1.28326090e-05]\n", + " [6.79336320e-02 5.59136813e-03 5.35899879e-02 2.18675752e-02]\n", + " [8.02083959e-02 3.60364770e-03 4.97032746e-03 1.14377502e-02]\n", + " [4.87374362e-02 3.36433325e-02 6.09190548e-02 7.33833971e-05]]\n" ] } ], @@ -495,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -530,14 +483,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "<Figure size 360x360 with 3 Axes>" ] @@ -563,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -602,7 +555,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.7" } }, "nbformat": 4, diff --git a/ot/__init__.py b/ot/__init__.py index b74b924..89c7936 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -1,6 +1,46 @@ -"""Python Optimal Transport toolbox +""" + +This is the main module of the POT toolbox. It provides easy access to +a number of sub-modules and functions described below. + +.. note:: + + + Here is a list of the submodules and short description of what they contain. + + - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems. + - :any:`ot.bregman` contains OT solvers for the entropic OT problems using + Bregman projections. + - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems. + - :any:`ot.smooth` contains OT solvers for the regularized (l2 and kl) smooth OT + problems. + - :any:`ot.gromov` contains solvers for Gromov-Wasserstein and Fused Gromov + Wasserstein problems. + - :any:`ot.optim` contains generic solvers OT based optimization problems + - :any:`ot.da` contains classes and function related to Monge mapping + estimation and Domain Adaptation (DA). + - :any:`ot.gpu` contains GPU (cupy) implementation of some OT solvers + - :any:`ot.dr` contains Dimension Reduction (DR) methods such as Wasserstein + Discriminant Analysis. + - :any:`ot.utils` contains utility functions such as distance computation and + timing. + - :any:`ot.datasets` contains toy dataset generation functions. + - :any:`ot.plot` contains visualization functions + - :any:`ot.stochastic` contains stochastic solvers for regularized OT. + - :any:`ot.unbalanced` contains solvers for regularized unbalanced OT. + +.. warning:: + The list of automatically imported sub-modules is as follows: + :py:mod:`ot.lp`, :py:mod:`ot.bregman`, :py:mod:`ot.optim` + :py:mod:`ot.utils`, :py:mod:`ot.datasets`, + :py:mod:`ot.gromov`, :py:mod:`ot.smooth` + :py:mod:`ot.stochastic` + The following sub-modules are not imported due to additional dependencies: + - :any:`ot.dr` : depends on :code:`pymanopt` and :code:`autograd`. + - :any:`ot.gpu` : depends on :code:`cupy` and a CUDA GPU. + - :any:`ot.plot` : depends on :code:`matplotlib` """ @@ -20,17 +60,22 @@ from . import da from . import gromov from . import smooth from . import stochastic +from . import unbalanced # OT functions -from .lp import emd, emd2 +from .lp import emd, emd2, emd_1d, emd2_1d, wasserstein_1d from .bregman import sinkhorn, sinkhorn2, barycenter +from .unbalanced import sinkhorn_unbalanced, barycenter_unbalanced, sinkhorn_unbalanced2 from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif, tic, toc, toq -__version__ = "0.5.1" +__version__ = "0.6.0" -__all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', +__all__ = ['emd', 'emd2', 'emd_1d', 'sinkhorn', 'sinkhorn2', 'utils', 'datasets', 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', - 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] + 'emd_1d', 'emd2_1d', 'wasserstein_1d', + 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', + 'sinkhorn_unbalanced', 'barycenter_unbalanced', + 'sinkhorn_unbalanced2'] diff --git a/ot/bregman.py b/ot/bregman.py index d1057ff..2707b7c 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -5,15 +5,22 @@ Bregman projections for regularized OT # Author: Remi Flamary <remi.flamary@unice.fr> # Nicolas Courty <ncourty@irisa.fr> +# Kilian Fatras <kilian.fatras@irisa.fr> +# Titouan Vayer <titouan.vayer@irisa.fr> +# Hicham Janati <hicham.janati@inria.fr> +# Mokhtar Z. Alaya <mokhtarzahdi.alaya@gmail.com> # # License: MIT License import numpy as np +import warnings +from .utils import unif, dist +from scipy.optimize import fmin_l_bfgs_b def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): - u""" + r""" Solve the entropic regularization optimal transport problem and return the OT matrix The function solves the following optimization problem: @@ -28,21 +35,21 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, \gamma\geq 0 where : - - M is the (ns,nt) metric cost matrix + - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - a and b are source and target weights (histograms, both sum to 1) The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (dim_a,) samples weights in the source domain - b : np.ndarray (nt,) or np.ndarray (nt,nbb) + b : ndarray, shape (dim_b,) or ndarray, shape (dim_b, n_hists) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : np.ndarray (ns,nt) + M : ndarray, shape (dim_a, dim_b) loss matrix reg : float Regularization term >0 @@ -61,7 +68,7 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (dim_a, dim_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -70,12 +77,12 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, -------- >>> import ot - >>> a=[.5,.5] - >>> b=[.5,.5] - >>> M=[[0.,1.],[1.,0.]] - >>> ot.sinkhorn(a,b,M,1) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.], [1., 0.]] + >>> ot.sinkhorn(a, b, M, 1) + array([[0.36552929, 0.13447071], + [0.13447071, 0.36552929]]) References @@ -100,34 +107,28 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, """ if method.lower() == 'sinkhorn': - def sink(): - return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, + **kwargs) elif method.lower() == 'greenkhorn': - def sink(): - return greenkhorn(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log) + return greenkhorn(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log) elif method.lower() == 'sinkhorn_stabilized': - def sink(): - return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) elif method.lower() == 'sinkhorn_epsilon_scaling': - def sink(): - return sinkhorn_epsilon_scaling( - a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return sinkhorn_epsilon_scaling(a, b, M, reg, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) else: - print('Warning : unknown method using classic Sinkhorn Knopp') - - def sink(): - return sinkhorn_knopp(a, b, M, reg, **kwargs) - - return sink() + raise ValueError("Unknown method '%s'." % method) def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): - u""" + r""" Solve the entropic regularization optimal transport problem and return the loss The function solves the following optimization problem: @@ -142,21 +143,21 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, \gamma\geq 0 where : - - M is the (ns,nt) metric cost matrix + - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - a and b are source and target weights (histograms, both sum to 1) The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (dim_a,) samples weights in the source domain - b : np.ndarray (nt,) or np.ndarray (nt,nbb) + b : ndarray, shape (dim_b,) or ndarray, shape (dim_b, n_hists) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : np.ndarray (ns,nt) + M : ndarray, shape (dim_a, dim_b) loss matrix reg : float Regularization term >0 @@ -172,11 +173,10 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, log : bool, optional record log if True - Returns ------- - W : (nt) ndarray or float - Optimal transportation matrix for the given parameters + W : (n_hists) ndarray or float + Optimal transportation loss for the given parameters log : dict log dictionary return only if log==True in parameters @@ -184,11 +184,11 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, -------- >>> import ot - >>> a=[.5,.5] - >>> b=[.5,.5] - >>> M=[[0.,1.],[1.,0.]] - >>> ot.sinkhorn2(a,b,M,1) - array([ 0.26894142]) + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.], [1., 0.]] + >>> ot.sinkhorn2(a, b, M, 1) + array([0.26894142]) @@ -215,36 +215,28 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, ot.bregman.sinkhorn_epsilon_scaling: Sinkhorn with epslilon scaling [9][10] """ - + b = np.asarray(b, dtype=np.float64) + if len(b.shape) < 2: + b = b[:, None] if method.lower() == 'sinkhorn': - def sink(): - return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, + **kwargs) elif method.lower() == 'sinkhorn_stabilized': - def sink(): - return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, + **kwargs) elif method.lower() == 'sinkhorn_epsilon_scaling': - def sink(): - return sinkhorn_epsilon_scaling( - a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) else: - print('Warning : unknown method using classic Sinkhorn Knopp') - - def sink(): - return sinkhorn_knopp(a, b, M, reg, **kwargs) - - b = np.asarray(b, dtype=np.float64) - if len(b.shape) < 2: - b = b.reshape((-1, 1)) - - return sink() + raise ValueError("Unknown method '%s'." % method) def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): - """ + r""" Solve the entropic regularization optimal transport problem and return the OT matrix The function solves the following optimization problem: @@ -259,21 +251,21 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, \gamma\geq 0 where : - - M is the (ns,nt) metric cost matrix + - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - a and b are source and target weights (histograms, both sum to 1) The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (dim_a,) samples weights in the source domain - b : np.ndarray (nt,) or np.ndarray (nt,nbb) + b : ndarray, shape (dim_b,) or ndarray, shape (dim_b, n_hists) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : np.ndarray (ns,nt) + M : ndarray, shape (dim_a, dim_b) loss matrix reg : float Regularization term >0 @@ -286,10 +278,9 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, log : bool, optional record log if True - Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (dim_a, dim_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -298,12 +289,12 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, -------- >>> import ot - >>> a=[.5,.5] - >>> b=[.5,.5] - >>> M=[[0.,1.],[1.,0.]] - >>> ot.sinkhorn(a,b,M,1) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.], [1., 0.]] + >>> ot.sinkhorn(a, b, M, 1) + array([[0.36552929, 0.13447071], + [0.13447071, 0.36552929]]) References @@ -329,25 +320,25 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] # init data - Nini = len(a) - Nfin = len(b) + dim_a = len(a) + dim_b = len(b) if len(b.shape) > 1: - nbb = b.shape[1] + n_hists = b.shape[1] else: - nbb = 0 + n_hists = 0 if log: log = {'err': []} # we assume that no distances are null except those of the diagonal of # distances - if nbb: - u = np.ones((Nini, nbb)) / Nini - v = np.ones((Nfin, nbb)) / Nfin + if n_hists: + u = np.ones((dim_a, n_hists)) / dim_a + v = np.ones((dim_b, n_hists)) / dim_b else: - u = np.ones(Nini) / Nini - v = np.ones(Nfin) / Nfin + u = np.ones(dim_a) / dim_a + v = np.ones(dim_b) / dim_b # print(reg) @@ -370,9 +361,9 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, v = np.divide(b, KtransposeU) u = 1. / np.dot(Kp, v) - if (np.any(KtransposeU == 0) or - np.any(np.isnan(u)) or np.any(np.isnan(v)) or - np.any(np.isinf(u)) or np.any(np.isinf(v))): + if (np.any(KtransposeU == 0) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): # we have reached the machine precision # come back to previous solution and quit loop print('Warning: numerical errors at iteration', cpt) @@ -382,13 +373,12 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, if cpt % 10 == 0: # we can speed up the process by checking for the error only all # the 10th iterations - if nbb: - err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ - np.sum((v - vprev)**2) / np.sum((v)**2) + if n_hists: + np.einsum('ik,ij,jk->jk', u, K, v, out=tmp2) else: # compute right marginal tmp2= (diag(u)Kdiag(v))^T1 np.einsum('i,ij,j->j', u, K, v, out=tmp2) - err = np.linalg.norm(tmp2 - b)**2 # violation of marginal + err = np.linalg.norm(tmp2 - b) # violation of marginal if log: log['err'].append(err) @@ -402,7 +392,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, log['u'] = u log['v'] = v - if nbb: # return only loss + if n_hists: # return only loss res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) if log: return res, log @@ -417,8 +407,9 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, return u.reshape((-1, 1)) * K * v.reshape((1, -1)) -def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log=False): - """ +def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, + log=False): + r""" Solve the entropic regularization optimal transport problem and return the OT matrix The algorithm used is based on the paper @@ -441,20 +432,20 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= \gamma\geq 0 where : - - M is the (ns,nt) metric cost matrix + - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - a and b are source and target weights (histograms, both sum to 1) Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (dim_a,) samples weights in the source domain - b : np.ndarray (nt,) or np.ndarray (nt,nbb) + b : ndarray, shape (dim_b,) or ndarray, shape (dim_b, n_hists) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : np.ndarray (ns,nt) + M : ndarray, shape (dim_a, dim_b) loss matrix reg : float Regularization term >0 @@ -465,10 +456,9 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= log : bool, optional record log if True - Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (dim_a, dim_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -477,12 +467,12 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= -------- >>> import ot - >>> a=[.5,.5] - >>> b=[.5,.5] - >>> M=[[0.,1.],[1.,0.]] - >>> ot.bregman.greenkhorn(a,b,M,1) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.], [1., 0.]] + >>> ot.bregman.greenkhorn(a, b, M, 1) + array([[0.36552929, 0.13447071], + [0.13447071, 0.36552929]]) References @@ -499,22 +489,33 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= """ - n = a.shape[0] - m = b.shape[0] + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) + + if len(a) == 0: + a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] + if len(b) == 0: + b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] + + dim_a = a.shape[0] + dim_b = b.shape[0] # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute K = np.empty_like(M) np.divide(M, -reg, out=K) np.exp(K, out=K) - u = np.full(n, 1. / n) - v = np.full(m, 1. / m) + u = np.full(dim_a, 1. / dim_a) + v = np.full(dim_b, 1. / dim_b) G = u[:, np.newaxis] * K * v[np.newaxis, :] viol = G.sum(1) - a viol_2 = G.sum(0) - b stopThr_val = 1 + if log: + log = dict() log['u'] = u log['v'] = v @@ -560,8 +561,9 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, - warmstart=None, verbose=False, print_period=20, log=False, **kwargs): - """ + warmstart=None, verbose=False, print_period=20, + log=False, **kwargs): + r""" Solve the entropic regularization OT problem with log stabilization The function solves the following optimization problem: @@ -576,9 +578,10 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, \gamma\geq 0 where : - - M is the (ns,nt) metric cost matrix + - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - a and b are source and target weights (histograms, both sum to 1) + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ but with the log stabilization @@ -587,11 +590,11 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (dim_a,) samples weights in the source domain - b : np.ndarray (nt,) + b : ndarray, shape (dim_b,) samples in the target domain - M : np.ndarray (ns,nt) + M : ndarray, shape (dim_a, dim_b) loss matrix reg : float Regularization term >0 @@ -608,10 +611,9 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, log : bool, optional record log if True - Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (dim_a, dim_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -623,9 +625,9 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, >>> a=[.5,.5] >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] - >>> ot.bregman.sinkhorn_stabilized(a,b,M,1) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) + >>> ot.bregman.sinkhorn_stabilized(a, b, M, 1) + array([[0.36552929, 0.13447071], + [0.13447071, 0.36552929]]) References @@ -656,14 +658,14 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, # test if multiple target if len(b.shape) > 1: - nbb = b.shape[1] + n_hists = b.shape[1] a = a[:, np.newaxis] else: - nbb = 0 + n_hists = 0 # init data - na = len(a) - nb = len(b) + dim_a = len(a) + dim_b = len(b) cpt = 0 if log: @@ -672,24 +674,25 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, # we assume that no distances are null except those of the diagonal of # distances if warmstart is None: - alpha, beta = np.zeros(na), np.zeros(nb) + alpha, beta = np.zeros(dim_a), np.zeros(dim_b) else: alpha, beta = warmstart - if nbb: - u, v = np.ones((na, nbb)) / na, np.ones((nb, nbb)) / nb + if n_hists: + u = np.ones((dim_a, n_hists)) / dim_a + v = np.ones((dim_b, n_hists)) / dim_b else: - u, v = np.ones(na) / na, np.ones(nb) / nb + u, v = np.ones(dim_a) / dim_a, np.ones(dim_b) / dim_b def get_K(alpha, beta): """log space computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - - beta.reshape((1, nb))) / reg) + return np.exp(-(M - alpha.reshape((dim_a, 1)) + - beta.reshape((1, dim_b))) / reg) def get_Gamma(alpha, beta, u, v): """log space gamma computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) / - reg + np.log(u.reshape((na, 1))) + np.log(v.reshape((1, nb)))) + return np.exp(-(M - alpha.reshape((dim_a, 1)) - beta.reshape((1, dim_b))) + / reg + np.log(u.reshape((dim_a, 1))) + np.log(v.reshape((1, dim_b)))) # print(np.min(K)) @@ -709,26 +712,29 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, # remove numerical problems and store them in K if np.abs(u).max() > tau or np.abs(v).max() > tau: - if nbb: + if n_hists: alpha, beta = alpha + reg * \ np.max(np.log(u), 1), beta + reg * np.max(np.log(v)) else: alpha, beta = alpha + reg * np.log(u), beta + reg * np.log(v) - if nbb: - u, v = np.ones((na, nbb)) / na, np.ones((nb, nbb)) / nb + if n_hists: + u, v = np.ones((dim_a, n_hists)) / dim_a, np.ones((dim_b, n_hists)) / dim_b else: - u, v = np.ones(na) / na, np.ones(nb) / nb + u, v = np.ones(dim_a) / dim_a, np.ones(dim_b) / dim_b K = get_K(alpha, beta) if cpt % print_period == 0: # we can speed up the process by checking for the error only all # the 10th iterations - if nbb: - err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ - np.sum((v - vprev)**2) / np.sum((v)**2) + if n_hists: + err_u = abs(u - uprev).max() + err_u /= max(abs(u).max(), abs(uprev).max(), 1.) + err_v = abs(v - vprev).max() + err_v /= max(abs(v).max(), abs(vprev).max(), 1.) + err = 0.5 * (err_u + err_v) else: transp = get_Gamma(alpha, beta, u, v) - err = np.linalg.norm((np.sum(transp, axis=0) - b))**2 + err = np.linalg.norm((np.sum(transp, axis=0) - b)) if log: log['err'].append(err) @@ -754,34 +760,40 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, cpt = cpt + 1 - # print('err=',err,' cpt=',cpt) if log: - log['logu'] = alpha / reg + np.log(u) - log['logv'] = beta / reg + np.log(v) + if n_hists: + alpha = alpha[:, None] + beta = beta[:, None] + logu = alpha / reg + np.log(u) + logv = beta / reg + np.log(v) + log['logu'] = logu + log['logv'] = logv log['alpha'] = alpha + reg * np.log(u) log['beta'] = beta + reg * np.log(v) log['warmstart'] = (log['alpha'], log['beta']) - if nbb: - res = np.zeros((nbb)) - for i in range(nbb): + if n_hists: + res = np.zeros((n_hists)) + for i in range(n_hists): res[i] = np.sum(get_Gamma(alpha, beta, u[:, i], v[:, i]) * M) return res, log else: return get_Gamma(alpha, beta, u, v), log else: - if nbb: - res = np.zeros((nbb)) - for i in range(nbb): + if n_hists: + res = np.zeros((n_hists)) + for i in range(n_hists): res[i] = np.sum(get_Gamma(alpha, beta, u[:, i], v[:, i]) * M) return res else: return get_Gamma(alpha, beta, u, v) -def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInnerItermax=100, - tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=10, log=False, **kwargs): - """ +def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, + numInnerItermax=100, tau=1e3, stopThr=1e-9, + warmstart=None, verbose=False, print_period=10, + log=False, **kwargs): + r""" Solve the entropic regularization optimal transport problem with log stabilization and epsilon scaling. @@ -797,9 +809,10 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne \gamma\geq 0 where : - - M is the (ns,nt) metric cost matrix + - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - a and b are source and target weights (histograms, both sum to 1) + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ but with the log stabilization @@ -808,19 +821,17 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (dim_a,) samples weights in the source domain - b : np.ndarray (nt,) + b : ndarray, shape (dim_b,) samples in the target domain - M : np.ndarray (ns,nt) + M : ndarray, shape (dim_a, dim_b) loss matrix reg : float Regularization term >0 tau : float thershold for max value in u or v for log scaling - tau : float - thershold for max value in u or v for log scaling - warmstart : tible of vectors + warmstart : tuple of vectors if given then sarting values for alpha an beta log scalings numItermax : int, optional Max number of iterations @@ -835,10 +846,9 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne log : bool, optional record log if True - Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (dim_a, dim_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -847,12 +857,12 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne -------- >>> import ot - >>> a=[.5,.5] - >>> b=[.5,.5] - >>> M=[[0.,1.],[1.,0.]] - >>> ot.bregman.sinkhorn_epsilon_scaling(a,b,M,1) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.], [1., 0.]] + >>> ot.bregman.sinkhorn_epsilon_scaling(a, b, M, 1) + array([[0.36552929, 0.13447071], + [0.13447071, 0.36552929]]) References @@ -879,8 +889,8 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] # init data - na = len(a) - nb = len(b) + dim_a = len(a) + dim_b = len(b) # nrelative umerical precision with 64 bits numItermin = 35 @@ -893,14 +903,14 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne # we assume that no distances are null except those of the diagonal of # distances if warmstart is None: - alpha, beta = np.zeros(na), np.zeros(nb) + alpha, beta = np.zeros(dim_a), np.zeros(dim_b) else: alpha, beta = warmstart def get_K(alpha, beta): """log space computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - - beta.reshape((1, nb))) / reg) + return np.exp(-(M - alpha.reshape((dim_a, 1)) + - beta.reshape((1, dim_b))) / reg) # print(np.min(K)) def get_reg(n): # exponential decreasing @@ -913,8 +923,10 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne regi = get_reg(cpt) - G, logi = sinkhorn_stabilized(a, b, M, regi, numItermax=numInnerItermax, stopThr=1e-9, warmstart=( - alpha, beta), verbose=False, print_period=20, tau=tau, log=True) + G, logi = sinkhorn_stabilized(a, b, M, regi, + numItermax=numInnerItermax, stopThr=1e-9, + warmstart=(alpha, beta), verbose=False, + print_period=20, tau=tau, log=True) alpha = logi['alpha'] beta = logi['beta'] @@ -972,9 +984,9 @@ def projC(gamma, q): return np.multiply(gamma, q / np.maximum(np.sum(gamma, axis=0), 1e-10)) -def barycenter(A, M, reg, weights=None, numItermax=1000, - stopThr=1e-4, verbose=False, log=False): - """Compute the entropic regularized wasserstein barycenter of distributions A +def barycenter(A, M, reg, weights=None, method="sinkhorn", numItermax=10000, + stopThr=1e-4, verbose=False, log=False, **kwargs): + r"""Compute the entropic regularized wasserstein barycenter of distributions A The function solves the following optimization problem: @@ -991,13 +1003,15 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, Parameters ---------- - A : np.ndarray (d,n) - n training distributions a_i of size d - M : np.ndarray (d,d) - loss matrix for OT + A : ndarray, shape (dim, n_hists) + n_hists training distributions a_i of size dim + M : ndarray, shape (dim, dim) + loss matrix for OT reg : float - Regularization term >0 - weights : np.ndarray (n,) + Regularization term > 0 + method : str (optional) + method used for the solver either 'sinkhorn' or 'sinkhorn_stabilized' + weights : ndarray, shape (n_hists,) Weights of each histogram a_i on the simplex (barycentric coodinates) numItermax : int, optional Max number of iterations @@ -1011,7 +1025,7 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, Returns ------- - a : (d,) ndarray + a : (dim,) ndarray Wasserstein barycenter log : dict log dictionary return only if log==True in parameters @@ -1022,7 +1036,71 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + """ + if method.lower() == 'sinkhorn': + return barycenter_sinkhorn(A, M, reg, weights=weights, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, + **kwargs) + elif method.lower() == 'sinkhorn_stabilized': + return barycenter_stabilized(A, M, reg, weights=weights, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + else: + raise ValueError("Unknown method '%s'." % method) + + +def barycenter_sinkhorn(A, M, reg, weights=None, numItermax=1000, + stopThr=1e-4, verbose=False, log=False): + r"""Compute the entropic regularized wasserstein barycenter of distributions A + + The function solves the following optimization problem: + + .. math:: + \mathbf{a} = arg\min_\mathbf{a} \sum_i W_{reg}(\mathbf{a},\mathbf{a}_i) + + where : + + - :math:`W_{reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance (see ot.bregman.sinkhorn) + - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` + - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix for OT + + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [3]_ + + Parameters + ---------- + A : ndarray, shape (dim, n_hists) + n_hists training distributions a_i of size dim + M : ndarray, shape (dim, dim) + loss matrix for OT + reg : float + Regularization term > 0 + weights : ndarray, shape (n_hists,) + Weights of each histogram a_i on the simplex (barycentric coodinates) + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + a : (dim,) ndarray + Wasserstein barycenter + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. """ @@ -1068,8 +1146,139 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, return geometricBar(weights, UKv) -def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1e-9, stabThr=1e-30, verbose=False, log=False): - """Compute the entropic regularized wasserstein barycenter of distributions A +def barycenter_stabilized(A, M, reg, tau=1e10, weights=None, numItermax=1000, + stopThr=1e-4, verbose=False, log=False): + r"""Compute the entropic regularized wasserstein barycenter of distributions A + with stabilization. + + The function solves the following optimization problem: + + .. math:: + \mathbf{a} = arg\min_\mathbf{a} \sum_i W_{reg}(\mathbf{a},\mathbf{a}_i) + + where : + + - :math:`W_{reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance (see ot.bregman.sinkhorn) + - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` + - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix for OT + + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [3]_ + + Parameters + ---------- + A : ndarray, shape (dim, n_hists) + n_hists training distributions a_i of size dim + M : ndarray, shape (dim, dim) + loss matrix for OT + reg : float + Regularization term > 0 + tau : float + thershold for max value in u or v for log scaling + weights : ndarray, shape (n_hists,) + Weights of each histogram a_i on the simplex (barycentric coodinates) + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + a : (dim,) ndarray + Wasserstein barycenter + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + + """ + + dim, n_hists = A.shape + if weights is None: + weights = np.ones(n_hists) / n_hists + else: + assert(len(weights) == A.shape[1]) + + if log: + log = {'err': []} + + u = np.ones((dim, n_hists)) / dim + v = np.ones((dim, n_hists)) / dim + + # print(reg) + # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + + cpt = 0 + err = 1. + alpha = np.zeros(dim) + beta = np.zeros(dim) + q = np.ones(dim) / dim + while (err > stopThr and cpt < numItermax): + qprev = q + Kv = K.dot(v) + u = A / (Kv + 1e-16) + Ktu = K.T.dot(u) + q = geometricBar(weights, Ktu) + Q = q[:, None] + v = Q / (Ktu + 1e-16) + absorbing = False + if (u > tau).any() or (v > tau).any(): + absorbing = True + alpha = alpha + reg * np.log(np.max(u, 1)) + beta = beta + reg * np.log(np.max(v, 1)) + K = np.exp((alpha[:, None] + beta[None, :] - + M) / reg) + v = np.ones_like(v) + Kv = K.dot(v) + if (np.any(Ktu == 0.) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): + # we have reached the machine precision + # come back to previous solution and quit loop + warnings.warn('Numerical errors at iteration %s' % cpt) + q = qprev + break + if (cpt % 10 == 0 and not absorbing) or cpt == 0: + # we can speed up the process by checking for the error only all + # the 10th iterations + err = abs(u * Kv - A).max() + if log: + log['err'].append(err) + if verbose: + if cpt % 50 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + + cpt += 1 + if err > stopThr: + warnings.warn("Stabilized Unbalanced Sinkhorn did not converge." + + "Try a larger entropy `reg`" + + "Or a larger absorption threshold `tau`.") + if log: + log['niter'] = cpt + log['logu'] = np.log(u + 1e-16) + log['logv'] = np.log(v + 1e-16) + return q, log + else: + return q + + +def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, + stopThr=1e-9, stabThr=1e-30, verbose=False, + log=False): + r"""Compute the entropic regularized wasserstein barycenter of distributions A where A is a collection of 2D images. The function solves the following optimization problem: @@ -1087,16 +1296,16 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 Parameters ---------- - A : np.ndarray (n,w,h) - n distributions (2D images) of size w x h + A : ndarray, shape (n_hists, width, height) + n distributions (2D images) of size width x height reg : float Regularization term >0 - weights : np.ndarray (n,) + weights : ndarray, shape (n_hists,) Weights of each image on the simplex (barycentric coodinates) numItermax : int, optional Max number of iterations stopThr : float, optional - Stop threshol on error (>0) + Stop threshol on error (> 0) stabThr : float, optional Stabilization threshold to avoid numerical precision issue verbose : bool, optional @@ -1104,15 +1313,13 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 log : bool, optional record log if True - Returns ------- - a : (w,h) ndarray + a : ndarray, shape (width, height) 2D Wasserstein barycenter log : dict log dictionary return only if log==True in parameters - References ---------- @@ -1180,7 +1387,7 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, stopThr=1e-3, verbose=False, log=False): - """ + r""" Compute the unmixing of an observation with a given dictionary using Wasserstein distance The function solve the following optimization problem: @@ -1192,9 +1399,12 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, where : - :math:`W_{M,reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance with M loss matrix (see ot.bregman.sinkhorn) - - :math:`\mathbf{a}` is an observed distribution, :math:`\mathbf{h}_0` is aprior on unmixing - - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix for OT data fitting - - reg0 and :math:`\mathbf{M0}` are respectively the regularization term and the cost matrix for regularization + - :math: `\mathbf{D}` is a dictionary of `n_atoms` atoms of dimension `dim_a`, its expected shape is `(dim_a, n_atoms)` + - :math:`\mathbf{h}` is the estimated unmixing of dimension `n_atoms` + - :math:`\mathbf{a}` is an observed distribution of dimension `dim_a` + - :math:`\mathbf{h}_0` is a prior on `h` of dimension `dim_prior` + - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix (dim_a, dim_a) for OT data fitting + - reg0 and :math:`\mathbf{M0}` are respectively the regularization term and the cost matrix (dim_prior, n_atoms) regularization - :math:`\\alpha`weight data fitting and regularization The optimization problem is solved suing the algorithm described in [4] @@ -1202,16 +1412,16 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, Parameters ---------- - a : np.ndarray (d) - observed distribution - D : np.ndarray (d,n) + a : ndarray, shape (dim_a) + observed distribution (histogram, sums to 1) + D : ndarray, shape (dim_a, n_atoms) dictionary matrix - M : np.ndarray (d,d) + M : ndarray, shape (dim_a, dim_a) loss matrix - M0 : np.ndarray (n,n) + M0 : ndarray, shape (n_atoms, dim_prior) loss matrix - h0 : np.ndarray (n,) - prior on h + h0 : ndarray, shape (n_atoms,) + prior on the estimated unmixing h reg : float Regularization term >0 (Wasserstein data fitting) reg0 : float @@ -1230,7 +1440,7 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, Returns ------- - a : (d,) ndarray + h : ndarray, shape (n_atoms,) Wasserstein barycenter log : dict log dictionary return only if log==True in parameters @@ -1284,3 +1494,635 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, return np.sum(K0, axis=1), log else: return np.sum(K0, axis=1) + + +def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', + numIterMax=10000, stopThr=1e-9, verbose=False, + log=False, **kwargs): + r''' + Solve the entropic regularization optimal transport problem and return the + OT matrix from empirical data + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - :math:`M` is the (n_samples_a, n_samples_b) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`a` and :math:`b` are source and target weights (sum to 1) + + + Parameters + ---------- + X_s : ndarray, shape (n_samples_a, dim) + samples in the source domain + X_t : ndarray, shape (n_samples_b, dim) + samples in the target domain + reg : float + Regularization term >0 + a : ndarray, shape (n_samples_a,) + samples weights in the source domain + b : ndarray, shape (n_samples_b,) + samples weights in the target domain + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : ndarray, shape (n_samples_a, n_samples_b) + Regularized optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> n_samples_a = 2 + >>> n_samples_b = 2 + >>> reg = 0.1 + >>> X_s = np.reshape(np.arange(n_samples_a), (n_samples_a, 1)) + >>> X_t = np.reshape(np.arange(0, n_samples_b), (n_samples_b, 1)) + >>> empirical_sinkhorn(X_s, X_t, reg, verbose=False) # doctest: +NORMALIZE_WHITESPACE + array([[4.99977301e-01, 2.26989344e-05], + [2.26989344e-05, 4.99977301e-01]]) + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + ''' + + if a is None: + a = unif(np.shape(X_s)[0]) + if b is None: + b = unif(np.shape(X_t)[0]) + + M = dist(X_s, X_t, metric=metric) + + if log: + pi, log = sinkhorn(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=True, **kwargs) + return pi, log + else: + pi = sinkhorn(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=False, **kwargs) + return pi + + +def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): + r''' + Solve the entropic regularization optimal transport problem from empirical + data and return the OT loss + + + The function solves the following optimization problem: + + .. math:: + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - :math:`M` is the (n_samples_a, n_samples_b) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`a` and :math:`b` are source and target weights (sum to 1) + + + Parameters + ---------- + X_s : ndarray, shape (n_samples_a, dim) + samples in the source domain + X_t : ndarray, shape (n_samples_b, dim) + samples in the target domain + reg : float + Regularization term >0 + a : ndarray, shape (n_samples_a,) + samples weights in the source domain + b : ndarray, shape (n_samples_b,) + samples weights in the target domain + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : ndarray, shape (n_samples_a, n_samples_b) + Regularized optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> n_samples_a = 2 + >>> n_samples_b = 2 + >>> reg = 0.1 + >>> X_s = np.reshape(np.arange(n_samples_a), (n_samples_a, 1)) + >>> X_t = np.reshape(np.arange(0, n_samples_b), (n_samples_b, 1)) + >>> empirical_sinkhorn2(X_s, X_t, reg, verbose=False) + array([4.53978687e-05]) + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + ''' + + if a is None: + a = unif(np.shape(X_s)[0]) + if b is None: + b = unif(np.shape(X_t)[0]) + + M = dist(X_s, X_t, metric=metric) + + if log: + sinkhorn_loss, log = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return sinkhorn_loss, log + else: + sinkhorn_loss = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return sinkhorn_loss + + +def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): + r''' + Compute the sinkhorn divergence loss from empirical data + + The function solves the following optimization problems and return the + sinkhorn divergence :math:`S`: + + .. math:: + + W &= \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + W_a &= \min_{\gamma_a} <\gamma_a,M_a>_F + reg\cdot\Omega(\gamma_a) + + W_b &= \min_{\gamma_b} <\gamma_b,M_b>_F + reg\cdot\Omega(\gamma_b) + + S &= W - 1/2 * (W_a + W_b) + + .. math:: + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + + \gamma_a 1 = a + + \gamma_a^T 1= a + + \gamma_a\geq 0 + + \gamma_b 1 = b + + \gamma_b^T 1= b + + \gamma_b\geq 0 + where : + + - :math:`M` (resp. :math:`M_a, M_b`) is the (n_samples_a, n_samples_b) metric cost matrix (resp (n_samples_a, n_samples_a) and (n_samples_b, n_samples_b)) + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`a` and :math:`b` are source and target weights (sum to 1) + + + Parameters + ---------- + X_s : ndarray, shape (n_samples_a, dim) + samples in the source domain + X_t : ndarray, shape (n_samples_b, dim) + samples in the target domain + reg : float + Regularization term >0 + a : ndarray, shape (n_samples_a,) + samples weights in the source domain + b : ndarray, shape (n_samples_b,) + samples weights in the target domain + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + Returns + ------- + gamma : ndarray, shape (n_samples_a, n_samples_b) + Regularized optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + >>> n_samples_a = 2 + >>> n_samples_b = 4 + >>> reg = 0.1 + >>> X_s = np.reshape(np.arange(n_samples_a), (n_samples_a, 1)) + >>> X_t = np.reshape(np.arange(0, n_samples_b), (n_samples_b, 1)) + >>> empirical_sinkhorn_divergence(X_s, X_t, reg) # doctest: +ELLIPSIS + array([1.499...]) + + + References + ---------- + .. [23] Aude Genevay, Gabriel Peyré, Marco Cuturi, Learning Generative Models with Sinkhorn Divergences, Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 + ''' + if log: + sinkhorn_loss_ab, log_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + + sinkhorn_loss_a, log_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + + sinkhorn_loss_b, log_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + + sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b) + + log = {} + log['sinkhorn_loss_ab'] = sinkhorn_loss_ab + log['sinkhorn_loss_a'] = sinkhorn_loss_a + log['sinkhorn_loss_b'] = sinkhorn_loss_b + log['log_sinkhorn_ab'] = log_ab + log['log_sinkhorn_a'] = log_a + log['log_sinkhorn_b'] = log_b + + return max(0, sinkhorn_div), log + + else: + sinkhorn_loss_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + + sinkhorn_loss_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + + sinkhorn_loss_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + + sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b) + return max(0, sinkhorn_div) + + +def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, restricted=True, + maxiter=10000, maxfun=10000, pgtol=1e-09, verbose=False, log=False): + r"""" + Screening Sinkhorn Algorithm for Regularized Optimal Transport + + The function solves an approximate dual of Sinkhorn divergence [2] which is written as the following optimization problem: + + ..math:: + (u, v) = \argmin_{u, v} 1_{ns}^T B(u,v) 1_{nt} - <\kappa u, a> - <v/\kappa, b> + + where B(u,v) = \diag(e^u) K \diag(e^v), with K = e^{-M/reg} and + + s.t. e^{u_i} \geq \epsilon / \kappa, for all i \in {1, ..., ns} + + e^{v_j} \geq \epsilon \kappa, for all j \in {1, ..., nt} + + The parameters \kappa and \epsilon are determined w.r.t the couple number budget of points (ns_budget, nt_budget), see Equation (5) in [26] + + + Parameters + ---------- + a : `numpy.ndarray`, shape=(ns,) + samples weights in the source domain + + b : `numpy.ndarray`, shape=(nt,) + samples weights in the target domain + + M : `numpy.ndarray`, shape=(ns, nt) + Cost matrix + + reg : `float` + Level of the entropy regularisation + + ns_budget : `int`, deafult=None + Number budget of points to be keeped in the source domain + If it is None then 50% of the source sample points will be keeped + + nt_budget : `int`, deafult=None + Number budget of points to be keeped in the target domain + If it is None then 50% of the target sample points will be keeped + + uniform : `bool`, default=False + If `True`, the source and target distribution are supposed to be uniform, i.e., a_i = 1 / ns and b_j = 1 / nt + + restricted : `bool`, default=True + If `True`, a warm-start initialization for the L-BFGS-B solver + using a restricted Sinkhorn algorithm with at most 5 iterations + + maxiter : `int`, default=10000 + Maximum number of iterations in LBFGS solver + + maxfun : `int`, default=10000 + Maximum number of function evaluations in LBFGS solver + + pgtol : `float`, default=1e-09 + Final objective function accuracy in LBFGS solver + + verbose : `bool`, default=False + If `True`, dispaly informations about the cardinals of the active sets and the paramerters kappa + and epsilon + + Dependency + ---------- + To gain more efficiency, screenkhorn needs to call the "Bottleneck" package (https://pypi.org/project/Bottleneck/) + in the screening pre-processing step. If Bottleneck isn't installed, the following error message appears: + "Bottleneck module doesn't exist. Install it from https://pypi.org/project/Bottleneck/" + + + Returns + ------- + gamma : `numpy.ndarray`, shape=(ns, nt) + Screened optimal transportation matrix for the given parameters + + log : `dict`, default=False + Log dictionary return only if log==True in parameters + + + References + ----------- + .. [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). Screening Sinkhorn Algorithm for Regularized Optimal Transport (NIPS) 33, 2019 + + """ + # check if bottleneck module exists + try: + import bottleneck + except ImportError: + warnings.warn("Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance.") + bottleneck = np + + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) + ns, nt = M.shape + + # by default, we keep only 50% of the sample data points + if ns_budget is None: + ns_budget = int(np.floor(0.5 * ns)) + if nt_budget is None: + nt_budget = int(np.floor(0.5 * nt)) + + # calculate the Gibbs kernel + K = np.empty_like(M) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + + def projection(u, epsilon): + u[u <= epsilon] = epsilon + return u + + # ----------------------------------------------------------------------------------------------------------------# + # Step 1: Screening pre-processing # + # ----------------------------------------------------------------------------------------------------------------# + + if ns_budget == ns and nt_budget == nt: + # full number of budget points (ns, nt) = (ns_budget, nt_budget) + Isel = np.ones(ns, dtype=bool) + Jsel = np.ones(nt, dtype=bool) + epsilon = 0.0 + kappa = 1.0 + + cst_u = 0. + cst_v = 0. + + bounds_u = [(0.0, np.inf)] * ns + bounds_v = [(0.0, np.inf)] * nt + + a_I = a + b_J = b + K_IJ = K + K_IJc = [] + K_IcJ = [] + + vec_eps_IJc = np.zeros(nt) + vec_eps_IcJ = np.zeros(ns) + + else: + # sum of rows and columns of K + K_sum_cols = K.sum(axis=1) + K_sum_rows = K.sum(axis=0) + + if uniform: + if ns / ns_budget < 4: + aK_sort = np.sort(K_sum_cols) + epsilon_u_square = a[0] / aK_sort[ns_budget - 1] + else: + aK_sort = bottleneck.partition(K_sum_cols, ns_budget - 1)[ns_budget - 1] + epsilon_u_square = a[0] / aK_sort + + if nt / nt_budget < 4: + bK_sort = np.sort(K_sum_rows) + epsilon_v_square = b[0] / bK_sort[nt_budget - 1] + else: + bK_sort = bottleneck.partition(K_sum_rows, nt_budget - 1)[nt_budget - 1] + epsilon_v_square = b[0] / bK_sort + else: + aK = a / K_sum_cols + bK = b / K_sum_rows + + aK_sort = np.sort(aK)[::-1] + epsilon_u_square = aK_sort[ns_budget - 1] + + bK_sort = np.sort(bK)[::-1] + epsilon_v_square = bK_sort[nt_budget - 1] + + # active sets I and J (see Lemma 1 in [26]) + Isel = a >= epsilon_u_square * K_sum_cols + Jsel = b >= epsilon_v_square * K_sum_rows + + if sum(Isel) != ns_budget: + if uniform: + aK = a / K_sum_cols + aK_sort = np.sort(aK)[::-1] + epsilon_u_square = aK_sort[ns_budget - 1:ns_budget + 1].mean() + Isel = a >= epsilon_u_square * K_sum_cols + ns_budget = sum(Isel) + + if sum(Jsel) != nt_budget: + if uniform: + bK = b / K_sum_rows + bK_sort = np.sort(bK)[::-1] + epsilon_v_square = bK_sort[nt_budget - 1:nt_budget + 1].mean() + Jsel = b >= epsilon_v_square * K_sum_rows + nt_budget = sum(Jsel) + + epsilon = (epsilon_u_square * epsilon_v_square) ** (1 / 4) + kappa = (epsilon_v_square / epsilon_u_square) ** (1 / 2) + + if verbose: + print("epsilon = %s\n" % epsilon) + print("kappa = %s\n" % kappa) + print('Cardinality of selected points: |Isel| = %s \t |Jsel| = %s \n' % (sum(Isel), sum(Jsel))) + + # Ic, Jc: complementary of the active sets I and J + Ic = ~Isel + Jc = ~Jsel + + K_IJ = K[np.ix_(Isel, Jsel)] + K_IcJ = K[np.ix_(Ic, Jsel)] + K_IJc = K[np.ix_(Isel, Jc)] + + K_min = K_IJ.min() + if K_min == 0: + K_min = np.finfo(float).tiny + + # a_I, b_J, a_Ic, b_Jc + a_I = a[Isel] + b_J = b[Jsel] + if not uniform: + a_I_min = a_I.min() + a_I_max = a_I.max() + b_J_max = b_J.max() + b_J_min = b_J.min() + else: + a_I_min = a_I[0] + a_I_max = a_I[0] + b_J_max = b_J[0] + b_J_min = b_J[0] + + # box constraints in L-BFGS-B (see Proposition 1 in [26]) + bounds_u = [(max(a_I_min / ((nt - nt_budget) * epsilon + nt_budget * (b_J_max / ( + ns * epsilon * kappa * K_min))), epsilon / kappa), a_I_max / (nt * epsilon * K_min))] * ns_budget + + bounds_v = [(max(b_J_min / ((ns - ns_budget) * epsilon + ns_budget * (kappa * a_I_max / (nt * epsilon * K_min))), + epsilon * kappa), b_J_max / (ns * epsilon * K_min))] * nt_budget + + # pre-calculated constants for the objective + vec_eps_IJc = epsilon * kappa * (K_IJc * np.ones(nt - nt_budget).reshape((1, -1))).sum(axis=1) + vec_eps_IcJ = (epsilon / kappa) * (np.ones(ns - ns_budget).reshape((-1, 1)) * K_IcJ).sum(axis=0) + + # initialisation + u0 = np.full(ns_budget, (1. / ns_budget) + epsilon / kappa) + v0 = np.full(nt_budget, (1. / nt_budget) + epsilon * kappa) + + # pre-calculed constants for Restricted Sinkhorn (see Algorithm 1 in supplementary of [26]) + if restricted: + if ns_budget != ns or nt_budget != nt: + cst_u = kappa * epsilon * K_IJc.sum(axis=1) + cst_v = epsilon * K_IcJ.sum(axis=0) / kappa + + cpt = 1 + while cpt < 5: # 5 iterations + K_IJ_v = np.dot(K_IJ.T, u0) + cst_v + v0 = b_J / (kappa * K_IJ_v) + KIJ_u = np.dot(K_IJ, v0) + cst_u + u0 = (kappa * a_I) / KIJ_u + cpt += 1 + + u0 = projection(u0, epsilon / kappa) + v0 = projection(v0, epsilon * kappa) + + else: + u0 = u0 + v0 = v0 + + def restricted_sinkhorn(usc, vsc, max_iter=5): + """ + Restricted Sinkhorn Algorithm as a warm-start initialized point for L-BFGS-B (see Algorithm 1 in supplementary of [26]) + """ + cpt = 1 + while cpt < max_iter: + K_IJ_v = np.dot(K_IJ.T, usc) + cst_v + vsc = b_J / (kappa * K_IJ_v) + KIJ_u = np.dot(K_IJ, vsc) + cst_u + usc = (kappa * a_I) / KIJ_u + cpt += 1 + + usc = projection(usc, epsilon / kappa) + vsc = projection(vsc, epsilon * kappa) + + return usc, vsc + + def screened_obj(usc, vsc): + part_IJ = np.dot(np.dot(usc, K_IJ), vsc) - kappa * np.dot(a_I, np.log(usc)) - (1. / kappa) * np.dot(b_J, np.log(vsc)) + part_IJc = np.dot(usc, vec_eps_IJc) + part_IcJ = np.dot(vec_eps_IcJ, vsc) + psi_epsilon = part_IJ + part_IJc + part_IcJ + return psi_epsilon + + def screened_grad(usc, vsc): + # gradients of Psi_(kappa,epsilon) w.r.t u and v + grad_u = np.dot(K_IJ, vsc) + vec_eps_IJc - kappa * a_I / usc + grad_v = np.dot(K_IJ.T, usc) + vec_eps_IcJ - (1. / kappa) * b_J / vsc + return grad_u, grad_v + + def bfgspost(theta): + u = theta[:ns_budget] + v = theta[ns_budget:] + # objective + f = screened_obj(u, v) + # gradient + g_u, g_v = screened_grad(u, v) + g = np.hstack([g_u, g_v]) + return f, g + + #----------------------------------------------------------------------------------------------------------------# + # Step 2: L-BFGS-B solver # + #----------------------------------------------------------------------------------------------------------------# + + u0, v0 = restricted_sinkhorn(u0, v0) + theta0 = np.hstack([u0, v0]) + + bounds = bounds_u + bounds_v # constraint bounds + + def obj(theta): + return bfgspost(theta) + + theta, _, _ = fmin_l_bfgs_b(func=obj, + x0=theta0, + bounds=bounds, + maxfun=maxfun, + pgtol=pgtol, + maxiter=maxiter) + + usc = theta[:ns_budget] + vsc = theta[ns_budget:] + + usc_full = np.full(ns, epsilon / kappa) + vsc_full = np.full(nt, epsilon * kappa) + usc_full[Isel] = usc + vsc_full[Jsel] = vsc + + if log: + log = {} + log['u'] = usc_full + log['v'] = vsc_full + log['Isel'] = Isel + log['Jsel'] = Jsel + + gamma = usc_full[:, None] * K * vsc_full[None, :] + gamma = gamma / gamma.sum() + + if log: + return gamma, log + else: + return gamma @@ -6,6 +6,7 @@ Domain adaptation with optimal transport # Author: Remi Flamary <remi.flamary@unice.fr> # Nicolas Courty <ncourty@irisa.fr> # Michael Perrot <michael.perrot@univ-st-etienne.fr> +# Nathalie Gayraud <nat.gayraud@gmail.com> # # License: MIT License @@ -16,6 +17,7 @@ from .bregman import sinkhorn from .lp import emd from .utils import unif, dist, kernel, cost_normalization from .utils import check_params, BaseEstimator +from .unbalanced import sinkhorn_unbalanced from .optim import cg from .optim import gcg @@ -41,15 +43,15 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, where : - M is the (ns,nt) metric cost matrix - - :math:`\Omega_e` is the entropic regularization term - :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - :math:`\Omega_g` is the group lasso regulaization term + - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e + (\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega_g` is the group lasso regularization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1` where :math:`\mathcal{I}_c` are the index of samples from class c in the source domain. - a and b are source and target weights (sum to 1) - The algorithm used for solving the problem is the generalised conditional + The algorithm used for solving the problem is the generalized conditional gradient as proposed in [5]_ [7]_ @@ -473,22 +475,24 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', Weight for the linear OT loss (>0) eta : float, optional Regularization term for the linear mapping L (>0) - bias : bool,optional - Estimate linear mapping with constant bias kerneltype : str,optional kernel used by calling function ot.utils.kernel (gaussian by default) sigma : float, optional Gaussian kernel bandwidth. + bias : bool,optional + Estimate linear mapping with constant bias + verbose : bool, optional + Print information along iterations + verbose2 : bool, optional + Print information along iterations numItermax : int, optional Max number of BCD iterations - stopThr : float, optional - Stop threshold on relative loss decrease (>0) numInnerItermax : int, optional Max number of iterations (inner CG solver) stopInnerThr : float, optional Stop threshold on error (inner CG solver) (>0) - verbose : bool, optional - Print information along iterations + stopThr : float, optional + Stop threshold on relative loss decrease (>0) log : bool, optional record log if True @@ -643,7 +647,8 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, The function estimates the optimal linear operator that aligns the two empirical distributions. This is equivalent to estimating the closed form mapping between two Gaussian distributions :math:`N(\mu_s,\Sigma_s)` - and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark 2.29 in [15]. + and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark + 2.29 in [15]. The linear operator from source to target :math:`M` @@ -1184,25 +1189,25 @@ class SinkhornTransport(BaseTransport): algorithm if no it has not converged tol : float, optional (default=10e-9) The precision required to stop the optimization algorithm. - mapping : string, optional (default="barycentric") - The kind of mapping to apply to transport samples from a domain into - another one. - if "barycentric" only the samples used to estimate the coupling can - be transported from a domain to another one. + verbose : bool, optional (default=False) + Controls the verbosity of the optimization algorithm + log : int, optional (default=False) + Controls the logs of the optimization algorithm metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem norm : string, optional (default=None) If given, normalize the ground metric to avoid numerical errors that can occur with large metric values. - distribution : string, optional (default="uniform") + distribution_estimation : callable, optional (defaults to the uniform) The kind of distribution estimation to employ - verbose : int, optional (default=0) - Controls the verbosity of the optimization algorithm - log : int, optional (default=0) - Controls the logs of the optimization algorithm + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. limit_max: float, optional (defaul=np.infty) Controls the semi supervised mode. Transport between labeled source - and target samples of different classes will exhibit an infinite cost + and target samples of different classes will exhibit an cost defined + by this variable Attributes ---------- @@ -1287,22 +1292,19 @@ class EMDTransport(BaseTransport): Parameters ---------- - mapping : string, optional (default="barycentric") - The kind of mapping to apply to transport samples from a domain into - another one. - if "barycentric" only the samples used to estimate the coupling can - be transported from a domain to another one. metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem norm : string, optional (default=None) If given, normalize the ground metric to avoid numerical errors that can occur with large metric values. - distribution : string, optional (default="uniform") - The kind of distribution estimation to employ - verbose : int, optional (default=0) - Controls the verbosity of the optimization algorithm - log : int, optional (default=0) + log : int, optional (default=False) Controls the logs of the optimization algorithm + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. limit_max: float, optional (default=10) Controls the semi supervised mode. Transport between labeled source and target samples of different classes will exhibit an infinite cost @@ -1387,28 +1389,32 @@ class SinkhornLpl1Transport(BaseTransport): Entropic regularization parameter reg_cl : float, optional (default=0.1) Class regularization parameter - mapping : string, optional (default="barycentric") - The kind of mapping to apply to transport samples from a domain into - another one. - if "barycentric" only the samples used to estimate the coupling can - be transported from a domain to another one. - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - distribution : string, optional (default="uniform") - The kind of distribution estimation to employ max_iter : int, float, optional (default=10) The minimum number of iteration before stopping the optimization algorithm if no it has not converged max_inner_iter : int, float, optional (default=200) The number of iteration in the inner loop - verbose : int, optional (default=0) + log : bool, optional (default=False) + Controls the logs of the optimization algorithm + tol : float, optional (default=10e-9) + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional (default=False) Controls the verbosity of the optimization algorithm + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. limit_max: float, optional (defaul=np.infty) Controls the semi supervised mode. Transport between labeled source - and target samples of different classes will exhibit an infinite cost + and target samples of different classes will exhibit a cost defined by + limit_max. Attributes ---------- @@ -1504,27 +1510,28 @@ class SinkhornL1l2Transport(BaseTransport): Entropic regularization parameter reg_cl : float, optional (default=0.1) Class regularization parameter - mapping : string, optional (default="barycentric") - The kind of mapping to apply to transport samples from a domain into - another one. - if "barycentric" only the samples used to estimate the coupling can - be transported from a domain to another one. - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - distribution : string, optional (default="uniform") - The kind of distribution estimation to employ max_iter : int, float, optional (default=10) The minimum number of iteration before stopping the optimization algorithm if no it has not converged max_inner_iter : int, float, optional (default=200) The number of iteration in the inner loop - verbose : int, optional (default=0) + tol : float, optional (default=10e-9) + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional (default=False) Controls the verbosity of the optimization algorithm - log : int, optional (default=0) + log : bool, optional (default=False) Controls the logs of the optimization algorithm + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. limit_max: float, optional (default=10) Controls the semi supervised mode. Transport between labeled source and target samples of different classes will exhibit an infinite cost @@ -1646,10 +1653,12 @@ class MappingTransport(BaseEstimator): Max number of iterations (inner CG solver) inner_tol : float, optional (default=1e-6) Stop threshold on error (inner CG solver) (>0) - verbose : bool, optional (default=False) - Print information along iterations log : bool, optional (default=False) record log if True + verbose : bool, optional (default=False) + Print information along iterations + verbose2 : bool, optional (default=False) + Print information along iterations Attributes ---------- @@ -1786,3 +1795,122 @@ class MappingTransport(BaseEstimator): transp_Xs = K.dot(self.mapping_) return transp_Xs + + +class UnbalancedSinkhornTransport(BaseTransport): + + """Domain Adapatation unbalanced OT method based on sinkhorn algorithm + + Parameters + ---------- + reg_e : float, optional (default=1) + Entropic regularization parameter + reg_m : float, optional (default=0.1) + Mass regularization parameter + method : str + method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or + 'sinkhorn_epsilon_scaling', see those function for specific parameters + max_iter : int, float, optional (default=10) + The minimum number of iteration before stopping the optimization + algorithm if no it has not converged + tol : float, optional (default=10e-9) + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional (default=False) + Controls the verbosity of the optimization algorithm + log : bool, optional (default=False) + Controls the logs of the optimization algorithm + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. + limit_max: float, optional (default=10) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit an infinite cost + (10 times the maximum value of the cost matrix) + + Attributes + ---------- + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + log_ : dictionary + The dictionary of log, empty dic if parameter log is not True + + References + ---------- + + .. [1] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprint + arXiv:1607.05816. + + """ + + def __init__(self, reg_e=1., reg_m=0.1, method='sinkhorn', + max_iter=10, tol=1e-9, verbose=False, log=False, + metric="sqeuclidean", norm=None, + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans', limit_max=10): + + self.reg_e = reg_e + self.reg_m = reg_m + self.method = method + self.max_iter = max_iter + self.tol = tol + self.verbose = verbose + self.log = log + self.metric = metric + self.norm = norm + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + self.limit_max = limit_max + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt): + + super(UnbalancedSinkhornTransport, self).fit(Xs, ys, Xt, yt) + + returned_ = sinkhorn_unbalanced( + a=self.mu_s, b=self.mu_t, M=self.cost_, + reg=self.reg_e, reg_m=self.reg_m, method=self.method, + numItermax=self.max_iter, stopThr=self.tol, + verbose=self.verbose, log=self.log) + + # deal with the value of log + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + + return self diff --git a/ot/datasets.py b/ot/datasets.py index e76e75d..ba0cfd9 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -17,7 +17,6 @@ def make_1D_gauss(n, m, s): Parameters ---------- - n : int number of bins in the histogram m : float @@ -25,12 +24,10 @@ def make_1D_gauss(n, m, s): s : float standard deviaton of the gaussian distribution - Returns ------- - h : np.array (n,) - 1D histogram for a gaussian distribution - + h : ndarray (n,) + 1D histogram for a gaussian distribution """ x = np.arange(n, dtype=np.float64) h = np.exp(-(x - m)**2 / (2 * s**2)) @@ -44,16 +41,15 @@ def get_1D_gauss(n, m, sigma): def make_2D_samples_gauss(n, m, sigma, random_state=None): - """return n samples drawn from 2D gaussian N(m,sigma) + """Return n samples drawn from 2D gaussian N(m,sigma) Parameters ---------- - n : int number of samples to make - m : np.array (2,) + m : ndarray, shape (2,) mean value of the gaussian distribution - sigma : np.array (2,2) + sigma : ndarray, shape (2, 2) covariance matrix of the gaussian distribution random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; @@ -63,9 +59,8 @@ def make_2D_samples_gauss(n, m, sigma, random_state=None): Returns ------- - X : np.array (n,2) - n samples drawn from N(m,sigma) - + X : ndarray, shape (n, 2) + n samples drawn from N(m, sigma). """ generator = check_random_state(random_state) @@ -86,11 +81,10 @@ def get_2D_samples_gauss(n, m, sigma, random_state=None): def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): - """ dataset generation for classification problems + """Dataset generation for classification problems Parameters ---------- - dataset : str type of classification problem (see code) n : int @@ -105,13 +99,11 @@ def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): Returns ------- - X : np.array (n,d) - n observation of size d - y : np.array (n,) - labels of the samples - + X : ndarray, shape (n, d) + n observation of size d + y : ndarray, shape (n,) + labels of the samples. """ - generator = check_random_state(random_state) if dataset.lower() == '3gauss': @@ -1,6 +1,12 @@ # -*- coding: utf-8 -*- """ Dimension reduction with optimal transport + + +.. warning:: + Note that by default the module is not import in :mod:`ot`. In order to + use it you need to explicitely import :mod:`ot.dr` + """ # Author: Remi Flamary <remi.flamary@unice.fr> @@ -43,30 +49,25 @@ def split_classes(X, y): def fda(X, y, p=2, reg=1e-16): - """ - Fisher Discriminant Analysis - + """Fisher Discriminant Analysis Parameters ---------- - X : numpy.ndarray (n,d) - Training samples - y : np.ndarray (n,) - labels for training samples + X : ndarray, shape (n, d) + Training samples. + y : ndarray, shape (n,) + Labels for training samples. p : int, optional - size of dimensionnality reduction + Size of dimensionnality reduction. reg : float, optional Regularization term >0 (ridge regularization) - Returns ------- - P : (d x p) ndarray + P : ndarray, shape (d, p) Optimal transportation matrix for the given parameters - proj : fun + proj : callable projection function including mean centering - - """ mx = np.mean(X) @@ -124,37 +125,33 @@ def wda(X, y, p=2, reg=1, k=10, solver=None, maxiter=100, verbose=0, P0=None): Parameters ---------- - X : numpy.ndarray (n,d) - Training samples - y : np.ndarray (n,) - labels for training samples + X : ndarray, shape (n, d) + Training samples. + y : ndarray, shape (n,) + Labels for training samples. p : int, optional - size of dimensionnality reduction + Size of dimensionnality reduction. reg : float, optional Regularization term >0 (entropic regularization) - solver : str, optional - None for steepest decsent or 'TrustRegions' for trust regions algorithm - else shoudl be a pymanopt.solvers - P0 : numpy.ndarray (d,p) - Initial starting point for projection + solver : None | str, optional + None for steepest descent or 'TrustRegions' for trust regions algorithm + else should be a pymanopt.solvers + P0 : ndarray, shape (d, p) + Initial starting point for projection. verbose : int, optional - Print information along iterations - - + Print information along iterations. Returns ------- - P : (d x p) ndarray + P : ndarray, shape (d, p) Optimal transportation matrix for the given parameters - proj : fun - projection function including mean centering - + proj : callable + Projection function including mean centering. References ---------- - - .. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. - + .. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). + Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. """ # noqa mx = np.mean(X) diff --git a/ot/externals/funcsigs.py b/ot/externals/funcsigs.py index c73fdc9..106bde7 100644 --- a/ot/externals/funcsigs.py +++ b/ot/externals/funcsigs.py @@ -126,8 +126,8 @@ def signature(obj): new_params[arg_name] = param.replace(default=arg_value, _partial_kwarg=True) - elif (param.kind not in (_VAR_KEYWORD, _VAR_POSITIONAL) and - not param._partial_kwarg): + elif (param.kind not in (_VAR_KEYWORD, _VAR_POSITIONAL) + and not param._partial_kwarg): new_params.pop(arg_name) return sig.replace(parameters=new_params.values()) @@ -333,11 +333,11 @@ class Parameter(object): raise TypeError(msg) def __eq__(self, other): - return (issubclass(other.__class__, Parameter) and - self._name == other._name and - self._kind == other._kind and - self._default == other._default and - self._annotation == other._annotation) + return (issubclass(other.__class__, Parameter) + and self._name == other._name + and self._kind == other._kind + and self._default == other._default + and self._annotation == other._annotation) def __ne__(self, other): return not self.__eq__(other) @@ -372,8 +372,8 @@ class BoundArguments(object): def args(self): args = [] for param_name, param in self._signature.parameters.items(): - if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) or - param._partial_kwarg): + if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) + or param._partial_kwarg): # Keyword arguments mapped by 'functools.partial' # (Parameter._partial_kwarg is True) are mapped # in 'BoundArguments.kwargs', along with VAR_KEYWORD & @@ -402,8 +402,8 @@ class BoundArguments(object): kwargs_started = False for param_name, param in self._signature.parameters.items(): if not kwargs_started: - if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) or - param._partial_kwarg): + if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) + or param._partial_kwarg): kwargs_started = True else: if param_name not in self.arguments: @@ -432,9 +432,9 @@ class BoundArguments(object): raise TypeError(msg) def __eq__(self, other): - return (issubclass(other.__class__, BoundArguments) and - self.signature == other.signature and - self.arguments == other.arguments) + return (issubclass(other.__class__, BoundArguments) + and self.signature == other.signature + and self.arguments == other.arguments) def __ne__(self, other): return not self.__eq__(other) @@ -612,9 +612,9 @@ class Signature(object): raise TypeError(msg) def __eq__(self, other): - if (not issubclass(type(other), Signature) or - self.return_annotation != other.return_annotation or - len(self.parameters) != len(other.parameters)): + if (not issubclass(type(other), Signature) + or self.return_annotation != other.return_annotation + or len(self.parameters) != len(other.parameters)): return False other_positions = dict((param, idx) @@ -635,8 +635,8 @@ class Signature(object): except KeyError: return False else: - if (idx != other_idx or - param != other.parameters[param_name]): + if (idx != other_idx + or param != other.parameters[param_name]): return False return True @@ -688,8 +688,8 @@ class Signature(object): raise TypeError(msg) parameters_ex = (param,) break - elif (param.kind == _VAR_KEYWORD or - param.default is not _empty): + elif (param.kind == _VAR_KEYWORD + or param.default is not _empty): # That's fine too - we have a default value for this # parameter. So, lets start parsing `kwargs`, starting # with the current parameter @@ -755,8 +755,8 @@ class Signature(object): # if it has a default value, or it is an '*args'-like # parameter, left alone by the processing of positional # arguments. - if (not partial and param.kind != _VAR_POSITIONAL and - param.default is _empty): + if (not partial and param.kind != _VAR_POSITIONAL + and param.default is _empty): raise TypeError('{arg!r} parameter lacking default value'. format(arg=param_name)) diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index deda6b1..1ab95bb 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -5,11 +5,15 @@ This module provides GPU implementation for several OT solvers and utility functions. The GPU backend in handled by `cupy <https://cupy.chainer.org/>`_. +.. warning:: + Note that by default the module is not import in :mod:`ot`. In order to + use it you need to explicitely import :mod:`ot.gpu` . + By default, the functions in this module accept and return numpy arrays in order to proide drop-in replacement for the other POT function but the transfer between CPU en GPU comes with a significant overhead. -In order to get the best erformances, we recommend to give only cupy +In order to get the best performances, we recommend to give only cupy arrays to the functions and desactivate the conversion to numpy of the result of the function with parameter ``to_numpy=False``. diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 978b307..2e2df83 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -70,17 +70,6 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, log : dict log dictionary return only if log==True in parameters - Examples - -------- - - >>> import ot - >>> a=[.5,.5] - >>> b=[.5,.5] - >>> M=[[0.,1.],[1.,0.]] - >>> ot.sinkhorn(a,b,M,1) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) - References ---------- diff --git a/ot/gromov.py b/ot/gromov.py index 0278e99..9869341 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -1,26 +1,25 @@ -
# -*- coding: utf-8 -*-
"""
Gromov-Wasserstein transport method
-
-
"""
# Author: Erwan Vautier <erwan.vautier@gmail.com>
# Nicolas Courty <ncourty@irisa.fr>
# Rémi Flamary <remi.flamary@unice.fr>
+# Titouan Vayer <titouan.vayer@irisa.fr>
#
# License: MIT License
import numpy as np
+
from .bregman import sinkhorn
-from .utils import dist
+from .utils import dist, UndefinedParameter
from .optim import cg
-def init_matrix(C1, C2, T, p, q, loss_fun='square_loss'):
- """ Return loss matrices and tensors for Gromov-Wasserstein fast computation
+def init_matrix(C1, C2, p, q, loss_fun='square_loss'):
+ """Return loss matrices and tensors for Gromov-Wasserstein fast computation
Returns the value of \mathcal{L}(C1,C2) \otimes T with the selected loss
function as the loss function of Gromow-Wasserstein discrepancy.
@@ -32,14 +31,14 @@ def init_matrix(C1, C2, T, p, q, loss_fun='square_loss'): * C2 : Metric cost matrix in the target space
* T : A coupling between those two spaces
- The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as :
+ The square-loss function L(a,b)=|a-b|^2 is read as :
L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with :
- * f1(a)=(a^2)/2
- * f2(b)=(b^2)/2
+ * f1(a)=(a^2)
+ * f2(b)=(b^2)
* h1(a)=a
- * h2(b)=b
+ * h2(b)=2*b
- The kl-loss function L(a,b)=(1/2)*|a-b|^2 is read as :
+ The kl-loss function L(a,b)=a*log(a/b)-a+b is read as :
L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with :
* f1(a)=a*log(a)-a
* f2(b)=b
@@ -49,44 +48,42 @@ def init_matrix(C1, C2, T, p, q, loss_fun='square_loss'): Parameters
----------
C1 : ndarray, shape (ns, ns)
- Metric cost matrix in the source space
+ Metric cost matrix in the source space
C2 : ndarray, shape (nt, nt)
- Metric costfr matrix in the target space
+ Metric costfr matrix in the target space
T : ndarray, shape (ns, nt)
- Coupling between source and target spaces
+ Coupling between source and target spaces
p : ndarray, shape (ns,)
-
Returns
-------
-
constC : ndarray, shape (ns, nt)
- Constant C matrix in Eq. (6)
+ Constant C matrix in Eq. (6)
hC1 : ndarray, shape (ns, ns)
- h1(C1) matrix in Eq. (6)
+ h1(C1) matrix in Eq. (6)
hC2 : ndarray, shape (nt, nt)
- h2(C) matrix in Eq. (6)
+ h2(C) matrix in Eq. (6)
References
----------
.. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
- "Gromov-Wasserstein averaging of kernel and distance matrices."
- International Conference on Machine Learning (ICML). 2016.
+ "Gromov-Wasserstein averaging of kernel and distance matrices."
+ International Conference on Machine Learning (ICML). 2016.
"""
if loss_fun == 'square_loss':
def f1(a):
- return (a**2) / 2
+ return (a**2)
def f2(b):
- return (b**2) / 2
+ return (b**2)
def h1(a):
return a
def h2(b):
- return b
+ return 2 * b
elif loss_fun == 'kl_loss':
def f1(a):
return a * np.log(a + 1e-15) - a
@@ -112,31 +109,29 @@ def init_matrix(C1, C2, T, p, q, loss_fun='square_loss'): def tensor_product(constC, hC1, hC2, T):
- """ Return the tensor for Gromov-Wasserstein fast computation
+ """Return the tensor for Gromov-Wasserstein fast computation
The tensor is computed as described in Proposition 1 Eq. (6) in [12].
Parameters
----------
constC : ndarray, shape (ns, nt)
- Constant C matrix in Eq. (6)
+ Constant C matrix in Eq. (6)
hC1 : ndarray, shape (ns, ns)
- h1(C1) matrix in Eq. (6)
+ h1(C1) matrix in Eq. (6)
hC2 : ndarray, shape (nt, nt)
- h2(C) matrix in Eq. (6)
-
+ h2(C) matrix in Eq. (6)
Returns
-------
-
tens : ndarray, shape (ns, nt)
- \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result
+ \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result
References
----------
.. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
- "Gromov-Wasserstein averaging of kernel and distance matrices."
- International Conference on Machine Learning (ICML). 2016.
+ "Gromov-Wasserstein averaging of kernel and distance matrices."
+ International Conference on Machine Learning (ICML). 2016.
"""
A = -np.dot(hC1, T).dot(hC2.T)
@@ -146,32 +141,31 @@ def tensor_product(constC, hC1, hC2, T): def gwloss(constC, hC1, hC2, T):
- """ Return the Loss for Gromov-Wasserstein
+ """Return the Loss for Gromov-Wasserstein
The loss is computed as described in Proposition 1 Eq. (6) in [12].
Parameters
----------
constC : ndarray, shape (ns, nt)
- Constant C matrix in Eq. (6)
+ Constant C matrix in Eq. (6)
hC1 : ndarray, shape (ns, ns)
- h1(C1) matrix in Eq. (6)
+ h1(C1) matrix in Eq. (6)
hC2 : ndarray, shape (nt, nt)
- h2(C) matrix in Eq. (6)
+ h2(C) matrix in Eq. (6)
T : ndarray, shape (ns, nt)
- Current value of transport matrix T
+ Current value of transport matrix T
Returns
-------
-
loss : float
- Gromov Wasserstein loss
+ Gromov Wasserstein loss
References
----------
.. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
- "Gromov-Wasserstein averaging of kernel and distance matrices."
- International Conference on Machine Learning (ICML). 2016.
+ "Gromov-Wasserstein averaging of kernel and distance matrices."
+ International Conference on Machine Learning (ICML). 2016.
"""
@@ -181,32 +175,31 @@ def gwloss(constC, hC1, hC2, T): def gwggrad(constC, hC1, hC2, T):
- """ Return the gradient for Gromov-Wasserstein
+ """Return the gradient for Gromov-Wasserstein
The gradient is computed as described in Proposition 2 in [12].
Parameters
----------
constC : ndarray, shape (ns, nt)
- Constant C matrix in Eq. (6)
+ Constant C matrix in Eq. (6)
hC1 : ndarray, shape (ns, ns)
- h1(C1) matrix in Eq. (6)
+ h1(C1) matrix in Eq. (6)
hC2 : ndarray, shape (nt, nt)
- h2(C) matrix in Eq. (6)
+ h2(C) matrix in Eq. (6)
T : ndarray, shape (ns, nt)
- Current value of transport matrix T
+ Current value of transport matrix T
Returns
-------
-
grad : ndarray, shape (ns, nt)
Gromov Wasserstein gradient
References
----------
.. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
- "Gromov-Wasserstein averaging of kernel and distance matrices."
- International Conference on Machine Learning (ICML). 2016.
+ "Gromov-Wasserstein averaging of kernel and distance matrices."
+ International Conference on Machine Learning (ICML). 2016.
"""
return 2 * tensor_product(constC, hC1, hC2,
@@ -220,19 +213,19 @@ def update_square_loss(p, lambdas, T, Cs): Parameters
----------
- p : ndarray, shape (N,)
- masses in the targeted barycenter
+ p : ndarray, shape (N,)
+ Masses in the targeted barycenter.
lambdas : list of float
- list of the S spaces' weights
- T : list of S np.ndarray(ns,N)
- the S Ts couplings calculated at each iteration
+ List of the S spaces' weights.
+ T : list of S np.ndarray of shape (ns,N)
+ The S Ts couplings calculated at each iteration.
Cs : list of S ndarray, shape(ns,ns)
- Metric cost matrices
+ Metric cost matrices.
Returns
----------
- C : ndarray, shape (nt,nt)
- updated C matrix
+ C : ndarray, shape (nt, nt)
+ Updated C matrix.
"""
tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s])
for s in range(len(T))])
@@ -249,12 +242,12 @@ def update_kl_loss(p, lambdas, T, Cs): Parameters
----------
p : ndarray, shape (N,)
- weights in the targeted barycenter
+ Weights in the targeted barycenter.
lambdas : list of the S spaces' weights
- T : list of S np.ndarray(ns,N)
- the S Ts couplings calculated at each iteration
+ T : list of S np.ndarray of shape (ns,N)
+ The S Ts couplings calculated at each iteration.
Cs : list of S ndarray, shape(ns,ns)
- Metric cost matrices
+ Metric cost matrices.
Returns
----------
@@ -268,34 +261,33 @@ def update_kl_loss(p, lambdas, T, Cs): return np.exp(np.divide(tmpsum, ppt))
-def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, **kwargs):
+def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs):
"""
Returns the gromov-wasserstein transport between (C1,p) and (C2,q)
The function solves the following optimization problem:
.. math::
- \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}
+ GW = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}
Where :
- C1 : Metric cost matrix in the source space
- C2 : Metric cost matrix in the target space
- p : distribution in the source space
- q : distribution in the target space
- L : loss function to account for the misfit between the similarity matrices
- H : entropy
+ - C1 : Metric cost matrix in the source space
+ - C2 : Metric cost matrix in the target space
+ - p : distribution in the source space
+ - q : distribution in the target space
+ - L : loss function to account for the misfit between the similarity matrices
Parameters
----------
C1 : ndarray, shape (ns, ns)
- Metric cost matrix in the source space
+ Metric cost matrix in the source space
C2 : ndarray, shape (nt, nt)
- Metric costfr matrix in the target space
- p : ndarray, shape (ns,)
- distribution in the source space
- q : ndarray, shape (nt,)
- distribution in the target space
- loss_fun : string
+ Metric costfr matrix in the target space
+ p : ndarray, shape (ns,)
+ Distribution in the source space
+ q : ndarray, shape (nt,)
+ Distribution in the target space
+ loss_fun : str
loss function used for the solver either 'square_loss' or 'kl_loss'
max_iter : int, optional
@@ -306,16 +298,19 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, **kwargs): Print information along iterations
log : bool, optional
record log if True
+ armijo : bool, optional
+ If True the steps of the line-search is found via an armijo research. Else closed form is used.
+ If there is convergence issues use False.
**kwargs : dict
- parameters can be directly pased to the ot.optim.cg solver
+ parameters can be directly passed to the ot.optim.cg solver
Returns
-------
T : ndarray, shape (ns, nt)
- coupling between the two spaces that minimizes :
+ Doupling between the two spaces that minimizes:
\sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}
log : dict
- convergence information and loss
+ Convergence information and loss.
References
----------
@@ -329,9 +324,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, **kwargs): """
- T = np.eye(len(p), len(q))
-
- constC, hC1, hC2 = init_matrix(C1, C2, T, p, q, loss_fun)
+ constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun)
G0 = p[:, None] * q[None, :]
@@ -342,43 +335,41 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, **kwargs): return gwggrad(constC, hC1, hC2, G)
if log:
- res, log = cg(p, q, 0, 1, f, df, G0, log=True, **kwargs)
+ res, log = cg(p, q, 0, 1, f, df, G0, log=True, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)
log['gw_dist'] = gwloss(constC, hC1, hC2, res)
return res, log
else:
- return cg(p, q, 0, 1, f, df, G0, **kwargs)
+ return cg(p, q, 0, 1, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)
-def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, **kwargs):
+def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs):
"""
Returns the gromov-wasserstein discrepancy between (C1,p) and (C2,q)
The function solves the following optimization problem:
.. math::
- \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}
+ GW = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}
Where :
- C1 : Metric cost matrix in the source space
- C2 : Metric cost matrix in the target space
- p : distribution in the source space
- q : distribution in the target space
- L : loss function to account for the misfit between the similarity matrices
- H : entropy
+ - C1 : Metric cost matrix in the source space
+ - C2 : Metric cost matrix in the target space
+ - p : distribution in the source space
+ - q : distribution in the target space
+ - L : loss function to account for the misfit between the similarity matrices
Parameters
----------
C1 : ndarray, shape (ns, ns)
- Metric cost matrix in the source space
+ Metric cost matrix in the source space
C2 : ndarray, shape (nt, nt)
- Metric costfr matrix in the target space
- p : ndarray, shape (ns,)
- distribution in the source space
+ Metric cost matrix in the target space
+ p : ndarray, shape (ns,)
+ Distribution in the source space.
q : ndarray, shape (nt,)
- distribution in the target space
- loss_fun : string
+ Distribution in the target space.
+ loss_fun : str
loss function used for the solver either 'square_loss' or 'kl_loss'
-
max_iter : int, optional
Max number of iterations
tol : float, optional
@@ -387,6 +378,9 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, **kwargs): Print information along iterations
log : bool, optional
record log if True
+ armijo : bool, optional
+ If True the steps of the line-search is found via an armijo research. Else closed form is used.
+ If there is convergence issues use False.
Returns
-------
@@ -407,9 +401,88 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, **kwargs): """
- T = np.eye(len(p), len(q))
+ constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun)
+
+ G0 = p[:, None] * q[None, :]
+
+ def f(G):
+ return gwloss(constC, hC1, hC2, G)
+
+ def df(G):
+ return gwggrad(constC, hC1, hC2, G)
+ res, log_gw = cg(p, q, 0, 1, f, df, G0, log=True, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)
+ log_gw['gw_dist'] = gwloss(constC, hC1, hC2, res)
+ log_gw['T'] = res
+ if log:
+ return log_gw['gw_dist'], log_gw
+ else:
+ return log_gw['gw_dist']
+
+
+def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, log=False, **kwargs):
+ """
+ Computes the FGW transport between two graphs see [24]
+
+ .. math::
+ \gamma = arg\min_\gamma (1-\\alpha)*<\gamma,M>_F + \\alpha* \sum_{i,j,k,l}
+ L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}
+
+ s.t. \gamma 1 = p
+ \gamma^T 1= q
+ \gamma\geq 0
+
+ where :
+ - M is the (ns,nt) metric cost matrix
+ - :math:`f` is the regularization term ( and df is its gradient)
+ - a and b are source and target weights (sum to 1)
+ - L is a loss function to account for the misfit between the similarity matrices
- constC, hC1, hC2 = init_matrix(C1, C2, T, p, q, loss_fun)
+ The algorithm used for solving the problem is conditional gradient as discussed in [24]_
+
+ Parameters
+ ----------
+ M : ndarray, shape (ns, nt)
+ Metric cost matrix between features across domains
+ C1 : ndarray, shape (ns, ns)
+ Metric cost matrix representative of the structure in the source space
+ C2 : ndarray, shape (nt, nt)
+ Metric cost matrix representative of the structure in the target space
+ p : ndarray, shape (ns,)
+ Distribution in the source space
+ q : ndarray, shape (nt,)
+ Distribution in the target space
+ loss_fun : str, optional
+ Loss function used for the solver
+ max_iter : int, optional
+ Max number of iterations
+ tol : float, optional
+ Stop threshold on error (>0)
+ verbose : bool, optional
+ Print information along iterations
+ log : bool, optional
+ record log if True
+ armijo : bool, optional
+ If True the steps of the line-search is found via an armijo research. Else closed form is used.
+ If there is convergence issues use False.
+ **kwargs : dict
+ parameters can be directly passed to the ot.optim.cg solver
+
+ Returns
+ -------
+ gamma : ndarray, shape (ns, nt)
+ Optimal transportation matrix for the given parameters.
+ log : dict
+ Log dictionary return only if log==True in parameters.
+
+ References
+ ----------
+ .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain
+ and Courty Nicolas "Optimal Transport for structured data with
+ application on graphs", International Conference on Machine Learning
+ (ICML). 2019.
+ """
+
+ constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun)
G0 = p[:, None] * q[None, :]
@@ -418,13 +491,95 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, **kwargs): def df(G):
return gwggrad(constC, hC1, hC2, G)
- res, log = cg(p, q, 0, 1, f, df, G0, log=True, **kwargs)
- log['gw_dist'] = gwloss(constC, hC1, hC2, res)
- log['T'] = res
+
if log:
- return log['gw_dist'], log
+ res, log = cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs)
+ log['fgw_dist'] = log['loss'][::-1][0]
+ return res, log
else:
- return log['gw_dist']
+ return cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)
+
+
+def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, log=False, **kwargs):
+ """
+ Computes the FGW distance between two graphs see [24]
+
+ .. math::
+ \min_\gamma (1-\\alpha)*<\gamma,M>_F + \\alpha* \sum_{i,j,k,l}
+ L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}
+
+
+ s.t. \gamma 1 = p
+ \gamma^T 1= q
+ \gamma\geq 0
+
+ where :
+ - M is the (ns,nt) metric cost matrix
+ - :math:`f` is the regularization term ( and df is its gradient)
+ - a and b are source and target weights (sum to 1)
+ - L is a loss function to account for the misfit between the similarity matrices
+ The algorithm used for solving the problem is conditional gradient as discussed in [1]_
+
+ Parameters
+ ----------
+ M : ndarray, shape (ns, nt)
+ Metric cost matrix between features across domains
+ C1 : ndarray, shape (ns, ns)
+ Metric cost matrix respresentative of the structure in the source space.
+ C2 : ndarray, shape (nt, nt)
+ Metric cost matrix espresentative of the structure in the target space.
+ p : ndarray, shape (ns,)
+ Distribution in the source space.
+ q : ndarray, shape (nt,)
+ Distribution in the target space.
+ loss_fun : str, optional
+ Loss function used for the solver.
+ max_iter : int, optional
+ Max number of iterations
+ tol : float, optional
+ Stop threshold on error (>0)
+ verbose : bool, optional
+ Print information along iterations
+ log : bool, optional
+ Record log if True.
+ armijo : bool, optional
+ If True the steps of the line-search is found via an armijo research.
+ Else closed form is used. If there is convergence issues use False.
+ **kwargs : dict
+ Parameters can be directly pased to the ot.optim.cg solver.
+
+ Returns
+ -------
+ gamma : ndarray, shape (ns, nt)
+ Optimal transportation matrix for the given parameters.
+ log : dict
+ Log dictionary return only if log==True in parameters.
+
+ References
+ ----------
+ .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain
+ and Courty Nicolas
+ "Optimal Transport for structured data with application on graphs"
+ International Conference on Machine Learning (ICML). 2019.
+ """
+
+ constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun)
+
+ G0 = p[:, None] * q[None, :]
+
+ def f(G):
+ return gwloss(constC, hC1, hC2, G)
+
+ def df(G):
+ return gwggrad(constC, hC1, hC2, G)
+
+ res, log = cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs)
+ if log:
+ log['fgw_dist'] = log['loss'][::-1][0]
+ log['T'] = res
+ return log['fgw_dist'], log
+ else:
+ return log['fgw_dist']
def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon,
@@ -437,56 +592,55 @@ def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, The function solves the following optimization problem:
.. math::
- \GW = arg\min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T))
+ GW = arg\min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T))
- s.t. \GW 1 = p
+ s.t. T 1 = p
- \GW^T 1= q
+ T^T 1= q
- \GW\geq 0
+ T\geq 0
Where :
- C1 : Metric cost matrix in the source space
- C2 : Metric cost matrix in the target space
- p : distribution in the source space
- q : distribution in the target space
- L : loss function to account for the misfit between the similarity matrices
- H : entropy
+ - C1 : Metric cost matrix in the source space
+ - C2 : Metric cost matrix in the target space
+ - p : distribution in the source space
+ - q : distribution in the target space
+ - L : loss function to account for the misfit between the similarity matrices
+ - H : entropy
Parameters
----------
C1 : ndarray, shape (ns, ns)
- Metric cost matrix in the source space
+ Metric cost matrix in the source space
C2 : ndarray, shape (nt, nt)
- Metric costfr matrix in the target space
+ Metric costfr matrix in the target space
p : ndarray, shape (ns,)
- distribution in the source space
+ Distribution in the source space
q : ndarray, shape (nt,)
- distribution in the target space
+ Distribution in the target space
loss_fun : string
- loss function used for the solver either 'square_loss' or 'kl_loss'
+ Loss function used for the solver either 'square_loss' or 'kl_loss'
epsilon : float
Regularization term >0
max_iter : int, optional
- Max number of iterations
+ Max number of iterations
tol : float, optional
Stop threshold on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
- record log if True
+ Record log if True.
Returns
-------
T : ndarray, shape (ns, nt)
- coupling between the two spaces that minimizes :
- \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T))
+ Optimal coupling between the two spaces
References
----------
.. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
- "Gromov-Wasserstein averaging of kernel and distance matrices."
- International Conference on Machine Learning (ICML). 2016.
+ "Gromov-Wasserstein averaging of kernel and distance matrices."
+ International Conference on Machine Learning (ICML). 2016.
"""
@@ -495,7 +649,7 @@ def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, T = np.outer(p, q) # Initialization
- constC, hC1, hC2 = init_matrix(C1, C2, T, p, q, loss_fun)
+ constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun)
cpt = 0
err = 1
@@ -545,28 +699,28 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, The function solves the following optimization problem:
.. math::
- \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T))
+ GW = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T))
Where :
- C1 : Metric cost matrix in the source space
- C2 : Metric cost matrix in the target space
- p : distribution in the source space
- q : distribution in the target space
- L : loss function to account for the misfit between the similarity matrices
- H : entropy
+ - C1 : Metric cost matrix in the source space
+ - C2 : Metric cost matrix in the target space
+ - p : distribution in the source space
+ - q : distribution in the target space
+ - L : loss function to account for the misfit between the similarity matrices
+ - H : entropy
Parameters
----------
C1 : ndarray, shape (ns, ns)
- Metric cost matrix in the source space
+ Metric cost matrix in the source space
C2 : ndarray, shape (nt, nt)
- Metric costfr matrix in the target space
+ Metric costfr matrix in the target space
p : ndarray, shape (ns,)
- distribution in the source space
+ Distribution in the source space
q : ndarray, shape (nt,)
- distribution in the target space
- loss_fun : string
- loss function used for the solver either 'square_loss' or 'kl_loss'
+ Distribution in the target space
+ loss_fun : str
+ Loss function used for the solver either 'square_loss' or 'kl_loss'
epsilon : float
Regularization term >0
max_iter : int, optional
@@ -576,7 +730,7 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, verbose : bool, optional
Print information along iterations
log : bool, optional
- record log if True
+ Record log if True.
Returns
-------
@@ -586,11 +740,10 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, References
----------
.. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
- "Gromov-Wasserstein averaging of kernel and distance matrices."
- International Conference on Machine Learning (ICML). 2016.
+ "Gromov-Wasserstein averaging of kernel and distance matrices."
+ International Conference on Machine Learning (ICML). 2016.
"""
-
gw, logv = entropic_gromov_wasserstein(
C1, C2, p, q, loss_fun, epsilon, max_iter, tol, verbose, log=True)
@@ -612,29 +765,31 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, The function solves the following optimization problem:
.. math::
- C = argmin_C\in R^{NxN} \sum_s \lambda_s GW(C,Cs,p,ps)
+ C = argmin_{C\in R^{NxN}} \sum_s \lambda_s GW(C,C_s,p,p_s)
Where :
- Cs : metric cost matrix
- ps : distribution
+ - :math:`C_s` : metric cost matrix
+ - :math:`p_s` : distribution
Parameters
----------
- N : Integer
- Size of the targeted barycenter
- Cs : list of S np.ndarray(ns,ns)
- Metric cost matrices
- ps : list of S np.ndarray(ns,)
- sample weights in the S spaces
- p : ndarray, shape(N,)
- weights in the targeted barycenter
+ N : int
+ Size of the targeted barycenter
+ Cs : list of S np.ndarray of shape (ns,ns)
+ Metric cost matrices
+ ps : list of S np.ndarray of shape (ns,)
+ Sample weights in the S spaces
+ p : ndarray, shape(N,)
+ Weights in the targeted barycenter
lambdas : list of float
- list of the S spaces' weights
- loss_fun : tensor-matrix multiplication function based on specific loss function
- update : function(p,lambdas,T,Cs) that updates C according to a specific Kernel
- with the S Ts couplings calculated at each iteration
+ List of the S spaces' weights.
+ loss_fun : callable
+ Tensor-matrix multiplication function based on specific loss function.
+ update : callable
+ function(p,lambdas,T,Cs) that updates C according to a specific Kernel
+ with the S Ts couplings calculated at each iteration
epsilon : float
Regularization term >0
max_iter : int, optional
@@ -642,11 +797,11 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, tol : float, optional
Stop threshol on error (>0)
verbose : bool, optional
- Print information along iterations
+ Print information along iterations.
log : bool, optional
- record log if True
- init_C : bool, ndarray, shape(N,N)
- random initial value for the C matrix provided by user
+ Record log if True.
+ init_C : bool | ndarray, shape (N, N)
+ Random initial value for the C matrix provided by user.
Returns
-------
@@ -656,9 +811,8 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, References
----------
.. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
- "Gromov-Wasserstein averaging of kernel and distance matrices."
- International Conference on Machine Learning (ICML). 2016.
-
+ "Gromov-Wasserstein averaging of kernel and distance matrices."
+ International Conference on Machine Learning (ICML). 2016.
"""
S = len(Cs)
@@ -668,6 +822,7 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, # Initialization of C : random SPD matrix (if not provided by user)
if init_C is None:
+ # XXX use random state
xalea = np.random.randn(N, 2)
C = dist(xalea, xalea)
C /= C.max()
@@ -679,7 +834,7 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, error = []
- while(err > tol and cpt < max_iter):
+ while (err > tol) and (cpt < max_iter):
Cprev = C
T = [entropic_gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon,
@@ -723,37 +878,36 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, .. math::
C = argmin_C\in R^NxN \sum_s \lambda_s GW(C,Cs,p,ps)
-
Where :
- Cs : metric cost matrix
- ps : distribution
+ - Cs : metric cost matrix
+ - ps : distribution
Parameters
----------
- N : Integer
- Size of the targeted barycenter
- Cs : list of S np.ndarray(ns,ns)
- Metric cost matrices
- ps : list of S np.ndarray(ns,)
- sample weights in the S spaces
- p : ndarray, shape(N,)
- weights in the targeted barycenter
+ N : int
+ Size of the targeted barycenter
+ Cs : list of S np.ndarray of shape (ns, ns)
+ Metric cost matrices
+ ps : list of S np.ndarray of shape (ns,)
+ Sample weights in the S spaces
+ p : ndarray, shape (N,)
+ Weights in the targeted barycenter
lambdas : list of float
- list of the S spaces' weights
+ List of the S spaces' weights
loss_fun : tensor-matrix multiplication function based on specific loss function
update : function(p,lambdas,T,Cs) that updates C according to a specific Kernel
with the S Ts couplings calculated at each iteration
max_iter : int, optional
Max number of iterations
tol : float, optional
- Stop threshol on error (>0)
+ Stop threshol on error (>0).
verbose : bool, optional
- Print information along iterations
+ Print information along iterations.
log : bool, optional
- record log if True
- init_C : bool, ndarray, shape(N,N)
- random initial value for the C matrix provided by user
+ Record log if True.
+ init_C : bool | ndarray, shape(N,N)
+ Random initial value for the C matrix provided by user.
Returns
-------
@@ -763,11 +917,10 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, References
----------
.. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
- "Gromov-Wasserstein averaging of kernel and distance matrices."
- International Conference on Machine Learning (ICML). 2016.
+ "Gromov-Wasserstein averaging of kernel and distance matrices."
+ International Conference on Machine Learning (ICML). 2016.
"""
-
S = len(Cs)
Cs = [np.asarray(Cs[s], dtype=np.float64) for s in range(S)]
@@ -775,6 +928,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, # Initialization of C : random SPD matrix (if not provided by user)
if init_C is None:
+ # XXX : should use a random state and not use the global seed
xalea = np.random.randn(N, 2)
C = dist(xalea, xalea)
C /= C.max()
@@ -815,3 +969,209 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, cpt += 1
return C
+
+
+def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_features=False,
+ p=None, loss_fun='square_loss', max_iter=100, tol=1e-9,
+ verbose=False, log=False, init_C=None, init_X=None):
+ """Compute the fgw barycenter as presented eq (5) in [24].
+
+ Parameters
+ ----------
+ N : integer
+ Desired number of samples of the target barycenter
+ Ys: list of ndarray, each element has shape (ns,d)
+ Features of all samples
+ Cs : list of ndarray, each element has shape (ns,ns)
+ Structure matrices of all samples
+ ps : list of ndarray, each element has shape (ns,)
+ Masses of all samples.
+ lambdas : list of float
+ List of the S spaces' weights
+ alpha : float
+ Alpha parameter for the fgw distance
+ fixed_structure : bool
+ Whether to fix the structure of the barycenter during the updates
+ fixed_features : bool
+ Whether to fix the feature of the barycenter during the updates
+ init_C : ndarray, shape (N,N), optional
+ Initialization for the barycenters' structure matrix. If not set
+ a random init is used.
+ init_X : ndarray, shape (N,d), optional
+ Initialization for the barycenters' features. If not set a
+ random init is used.
+
+ Returns
+ -------
+ X : ndarray, shape (N, d)
+ Barycenters' features
+ C : ndarray, shape (N, N)
+ Barycenters' structure matrix
+ log_: dict
+ Only returned when log=True. It contains the keys:
+ T : list of (N,ns) transport matrices
+ Ms : all distance matrices between the feature of the barycenter and the
+ other features dist(X,Ys) shape (N,ns)
+
+ References
+ ----------
+ .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain
+ and Courty Nicolas
+ "Optimal Transport for structured data with application on graphs"
+ International Conference on Machine Learning (ICML). 2019.
+ """
+ S = len(Cs)
+ d = Ys[0].shape[1] # dimension on the node features
+ if p is None:
+ p = np.ones(N) / N
+
+ Cs = [np.asarray(Cs[s], dtype=np.float64) for s in range(S)]
+ Ys = [np.asarray(Ys[s], dtype=np.float64) for s in range(S)]
+
+ lambdas = np.asarray(lambdas, dtype=np.float64)
+
+ if fixed_structure:
+ if init_C is None:
+ raise UndefinedParameter('If C is fixed it must be initialized')
+ else:
+ C = init_C
+ else:
+ if init_C is None:
+ xalea = np.random.randn(N, 2)
+ C = dist(xalea, xalea)
+ else:
+ C = init_C
+
+ if fixed_features:
+ if init_X is None:
+ raise UndefinedParameter('If X is fixed it must be initialized')
+ else:
+ X = init_X
+ else:
+ if init_X is None:
+ X = np.zeros((N, d))
+ else:
+ X = init_X
+
+ T = [np.outer(p, q) for q in ps]
+
+ Ms = [np.asarray(dist(X, Ys[s]), dtype=np.float64) for s in range(len(Ys))] # Ms is N,ns
+
+ cpt = 0
+ err_feature = 1
+ err_structure = 1
+
+ if log:
+ log_ = {}
+ log_['err_feature'] = []
+ log_['err_structure'] = []
+ log_['Ts_iter'] = []
+
+ while((err_feature > tol or err_structure > tol) and cpt < max_iter):
+ Cprev = C
+ Xprev = X
+
+ if not fixed_features:
+ Ys_temp = [y.T for y in Ys]
+ X = update_feature_matrix(lambdas, Ys_temp, T, p).T
+
+ Ms = [np.asarray(dist(X, Ys[s]), dtype=np.float64) for s in range(len(Ys))]
+
+ if not fixed_structure:
+ if loss_fun == 'square_loss':
+ T_temp = [t.T for t in T]
+ C = update_sructure_matrix(p, lambdas, T_temp, Cs)
+
+ T = [fused_gromov_wasserstein((1 - alpha) * Ms[s], C, Cs[s], p, ps[s], loss_fun, alpha,
+ numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)]
+
+ # T is N,ns
+ err_feature = np.linalg.norm(X - Xprev.reshape(N, d))
+ err_structure = np.linalg.norm(C - Cprev)
+
+ if log:
+ log_['err_feature'].append(err_feature)
+ log_['err_structure'].append(err_structure)
+ log_['Ts_iter'].append(T)
+
+ if verbose:
+ if cpt % 200 == 0:
+ print('{:5s}|{:12s}'.format(
+ 'It.', 'Err') + '\n' + '-' * 19)
+ print('{:5d}|{:8e}|'.format(cpt, err_structure))
+ print('{:5d}|{:8e}|'.format(cpt, err_feature))
+
+ cpt += 1
+
+ if log:
+ log_['T'] = T # from target to Ys
+ log_['p'] = p
+ log_['Ms'] = Ms
+
+ if log:
+ return X, C, log_
+ else:
+ return X, C
+
+
+def update_sructure_matrix(p, lambdas, T, Cs):
+ """Updates C according to the L2 Loss kernel with the S Ts couplings.
+
+ It is calculated at each iteration
+
+ Parameters
+ ----------
+ p : ndarray, shape (N,)
+ Masses in the targeted barycenter.
+ lambdas : list of float
+ List of the S spaces' weights.
+ T : list of S ndarray of shape (ns, N)
+ The S Ts couplings calculated at each iteration.
+ Cs : list of S ndarray, shape (ns, ns)
+ Metric cost matrices.
+
+ Returns
+ -------
+ C : ndarray, shape (nt, nt)
+ Updated C matrix.
+ """
+ tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))])
+ ppt = np.outer(p, p)
+
+ return np.divide(tmpsum, ppt)
+
+
+def update_feature_matrix(lambdas, Ys, Ts, p):
+ """Updates the feature with respect to the S Ts couplings.
+
+
+ See "Solving the barycenter problem with Block Coordinate Descent (BCD)"
+ in [24] calculated at each iteration
+
+ Parameters
+ ----------
+ p : ndarray, shape (N,)
+ masses in the targeted barycenter
+ lambdas : list of float
+ List of the S spaces' weights
+ Ts : list of S np.ndarray(ns,N)
+ the S Ts couplings calculated at each iteration
+ Ys : list of S ndarray, shape(d,ns)
+ The features.
+
+ Returns
+ -------
+ X : ndarray, shape (d, N)
+
+ References
+ ----------
+ .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain
+ and Courty Nicolas
+ "Optimal Transport for structured data with application on graphs"
+ International Conference on Machine Learning (ICML). 2019.
+ """
+ p = np.array(1. / p).reshape(-1,)
+
+ tmpsum = sum([lambdas[s] * np.dot(Ys[s], Ts[s].T) * p[None, :] for s in range(len(Ts))])
+
+ return tmpsum
diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h index f42e222..2adaace 100644 --- a/ot/lp/EMD.h +++ b/ot/lp/EMD.h @@ -32,4 +32,9 @@ enum ProblemType { int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter); +int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, + long *iG, long *jG, double *G, long * nG, + double* alpha, double* beta, double *cost, int maxIter); + + #endif diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index fc7ca63..28e4af2 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -17,13 +17,13 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, double* alpha, double* beta, double *cost, int maxIter) { -// beware M and C anre strored in row major C style!!! - int n, m, i, cur; + // beware M and C anre strored in row major C style!!! + int n, m, i, cur; typedef FullBipartiteDigraph Digraph; - DIGRAPH_TYPEDEFS(FullBipartiteDigraph); + DIGRAPH_TYPEDEFS(FullBipartiteDigraph); - // Get the number of non zero coordinates for r and c + // Get the number of non zero coordinates for r and c n=0; for (int i=0; i<n1; i++) { double val=*(X+i); @@ -105,3 +105,186 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, return ret; } + + +int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, + long *iG, long *jG, double *G, long * nG, + double* alpha, double* beta, double *cost, int maxIter) { + // beware M and C anre strored in row major C style!!! + + // Get the number of non zero coordinates for r and c and vectors + int n, m, i, cur; + + typedef FullBipartiteDigraph Digraph; + DIGRAPH_TYPEDEFS(FullBipartiteDigraph); + + // Get the number of non zero coordinates for r and c + n=0; + for (int i=0; i<n1; i++) { + double val=*(X+i); + if (val>0) { + n++; + }else if(val<0){ + return INFEASIBLE; + } + } + m=0; + for (int i=0; i<n2; i++) { + double val=*(Y+i); + if (val>0) { + m++; + }else if(val<0){ + return INFEASIBLE; + } + } + + // Define the graph + + std::vector<int> indI(n), indJ(m); + std::vector<double> weights1(n), weights2(m); + Digraph di(n, m); + NetworkSimplexSimple<Digraph,double,double, node_id_type> net(di, true, n+m, n*m, maxIter); + + // Set supply and demand, don't account for 0 values (faster) + + cur=0; + for (int i=0; i<n1; i++) { + double val=*(X+i); + if (val>0) { + weights1[ cur ] = val; + indI[cur++]=i; + } + } + + // Demand is actually negative supply... + + cur=0; + for (int i=0; i<n2; i++) { + double val=*(Y+i); + if (val>0) { + weights2[ cur ] = -val; + indJ[cur++]=i; + } + } + + // Define the graph + net.supplyMap(&weights1[0], n, &weights2[0], m); + + // Set the cost of each edge + for (int i=0; i<n; i++) { + for (int j=0; j<m; j++) { + double val=*(D+indI[i]*n2+indJ[j]); + net.setCost(di.arcFromId(i*m+j), val); + } + } + + + // Solve the problem with the network simplex algorithm + + int ret=net.run(); + if (ret==(int)net.OPTIMAL || ret==(int)net.MAX_ITER_REACHED) { + *cost = 0; + Arc a; di.first(a); + cur=0; + for (; a != INVALID; di.next(a)) { + int i = di.source(a); + int j = di.target(a); + double flow = net.flow(a); + if (flow>0) + { + *cost += flow * (*(D+indI[i]*n2+indJ[j-n])); + + *(G+cur) = flow; + *(iG+cur) = indI[i]; + *(jG+cur) = indJ[j-n]; + *(alpha + indI[i]) = -net.potential(i); + *(beta + indJ[j-n]) = net.potential(j); + cur++; + } + } + *nG=cur; // nb of value +1 for numpy indexing + + } + + + return ret; +} + +int EMD_wrap_all_sparse(int n1, int n2, double *X, double *Y, + long *iD, long *jD, double *D, long nD, + long *iG, long *jG, double *G, long * nG, + double* alpha, double* beta, double *cost, int maxIter) { + // beware M and C anre strored in row major C style!!! + + // Get the number of non zero coordinates for r and c and vectors + int n, m, cur; + + typedef FullBipartiteDigraph Digraph; + DIGRAPH_TYPEDEFS(FullBipartiteDigraph); + + n=n1; + m=n2; + + + // Define the graph + + + std::vector<double> weights2(m); + Digraph di(n, m); + NetworkSimplexSimple<Digraph,double,double, node_id_type> net(di, true, n+m, n*m, maxIter); + + // Set supply and demand, don't account for 0 values (faster) + + + // Demand is actually negative supply... + + cur=0; + for (int i=0; i<n2; i++) { + double val=*(Y+i); + if (val>0) { + weights2[ cur ] = -val; + } + } + + // Define the graph + net.supplyMap(X, n, &weights2[0], m); + + // Set the cost of each edge + for (int k=0; k<nD; k++) { + int i = iD[k]; + int j = jD[k]; + net.setCost(di.arcFromId(i*m+j), D[k]); + + } + + + // Solve the problem with the network simplex algorithm + + int ret=net.run(); + if (ret==(int)net.OPTIMAL || ret==(int)net.MAX_ITER_REACHED) { + *cost = net.totalCost(); + Arc a; di.first(a); + cur=0; + for (; a != INVALID; di.next(a)) { + int i = di.source(a); + int j = di.target(a); + double flow = net.flow(a); + if (flow>0) + { + + *(G+cur) = flow; + *(iG+cur) = i; + *(jG+cur) = j-n; + *(alpha + i) = -net.potential(i); + *(beta + j-n) = net.potential(j); + cur++; + } + } + *nG=cur; // nb of value +1 for numpy indexing + + } + + + return ret; +} + diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 02cbd8c..cdd505d 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -1,6 +1,9 @@ # -*- coding: utf-8 -*- """ Solvers for the original linear program OT problem + + + """ # Author: Remi Flamary <remi.flamary@unice.fr> @@ -8,22 +11,171 @@ Solvers for the original linear program OT problem # License: MIT License import multiprocessing - +import sys import numpy as np +from scipy.sparse import coo_matrix from .import cvx # import compiled emd -from .emd_wrap import emd_c, check_result +from .emd_wrap import emd_c, check_result, emd_1d_sorted from ..utils import parmap from .cvx import barycenter from ..utils import dist -__all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx'] +__all__ = ['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', + 'emd_1d', 'emd2_1d', 'wasserstein_1d'] -def emd(a, b, M, numItermax=100000, log=False): - """Solves the Earth Movers distance problem and returns the OT matrix +def center_ot_dual(alpha0, beta0, a=None, b=None): + r"""Center dual OT potentials w.r.t. theirs weights + + The main idea of this function is to find unique dual potentials + that ensure some kind of centering/fairness. The main idea is to find dual potentials that lead to the same final objective value for both source and targets (see below for more details). It will help having + stability when multiple calling of the OT solver with small changes. + + Basically we add another constraint to the potential that will not + change the objective value but will ensure unicity. The constraint + is the following: + + .. math:: + \alpha^T a= \beta^T b + + in addition to the OT problem constraints. + + since :math:`\sum_i a_i=\sum_j b_j` this can be solved by adding/removing + a constant from both :math:`\alpha_0` and :math:`\beta_0`. + + .. math:: + c=\frac{\beta0^T b-\alpha_0^T a}{1^Tb+1^Ta} + + \alpha=\alpha_0+c + + \beta=\beta0+c + + Parameters + ---------- + alpha0 : (ns,) numpy.ndarray, float64 + Source dual potential + beta0 : (nt,) numpy.ndarray, float64 + Target dual potential + a : (ns,) numpy.ndarray, float64 + Source histogram (uniform weight if empty list) + b : (nt,) numpy.ndarray, float64 + Target histogram (uniform weight if empty list) + + Returns + ------- + alpha : (ns,) numpy.ndarray, float64 + Source centered dual potential + beta : (nt,) numpy.ndarray, float64 + Target centered dual potential + + """ + # if no weights are provided, use uniform + if a is None: + a = np.ones(alpha0.shape[0]) / alpha0.shape[0] + if b is None: + b = np.ones(beta0.shape[0]) / beta0.shape[0] + + # compute constant that balances the weighted sums of the duals + c = (b.dot(beta0) - a.dot(alpha0)) / (a.sum() + b.sum()) + + # update duals + alpha = alpha0 + c + beta = beta0 - c + + return alpha, beta + + +def estimate_dual_null_weights(alpha0, beta0, a, b, M): + r"""Estimate feasible values for 0-weighted dual potentials + + The feasible values are computed efficiently but rather coarsely. + + .. warning:: + This function is necessary because the C++ solver in emd_c + discards all samples in the distributions with + zeros weights. This means that while the primal variable (transport + matrix) is exact, the solver only returns feasible dual potentials + on the samples with weights different from zero. + + First we compute the constraints violations: + + .. math:: + V=\alpha+\beta^T-M + + Next we compute the max amount of violation per row (alpha) and + columns (beta) + + .. math:: + v^a_i=\max_j V_{i,j} + + v^b_j=\max_i V_{i,j} + + Finally we update the dual potential with 0 weights if a + constraint is violated + + .. math:: + \alpha_i = \alpha_i -v^a_i \quad \text{ if } a_i=0 \text{ and } v^a_i>0 + + \beta_j = \beta_j -v^b_j \quad \text{ if } b_j=0 \text{ and } v^b_j>0 + + In the end the dual potentials are centered using function + :ref:`center_ot_dual`. + + Note that all those updates do not change the objective value of the + solution but provide dual potentials that do not violate the constraints. + + Parameters + ---------- + alpha0 : (ns,) numpy.ndarray, float64 + Source dual potential + beta0 : (nt,) numpy.ndarray, float64 + Target dual potential + alpha0 : (ns,) numpy.ndarray, float64 + Source dual potential + beta0 : (nt,) numpy.ndarray, float64 + Target dual potential + a : (ns,) numpy.ndarray, float64 + Source distribution (uniform weights if empty list) + b : (nt,) numpy.ndarray, float64 + Target distribution (uniform weights if empty list) + M : (ns,nt) numpy.ndarray, float64 + Loss matrix (c-order array with type float64) + + Returns + ------- + alpha : (ns,) numpy.ndarray, float64 + Source corrected dual potential + beta : (nt,) numpy.ndarray, float64 + Target corrected dual potential + + """ + + # binary indexing of non-zeros weights + asel = a != 0 + bsel = b != 0 + + # compute dual constraints violation + constraint_violation = alpha0[:, None] + beta0[None, :] - M + + # Compute largest violation per line and columns + aviol = np.max(constraint_violation, 1) + bviol = np.max(constraint_violation, 0) + + # update corrects violation of + alpha_up = -1 * ~asel * np.maximum(aviol, 0) + beta_up = -1 * ~bsel * np.maximum(bviol, 0) + + alpha = alpha0 + alpha_up + beta = beta0 + beta_up + + return center_ot_dual(alpha, beta, a, b) + + +def emd(a, b, M, numItermax=100000, log=False, dense=True, center_dual=True): + r"""Solves the Earth Movers distance problem and returns the OT matrix .. math:: @@ -37,26 +189,37 @@ def emd(a, b, M, numItermax=100000, log=False): - M is the metric cost matrix - a and b are the sample weights + .. warning:: + Note that the M matrix needs to be a C-order numpy.array in float64 + format. + Uses the algorithm proposed in [1]_ Parameters ---------- - a : (ns,) ndarray, float64 - Source histogram (uniform weigth if empty list) - b : (nt,) ndarray, float64 - Target histogram (uniform weigth if empty list) - M : (ns,nt) ndarray, float64 - loss matrix + a : (ns,) numpy.ndarray, float64 + Source histogram (uniform weight if empty list) + b : (nt,) numpy.ndarray, float64 + Target histogram (uniform weight if empty list) + M : (ns,nt) numpy.ndarray, float64 + Loss matrix (c-order array with type float64) numItermax : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. - log: boolean, optional (default=False) + log: bool, optional (default=False) If True, returns a dictionary containing the cost and dual variables. Otherwise returns only the optimal transportation matrix. + dense: boolean, optional (default=True) + If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). + Otherwise returns a sparse representation using scipy's `coo_matrix` + format. + center_dual: boolean, optional (default=True) + If True, centers the dual potential using function + :ref:`center_ot_dual`. Returns ------- - gamma: (ns x nt) ndarray + gamma: (ns x nt) numpy.ndarray Optimal transportation matrix for the given parameters log: dict If input log is true, a dictionary containing the cost and dual @@ -74,8 +237,8 @@ def emd(a, b, M, numItermax=100000, log=False): >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] >>> ot.emd(a,b,M) - array([[ 0.5, 0. ], - [ 0. , 0.5]]) + array([[0.5, 0. ], + [0. , 0.5]]) References ---------- @@ -94,13 +257,37 @@ def emd(a, b, M, numItermax=100000, log=False): b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) - # if empty array given then use unifor distributions + # if empty array given then use uniform distributions if len(a) == 0: a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] - G, cost, u, v, result_code = emd_c(a, b, M, numItermax) + assert (a.shape[0] == M.shape[0] and b.shape[0] == M.shape[1]), \ + "Dimension mismatch, check dimensions of M with a and b" + + asel = a != 0 + bsel = b != 0 + + if dense: + G, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) + + if center_dual: + u, v = center_ot_dual(u, v, a, b) + + if np.any(~asel) or np.any(~bsel): + u, v = estimate_dual_null_weights(u, v, a, b, M) + + else: + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + + if center_dual: + u, v = center_ot_dual(u, v, a, b) + + if np.any(~asel) or np.any(~bsel): + u, v = estimate_dual_null_weights(u, v, a, b, M) + result_code_string = check_result(result_code) if log: log = {} @@ -114,8 +301,9 @@ def emd(a, b, M, numItermax=100000, log=False): def emd2(a, b, M, processes=multiprocessing.cpu_count(), - numItermax=100000, log=False, return_matrix=False): - """Solves the Earth Movers distance problem and returns the loss + numItermax=100000, log=False, dense=True, return_matrix=False, + center_dual=True): + r"""Solves the Earth Movers distance problem and returns the loss .. math:: \gamma = arg\min_\gamma <\gamma,M>_F @@ -128,16 +316,22 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), - M is the metric cost matrix - a and b are the sample weights + .. warning:: + Note that the M matrix needs to be a C-order numpy.array in float64 + format. + Uses the algorithm proposed in [1]_ Parameters ---------- - a : (ns,) ndarray, float64 - Source histogram (uniform weigth if empty list) - b : (nt,) ndarray, float64 - Target histogram (uniform weigth if empty list) - M : (ns,nt) ndarray, float64 - loss matrix + a : (ns,) numpy.ndarray, float64 + Source histogram (uniform weight if empty list) + b : (nt,) numpy.ndarray, float64 + Target histogram (uniform weight if empty list) + M : (ns,nt) numpy.ndarray, float64 + Loss matrix (c-order array with type float64) + processes : int, optional (default=nb cpu) + Nb of processes used for multiple emd computation (not used on windows) numItermax : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -146,12 +340,19 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), variables. Otherwise returns only the optimal transportation cost. return_matrix: boolean, optional (default=False) If True, returns the optimal transportation matrix in the log. + dense: boolean, optional (default=True) + If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). + Otherwise returns a sparse representation using scipy's `coo_matrix` + format. + center_dual: boolean, optional (default=True) + If True, centers the dual potential using function + :ref:`center_ot_dual`. Returns ------- gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters - log: dict + log: dictnp If input log is true, a dictionary containing the cost and dual variables and exit status @@ -187,27 +388,61 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) - # if empty array given then use unifor distributions + # problem with pikling Forks + if sys.platform.endswith('win32'): + processes = 1 + + # if empty array given then use uniform distributions if len(a) == 0: a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] + assert (a.shape[0] == M.shape[0] and b.shape[0] == M.shape[1]), \ + "Dimension mismatch, check dimensions of M with a and b" + + asel = a != 0 + if log or return_matrix: def f(b): - G, cost, u, v, resultCode = emd_c(a, b, M, numItermax) - result_code_string = check_result(resultCode) + bsel = b != 0 + if dense: + G, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) + else: + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + + if center_dual: + u, v = center_ot_dual(u, v, a, b) + + if np.any(~asel) or np.any(~bsel): + u, v = estimate_dual_null_weights(u, v, a, b, M) + + result_code_string = check_result(result_code) log = {} if return_matrix: log['G'] = G log['u'] = u log['v'] = v log['warning'] = result_code_string - log['result_code'] = resultCode + log['result_code'] = result_code return [cost, log] else: def f(b): - G, cost, u, v, result_code = emd_c(a, b, M, numItermax) + bsel = b != 0 + if dense: + G, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) + else: + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + + if center_dual: + u, v = center_ot_dual(u, v, a, b) + + if np.any(~asel) or np.any(~bsel): + u, v = estimate_dual_null_weights(u, v, a, b, M) + + result_code_string = check_result(result_code) check_result(result_code) return cost @@ -215,9 +450,12 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), return f(b) nb = b.shape[1] - res = parmap(f, [b[:, i] for i in range(nb)], processes) - return res + if processes > 1: + res = parmap(f, [b[:, i] for i in range(nb)], processes) + else: + res = list(map(f, [b[:, i].copy() for i in range(nb)])) + return res def free_support_barycenter(measures_locations, measures_weights, X_init, b=None, weights=None, numItermax=100, stopThr=1e-7, verbose=False, log=None): @@ -231,9 +469,9 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None Parameters ---------- - measures_locations : list of (k_i,d) np.ndarray + measures_locations : list of (k_i,d) numpy.ndarray The discrete support of a measure supported on k_i locations of a d-dimensional space (k_i can be different for each element of the list) - measures_weights : list of (k_i,) np.ndarray + measures_weights : list of (k_i,) numpy.ndarray Numpy arrays where each numpy array has k_i non-negatives values summing to one representing the weights of each discrete input measure X_init : (k,d) np.ndarray @@ -246,7 +484,7 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None numItermax : int, optional Max number of iterations stopThr : float, optional - Stop threshol on error (>0) + Stop threshold on error (>0) verbose : bool, optional Print information along iterations log : bool, optional @@ -272,7 +510,7 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None k = X_init.shape[0] d = X_init.shape[1] if b is None: - b = np.ones((k,))/k + b = np.ones((k,)) / k if weights is None: weights = np.ones((N,)) / N @@ -283,7 +521,7 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None displacement_square_norm = stopThr + 1. - while ( displacement_square_norm > stopThr and iter_count < numItermax ): + while (displacement_square_norm > stopThr and iter_count < numItermax): T_sum = np.zeros((k, d)) @@ -293,7 +531,7 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None T_i = emd(b, measure_weights_i, M_i) T_sum = T_sum + weight_i * np.reshape(1. / b, (-1, 1)) * np.matmul(T_i, measure_locations_i) - displacement_square_norm = np.sum(np.square(T_sum-X)) + displacement_square_norm = np.sum(np.square(T_sum - X)) if log: displacement_square_norms.append(displacement_square_norm) @@ -308,4 +546,288 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None log_dict['displacement_square_norms'] = displacement_square_norms return X, log_dict else: - return X
\ No newline at end of file + return X + + +def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, + log=False): + r"""Solves the Earth Movers distance problem between 1d measures and returns + the OT matrix + + + .. math:: + \gamma = arg\min_\gamma \sum_i \sum_j \gamma_{ij} d(x_a[i], x_b[j]) + + s.t. \gamma 1 = a, + \gamma^T 1= b, + \gamma\geq 0 + where : + + - d is the metric + - x_a and x_b are the samples + - a and b are the sample weights + + When 'minkowski' is used as a metric, :math:`d(x, y) = |x - y|^p`. + + Uses the algorithm detailed in [1]_ + + Parameters + ---------- + x_a : (ns,) or (ns, 1) ndarray, float64 + Source dirac locations (on the real line) + x_b : (nt,) or (ns, 1) ndarray, float64 + Target dirac locations (on the real line) + a : (ns,) ndarray, float64, optional + Source histogram (default is uniform weight) + b : (nt,) ndarray, float64, optional + Target histogram (default is uniform weight) + metric: str, optional (default='sqeuclidean') + Metric to be used. Only strings listed in :func:`ot.dist` are accepted. + Due to implementation details, this function runs faster when + `'sqeuclidean'`, `'cityblock'`, or `'euclidean'` metrics are used. + p: float, optional (default=1.0) + The p-norm to apply for if metric='minkowski' + dense: boolean, optional (default=True) + If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). + Otherwise returns a sparse representation using scipy's `coo_matrix` + format. Due to implementation details, this function runs faster when + `'sqeuclidean'`, `'minkowski'`, `'cityblock'`, or `'euclidean'` metrics + are used. + log: boolean, optional (default=False) + If True, returns a dictionary containing the cost. + Otherwise returns only the optimal transportation matrix. + + Returns + ------- + gamma: (ns, nt) ndarray + Optimal transportation matrix for the given parameters + log: dict + If input log is True, a dictionary containing the cost + + + Examples + -------- + + Simple example with obvious solution. The function emd_1d accepts lists and + performs automatic conversion to numpy arrays + + >>> import ot + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> x_a = [2., 0.] + >>> x_b = [0., 3.] + >>> ot.emd_1d(x_a, x_b, a, b) + array([[0. , 0.5], + [0.5, 0. ]]) + >>> ot.emd_1d(x_a, x_b) + array([[0. , 0.5], + [0.5, 0. ]]) + + References + ---------- + + .. [1] Peyré, G., & Cuturi, M. (2017). "Computational Optimal + Transport", 2018. + + See Also + -------- + ot.lp.emd : EMD for multidimensional distributions + ot.lp.emd2_1d : EMD for 1d distributions (returns cost instead of the + transportation matrix) + """ + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + x_a = np.asarray(x_a, dtype=np.float64) + x_b = np.asarray(x_b, dtype=np.float64) + + assert (x_a.ndim == 1 or x_a.ndim == 2 and x_a.shape[1] == 1), \ + "emd_1d should only be used with monodimensional data" + assert (x_b.ndim == 1 or x_b.ndim == 2 and x_b.shape[1] == 1), \ + "emd_1d should only be used with monodimensional data" + + # if empty array given then use uniform distributions + if a.ndim == 0 or len(a) == 0: + a = np.ones((x_a.shape[0],), dtype=np.float64) / x_a.shape[0] + if b.ndim == 0 or len(b) == 0: + b = np.ones((x_b.shape[0],), dtype=np.float64) / x_b.shape[0] + + x_a_1d = x_a.reshape((-1, )) + x_b_1d = x_b.reshape((-1, )) + perm_a = np.argsort(x_a_1d) + perm_b = np.argsort(x_b_1d) + + G_sorted, indices, cost = emd_1d_sorted(a, b, + x_a_1d[perm_a], x_b_1d[perm_b], + metric=metric, p=p) + G = coo_matrix((G_sorted, (perm_a[indices[:, 0]], perm_b[indices[:, 1]])), + shape=(a.shape[0], b.shape[0])) + if dense: + G = G.toarray() + if log: + log = {'cost': cost} + return G, log + return G + + +def emd2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, + log=False): + r"""Solves the Earth Movers distance problem between 1d measures and returns + the loss + + + .. math:: + \gamma = arg\min_\gamma \sum_i \sum_j \gamma_{ij} d(x_a[i], x_b[j]) + + s.t. \gamma 1 = a, + \gamma^T 1= b, + \gamma\geq 0 + where : + + - d is the metric + - x_a and x_b are the samples + - a and b are the sample weights + + When 'minkowski' is used as a metric, :math:`d(x, y) = |x - y|^p`. + + Uses the algorithm detailed in [1]_ + + Parameters + ---------- + x_a : (ns,) or (ns, 1) ndarray, float64 + Source dirac locations (on the real line) + x_b : (nt,) or (ns, 1) ndarray, float64 + Target dirac locations (on the real line) + a : (ns,) ndarray, float64, optional + Source histogram (default is uniform weight) + b : (nt,) ndarray, float64, optional + Target histogram (default is uniform weight) + metric: str, optional (default='sqeuclidean') + Metric to be used. Only strings listed in :func:`ot.dist` are accepted. + Due to implementation details, this function runs faster when + `'sqeuclidean'`, `'minkowski'`, `'cityblock'`, or `'euclidean'` metrics + are used. + p: float, optional (default=1.0) + The p-norm to apply for if metric='minkowski' + dense: boolean, optional (default=True) + If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). + Otherwise returns a sparse representation using scipy's `coo_matrix` + format. Only used if log is set to True. Due to implementation details, + this function runs faster when dense is set to False. + log: boolean, optional (default=False) + If True, returns a dictionary containing the transportation matrix. + Otherwise returns only the loss. + + Returns + ------- + loss: float + Cost associated to the optimal transportation + log: dict + If input log is True, a dictionary containing the Optimal transportation + matrix for the given parameters + + + Examples + -------- + + Simple example with obvious solution. The function emd2_1d accepts lists and + performs automatic conversion to numpy arrays + + >>> import ot + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> x_a = [2., 0.] + >>> x_b = [0., 3.] + >>> ot.emd2_1d(x_a, x_b, a, b) + 0.5 + >>> ot.emd2_1d(x_a, x_b) + 0.5 + + References + ---------- + + .. [1] Peyré, G., & Cuturi, M. (2017). "Computational Optimal + Transport", 2018. + + See Also + -------- + ot.lp.emd2 : EMD for multidimensional distributions + ot.lp.emd_1d : EMD for 1d distributions (returns the transportation matrix + instead of the cost) + """ + # If we do not return G (log==False), then we should not to cast it to dense + # (useless overhead) + G, log_emd = emd_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric=metric, p=p, + dense=dense and log, log=True) + cost = log_emd['cost'] + if log: + log_emd = {'G': G} + return cost, log_emd + return cost + + +def wasserstein_1d(x_a, x_b, a=None, b=None, p=1.): + r"""Solves the p-Wasserstein distance problem between 1d measures and returns + the distance + + .. math:: + \min_\gamma \left( \sum_i \sum_j \gamma_{ij} \|x_a[i] - x_b[j]\|^p \right)^{1/p} + + s.t. \gamma 1 = a, + \gamma^T 1= b, + \gamma\geq 0 + + where : + + - x_a and x_b are the samples + - a and b are the sample weights + + Uses the algorithm detailed in [1]_ + + Parameters + ---------- + x_a : (ns,) or (ns, 1) ndarray, float64 + Source dirac locations (on the real line) + x_b : (nt,) or (ns, 1) ndarray, float64 + Target dirac locations (on the real line) + a : (ns,) ndarray, float64, optional + Source histogram (default is uniform weight) + b : (nt,) ndarray, float64, optional + Target histogram (default is uniform weight) + p: float, optional (default=1.0) + The order of the p-Wasserstein distance to be computed + + Returns + ------- + dist: float + p-Wasserstein distance + + + Examples + -------- + + Simple example with obvious solution. The function wasserstein_1d accepts + lists and performs automatic conversion to numpy arrays + + >>> import ot + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> x_a = [2., 0.] + >>> x_b = [0., 3.] + >>> ot.wasserstein_1d(x_a, x_b, a, b) + 0.5 + >>> ot.wasserstein_1d(x_a, x_b) + 0.5 + + References + ---------- + + .. [1] Peyré, G., & Cuturi, M. (2017). "Computational Optimal + Transport", 2018. + + See Also + -------- + ot.lp.emd_1d : EMD for 1d distributions + """ + cost_emd = emd2_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric='minkowski', p=p, + dense=False, log=False) + return np.power(cost_emd, 1. / p) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 83ee6aa..d345fd4 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -10,13 +10,19 @@ Cython linker with C solver import numpy as np cimport numpy as np +from ..utils import dist + cimport cython +cimport libc.math as math import warnings cdef extern from "EMD.h": int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter) + int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, + long *iG, long *jG, double *G, long * nG, + double* alpha, double* beta, double *cost, int maxIter) cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED @@ -34,9 +40,11 @@ def check_result(result_code): return message + + @cython.boundscheck(False) @cython.wraparound(False) -def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int max_iter): +def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int max_iter, bint dense): """ Solves the Earth Movers distance problem and returns the optimal transport matrix @@ -55,33 +63,50 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod - M is the metric cost matrix - a and b are the sample weights + .. warning:: + Note that the M matrix needs to be a C-order :py.cls:`numpy.array` + + .. warning:: + The C++ solver discards all samples in the distributions with + zeros weights. This means that while the primal variable (transport + matrix) is exact, the solver only returns feasible dual potentials + on the samples with weights different from zero. + Parameters ---------- - a : (ns,) ndarray, float64 + a : (ns,) numpy.ndarray, float64 source histogram - b : (nt,) ndarray, float64 + b : (nt,) numpy.ndarray, float64 target histogram - M : (ns,nt) ndarray, float64 + M : (ns,nt) numpy.ndarray, float64 loss matrix max_iter : int The maximum number of iterations before stopping the optimization algorithm if it has not converged. - + dense : bool + Return a sparse transport matrix if set to False Returns ------- - gamma: (ns x nt) ndarray + gamma: (ns x nt) numpy.ndarray Optimal transportation matrix for the given parameters """ cdef int n1= M.shape[0] cdef int n2= M.shape[1] + cdef int nmax=n1+n2-1 + cdef int result_code = 0 + cdef int nG=0 cdef double cost=0 - cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) cdef np.ndarray[double, ndim=1, mode="c"] alpha=np.zeros(n1) cdef np.ndarray[double, ndim=1, mode="c"] beta=np.zeros(n2) + cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([0, 0]) + + cdef np.ndarray[double, ndim=1, mode="c"] Gv=np.zeros(0) + cdef np.ndarray[long, ndim=1, mode="c"] iG=np.zeros(0,dtype=np.int) + cdef np.ndarray[long, ndim=1, mode="c"] jG=np.zeros(0,dtype=np.int) if not len(a): a=np.ones((n1,))/n1 @@ -89,7 +114,112 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod if not len(b): b=np.ones((n2,))/n2 - # calling the function - cdef int result_code = EMD_wrap(n1, n2, <double*> a.data, <double*> b.data, <double*> M.data, <double*> G.data, <double*> alpha.data, <double*> beta.data, <double*> &cost, max_iter) + if dense: + # init OT matrix + G=np.zeros([n1, n2]) + + # calling the function + result_code = EMD_wrap(n1, n2, <double*> a.data, <double*> b.data, <double*> M.data, <double*> G.data, <double*> alpha.data, <double*> beta.data, <double*> &cost, max_iter) + + return G, cost, alpha, beta, result_code + + + else: + + # init sparse OT matrix + Gv=np.zeros(nmax) + iG=np.zeros(nmax,dtype=np.int) + jG=np.zeros(nmax,dtype=np.int) + + + result_code = EMD_wrap_return_sparse(n1, n2, <double*> a.data, <double*> b.data, <double*> M.data, <long*> iG.data, <long*> jG.data, <double*> Gv.data, <long*> &nG, <double*> alpha.data, <double*> beta.data, <double*> &cost, max_iter) - return G, cost, alpha, beta, result_code + + return Gv[:nG], iG[:nG], jG[:nG], cost, alpha, beta, result_code + + + +@cython.boundscheck(False) +@cython.wraparound(False) +def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, + np.ndarray[double, ndim=1, mode="c"] v_weights, + np.ndarray[double, ndim=1, mode="c"] u, + np.ndarray[double, ndim=1, mode="c"] v, + str metric='sqeuclidean', + double p=1.): + r""" + Solves the Earth Movers distance problem between sorted 1d measures and + returns the OT matrix and the associated cost + + Parameters + ---------- + u_weights : (ns,) ndarray, float64 + Source histogram + v_weights : (nt,) ndarray, float64 + Target histogram + u : (ns,) ndarray, float64 + Source dirac locations (on the real line) + v : (nt,) ndarray, float64 + Target dirac locations (on the real line) + metric: str, optional (default='sqeuclidean') + Metric to be used. Only strings listed in :func:`ot.dist` are accepted. + Due to implementation details, this function runs faster when + `'sqeuclidean'`, `'minkowski'`, `'cityblock'`, or `'euclidean'` metrics + are used. + p: float, optional (default=1.0) + The p-norm to apply for if metric='minkowski' + + Returns + ------- + gamma: (n, ) ndarray, float64 + Values in the Optimal transportation matrix + indices: (n, 2) ndarray, int64 + Indices of the values stored in gamma for the Optimal transportation + matrix + cost + cost associated to the optimal transportation + """ + cdef double cost = 0. + cdef int n = u_weights.shape[0] + cdef int m = v_weights.shape[0] + + cdef int i = 0 + cdef double w_i = u_weights[0] + cdef int j = 0 + cdef double w_j = v_weights[0] + + cdef double m_ij = 0. + + cdef np.ndarray[double, ndim=1, mode="c"] G = np.zeros((n + m - 1, ), + dtype=np.float64) + cdef np.ndarray[long, ndim=2, mode="c"] indices = np.zeros((n + m - 1, 2), + dtype=np.int) + cdef int cur_idx = 0 + while i < n and j < m: + if metric == 'sqeuclidean': + m_ij = (u[i] - v[j]) * (u[i] - v[j]) + elif metric == 'cityblock' or metric == 'euclidean': + m_ij = math.fabs(u[i] - v[j]) + elif metric == 'minkowski': + m_ij = math.pow(math.fabs(u[i] - v[j]), p) + else: + m_ij = dist(u[i].reshape((1, 1)), v[j].reshape((1, 1)), + metric=metric)[0, 0] + if w_i < w_j or j == m - 1: + cost += m_ij * w_i + G[cur_idx] = w_i + indices[cur_idx, 0] = i + indices[cur_idx, 1] = j + i += 1 + w_j -= w_i + w_i = u_weights[i] + else: + cost += m_ij * w_j + G[cur_idx] = w_j + indices[cur_idx, 0] = i + indices[cur_idx, 1] = j + j += 1 + w_i -= w_j + w_j = v_weights[j] + cur_idx += 1 + return G[:cur_idx], indices[:cur_idx], cost diff --git a/ot/lp/network_simplex_simple.h b/ot/lp/network_simplex_simple.h index 7c6a4ce..498e921 100644 --- a/ot/lp/network_simplex_simple.h +++ b/ot/lp/network_simplex_simple.h @@ -686,7 +686,7 @@ namespace lemon { /// \see resetParams(), reset() ProblemType run() { #if DEBUG_LVL>0 - std::cout << "OPTIMAL = " << OPTIMAL << "\nINFEASIBLE = " << INFEASIBLE << "\nUNBOUNDED = " << UNBOUNDED << "\nMAX_ITER_REACHED" << MAX_ITER_REACHED\n"; + std::cout << "OPTIMAL = " << OPTIMAL << "\nINFEASIBLE = " << INFEASIBLE << "\nUNBOUNDED = " << UNBOUNDED << "\nMAX_ITER_REACHED" << MAX_ITER_REACHED << "\n" ; #endif if (!init()) return INFEASIBLE; diff --git a/ot/optim.py b/ot/optim.py index f31fae2..4012e0d 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -4,6 +4,7 @@ Optimization algorithms for OT """ # Author: Remi Flamary <remi.flamary@unice.fr> +# Titouan Vayer <titouan.vayer@irisa.fr> # # License: MIT License @@ -25,14 +26,13 @@ def line_search_armijo(f, xk, pk, gfk, old_fval, Parameters ---------- - - f : function + f : callable loss function - xk : np.ndarray + xk : ndarray initial position - pk : np.ndarray + pk : ndarray descent direction - gfk : np.ndarray + gfk : ndarray gradient of f at xk old_fval : float loss value at xk @@ -72,8 +72,70 @@ def line_search_armijo(f, xk, pk, gfk, old_fval, return alpha, fc[0], phi1 -def cg(a, b, M, reg, f, df, G0=None, numItermax=200, - stopThr=1e-9, verbose=False, log=False): +def solve_linesearch(cost, G, deltaG, Mi, f_val, + armijo=True, C1=None, C2=None, reg=None, Gc=None, constC=None, M=None): + """ + Solve the linesearch in the FW iterations + Parameters + ---------- + cost : method + Cost in the FW for the linesearch + G : ndarray, shape(ns,nt) + The transport map at a given iteration of the FW + deltaG : ndarray (ns,nt) + Difference between the optimal map found by linearization in the FW algorithm and the value at a given iteration + Mi : ndarray (ns,nt) + Cost matrix of the linearized transport problem. Corresponds to the gradient of the cost + f_val : float + Value of the cost at G + armijo : bool, optional + If True the steps of the line-search is found via an armijo research. Else closed form is used. + If there is convergence issues use False. + C1 : ndarray (ns,ns), optional + Structure matrix in the source domain. Only used and necessary when armijo=False + C2 : ndarray (nt,nt), optional + Structure matrix in the target domain. Only used and necessary when armijo=False + reg : float, optional + Regularization parameter. Only used and necessary when armijo=False + Gc : ndarray (ns,nt) + Optimal map found by linearization in the FW algorithm. Only used and necessary when armijo=False + constC : ndarray (ns,nt) + Constant for the gromov cost. See [24]. Only used and necessary when armijo=False + M : ndarray (ns,nt), optional + Cost matrix between the features. Only used and necessary when armijo=False + Returns + ------- + alpha : float + The optimal step size of the FW + fc : int + nb of function call. Useless here + f_val : float + The value of the cost for the next iteration + References + ---------- + .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + """ + if armijo: + alpha, fc, f_val = line_search_armijo(cost, G, deltaG, Mi, f_val) + else: # requires symetric matrices + dot1 = np.dot(C1, deltaG) + dot12 = dot1.dot(C2) + a = -2 * reg * np.sum(dot12 * deltaG) + b = np.sum((M + reg * constC) * deltaG) - 2 * reg * (np.sum(dot12 * G) + np.sum(np.dot(C1, G).dot(C2) * deltaG)) + c = cost(G) + + alpha = solve_1d_linesearch_quad(a, b, c) + fc = None + f_val = cost(G + alpha * deltaG) + + return alpha, fc, f_val + + +def cg(a, b, M, reg, f, df, G0=None, numItermax=200, numItermaxEmd=100000, + stopThr=1e-9, stopThr2=1e-9, verbose=False, log=False, **kwargs): """ Solve the general regularized OT problem with conditional gradient @@ -98,24 +160,30 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (ns,) samples weights in the source domain - b : np.ndarray (nt,) + b : ndarray, shape (nt,) samples in the target domain - M : np.ndarray (ns,nt) + M : ndarray, shape (ns, nt) loss matrix reg : float Regularization term >0 - G0 : np.ndarray (ns,nt), optional + G0 : ndarray, shape (ns,nt), optional initial guess (default is indep joint density) numItermax : int, optional Max number of iterations + numItermaxEmd : int, optional + Max number of iterations for emd stopThr : float, optional - Stop threshol on error (>0) + Stop threshol on the relative variation (>0) + stopThr2 : float, optional + Stop threshol on the absolute variation (>0) verbose : bool, optional Print information along iterations log : bool, optional record log if True + **kwargs : dict + Parameters for linesearch Returns ------- @@ -157,9 +225,9 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, it = 0 if verbose: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) - print('{:5d}|{:8e}|{:8e}'.format(it, f_val, 0)) + print('{:5s}|{:12s}|{:8s}|{:8s}'.format( + 'It.', 'Loss', 'Relative loss', 'Absolute loss') + '\n' + '-' * 48) + print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, 0, 0)) while loop: @@ -172,12 +240,12 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, Mi += Mi.min() # solve linear program - Gc = emd(a, b, Mi) + Gc = emd(a, b, Mi, numItermax=numItermaxEmd) deltaG = Gc - G # line search - alpha, fc, f_val = line_search_armijo(cost, G, deltaG, Mi, f_val) + alpha, fc, f_val = solve_linesearch(cost, G, deltaG, Mi, f_val, reg=reg, M=M, Gc=Gc, **kwargs) G = G + alpha * deltaG @@ -185,8 +253,9 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, if it >= numItermax: loop = 0 - delta_fval = (f_val - old_fval) / abs(f_val) - if abs(delta_fval) < stopThr: + abs_delta_fval = abs(f_val - old_fval) + relative_delta_fval = abs_delta_fval / abs(f_val) + if relative_delta_fval < stopThr or abs_delta_fval < stopThr2: loop = 0 if log: @@ -194,9 +263,9 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, if verbose: if it % 20 == 0: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) - print('{:5d}|{:8e}|{:8e}'.format(it, f_val, delta_fval)) + print('{:5s}|{:12s}|{:8s}|{:8s}'.format( + 'It.', 'Loss', 'Relative loss', 'Absolute loss') + '\n' + '-' * 48) + print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, relative_delta_fval, abs_delta_fval)) if log: return G, log @@ -205,7 +274,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, - numInnerItermax=200, stopThr=1e-9, verbose=False, log=False): + numInnerItermax=200, stopThr=1e-9, stopThr2=1e-9, verbose=False, log=False): """ Solve the general regularized OT problem with the generalized conditional gradient @@ -231,24 +300,26 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (ns,) samples weights in the source domain - b : np.ndarray (nt,) + b : ndarrayv (nt,) samples in the target domain - M : np.ndarray (ns,nt) + M : ndarray, shape (ns, nt) loss matrix reg1 : float Entropic Regularization term >0 reg2 : float Second Regularization term >0 - G0 : np.ndarray (ns,nt), optional + G0 : ndarray, shape (ns, nt), optional initial guess (default is indep joint density) numItermax : int, optional Max number of iterations numInnerItermax : int, optional Max number of iterations of Sinkhorn stopThr : float, optional - Stop threshol on error (>0) + Stop threshol on the relative variation (>0) + stopThr2 : float, optional + Stop threshol on the absolute variation (>0) verbose : bool, optional Print information along iterations log : bool, optional @@ -256,15 +327,13 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (ns, nt) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters - References ---------- - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. @@ -294,9 +363,9 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, it = 0 if verbose: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) - print('{:5d}|{:8e}|{:8e}'.format(it, f_val, 0)) + print('{:5s}|{:12s}|{:8s}|{:8s}'.format( + 'It.', 'Loss', 'Relative loss', 'Absolute loss') + '\n' + '-' * 48) + print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, 0, 0)) while loop: @@ -322,8 +391,10 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, if it >= numItermax: loop = 0 - delta_fval = (f_val - old_fval) / abs(f_val) - if abs(delta_fval) < stopThr: + abs_delta_fval = abs(f_val - old_fval) + relative_delta_fval = abs_delta_fval / abs(f_val) + + if relative_delta_fval < stopThr or abs_delta_fval < stopThr2: loop = 0 if log: @@ -331,11 +402,41 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, if verbose: if it % 20 == 0: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) - print('{:5d}|{:8e}|{:8e}'.format(it, f_val, delta_fval)) + print('{:5s}|{:12s}|{:8s}|{:8s}'.format( + 'It.', 'Loss', 'Relative loss', 'Absolute loss') + '\n' + '-' * 48) + print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, relative_delta_fval, abs_delta_fval)) if log: return G, log else: return G + + +def solve_1d_linesearch_quad(a, b, c): + """ + For any convex or non-convex 1d quadratic function f, solve on [0,1] the following problem: + .. math:: + \argmin f(x)=a*x^{2}+b*x+c + + Parameters + ---------- + a,b,c : float + The coefficients of the quadratic function + + Returns + ------- + x : float + The optimal value which leads to the minimal cost + """ + f0 = c + df0 = b + f1 = a + f0 + df0 + + if a > 0: # convex + minimum = min(1, max(0, np.divide(-b, 2.0 * a))) + return minimum + else: # non convex + if f0 > f1: + return 1 + else: + return 0 @@ -1,5 +1,11 @@ """ Functions for plotting OT matrices + +.. warning:: + Note that by default the module is not import in :mod:`ot`. In order to + use it you need to explicitely import :mod:`ot.plot` + + """ # Author: Remi Flamary <remi.flamary@unice.fr> @@ -20,11 +26,11 @@ def plot1D_mat(a, b, M, title=''): Parameters ---------- - a : np.array, shape (na,) + a : ndarray, shape (na,) Source distribution - b : np.array, shape (nb,) + b : ndarray, shape (nb,) Target distribution - M : np.array, shape (na,nb) + M : ndarray, shape (na, nb) Matrix to plot """ na, nb = M.shape diff --git a/ot/stochastic.py b/ot/stochastic.py index 4795d88..13ed9cc 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -1,3 +1,9 @@ +""" +Stochastic solvers for regularized OT. + + +""" + # Author: Kilian Fatras <kilian.fatras@gmail.com> # # License: MIT License @@ -11,7 +17,7 @@ import numpy as np def coordinate_grad_semi_dual(b, M, reg, beta, i): - ''' + r''' Compute the coordinate gradient update for regularized discrete distributions for (i, :) The function computes the gradient of the semi dual problem: @@ -32,51 +38,49 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i): Parameters ---------- - - b : np.ndarray(nt,) - target measure - M : np.ndarray(ns, nt) - cost matrix - reg : float nu - Regularization term > 0 - v : np.ndarray(nt,) - dual variable - i : number int - picked number i + b : ndarray, shape (nt,) + Target measure. + M : ndarray, shape (ns, nt) + Cost matrix. + reg : float + Regularization term > 0. + v : ndarray, shape (nt,) + Dual variable. + i : int + Picked number i. Returns ------- - - coordinate gradient : np.ndarray(nt,) + coordinate gradient : ndarray, shape (nt,) Examples -------- - + >>> import ot + >>> np.random.seed(0) >>> n_source = 7 >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) + >>> X_source = np.random.randn(n_source, 2) + >>> Y_target = np.random.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) - >>> print(asgd_pi) + >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000) + array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06], + [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03], + [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07], + [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04], + [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01], + [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01], + [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]]) + References ---------- - [Genevay et al., 2016] : - Stochastic Optimization for Large-scale Optimal Transport, - Advances in Neural Information Processing Systems (2016), - arXiv preprint arxiv:1605.08527. - + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. ''' - r = M[i, :] - beta exp_beta = np.exp(-r / reg) * b khi = exp_beta / (np.sum(exp_beta)) @@ -84,7 +88,7 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i): def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): - ''' + r''' Compute the SAG algorithm to solve the regularized discrete measures optimal transport max problem @@ -112,42 +116,43 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): Parameters ---------- - a : np.ndarray(ns,), - source measure - b : np.ndarray(nt,), - target measure - M : np.ndarray(ns, nt), - cost matrix - reg : float number, + a : ndarray, shape (ns,), + Source measure. + b : ndarray, shape (nt,), + Target measure. + M : ndarray, shape (ns, nt), + Cost matrix. + reg : float Regularization term > 0 - numItermax : int number - number of iteration - lr : float number - learning rate + numItermax : int + Number of iteration. + lr : float + Learning rate. Returns ------- - - v : np.ndarray(nt,) - dual variable + v : ndarray, shape (nt,) + Dual variable. Examples -------- - + >>> import ot + >>> np.random.seed(0) >>> n_source = 7 >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) + >>> X_source = np.random.randn(n_source, 2) + >>> Y_target = np.random.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) - >>> print(asgd_pi) + >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000) + array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06], + [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03], + [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07], + [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04], + [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01], + [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01], + [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]]) References ---------- @@ -176,7 +181,7 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): - ''' + r''' Compute the ASGD algorithm to solve the regularized semi continous measures optimal transport max problem The function solves the following optimization problem: @@ -202,50 +207,49 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): Parameters ---------- - - b : np.ndarray(nt,) + b : ndarray, shape (nt,) target measure - M : np.ndarray(ns, nt) + M : ndarray, shape (ns, nt) cost matrix - reg : float number + reg : float Regularization term > 0 - numItermax : int number - number of iteration - lr : float number - learning rate - + numItermax : int + Number of iteration. + lr : float + Learning rate. Returns ------- - - ave_v : np.ndarray(nt,) + ave_v : ndarray, shape (nt,) dual variable Examples -------- - + >>> import ot + >>> np.random.seed(0) >>> n_source = 7 >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) + >>> X_source = np.random.randn(n_source, 2) + >>> Y_target = np.random.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) - >>> print(asgd_pi) + >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000) + array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06], + [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03], + [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07], + [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04], + [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01], + [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01], + [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]]) References ---------- [Genevay et al., 2016] : - Stochastic Optimization for Large-scale Optimal Transport, - Advances in Neural Information Processing Systems (2016), - arXiv preprint arxiv:1605.08527. + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. ''' if lr is None: @@ -264,7 +268,7 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): def c_transform_entropic(b, M, reg, beta): - ''' + r''' The goal is to recover u from the c-transform. The function computes the c_transform of a dual variable from the other @@ -285,47 +289,47 @@ def c_transform_entropic(b, M, reg, beta): Parameters ---------- - - b : np.ndarray(nt,) - target measure - M : np.ndarray(ns, nt) - cost matrix + b : ndarray, shape (nt,) + Target measure + M : ndarray, shape (ns, nt) + Cost matrix reg : float - regularization term > 0 - v : np.ndarray(nt,) - dual variable + Regularization term > 0 + v : ndarray, shape (nt,) + Dual variable. Returns ------- - - u : np.ndarray(ns,) - dual variable + u : ndarray, shape (ns,) + Dual variable. Examples -------- - + >>> import ot + >>> np.random.seed(0) >>> n_source = 7 >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) + >>> X_source = np.random.randn(n_source, 2) + >>> Y_target = np.random.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) - >>> print(asgd_pi) + >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000) + array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06], + [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03], + [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07], + [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04], + [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01], + [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01], + [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]]) References ---------- [Genevay et al., 2016] : - Stochastic Optimization for Large-scale Optimal Transport, - Advances in Neural Information Processing Systems (2016), - arXiv preprint arxiv:1605.08527. + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. ''' n_source = np.shape(M)[0] @@ -340,7 +344,7 @@ def c_transform_entropic(b, M, reg, beta): def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, log=False): - ''' + r''' Compute the transportation matrix to solve the regularized discrete measures optimal transport max problem @@ -348,8 +352,11 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma \geq 0 Where : @@ -364,52 +371,53 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, Parameters ---------- - a : np.ndarray(ns,) + a : ndarray, shape (ns,) source measure - b : np.ndarray(nt,) + b : ndarray, shape (nt,) target measure - M : np.ndarray(ns, nt) + M : ndarray, shape (ns, nt) cost matrix - reg : float number + reg : float Regularization term > 0 methode : str used method (SAG or ASGD) - numItermax : int number + numItermax : int number of iteration - lr : float number + lr : float learning rate - n_source : int number + n_source : int size of the source measure - n_target : int number + n_target : int size of the target measure log : bool, optional record log if True Returns ------- - - pi : np.ndarray(ns, nt) + pi : ndarray, shape (ns, nt) transportation matrix log : dict log dictionary return only if log==True in parameters Examples -------- - + >>> import ot + >>> np.random.seed(0) >>> n_source = 7 >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) + >>> X_source = np.random.randn(n_source, 2) + >>> Y_target = np.random.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) - >>> print(asgd_pi) + >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000) + array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06], + [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03], + [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07], + [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04], + [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01], + [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01], + [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]]) References ---------- @@ -448,7 +456,7 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, batch_beta): - ''' + r''' Computes the partial gradient of the dual optimal transport problem. For each (i,j) in a batch of coordinates, the partial gradients are : @@ -475,53 +483,55 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, Parameters ---------- - - a : np.ndarray(ns,) + a : ndarray, shape (ns,) source measure - b : np.ndarray(nt,) + b : ndarray, shape (nt,) target measure - M : np.ndarray(ns, nt) + M : ndarray, shape (ns, nt) cost matrix - reg : float number + reg : float Regularization term > 0 - alpha : np.ndarray(ns,) + alpha : ndarray, shape (ns,) dual variable - beta : np.ndarray(nt,) + beta : ndarray, shape (nt,) dual variable - batch_size : int number + batch_size : int size of the batch - batch_alpha : np.ndarray(bs,) + batch_alpha : ndarray, shape (bs,) batch of index of alpha - batch_beta : np.ndarray(bs,) + batch_beta : ndarray, shape (bs,) batch of index of beta Returns ------- - - grad : np.ndarray(ns,) + grad : ndarray, shape (ns,) partial grad F Examples -------- - + >>> import ot + >>> np.random.seed(0) >>> n_source = 7 >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 20000 - >>> lr = 0.1 - >>> batch_size = 3 - >>> log = True >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) + >>> X_source = np.random.randn(n_source, 2) + >>> Y_target = np.random.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> sgd_dual_pi, log = stochastic.solve_dual_entropic(a, b, M, reg, - batch_size, - numItermax, lr, log) - >>> print(log['alpha'], log['beta']) - >>> print(sgd_dual_pi) + >>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg=1, batch_size=3, numItermax=30000, lr=0.1, log=True) + >>> log['alpha'] + array([0.71759102, 1.57057384, 0.85576566, 0.1208211 , 0.59190466, + 1.197148 , 0.17805133]) + >>> log['beta'] + array([0.49741367, 0.57478564, 1.40075528, 2.75890102]) + >>> sgd_dual_pi + array([[2.09730063e-02, 8.38169324e-02, 7.50365455e-03, 8.72731415e-09], + [5.58432437e-03, 5.89881299e-04, 3.09558411e-05, 8.35469849e-07], + [3.26489515e-03, 7.15536035e-02, 2.99778211e-02, 3.02601593e-10], + [4.05390622e-02, 5.31085068e-02, 6.65191787e-02, 1.55812785e-06], + [7.82299812e-02, 6.12099102e-03, 4.44989098e-02, 2.37719187e-03], + [5.06266486e-02, 2.16230494e-03, 2.26215141e-03, 6.81514609e-04], + [6.06713990e-02, 3.98139808e-02, 5.46829338e-02, 8.62371424e-06]]) References ---------- @@ -530,22 +540,21 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, International Conference on Learning Representation (2018), arXiv preprint arxiv:1711.02283. ''' - G = - (np.exp((alpha[batch_alpha, None] + beta[None, batch_beta] - M[batch_alpha, :][:, batch_beta]) / reg) * a[batch_alpha, None] * b[None, batch_beta]) grad_beta = np.zeros(np.shape(M)[1]) grad_alpha = np.zeros(np.shape(M)[0]) - grad_beta[batch_beta] = (b[batch_beta] * len(batch_alpha) / np.shape(M)[0] + - G.sum(0)) - grad_alpha[batch_alpha] = (a[batch_alpha] * len(batch_beta) / - np.shape(M)[1] + G.sum(1)) + grad_beta[batch_beta] = (b[batch_beta] * len(batch_alpha) / np.shape(M)[0] + + G.sum(0)) + grad_alpha[batch_alpha] = (a[batch_alpha] * len(batch_beta) + / np.shape(M)[1] + G.sum(1)) return grad_alpha, grad_beta def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): - ''' + r''' Compute the sgd algorithm to solve the regularized discrete measures optimal transport dual problem @@ -568,33 +577,31 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): Parameters ---------- - - a : np.ndarray(ns,) + a : ndarray, shape (ns,) source measure - b : np.ndarray(nt,) + b : ndarray, shape (nt,) target measure - M : np.ndarray(ns, nt) + M : ndarray, shape (ns, nt) cost matrix - reg : float number + reg : float Regularization term > 0 - batch_size : int number + batch_size : int size of the batch - numItermax : int number + numItermax : int number of iteration - lr : float number + lr : float learning rate Returns ------- - - alpha : np.ndarray(ns,) + alpha : ndarray, shape (ns,) dual variable - beta : np.ndarray(nt,) + beta : ndarray, shape (nt,) dual variable Examples -------- - + >>> import ot >>> n_source = 7 >>> n_target = 4 >>> reg = 1 @@ -608,18 +615,26 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): >>> X_source = rng.randn(n_source, 2) >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> sgd_dual_pi, log = stochastic.solve_dual_entropic(a, b, M, reg, - batch_size, - numItermax, lr, log) - >>> print(log['alpha'], log['beta']) - >>> print(sgd_dual_pi) + >>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, numItermax, lr, log) + >>> log['alpha'] + array([0.64171798, 1.27932201, 0.78132257, 0.15638935, 0.54888354, + 1.03663469, 0.20595781]) + >>> log['beta'] + array([0.51207194, 0.58033189, 1.28922676, 2.26859736]) + >>> sgd_dual_pi + array([[1.97276541e-02, 7.81248547e-02, 6.22136048e-03, 4.95442423e-09], + [4.23494310e-03, 4.43286263e-04, 2.06927079e-05, 3.82389139e-07], + [3.07542414e-03, 6.67897769e-02, 2.48904999e-02, 1.72030247e-10], + [4.26271990e-02, 5.53375455e-02, 6.16535024e-02, 9.88812650e-07], + [7.60423265e-02, 5.89585256e-03, 3.81267087e-02, 1.39458256e-03], + [4.37557504e-02, 1.85189176e-03, 1.72335760e-03, 3.55491279e-04], + [6.33096109e-02, 4.11683954e-02, 5.02962051e-02, 5.43097516e-06]]) References ---------- - [Seguy et al., 2018] : - International Conference on Learning Representation (2018), - arXiv preprint arxiv:1711.02283. + International Conference on Learning Representation (2018), + arXiv preprint arxiv:1711.02283. ''' n_source = np.shape(M)[0] @@ -641,7 +656,7 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, log=False): - ''' + r''' Compute the transportation matrix to solve the regularized discrete measures optimal transport dual problem @@ -664,35 +679,33 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, Parameters ---------- - - a : np.ndarray(ns,) + a : ndarray, shape (ns,) source measure - b : np.ndarray(nt,) + b : ndarray, shape (nt,) target measure - M : np.ndarray(ns, nt) + M : ndarray, shape (ns, nt) cost matrix - reg : float number + reg : float Regularization term > 0 - batch_size : int number + batch_size : int size of the batch - numItermax : int number + numItermax : int number of iteration - lr : float number + lr : float learning rate log : bool, optional record log if True Returns ------- - - pi : np.ndarray(ns, nt) + pi : ndarray, shape (ns, nt) transportation matrix log : dict log dictionary return only if log==True in parameters Examples -------- - + >>> import ot >>> n_source = 7 >>> n_target = 4 >>> reg = 1 @@ -706,18 +719,27 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, >>> X_source = rng.randn(n_source, 2) >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> sgd_dual_pi, log = stochastic.solve_dual_entropic(a, b, M, reg, - batch_size, - numItermax, lr, log) - >>> print(log['alpha'], log['beta']) - >>> print(sgd_dual_pi) + >>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, numItermax, lr, log) + >>> log['alpha'] + array([0.64057733, 1.2683513 , 0.75610161, 0.16024284, 0.54926534, + 1.0514201 , 0.19958936]) + >>> log['beta'] + array([0.51372571, 0.58843489, 1.27993921, 2.24344807]) + >>> sgd_dual_pi + array([[1.97377795e-02, 7.86706853e-02, 6.15682001e-03, 4.82586997e-09], + [4.19566963e-03, 4.42016865e-04, 2.02777272e-05, 3.68823708e-07], + [3.00379244e-03, 6.56562018e-02, 2.40462171e-02, 1.63579656e-10], + [4.28626062e-02, 5.60031599e-02, 6.13193826e-02, 9.67977735e-07], + [7.61972739e-02, 5.94609051e-03, 3.77886693e-02, 1.36046648e-03], + [4.44810042e-02, 1.89476742e-03, 1.73285847e-03, 3.51826036e-04], + [6.30118293e-02, 4.12398660e-02, 4.95148998e-02, 5.26247246e-06]]) References ---------- [Seguy et al., 2018] : - International Conference on Learning Representation (2018), - arXiv preprint arxiv:1711.02283. + International Conference on Learning Representation (2018), + arXiv preprint arxiv:1711.02283. ''' opt_alpha, opt_beta = sgd_entropic_regularization(a, b, M, reg, batch_size, diff --git a/ot/unbalanced.py b/ot/unbalanced.py new file mode 100644 index 0000000..23f6607 --- /dev/null +++ b/ot/unbalanced.py @@ -0,0 +1,1023 @@ +# -*- coding: utf-8 -*- +""" +Regularized Unbalanced OT +""" + +# Author: Hicham Janati <hicham.janati@inria.fr> +# License: MIT License + +from __future__ import division +import warnings +import numpy as np +from scipy.special import logsumexp + +# from .utils import unif, dist + + +def sinkhorn_unbalanced(a, b, M, reg, reg_m, method='sinkhorn', numItermax=1000, + stopThr=1e-6, verbose=False, log=False, **kwargs): + r""" + Solve the unbalanced entropic regularization optimal transport problem + and return the OT plan + + The function solves the following optimization problem: + + .. math:: + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + reg_m KL(\gamma 1, a) + reg_m KL(\gamma^T 1, b) + + s.t. + \gamma\geq 0 + where : + + - M is the (dim_a, dim_b) metric cost matrix + - :math:`\Omega` is the entropic regularization + term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target unbalanced distributions + - KL is the Kullback-Leibler divergence + + The algorithm used for solving the problem is the generalized + Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ + + + Parameters + ---------- + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) or np.ndarray (dim_b, n_hists) + One or multiple unnormalized histograms of dimension dim_b + If many, compute all the OT distances (a, b_i) + M : np.ndarray (dim_a, dim_b) + loss matrix + reg : float + Entropy regularization term > 0 + reg_m: float + Marginal relaxation term > 0 + method : str + method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or + 'sinkhorn_reg_scaling', see those function for specific parameters + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + if n_hists == 1: + gamma : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + else: + ot_distance : (n_hists,) ndarray + the OT distance between `a` and each of the histograms `b_i` + log : dict + log dictionary returned only if `log` is `True` + + Examples + -------- + + >>> import ot + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.], [1., 0.]] + >>> ot.sinkhorn_unbalanced(a, b, M, 1, 1) + array([[0.51122823, 0.18807035], + [0.18807035, 0.51122823]]) + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal + Transport, Advances in Neural Information Processing Systems + (NIPS) 26, 2013 + + .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for + Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprint + arXiv:1607.05816. + + .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : + Learning with a Wasserstein Loss, Advances in Neural Information + Processing Systems (NIPS) 2015 + + + See Also + -------- + ot.unbalanced.sinkhorn_knopp_unbalanced : Unbalanced Classic Sinkhorn [10] + ot.unbalanced.sinkhorn_stabilized_unbalanced: + Unbalanced Stabilized sinkhorn [9][10] + ot.unbalanced.sinkhorn_reg_scaling_unbalanced: + Unbalanced Sinkhorn with epslilon scaling [9][10] + + """ + + if method.lower() == 'sinkhorn': + return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + + elif method.lower() == 'sinkhorn_stabilized': + return sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, + verbose=verbose, + log=log, **kwargs) + elif method.lower() in ['sinkhorn_reg_scaling']: + warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') + return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + else: + raise ValueError("Unknown method '%s'." % method) + + +def sinkhorn_unbalanced2(a, b, M, reg, reg_m, method='sinkhorn', + numItermax=1000, stopThr=1e-6, verbose=False, + log=False, **kwargs): + r""" + Solve the entropic regularization unbalanced optimal transport problem and + return the loss + + The function solves the following optimization problem: + + .. math:: + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + reg_m KL(\gamma 1, a) + reg_m KL(\gamma^T 1, b) + + s.t. + \gamma\geq 0 + where : + + - M is the (dim_a, dim_b) metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target unbalanced distributions + - KL is the Kullback-Leibler divergence + + The algorithm used for solving the problem is the generalized + Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ + + + Parameters + ---------- + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) or np.ndarray (dim_b, n_hists) + One or multiple unnormalized histograms of dimension dim_b + If many, compute all the OT distances (a, b_i) + M : np.ndarray (dim_a, dim_b) + loss matrix + reg : float + Entropy regularization term > 0 + reg_m: float + Marginal relaxation term > 0 + method : str + method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or + 'sinkhorn_reg_scaling', see those function for specific parameters + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + ot_distance : (n_hists,) ndarray + the OT distance between `a` and each of the histograms `b_i` + log : dict + log dictionary returned only if `log` is `True` + + Examples + -------- + + >>> import ot + >>> a=[.5, .10] + >>> b=[.5, .5] + >>> M=[[0., 1.],[1., 0.]] + >>> ot.unbalanced.sinkhorn_unbalanced2(a, b, M, 1., 1.) + array([0.31912866]) + + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal + Transport, Advances in Neural Information Processing Systems + (NIPS) 26, 2013 + + .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for + Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprint + arXiv:1607.05816. + + .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : + Learning with a Wasserstein Loss, Advances in Neural Information + Processing Systems (NIPS) 2015 + + See Also + -------- + ot.unbalanced.sinkhorn_knopp : Unbalanced Classic Sinkhorn [10] + ot.unbalanced.sinkhorn_stabilized: Unbalanced Stabilized sinkhorn [9][10] + ot.unbalanced.sinkhorn_reg_scaling: Unbalanced Sinkhorn with epslilon scaling [9][10] + + """ + b = np.asarray(b, dtype=np.float64) + if len(b.shape) < 2: + b = b[:, None] + if method.lower() == 'sinkhorn': + return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + + elif method.lower() == 'sinkhorn_stabilized': + return sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, + verbose=verbose, + log=log, **kwargs) + elif method.lower() in ['sinkhorn_reg_scaling']: + warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') + return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + else: + raise ValueError('Unknown method %s.' % method) + + +def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000, + stopThr=1e-6, verbose=False, log=False, **kwargs): + r""" + Solve the entropic regularization unbalanced optimal transport problem and return the loss + + The function solves the following optimization problem: + + .. math:: + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \reg_m KL(\gamma 1, a) + \reg_m KL(\gamma^T 1, b) + + s.t. + \gamma\geq 0 + where : + + - M is the (dim_a, dim_b) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target unbalanced distributions + - KL is the Kullback-Leibler divergence + + The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ + + + Parameters + ---------- + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) or np.ndarray (dim_b, n_hists) + One or multiple unnormalized histograms of dimension dim_b + If many, compute all the OT distances (a, b_i) + M : np.ndarray (dim_a, dim_b) + loss matrix + reg : float + Entropy regularization term > 0 + reg_m: float + Marginal relaxation term > 0 + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (> 0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + if n_hists == 1: + gamma : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + else: + ot_distance : (n_hists,) ndarray + the OT distance between `a` and each of the histograms `b_i` + log : dict + log dictionary returned only if `log` is `True` + Examples + -------- + + >>> import ot + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.],[1., 0.]] + >>> ot.unbalanced.sinkhorn_knopp_unbalanced(a, b, M, 1., 1.) + array([[0.51122823, 0.18807035], + [0.18807035, 0.51122823]]) + + References + ---------- + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprint + arXiv:1607.05816. + + .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : + Learning with a Wasserstein Loss, Advances in Neural Information + Processing Systems (NIPS) 2015 + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) + + dim_a, dim_b = M.shape + + if len(a) == 0: + a = np.ones(dim_a, dtype=np.float64) / dim_a + if len(b) == 0: + b = np.ones(dim_b, dtype=np.float64) / dim_b + + if len(b.shape) > 1: + n_hists = b.shape[1] + else: + n_hists = 0 + + if log: + log = {'err': []} + + # we assume that no distances are null except those of the diagonal of + # distances + if n_hists: + u = np.ones((dim_a, 1)) / dim_a + v = np.ones((dim_b, n_hists)) / dim_b + a = a.reshape(dim_a, 1) + else: + u = np.ones(dim_a) / dim_a + v = np.ones(dim_b) / dim_b + + # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + + fi = reg_m / (reg_m + reg) + + err = 1. + + for i in range(numItermax): + uprev = u + vprev = v + + Kv = K.dot(v) + u = (a / Kv) ** fi + Ktu = K.T.dot(u) + v = (b / Ktu) ** fi + + if (np.any(Ktu == 0.) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): + # we have reached the machine precision + # come back to previous solution and quit loop + warnings.warn('Numerical errors at iteration %s' % i) + u = uprev + v = vprev + break + + err_u = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), 1.) + err_v = abs(v - vprev).max() / max(abs(v).max(), abs(vprev).max(), 1.) + err = 0.5 * (err_u + err_v) + if log: + log['err'].append(err) + if verbose: + if i % 50 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(i, err)) + if err < stopThr: + break + + if log: + log['logu'] = np.log(u + 1e-300) + log['logv'] = np.log(v + 1e-300) + + if n_hists: # return only loss + res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) + if log: + return res, log + else: + return res + + else: # return OT matrix + + if log: + return u[:, None] * K * v[None, :], log + else: + return u[:, None] * K * v[None, :] + + +def sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, tau=1e5, numItermax=1000, + stopThr=1e-6, verbose=False, log=False, + **kwargs): + r""" + Solve the entropic regularization unbalanced optimal transport + problem and return the loss + + The function solves the following optimization problem using log-domain + stabilization as proposed in [10]: + + .. math:: + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + reg_m KL(\gamma 1, a) + reg_m KL(\gamma^T 1, b) + + s.t. + \gamma\geq 0 + where : + + - M is the (dim_a, dim_b) metric cost matrix + - :math:`\Omega` is the entropic regularization + term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target unbalanced distributions + - KL is the Kullback-Leibler divergence + + The algorithm used for solving the problem is the generalized + Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ + + + Parameters + ---------- + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) or np.ndarray (dim_b, n_hists) + One or multiple unnormalized histograms of dimension dim_b + If many, compute all the OT distances (a, b_i) + M : np.ndarray (dim_a, dim_b) + loss matrix + reg : float + Entropy regularization term > 0 + reg_m: float + Marginal relaxation term > 0 + tau : float + thershold for max value in u or v for log scaling + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + if n_hists == 1: + gamma : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + else: + ot_distance : (n_hists,) ndarray + the OT distance between `a` and each of the histograms `b_i` + log : dict + log dictionary returned only if `log` is `True` + Examples + -------- + + >>> import ot + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.],[1., 0.]] + >>> ot.unbalanced.sinkhorn_stabilized_unbalanced(a, b, M, 1., 1.) + array([[0.51122823, 0.18807035], + [0.18807035, 0.51122823]]) + + References + ---------- + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + + .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : + Learning with a Wasserstein Loss, Advances in Neural Information + Processing Systems (NIPS) 2015 + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) + + dim_a, dim_b = M.shape + + if len(a) == 0: + a = np.ones(dim_a, dtype=np.float64) / dim_a + if len(b) == 0: + b = np.ones(dim_b, dtype=np.float64) / dim_b + + if len(b.shape) > 1: + n_hists = b.shape[1] + else: + n_hists = 0 + + if log: + log = {'err': []} + + # we assume that no distances are null except those of the diagonal of + # distances + if n_hists: + u = np.ones((dim_a, n_hists)) / dim_a + v = np.ones((dim_b, n_hists)) / dim_b + a = a.reshape(dim_a, 1) + else: + u = np.ones(dim_a) / dim_a + v = np.ones(dim_b) / dim_b + + # print(reg) + # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + + fi = reg_m / (reg_m + reg) + + cpt = 0 + err = 1. + alpha = np.zeros(dim_a) + beta = np.zeros(dim_b) + while (err > stopThr and cpt < numItermax): + uprev = u + vprev = v + + Kv = K.dot(v) + f_alpha = np.exp(- alpha / (reg + reg_m)) + f_beta = np.exp(- beta / (reg + reg_m)) + + if n_hists: + f_alpha = f_alpha[:, None] + f_beta = f_beta[:, None] + u = ((a / (Kv + 1e-16)) ** fi) * f_alpha + Ktu = K.T.dot(u) + v = ((b / (Ktu + 1e-16)) ** fi) * f_beta + absorbing = False + if (u > tau).any() or (v > tau).any(): + absorbing = True + if n_hists: + alpha = alpha + reg * np.log(np.max(u, 1)) + beta = beta + reg * np.log(np.max(v, 1)) + else: + alpha = alpha + reg * np.log(np.max(u)) + beta = beta + reg * np.log(np.max(v)) + K = np.exp((alpha[:, None] + beta[None, :] - + M) / reg) + v = np.ones_like(v) + Kv = K.dot(v) + + if (np.any(Ktu == 0.) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): + # we have reached the machine precision + # come back to previous solution and quit loop + warnings.warn('Numerical errors at iteration %s' % cpt) + u = uprev + v = vprev + break + if (cpt % 10 == 0 and not absorbing) or cpt == 0: + # we can speed up the process by checking for the error only all + # the 10th iterations + err = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), + 1.) + if log: + log['err'].append(err) + if verbose: + if cpt % 200 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + cpt = cpt + 1 + + if err > stopThr: + warnings.warn("Stabilized Unbalanced Sinkhorn did not converge." + + "Try a larger entropy `reg` or a lower mass `reg_m`." + + "Or a larger absorption threshold `tau`.") + if n_hists: + logu = alpha[:, None] / reg + np.log(u) + logv = beta[:, None] / reg + np.log(v) + else: + logu = alpha / reg + np.log(u) + logv = beta / reg + np.log(v) + if log: + log['logu'] = logu + log['logv'] = logv + if n_hists: # return only loss + res = logsumexp(np.log(M + 1e-100)[:, :, None] + logu[:, None, :] + + logv[None, :, :] - M[:, :, None] / reg, axis=(0, 1)) + res = np.exp(res) + if log: + return res, log + else: + return res + + else: # return OT matrix + ot_matrix = np.exp(logu[:, None] + logv[None, :] - M / reg) + if log: + return ot_matrix, log + else: + return ot_matrix + + +def barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3, + numItermax=1000, stopThr=1e-6, + verbose=False, log=False): + r"""Compute the entropic unbalanced wasserstein barycenter of A with stabilization. + + The function solves the following optimization problem: + + .. math:: + \mathbf{a} = arg\min_\mathbf{a} \sum_i Wu_{reg}(\mathbf{a},\mathbf{a}_i) + + where : + + - :math:`Wu_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized + Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced) + - :math:`\mathbf{a}_i` are training distributions in the columns of + matrix :math:`\mathbf{A}` + - reg and :math:`\mathbf{M}` are respectively the regularization term and + the cost matrix for OT + - reg_mis the marginal relaxation hyperparameter + The algorithm used for solving the problem is the generalized + Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ + + Parameters + ---------- + A : np.ndarray (dim, n_hists) + `n_hists` training distributions a_i of dimension dim + M : np.ndarray (dim, dim) + ground metric matrix for OT. + reg : float + Entropy regularization term > 0 + reg_m : float + Marginal relaxation term > 0 + tau : float + Stabilization threshold for log domain absorption. + weights : np.ndarray (n_hists,) optional + Weight of each distribution (barycentric coodinates) + If None, uniform weights are used. + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (> 0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + a : (dim,) ndarray + Unbalanced Wasserstein barycenter + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, + G. (2015). Iterative Bregman projections for regularized transportation + problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprint + arXiv:1607.05816. + + + """ + dim, n_hists = A.shape + if weights is None: + weights = np.ones(n_hists) / n_hists + else: + assert(len(weights) == A.shape[1]) + + if log: + log = {'err': []} + + fi = reg_m / (reg_m + reg) + + u = np.ones((dim, n_hists)) / dim + v = np.ones((dim, n_hists)) / dim + + # print(reg) + # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + + fi = reg_m / (reg_m + reg) + + cpt = 0 + err = 1. + alpha = np.zeros(dim) + beta = np.zeros(dim) + q = np.ones(dim) / dim + for i in range(numItermax): + qprev = q.copy() + Kv = K.dot(v) + f_alpha = np.exp(- alpha / (reg + reg_m)) + f_beta = np.exp(- beta / (reg + reg_m)) + f_alpha = f_alpha[:, None] + f_beta = f_beta[:, None] + u = ((A / (Kv + 1e-16)) ** fi) * f_alpha + Ktu = K.T.dot(u) + q = (Ktu ** (1 - fi)) * f_beta + q = q.dot(weights) ** (1 / (1 - fi)) + Q = q[:, None] + v = ((Q / (Ktu + 1e-16)) ** fi) * f_beta + absorbing = False + if (u > tau).any() or (v > tau).any(): + absorbing = True + alpha = alpha + reg * np.log(np.max(u, 1)) + beta = beta + reg * np.log(np.max(v, 1)) + K = np.exp((alpha[:, None] + beta[None, :] - + M) / reg) + v = np.ones_like(v) + Kv = K.dot(v) + if (np.any(Ktu == 0.) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): + # we have reached the machine precision + # come back to previous solution and quit loop + warnings.warn('Numerical errors at iteration %s' % cpt) + q = qprev + break + if (i % 10 == 0 and not absorbing) or i == 0: + # we can speed up the process by checking for the error only all + # the 10th iterations + err = abs(q - qprev).max() / max(abs(q).max(), + abs(qprev).max(), 1.) + if log: + log['err'].append(err) + if verbose: + if i % 50 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(i, err)) + if err < stopThr: + break + + if err > stopThr: + warnings.warn("Stabilized Unbalanced Sinkhorn did not converge." + + "Try a larger entropy `reg` or a lower mass `reg_m`." + + "Or a larger absorption threshold `tau`.") + if log: + log['niter'] = i + log['logu'] = np.log(u + 1e-300) + log['logv'] = np.log(v + 1e-300) + return q, log + else: + return q + + +def barycenter_unbalanced_sinkhorn(A, M, reg, reg_m, weights=None, + numItermax=1000, stopThr=1e-6, + verbose=False, log=False): + r"""Compute the entropic unbalanced wasserstein barycenter of A. + + The function solves the following optimization problem with a + + .. math:: + \mathbf{a} = arg\min_\mathbf{a} \sum_i Wu_{reg}(\mathbf{a},\mathbf{a}_i) + + where : + + - :math:`Wu_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized + Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced) + - :math:`\mathbf{a}_i` are training distributions in the columns of matrix + :math:`\mathbf{A}` + - reg and :math:`\mathbf{M}` are respectively the regularization term and + the cost matrix for OT + - reg_mis the marginal relaxation hyperparameter + The algorithm used for solving the problem is the generalized + Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ + + Parameters + ---------- + A : np.ndarray (dim, n_hists) + `n_hists` training distributions a_i of dimension dim + M : np.ndarray (dim, dim) + ground metric matrix for OT. + reg : float + Entropy regularization term > 0 + reg_m: float + Marginal relaxation term > 0 + weights : np.ndarray (n_hists,) optional + Weight of each distribution (barycentric coodinates) + If None, uniform weights are used. + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (> 0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + a : (dim,) ndarray + Unbalanced Wasserstein barycenter + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. + (2015). Iterative Bregman projections for regularized transportation + problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprin + arXiv:1607.05816. + + + """ + dim, n_hists = A.shape + if weights is None: + weights = np.ones(n_hists) / n_hists + else: + assert(len(weights) == A.shape[1]) + + if log: + log = {'err': []} + + K = np.exp(- M / reg) + + fi = reg_m / (reg_m + reg) + + v = np.ones((dim, n_hists)) + u = np.ones((dim, 1)) + q = np.ones(dim) + err = 1. + + for i in range(numItermax): + uprev = u.copy() + vprev = v.copy() + qprev = q.copy() + + Kv = K.dot(v) + u = (A / Kv) ** fi + Ktu = K.T.dot(u) + q = ((Ktu ** (1 - fi)).dot(weights)) + q = q ** (1 / (1 - fi)) + Q = q[:, None] + v = (Q / Ktu) ** fi + + if (np.any(Ktu == 0.) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): + # we have reached the machine precision + # come back to previous solution and quit loop + warnings.warn('Numerical errors at iteration %s' % i) + u = uprev + v = vprev + q = qprev + break + # compute change in barycenter + err = abs(q - qprev).max() + err /= max(abs(q).max(), abs(qprev).max(), 1.) + if log: + log['err'].append(err) + # if barycenter did not change + at least 10 iterations - stop + if err < stopThr and i > 10: + break + + if verbose: + if i % 10 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(i, err)) + + if log: + log['niter'] = i + log['logu'] = np.log(u + 1e-300) + log['logv'] = np.log(v + 1e-300) + return q, log + else: + return q + + +def barycenter_unbalanced(A, M, reg, reg_m, method="sinkhorn", weights=None, + numItermax=1000, stopThr=1e-6, + verbose=False, log=False, **kwargs): + r"""Compute the entropic unbalanced wasserstein barycenter of A. + + The function solves the following optimization problem with a + + .. math:: + \mathbf{a} = arg\min_\mathbf{a} \sum_i Wu_{reg}(\mathbf{a},\mathbf{a}_i) + + where : + + - :math:`Wu_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized + Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced) + - :math:`\mathbf{a}_i` are training distributions in the columns of matrix + :math:`\mathbf{A}` + - reg and :math:`\mathbf{M}` are respectively the regularization term and + the cost matrix for OT + - reg_mis the marginal relaxation hyperparameter + The algorithm used for solving the problem is the generalized + Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ + + Parameters + ---------- + A : np.ndarray (dim, n_hists) + `n_hists` training distributions a_i of dimension dim + M : np.ndarray (dim, dim) + ground metric matrix for OT. + reg : float + Entropy regularization term > 0 + reg_m: float + Marginal relaxation term > 0 + weights : np.ndarray (n_hists,) optional + Weight of each distribution (barycentric coodinates) + If None, uniform weights are used. + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (> 0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + a : (dim,) ndarray + Unbalanced Wasserstein barycenter + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. + (2015). Iterative Bregman projections for regularized transportation + problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprin + arXiv:1607.05816. + + """ + + if method.lower() == 'sinkhorn': + return barycenter_unbalanced_sinkhorn(A, M, reg, reg_m, + weights=weights, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + + elif method.lower() == 'sinkhorn_stabilized': + return barycenter_unbalanced_stabilized(A, M, reg, reg_m, + weights=weights, + numItermax=numItermax, + stopThr=stopThr, + verbose=verbose, + log=log, **kwargs) + elif method.lower() in ['sinkhorn_reg_scaling']: + warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') + return barycenter_unbalanced(A, M, reg, reg_m, + weights=weights, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + else: + raise ValueError("Unknown method '%s'." % method) diff --git a/ot/utils.py b/ot/utils.py index bb21b38..b71458b 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -Various function that can be usefull +Various useful functions """ # Author: Remi Flamary <remi.flamary@unice.fr> @@ -111,12 +111,12 @@ def dist(x1, x2=None, metric='sqeuclidean'): Parameters ---------- - x1 : np.array (n1,d) + x1 : ndarray, shape (n1,d) matrix with n1 samples of size d - x2 : np.array (n2,d), optional + x2 : array, shape (n2,d), optional matrix with n2 samples of size d (if None then x2=x1) - metric : str, fun, optional - name of the metric to be computed (full list in the doc of scipy), If a string, + metric : str | callable, optional + Name of the metric to be computed (full list in the doc of scipy), If a string, the distance function can be 'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', @@ -138,26 +138,21 @@ def dist(x1, x2=None, metric='sqeuclidean'): def dist0(n, method='lin_square'): - """Compute standard cost matrices of size (n,n) for OT problems + """Compute standard cost matrices of size (n, n) for OT problems Parameters ---------- - n : int - size of the cost matrix + Size of the cost matrix. method : str, optional Type of loss matrix chosen from: * 'lin_square' : linear sampling between 0 and n-1, quadratic loss - Returns ------- - - M : np.array (n1,n2) - distance matrix computed with given metric - - + M : ndarray, shape (n1,n2) + Distance matrix computed with given metric. """ res = 0 if method == 'lin_square': @@ -169,33 +164,34 @@ def dist0(n, method='lin_square'): def cost_normalization(C, norm=None): """ Apply normalization to the loss matrix - Parameters ---------- - C : np.array (n1, n2) + C : ndarray, shape (n1, n2) The cost matrix to normalize. norm : str - type of normalization from 'median','max','log','loglog'. Any other - value do not normalize. - + Type of normalization from 'median', 'max', 'log', 'loglog'. Any + other value do not normalize. Returns ------- - - C : np.array (n1, n2) + C : ndarray, shape (n1, n2) The input cost matrix normalized according to given norm. - """ - if norm == "median": + if norm is None: + pass + elif norm == "median": C /= float(np.median(C)) elif norm == "max": C /= float(np.max(C)) elif norm == "log": C = np.log(1 + C) elif norm == "loglog": - C = np.log(1 + np.log(1 + C)) - + C = np.log1p(np.log1p(C)) + else: + raise ValueError('Norm %s is not a valid option.\n' + 'Valid options are:\n' + 'median, max, log, loglog' % norm) return C @@ -214,23 +210,28 @@ def fun(f, q_in, q_out): def parmap(f, X, nprocs=multiprocessing.cpu_count()): - """ paralell map for multiprocessing """ - q_in = multiprocessing.Queue(1) - q_out = multiprocessing.Queue() + """ paralell map for multiprocessing (only map on windows)""" - proc = [multiprocessing.Process(target=fun, args=(f, q_in, q_out)) - for _ in range(nprocs)] - for p in proc: - p.daemon = True - p.start() + if not sys.platform.endswith('win32'): - sent = [q_in.put((i, x)) for i, x in enumerate(X)] - [q_in.put((None, None)) for _ in range(nprocs)] - res = [q_out.get() for _ in range(len(sent))] + q_in = multiprocessing.Queue(1) + q_out = multiprocessing.Queue() - [p.join() for p in proc] + proc = [multiprocessing.Process(target=fun, args=(f, q_in, q_out)) + for _ in range(nprocs)] + for p in proc: + p.daemon = True + p.start() - return [x for i, x in sorted(res)] + sent = [q_in.put((i, x)) for i, x in enumerate(X)] + [q_in.put((None, None)) for _ in range(nprocs)] + res = [q_out.get() for _ in range(len(sent))] + + [p.join() for p in proc] + + return [x for i, x in sorted(res)] + else: + return list(map(f, X)) def check_params(**kwargs): @@ -256,6 +257,7 @@ def check_params(**kwargs): def check_random_state(seed): """Turn seed into a np.random.RandomState instance + Parameters ---------- seed : None | int | instance of RandomState @@ -275,7 +277,6 @@ def check_random_state(seed): class deprecated(object): - """Decorator to mark a function or class as deprecated. deprecated class from scikit-learn package @@ -285,14 +286,14 @@ class deprecated(object): The optional extra argument will be appended to the deprecation message and the docstring. Note: to use this with the default value for extra, put in an empty of parentheses: - >>> from ot.deprecation import deprecated - >>> @deprecated() - ... def some_function(): pass + >>> from ot.deprecation import deprecated # doctest: +SKIP + >>> @deprecated() # doctest: +SKIP + ... def some_function(): pass # doctest: +SKIP Parameters ---------- - extra : string - to be added to the deprecation messages + extra : str + To be added to the deprecation messages. """ # Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary, @@ -373,9 +374,9 @@ def _is_deprecated(func): class BaseEstimator(object): - """Base class for most objects in POT - adapted from sklearn BaseEstimator class + + Code adapted from sklearn BaseEstimator class Notes ----- @@ -417,7 +418,7 @@ class BaseEstimator(object): Parameters ---------- - deep : boolean, optional + deep : bool, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. @@ -487,3 +488,11 @@ class BaseEstimator(object): (key, self.__class__.__name__)) setattr(self, key, value) return self + + +class UndefinedParameter(Exception): + """ + Aim at raising an Exception when a undefined parameter is called + + """ + pass diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/pytest.ini diff --git a/requirements.txt b/requirements.txt index 97d165b..c08822e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,5 +4,7 @@ cython matplotlib sphinx-gallery autograd -pymanopt -pytest +pymanopt==0.2.4; python_version <'3' +pymanopt; python_version >= '3' +cvxopt +pytest
\ No newline at end of file @@ -3,4 +3,22 @@ description-file = README.md [flake8] exclude = __init__.py -ignore = E265,E501 +ignore = E265,E501,W605,W503,W504 + +[tool:pytest] +addopts = + --showlocals --durations=20 --doctest-modules -ra --cov-report= --cov=ot + --doctest-ignore-import-errors --junit-xml=junit-results.xml + --ignore=docs --ignore=examples --ignore=notebooks + +[pycodestyle] +exclude = __init__.py,*externals*,constants.py,fixes.py +ignore = E241,E305,W504 + +[pydocstyle] +convention = pep257 +match_dir = ^(?!\.|docs|examples).*$ +match = (?!tests/__init__\.py|fixes).*\.py +add-ignore = D100,D104,D107,D413 +add-select = D214,D215,D404,D405,D406,D407,D408,D409,D410,D411 +ignore-decorators = ^(copy_.*_doc_to_|on_trait_change|cached_property|deprecated|property|.*setter).* @@ -60,17 +60,26 @@ setup(name='POT', license = 'MIT', scripts=[], data_files=[], - requires=["numpy","scipy","cython","matplotlib"], - install_requires=["numpy","scipy","cython","matplotlib"], + requires=["numpy","scipy","cython"], + install_requires=["numpy","scipy","cython"], classifiers=[ - 'Development Status :: 4 - Beta', + 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', + 'Intended Audience :: Education', + 'Intended Audience :: Science/Research', + 'License :: OSI Approved :: MIT License', 'Environment :: Console', 'Operating System :: OS Independent', 'Operating System :: MacOS', 'Operating System :: POSIX', 'Programming Language :: Python', + 'Programming Language :: C++', + 'Programming Language :: C', + 'Programming Language :: Cython', 'Topic :: Utilities', + 'Topic :: Scientific/Engineering :: Artificial Intelligence', + 'Topic :: Scientific/Engineering :: Mathematics', + 'Topic :: Scientific/Engineering :: Information Analysis', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', diff --git a/test/test_bregman.py b/test/test_bregman.py index 14edaf5..f54ba9f 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -1,11 +1,13 @@ """Tests for module bregman on OT with bregman projections """ # Author: Remi Flamary <remi.flamary@unice.fr> +# Kilian Fatras <kilian.fatras@irisa.fr> # # License: MIT License import numpy as np import ot +import pytest def test_sinkhorn(): @@ -70,19 +72,40 @@ def test_sinkhorn_variants(): Gs = ot.sinkhorn(u, u, M, 1, method='sinkhorn_stabilized', stopThr=1e-10) Ges = ot.sinkhorn( u, u, M, 1, method='sinkhorn_epsilon_scaling', stopThr=1e-10) - Gerr = ot.sinkhorn(u, u, M, 1, method='do_not_exists', stopThr=1e-10) G_green = ot.sinkhorn(u, u, M, 1, method='greenkhorn', stopThr=1e-10) # check values np.testing.assert_allclose(G0, Gs, atol=1e-05) np.testing.assert_allclose(G0, Ges, atol=1e-05) - np.testing.assert_allclose(G0, Gerr) np.testing.assert_allclose(G0, G_green, atol=1e-5) print(G0, G_green) -def test_bary(): +def test_sinkhorn_variants_log(): + # test sinkhorn + n = 100 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + M = ot.dist(x, x) + + G0, log0 = ot.sinkhorn(u, u, M, 1, method='sinkhorn', stopThr=1e-10, log=True) + Gs, logs = ot.sinkhorn(u, u, M, 1, method='sinkhorn_stabilized', stopThr=1e-10, log=True) + Ges, loges = ot.sinkhorn( + u, u, M, 1, method='sinkhorn_epsilon_scaling', stopThr=1e-10, log=True) + G_green, loggreen = ot.sinkhorn(u, u, M, 1, method='greenkhorn', stopThr=1e-10, log=True) + + # check values + np.testing.assert_allclose(G0, Gs, atol=1e-05) + np.testing.assert_allclose(G0, Ges, atol=1e-05) + np.testing.assert_allclose(G0, G_green, atol=1e-5) + print(G0, G_green) + + +@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) +def test_barycenter(method): n_bins = 100 # nb bins # Gaussian distributions @@ -100,16 +123,42 @@ def test_bary(): weights = np.array([1 - alpha, alpha]) # wasserstein - reg = 1e-3 - bary_wass = ot.bregman.barycenter(A, M, reg, weights) + reg = 1e-2 + bary_wass = ot.bregman.barycenter(A, M, reg, weights, method=method) np.testing.assert_allclose(1, np.sum(bary_wass)) ot.bregman.barycenter(A, M, reg, log=True, verbose=True) -def test_wasserstein_bary_2d(): +def test_barycenter_stabilization(): + n_bins = 100 # nb bins + # Gaussian distributions + a1 = ot.datasets.make_1D_gauss(n_bins, m=30, s=10) # m= mean, s= std + a2 = ot.datasets.make_1D_gauss(n_bins, m=40, s=10) + + # creating matrix A containing all distributions + A = np.vstack((a1, a2)).T + + # loss matrix + normalization + M = ot.utils.dist0(n_bins) + M /= M.max() + + alpha = 0.5 # 0<=alpha<=1 + weights = np.array([1 - alpha, alpha]) + + # wasserstein + reg = 1e-2 + bar_stable = ot.bregman.barycenter(A, M, reg, weights, + method="sinkhorn_stabilized", + stopThr=1e-8) + bar = ot.bregman.barycenter(A, M, reg, weights, method="sinkhorn", + stopThr=1e-8) + np.testing.assert_allclose(bar, bar_stable) + + +def test_wasserstein_bary_2d(): size = 100 # size of a square image a1 = np.random.randn(size, size) a1 += a1.min() @@ -133,7 +182,6 @@ def test_wasserstein_bary_2d(): def test_unmix(): - n_bins = 50 # nb bins # Gaussian distributions @@ -155,10 +203,151 @@ def test_unmix(): # wasserstein reg = 1e-3 - um = ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01,) + um = ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01, ) np.testing.assert_allclose(1, np.sum(um), rtol=1e-03, atol=1e-03) np.testing.assert_allclose([0.5, 0.5], um, rtol=1e-03, atol=1e-03) ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01, log=True, verbose=True) + + +def test_empirical_sinkhorn(): + # test sinkhorn + n = 100 + a = ot.unif(n) + b = ot.unif(n) + + X_s = np.reshape(np.arange(n), (n, 1)) + X_t = np.reshape(np.arange(0, n), (n, 1)) + M = ot.dist(X_s, X_t) + M_m = ot.dist(X_s, X_t, metric='minkowski') + + G_sqe = ot.bregman.empirical_sinkhorn(X_s, X_t, 1) + sinkhorn_sqe = ot.sinkhorn(a, b, M, 1) + + G_log, log_es = ot.bregman.empirical_sinkhorn(X_s, X_t, 0.1, log=True) + sinkhorn_log, log_s = ot.sinkhorn(a, b, M, 0.1, log=True) + + G_m = ot.bregman.empirical_sinkhorn(X_s, X_t, 1, metric='minkowski') + sinkhorn_m = ot.sinkhorn(a, b, M_m, 1) + + loss_emp_sinkhorn = ot.bregman.empirical_sinkhorn2(X_s, X_t, 1) + loss_sinkhorn = ot.sinkhorn2(a, b, M, 1) + + # check constratints + np.testing.assert_allclose( + sinkhorn_sqe.sum(1), G_sqe.sum(1), atol=1e-05) # metric sqeuclidian + np.testing.assert_allclose( + sinkhorn_sqe.sum(0), G_sqe.sum(0), atol=1e-05) # metric sqeuclidian + np.testing.assert_allclose( + sinkhorn_log.sum(1), G_log.sum(1), atol=1e-05) # log + np.testing.assert_allclose( + sinkhorn_log.sum(0), G_log.sum(0), atol=1e-05) # log + np.testing.assert_allclose( + sinkhorn_m.sum(1), G_m.sum(1), atol=1e-05) # metric euclidian + np.testing.assert_allclose( + sinkhorn_m.sum(0), G_m.sum(0), atol=1e-05) # metric euclidian + np.testing.assert_allclose(loss_emp_sinkhorn, loss_sinkhorn, atol=1e-05) + + +def test_empirical_sinkhorn_divergence(): + # Test sinkhorn divergence + n = 10 + a = ot.unif(n) + b = ot.unif(n) + X_s = np.reshape(np.arange(n), (n, 1)) + X_t = np.reshape(np.arange(0, n * 2, 2), (n, 1)) + M = ot.dist(X_s, X_t) + M_s = ot.dist(X_s, X_s) + M_t = ot.dist(X_t, X_t) + + emp_sinkhorn_div = ot.bregman.empirical_sinkhorn_divergence(X_s, X_t, 1) + sinkhorn_div = (ot.sinkhorn2(a, b, M, 1) - 1 / 2 * ot.sinkhorn2(a, a, M_s, 1) - 1 / 2 * ot.sinkhorn2(b, b, M_t, 1)) + + emp_sinkhorn_div_log, log_es = ot.bregman.empirical_sinkhorn_divergence(X_s, X_t, 1, log=True) + sink_div_log_ab, log_s_ab = ot.sinkhorn2(a, b, M, 1, log=True) + sink_div_log_a, log_s_a = ot.sinkhorn2(a, a, M_s, 1, log=True) + sink_div_log_b, log_s_b = ot.sinkhorn2(b, b, M_t, 1, log=True) + sink_div_log = sink_div_log_ab - 1 / 2 * (sink_div_log_a + sink_div_log_b) + + # check constratints + np.testing.assert_allclose( + emp_sinkhorn_div, sinkhorn_div, atol=1e-05) # cf conv emp sinkhorn + np.testing.assert_allclose( + emp_sinkhorn_div_log, sink_div_log, atol=1e-05) # cf conv emp sinkhorn + + +def test_stabilized_vs_sinkhorn_multidim(): + # test if stable version matches sinkhorn + # for multidimensional inputs + n = 100 + + # Gaussian distributions + a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + b1 = ot.datasets.make_1D_gauss(n, m=60, s=8) + b2 = ot.datasets.make_1D_gauss(n, m=30, s=4) + + # creating matrix A containing all distributions + b = np.vstack((b1, b2)).T + + M = ot.utils.dist0(n) + M /= np.median(M) + epsilon = 0.1 + G, log = ot.bregman.sinkhorn(a, b, M, reg=epsilon, + method="sinkhorn_stabilized", + log=True) + G2, log2 = ot.bregman.sinkhorn(a, b, M, epsilon, + method="sinkhorn", log=True) + + np.testing.assert_allclose(G, G2) + + +def test_implemented_methods(): + IMPLEMENTED_METHODS = ['sinkhorn', 'sinkhorn_stabilized'] + ONLY_1D_methods = ['greenkhorn', 'sinkhorn_epsilon_scaling'] + NOT_VALID_TOKENS = ['foo'] + # test generalized sinkhorn for unbalanced OT barycenter + n = 3 + rng = np.random.RandomState(42) + + x = rng.randn(n, 2) + a = ot.utils.unif(n) + + # make dists unbalanced + b = ot.utils.unif(n) + A = rng.rand(n, 2) + M = ot.dist(x, x) + epsilon = 1. + + for method in IMPLEMENTED_METHODS: + ot.bregman.sinkhorn(a, b, M, epsilon, method=method) + ot.bregman.sinkhorn2(a, b, M, epsilon, method=method) + ot.bregman.barycenter(A, M, reg=epsilon, method=method) + with pytest.raises(ValueError): + for method in set(NOT_VALID_TOKENS): + ot.bregman.sinkhorn(a, b, M, epsilon, method=method) + ot.bregman.sinkhorn2(a, b, M, epsilon, method=method) + ot.bregman.barycenter(A, M, reg=epsilon, method=method) + for method in ONLY_1D_methods: + ot.bregman.sinkhorn(a, b, M, epsilon, method=method) + with pytest.raises(ValueError): + ot.bregman.sinkhorn2(a, b, M, epsilon, method=method) + + +def test_screenkhorn(): + # test screenkhorn + rng = np.random.RandomState(0) + n = 100 + a = ot.unif(n) + b = ot.unif(n) + + x = rng.randn(n, 2) + M = ot.dist(x, x) + # sinkhorn + G_sink = ot.sinkhorn(a, b, M, 1e-03) + # screenkhorn + G_screen = ot.bregman.screenkhorn(a, b, M, 1e-03, uniform=True, verbose=True) + # check marginals + np.testing.assert_allclose(G_sink.sum(0), G_screen.sum(0), atol=1e-02) + np.testing.assert_allclose(G_sink.sum(1), G_screen.sum(1), atol=1e-02) diff --git a/test/test_da.py b/test/test_da.py index f7f3a9d..2a5e50e 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -245,6 +245,71 @@ def test_sinkhorn_transport_class(): assert len(otda.log_.keys()) != 0 +def test_unbalanced_sinkhorn_transport_class(): + """test_sinkhorn_transport + """ + + ns = 150 + nt = 200 + + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) + + otda = ot.da.UnbalancedSinkhornTransport() + + # test its computed + otda.fit(Xs=Xs, Xt=Xt) + assert hasattr(otda, "cost_") + assert hasattr(otda, "coupling_") + assert hasattr(otda, "log_") + + # test dimensions of coupling + assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test transform + transp_Xs = otda.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + Xs_new, _ = make_data_classif('3gauss', ns + 1) + transp_Xs_new = otda.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + # test inverse transform + transp_Xt = otda.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = make_data_classif('3gauss2', nt + 1) + transp_Xt_new = otda.inverse_transform(Xt=Xt_new) + + # check that the oos method is working + assert_equal(transp_Xt_new.shape, Xt_new.shape) + + # test fit_transform + transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) + assert_equal(transp_Xs.shape, Xs.shape) + + # test unsupervised vs semi-supervised mode + otda_unsup = ot.da.SinkhornTransport() + otda_unsup.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(otda_unsup.cost_) + + otda_semi = ot.da.SinkhornTransport() + otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(otda_semi.cost_) + + # check that the cost matrix norms are indeed different + assert n_unsup != n_semisup, "semisupervised mode not working" + + # check everything runs well with log=True + otda = ot.da.SinkhornTransport(log=True) + otda.fit(Xs=Xs, ys=ys, Xt=Xt) + assert len(otda.log_.keys()) != 0 + + def test_emd_transport_class(): """test_sinkhorn_transport """ diff --git a/test/test_gpu.py b/test/test_gpu.py index 6b7fdd4..8e62a74 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -16,6 +16,16 @@ except ImportError: @pytest.mark.skipif(nogpu, reason="No GPU available") +def test_gpu_old_doctests(): + a = [.5, .5] + b = [.5, .5] + M = [[0., 1.], [1., 0.]] + G = ot.sinkhorn(a, b, M, 1) + np.testing.assert_allclose(G, np.array([[0.36552929, 0.13447071], + [0.13447071, 0.36552929]])) + + +@pytest.mark.skipif(nogpu, reason="No GPU available") def test_gpu_dist(): rng = np.random.RandomState(0) diff --git a/test/test_gromov.py b/test/test_gromov.py index 305ae84..43da9fc 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -2,6 +2,7 @@ # Author: Erwan Vautier <erwan.vautier@gmail.com>
# Nicolas Courty <ncourty@irisa.fr>
+# Titouan Vayer <titouan.vayer@irisa.fr>
#
# License: MIT License
@@ -15,7 +16,7 @@ def test_gromov(): mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
- xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)
+ xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=4)
xt = xs[::-1].copy()
@@ -36,12 +37,21 @@ def test_gromov(): np.testing.assert_allclose(
q, G.sum(0), atol=1e-04) # cf convergence gromov
+ Id = (1 / (1.0 * n_samples)) * np.eye(n_samples, n_samples)
+
+ np.testing.assert_allclose(
+ G, np.flipud(Id), atol=1e-04)
+
gw, log = ot.gromov.gromov_wasserstein2(C1, C2, p, q, 'kl_loss', log=True)
+ gw_val = ot.gromov.gromov_wasserstein2(C1, C2, p, q, 'kl_loss', log=False)
+
G = log['T']
np.testing.assert_allclose(gw, 0, atol=1e-1, rtol=1e-1)
+ np.testing.assert_allclose(gw, gw_val, atol=1e-1, rtol=1e-1) # cf log=False
+
# check constratints
np.testing.assert_allclose(
p, G.sum(1), atol=1e-04) # cf convergence gromov
@@ -55,7 +65,7 @@ def test_entropic_gromov(): mu_s = np.array([0, 0])
cov_s = np.array([[1, 0], [0, 1]])
- xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)
+ xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=42)
xt = xs[::-1].copy()
@@ -92,12 +102,11 @@ def test_entropic_gromov(): def test_gromov_barycenter():
-
ns = 50
nt = 60
- Xs, ys = ot.datasets.make_data_classif('3gauss', ns)
- Xt, yt = ot.datasets.make_data_classif('3gauss2', nt)
+ Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42)
+ Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42)
C1 = ot.dist(Xs)
C2 = ot.dist(Xt)
@@ -120,12 +129,11 @@ def test_gromov_barycenter(): def test_gromov_entropic_barycenter():
-
ns = 50
nt = 60
- Xs, ys = ot.datasets.make_data_classif('3gauss', ns)
- Xt, yt = ot.datasets.make_data_classif('3gauss2', nt)
+ Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42)
+ Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42)
C1 = ot.dist(Xs)
C2 = ot.dist(Xt)
@@ -145,3 +153,98 @@ def test_gromov_entropic_barycenter(): 'kl_loss', 2e-3,
max_iter=100, tol=1e-3)
np.testing.assert_allclose(Cb2.shape, (n_samples, n_samples))
+
+
+def test_fgw():
+
+ n_samples = 50 # nb samples
+
+ mu_s = np.array([0, 0])
+ cov_s = np.array([[1, 0], [0, 1]])
+
+ xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=42)
+
+ xt = xs[::-1].copy()
+
+ ys = np.random.randn(xs.shape[0], 2)
+ yt = ys[::-1].copy()
+
+ p = ot.unif(n_samples)
+ q = ot.unif(n_samples)
+
+ C1 = ot.dist(xs, xs)
+ C2 = ot.dist(xt, xt)
+
+ C1 /= C1.max()
+ C2 /= C2.max()
+
+ M = ot.dist(ys, yt)
+ M /= M.max()
+
+ G = ot.gromov.fused_gromov_wasserstein(M, C1, C2, p, q, 'square_loss', alpha=0.5)
+
+ # check constratints
+ np.testing.assert_allclose(
+ p, G.sum(1), atol=1e-04) # cf convergence fgw
+ np.testing.assert_allclose(
+ q, G.sum(0), atol=1e-04) # cf convergence fgw
+
+ Id = (1 / (1.0 * n_samples)) * np.eye(n_samples, n_samples)
+
+ np.testing.assert_allclose(
+ G, np.flipud(Id), atol=1e-04) # cf convergence gromov
+
+ fgw, log = ot.gromov.fused_gromov_wasserstein2(M, C1, C2, p, q, 'square_loss', alpha=0.5, log=True)
+
+ G = log['T']
+
+ np.testing.assert_allclose(fgw, 0, atol=1e-1, rtol=1e-1)
+
+ # check constratints
+ np.testing.assert_allclose(
+ p, G.sum(1), atol=1e-04) # cf convergence gromov
+ np.testing.assert_allclose(
+ q, G.sum(0), atol=1e-04) # cf convergence gromov
+
+
+def test_fgw_barycenter():
+ np.random.seed(42)
+
+ ns = 50
+ nt = 60
+
+ Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42)
+ Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42)
+
+ ys = np.random.randn(Xs.shape[0], 2)
+ yt = np.random.randn(Xt.shape[0], 2)
+
+ C1 = ot.dist(Xs)
+ C2 = ot.dist(Xt)
+
+ n_samples = 3
+ X, C = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], [ot.unif(ns), ot.unif(nt)], [.5, .5], 0.5,
+ fixed_structure=False, fixed_features=False,
+ p=ot.unif(n_samples), loss_fun='square_loss',
+ max_iter=100, tol=1e-3)
+ np.testing.assert_allclose(C.shape, (n_samples, n_samples))
+ np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1]))
+
+ xalea = np.random.randn(n_samples, 2)
+ init_C = ot.dist(xalea, xalea)
+
+ X, C = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], ps=[ot.unif(ns), ot.unif(nt)], lambdas=[.5, .5], alpha=0.5,
+ fixed_structure=True, init_C=init_C, fixed_features=False,
+ p=ot.unif(n_samples), loss_fun='square_loss',
+ max_iter=100, tol=1e-3)
+ np.testing.assert_allclose(C.shape, (n_samples, n_samples))
+ np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1]))
+
+ init_X = np.random.randn(n_samples, ys.shape[1])
+
+ X, C = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], [ot.unif(ns), ot.unif(nt)], [.5, .5], 0.5,
+ fixed_structure=False, fixed_features=True, init_X=init_X,
+ p=ot.unif(n_samples), loss_fun='square_loss',
+ max_iter=100, tol=1e-3)
+ np.testing.assert_allclose(C.shape, (n_samples, n_samples))
+ np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1]))
diff --git a/test/test_optim.py b/test/test_optim.py index dfefe59..aade36e 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -37,6 +37,39 @@ def test_conditional_gradient(): np.testing.assert_allclose(b, G.sum(0)) +def test_conditional_gradient2(): + n = 4000 # nb samples + + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) + + mu_t = np.array([4, 4]) + cov_t = np.array([[1, -.8], [-.8, 1]]) + + xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s) + xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t) + + a, b = np.ones((n,)) / n, np.ones((n,)) / n + + # loss matrix + M = ot.dist(xs, xt) + M /= M.max() + + def f(G): + return 0.5 * np.sum(G**2) + + def df(G): + return G + + reg = 1e-1 + + G, log = ot.optim.cg(a, b, M, reg, f, df, numItermaxEmd=200000, + verbose=True, log=True) + + np.testing.assert_allclose(a, G.sum(1)) + np.testing.assert_allclose(b, G.sum(0)) + + def test_generalized_conditional_gradient(): n_bins = 100 # nb bins @@ -65,3 +98,9 @@ def test_generalized_conditional_gradient(): np.testing.assert_allclose(a, G.sum(1), atol=1e-05) np.testing.assert_allclose(b, G.sum(0), atol=1e-05) + + +def test_solve_1d_linesearch_quad_funct(): + np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad(1, -1, 0), 0.5) + np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad(-1, 5, 0), 0) + np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad(-1, 0.5, 0), 1) diff --git a/test/test_ot.py b/test/test_ot.py index 7652394..47df946 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -7,20 +7,27 @@ import warnings import numpy as np +from scipy.stats import wasserstein_distance import ot from ot.datasets import make_1D_gauss as gauss import pytest -def test_doctest(): - import doctest +def test_emd_dimension_mismatch(): + # test emd and emd2 for dimension mismatch + n_samples = 100 + n_features = 2 + rng = np.random.RandomState(0) + + x = rng.randn(n_samples, n_features) + a = ot.utils.unif(n_samples + 1) - # test lp solver - doctest.testmod(ot.lp, verbose=True) + M = ot.dist(x, x) - # test bregman solver - doctest.testmod(ot.bregman, verbose=True) + np.testing.assert_raises(AssertionError, ot.emd, a, a, M) + + np.testing.assert_raises(AssertionError, ot.emd2, a, a, M) def test_emd_emd2(): @@ -37,7 +44,7 @@ def test_emd_emd2(): # check G is identity np.testing.assert_allclose(G, np.eye(n) / n) - # check constratints + # check constraints np.testing.assert_allclose(u, G.sum(1)) # cf convergence sinkhorn np.testing.assert_allclose(u, G.sum(0)) # cf convergence sinkhorn @@ -46,6 +53,64 @@ def test_emd_emd2(): np.testing.assert_allclose(w, 0) +def test_emd_1d_emd2_1d(): + # test emd1d gives similar results as emd + n = 20 + m = 30 + rng = np.random.RandomState(0) + u = rng.randn(n, 1) + v = rng.randn(m, 1) + + M = ot.dist(u, v, metric='sqeuclidean') + + G, log = ot.emd([], [], M, log=True) + wass = log["cost"] + G_1d, log = ot.emd_1d(u, v, [], [], metric='sqeuclidean', log=True) + wass1d = log["cost"] + wass1d_emd2 = ot.emd2_1d(u, v, [], [], metric='sqeuclidean', log=False) + wass1d_euc = ot.emd2_1d(u, v, [], [], metric='euclidean', log=False) + + # check loss is similar + np.testing.assert_allclose(wass, wass1d) + np.testing.assert_allclose(wass, wass1d_emd2) + + # check loss is similar to scipy's implementation for Euclidean metric + wass_sp = wasserstein_distance(u.reshape((-1, )), v.reshape((-1, ))) + np.testing.assert_allclose(wass_sp, wass1d_euc) + + # check constraints + np.testing.assert_allclose(np.ones((n, )) / n, G.sum(1)) + np.testing.assert_allclose(np.ones((m, )) / m, G.sum(0)) + + # check G is similar + np.testing.assert_allclose(G, G_1d) + + # check AssertionError is raised if called on non 1d arrays + u = np.random.randn(n, 2) + v = np.random.randn(m, 2) + with pytest.raises(AssertionError): + ot.emd_1d(u, v, [], []) + + +def test_wass_1d(): + # test emd1d gives similar results as emd + n = 20 + m = 30 + rng = np.random.RandomState(0) + u = rng.randn(n, 1) + v = rng.randn(m, 1) + + M = ot.dist(u, v, metric='sqeuclidean') + + G, log = ot.emd([], [], M, log=True) + wass = log["cost"] + + wass1d = ot.wasserstein_1d(u, v, [], [], p=2.) + + # check loss is similar + np.testing.assert_allclose(np.sqrt(wass), wass1d) + + def test_emd_empty(): # test emd and emd2 for simple identity n = 100 @@ -60,7 +125,7 @@ def test_emd_empty(): # check G is identity np.testing.assert_allclose(G, np.eye(n) / n) - # check constratints + # check constraints np.testing.assert_allclose(u, G.sum(1)) # cf convergence sinkhorn np.testing.assert_allclose(u, G.sum(0)) # cf convergence sinkhorn @@ -69,6 +134,28 @@ def test_emd_empty(): np.testing.assert_allclose(w, 0) +def test_emd_sparse(): + + n = 100 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + x2 = rng.randn(n, 2) + + M = ot.dist(x, x2) + + G = ot.emd([], [], M, dense=True) + + Gs = ot.emd([], [], M, dense=False) + + ws = ot.emd2([], [], M, dense=False) + + # check G is the same + np.testing.assert_allclose(G, Gs.todense()) + # check value + np.testing.assert_allclose(Gs.multiply(M).sum(), ws, rtol=1e-6) + + def test_emd2_multi(): n = 500 # nb bins @@ -100,7 +187,12 @@ def test_emd2_multi(): emdn = ot.emd2(a, b, M) ot.toc('multi proc : {} s') + ot.tic() + emdn2 = ot.emd2(a, b, M, dense=False) + ot.toc('multi proc : {} s') + np.testing.assert_allclose(emd1, emdn) + np.testing.assert_allclose(emd1, emdn2, rtol=1e-6) # emd loss multipro proc with log ot.tic() @@ -246,6 +338,10 @@ def test_dual_variables(): np.testing.assert_almost_equal(cost1, log['cost']) check_duality_gap(a, b, M, G, log['u'], log['v'], log['cost']) + constraint_violation = log['u'][:, None] + log['v'][None, :] - M + + assert constraint_violation.max() < 1e-8 + def check_duality_gap(a, b, M, G, u, v, cost): cost_dual = np.vdot(a, u) + np.vdot(b, v) diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py new file mode 100644 index 0000000..ca1efba --- /dev/null +++ b/test/test_unbalanced.py @@ -0,0 +1,221 @@ +"""Tests for module Unbalanced OT with entropy regularization""" + +# Author: Hicham Janati <hicham.janati@inria.fr> +# +# License: MIT License + +import numpy as np +import ot +import pytest +from ot.unbalanced import barycenter_unbalanced + +from scipy.special import logsumexp + + +@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) +def test_unbalanced_convergence(method): + # test generalized sinkhorn for unbalanced OT + n = 100 + rng = np.random.RandomState(42) + + x = rng.randn(n, 2) + a = ot.utils.unif(n) + + # make dists unbalanced + b = ot.utils.unif(n) * 1.5 + + M = ot.dist(x, x) + epsilon = 1. + reg_m = 1. + + G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, + reg_m=reg_m, + method=method, + log=True) + loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, + method=method) + # check fixed point equations + # in log-domain + fi = reg_m / (reg_m + epsilon) + logb = np.log(b + 1e-16) + loga = np.log(a + 1e-16) + logKtu = logsumexp(log["logu"][None, :] - M.T / epsilon, axis=1) + logKv = logsumexp(log["logv"][None, :] - M / epsilon, axis=1) + + v_final = fi * (logb - logKtu) + u_final = fi * (loga - logKv) + + np.testing.assert_allclose( + u_final, log["logu"], atol=1e-05) + np.testing.assert_allclose( + v_final, log["logv"], atol=1e-05) + + # check if sinkhorn_unbalanced2 returns the correct loss + np.testing.assert_allclose((G * M).sum(), loss, atol=1e-5) + + +@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) +def test_unbalanced_multiple_inputs(method): + # test generalized sinkhorn for unbalanced OT + n = 100 + rng = np.random.RandomState(42) + + x = rng.randn(n, 2) + a = ot.utils.unif(n) + + # make dists unbalanced + b = rng.rand(n, 2) + + M = ot.dist(x, x) + epsilon = 1. + reg_m = 1. + + loss, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, + reg_m=reg_m, + method=method, + log=True) + # check fixed point equations + # in log-domain + fi = reg_m / (reg_m + epsilon) + logb = np.log(b + 1e-16) + loga = np.log(a + 1e-16)[:, None] + logKtu = logsumexp(log["logu"][:, None, :] - M[:, :, None] / epsilon, + axis=0) + logKv = logsumexp(log["logv"][None, :] - M[:, :, None] / epsilon, axis=1) + v_final = fi * (logb - logKtu) + u_final = fi * (loga - logKv) + + np.testing.assert_allclose( + u_final, log["logu"], atol=1e-05) + np.testing.assert_allclose( + v_final, log["logv"], atol=1e-05) + + assert len(loss) == b.shape[1] + + +def test_stabilized_vs_sinkhorn(): + # test if stable version matches sinkhorn + n = 100 + + # Gaussian distributions + a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + b1 = ot.datasets.make_1D_gauss(n, m=60, s=8) + b2 = ot.datasets.make_1D_gauss(n, m=30, s=4) + + # creating matrix A containing all distributions + b = np.vstack((b1, b2)).T + + M = ot.utils.dist0(n) + M /= np.median(M) + epsilon = 0.1 + reg_m = 1. + G, log = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, reg=epsilon, + method="sinkhorn_stabilized", + reg_m=reg_m, + log=True) + G2, log2 = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, + method="sinkhorn", log=True) + + np.testing.assert_allclose(G, G2, atol=1e-5) + + +@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) +def test_unbalanced_barycenter(method): + # test generalized sinkhorn for unbalanced OT barycenter + n = 100 + rng = np.random.RandomState(42) + + x = rng.randn(n, 2) + A = rng.rand(n, 2) + + # make dists unbalanced + A = A * np.array([1, 2])[None, :] + M = ot.dist(x, x) + epsilon = 1. + reg_m = 1. + + q, log = barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method=method, log=True) + # check fixed point equations + fi = reg_m / (reg_m + epsilon) + logA = np.log(A + 1e-16) + logq = np.log(q + 1e-16)[:, None] + logKtu = logsumexp(log["logu"][:, None, :] - M[:, :, None] / epsilon, + axis=0) + logKv = logsumexp(log["logv"][None, :] - M[:, :, None] / epsilon, axis=1) + v_final = fi * (logq - logKtu) + u_final = fi * (logA - logKv) + + np.testing.assert_allclose( + u_final, log["logu"], atol=1e-05) + np.testing.assert_allclose( + v_final, log["logv"], atol=1e-05) + + +def test_barycenter_stabilized_vs_sinkhorn(): + # test generalized sinkhorn for unbalanced OT barycenter + n = 100 + rng = np.random.RandomState(42) + + x = rng.randn(n, 2) + A = rng.rand(n, 2) + + # make dists unbalanced + A = A * np.array([1, 4])[None, :] + M = ot.dist(x, x) + epsilon = 0.5 + reg_m = 10 + + qstable, log = barycenter_unbalanced(A, M, reg=epsilon, + reg_m=reg_m, log=True, + tau=100, + method="sinkhorn_stabilized", + ) + q, log = barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method="sinkhorn", + log=True) + + np.testing.assert_allclose( + q, qstable, atol=1e-05) + + +def test_implemented_methods(): + IMPLEMENTED_METHODS = ['sinkhorn', 'sinkhorn_stabilized'] + TO_BE_IMPLEMENTED_METHODS = ['sinkhorn_reg_scaling'] + NOT_VALID_TOKENS = ['foo'] + # test generalized sinkhorn for unbalanced OT barycenter + n = 3 + rng = np.random.RandomState(42) + + x = rng.randn(n, 2) + a = ot.utils.unif(n) + + # make dists unbalanced + b = ot.utils.unif(n) * 1.5 + A = rng.rand(n, 2) + M = ot.dist(x, x) + epsilon = 1. + reg_m = 1. + for method in IMPLEMENTED_METHODS: + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, reg_m, + method=method) + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, + method=method) + barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method=method) + with pytest.warns(UserWarning, match='not implemented'): + for method in set(TO_BE_IMPLEMENTED_METHODS): + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, reg_m, + method=method) + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, + method=method) + barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method=method) + with pytest.raises(ValueError): + for method in set(NOT_VALID_TOKENS): + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, reg_m, + method=method) + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, + method=method) + barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method=method) |
