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108 files changed, 6782 insertions, 1151 deletions
diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 0000000..2114fb4 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,6 @@ +[run] + +omit= + ot/externals/* + ot/externals/funcsigs.py + ot/gpu/* diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..7f8acda --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,36 @@ +--- +name: Bug report +about: Create a report to help us improve POT + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1 ... +2. + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Desktop (please complete the following information):** + - OS: [e.g. MacOSX, Windows, Ubuntu] + - Python version [2.7,3.6] +- How was POT installed [source, pip, conda] + +Output of the following code snippet: +```python +import platform; print(platform.platform()) +import sys; print("Python", sys.version) +import numpy; print("NumPy", numpy.__version__) +import scipy; print("SciPy", scipy.__version__) +import ot; print("POT", ot.__version__) +``` + +**Additional context** +Add any other context about the problem here. diff --git a/.travis.yml b/.travis.yml index d146395..50ff22c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,11 +26,11 @@ before_script: # configure a headless display to test plot generation # command to install dependencies install: - pip install -r requirements.txt - - pip install flake8 pytest + - pip install flake8 pytest "pytest-cov<2.6" - pip install . # command to run tests + check syntax style script: - python setup.py develop - flake8 examples/ ot/ test/ - - python -m pytest -v test/ + - python -m pytest -v test/ --cov=ot # - py.test ot test diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 84ef29a..54e7e42 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -17,7 +17,7 @@ GitHub, clone, and develop on a branch. Steps: a copy of the code under your GitHub user account. For more details on how to fork a repository see [this guide](https://help.github.com/articles/fork-a-repo/). -2. Clone your fork of the scikit-learn repo from your GitHub account to your local disk: +2. Clone your fork of the POT repo from your GitHub account to your local disk: ```bash $ git clone git@github.com:YourLogin/POT.git @@ -84,7 +84,7 @@ following rules before you submit a pull request: example script in the ``examples/`` folder. Have a look at other examples for reference. Examples should demonstrate why the new functionality is useful in practice and, if possible, compare it - to other methods available in scikit-learn. + to other methods available in POT. - Documentation and high-coverage tests are necessary for enhancements to be accepted. Bug-fixes or new features should be provided with @@ -145,7 +145,7 @@ following rules before submitting: See [Creating and highlighting code blocks](https://help.github.com/articles/creating-and-highlighting-code-blocks). - Please include your operating system type and version number, as well - as your Python, scikit-learn, numpy, and scipy versions. This information + as your Python, POT, numpy, and scipy versions. This information can be found by running the following code snippet: ```python @@ -165,8 +165,8 @@ following rules before submitting: New contributor tips -------------------- -A great way to start contributing to scikit-learn is to pick an item -from the list of [Easy issues](https://github.com/scikit-learn/scikit-learn/issues?labels=Easy) +A great way to start contributing to POT is to pick an item +from the list of [Easy issues](https://github.com/rflamary/POT/issues?labels=Easy) in the issue tracker. Resolving these issues allow you to start contributing to the project without much prior knowledge. Your assistance in this area will be greatly appreciated by the more @@ -201,4 +201,4 @@ method does to the data and a figure (coming from an example) illustrating it. -This Contrubution guide is strongly inpired by the one of the [scikit-learn](https://github.com/scikit-learn/scikit-learn) team. +This Contribution guide is strongly inpired by the one of the [scikit-learn](https://github.com/scikit-learn/scikit-learn) team. @@ -1,6 +1,7 @@ PYTHON=python3 +branch := $(shell git symbolic-ref --short -q HEAD) help : @echo "The following make targets are available:" @@ -57,6 +58,16 @@ rdoc : notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ +bench : + @git stash >/dev/null 2>&1 + @echo 'Branch master' + @git checkout master >/dev/null 2>&1 + python3 $(script) + @echo 'Branch $(branch)' + @git checkout $(branch) >/dev/null 2>&1 + python3 $(script) + @git stash apply >/dev/null 2>&1 + autopep8 : autopep8 -ir test ot examples --jobs -1 @@ -222,18 +222,3 @@ You can also post bug reports and feature requests in Github issues. Make sure t [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). - diff --git a/RELEASES.md b/RELEASES.md index 58712c8..a617441 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -1,5 +1,64 @@ # POT Releases + +## 0.5.0 Year 2 +*Sep 2018* + +POT is 2 years old! This release brings numerous new features to the +toolbox as listed below but also several bug correction. + +Among the new features, we can highlight a [non-regularized Gromov-Wasserstein +solver](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb), +a new [greedy variant of sinkhorn](https://pot.readthedocs.io/en/latest/all.html#ot.bregman.greenkhorn), +[non-regularized](https://pot.readthedocs.io/en/latest/all.html#ot.lp.barycenter), +[convolutional (2D)](https://github.com/rflamary/POT/blob/master/notebooks/plot_convolutional_barycenter.ipynb) +and [free support](https://github.com/rflamary/POT/blob/master/notebooks/plot_free_support_barycenter.ipynb) + Wasserstein barycenters and [smooth](https://github.com/rflamary/POT/blob/prV0.5/notebooks/plot_OT_1D_smooth.ipynb) + and [stochastic](https://pot.readthedocs.io/en/latest/all.html#ot.stochastic.sgd_entropic_regularization) +implementation of entropic OT. + +POT 0.5 also comes with a rewriting of ot.gpu using the cupy framework instead of +the unmaintained cudamat. Note that while we tried to keed changes to the +minimum, the OTDA classes were deprecated. If you are happy with the cudamat +implementation, we recommend you stay with stable release 0.4 for now. + +The code quality has also improved with 92% code coverage in tests that is now +printed to the log in the Travis builds. The documentation has also been +greatly improved with new modules and examples/notebooks. + +This new release is so full of new stuff and corrections thanks to the old +and new POT contributors (you can see the list in the [readme](https://github.com/rflamary/POT/blob/master/README.md)). + +#### Features + +* Add non regularized Gromov-Wasserstein solver (PR #41) +* Linear OT mapping between empirical distributions and 90\% test coverage (PR #42) +* Add log parameter in class EMDTransport and SinkhornLpL1Transport (PR #44) +* Add Markdown format for Pipy (PR #45) +* Test for Python 3.5 and 3.6 on Travis (PR #46) +* Non regularized Wasserstein barycenter with scipy linear solver and/or cvxopt (PR #47) +* Rename dataset functions to be more sklearn compliant (PR #49) +* Smooth and sparse Optimal transport implementation with entropic and quadratic regularization (PR #50) +* Stochastic OT in the dual and semi-dual (PR #52 and PR #62) +* Free support barycenters (PR #56) +* Speed-up Sinkhorn function (PR #57 and PR #58) +* Add convolutional Wassersein barycenters for 2D images (PR #64) +* Add Greedy Sinkhorn variant (Greenkhorn) (PR #66) +* Big ot.gpu update with cupy implementation (instead of un-maintained cudamat) (PR #67) + +#### Deprecation + +Deprecated OTDA Classes were removed from ot.da and ot.gpu for version 0.5 +(PR #48 and PR #67). The deprecation message has been for a year here since +0.4 and it is time to pull the plug. + +#### Closed issues + +* Issue #35 : remove import plot from ot/__init__.py (See PR #41) +* Issue #43 : Unusable parameter log for EMDTransport (See PR #44) +* Issue #55 : UnicodeDecodeError: 'ascii' while installing with pip + + ## 0.4 Community edition *15 Sep 2017* diff --git a/data/duck.png b/data/duck.png Binary files differnew file mode 100644 index 0000000..9181697 --- /dev/null +++ b/data/duck.png diff --git a/data/heart.png b/data/heart.png Binary files differnew file mode 100644 index 0000000..44a6385 --- /dev/null +++ b/data/heart.png diff --git a/data/redcross.png b/data/redcross.png Binary files differnew file mode 100644 index 0000000..8d0a6fa --- /dev/null +++ b/data/redcross.png diff --git a/data/tooth.png b/data/tooth.png Binary files differnew file mode 100644 index 0000000..cd92c9d --- /dev/null +++ b/data/tooth.png diff --git a/docs/cache_nbrun b/docs/cache_nbrun index 318bcf4..6f10375 100644 --- a/docs/cache_nbrun +++ b/docs/cache_nbrun @@ -1 +1 @@ -{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_barycenter_1D.ipynb": "6063193f9ac87517acced2625edb9a54", "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_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 bbb9833..32930fd 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -19,7 +19,7 @@ ot.bregman .. automodule:: ot.bregman :members: - + ot.smooth ----- .. automodule:: ot.smooth @@ -43,6 +43,12 @@ ot.da .. automodule:: ot.da :members: + +ot.gpu +-------- + +.. automodule:: ot.gpu + :members: ot.dr -------- @@ -68,3 +74,9 @@ ot.plot .. automodule:: ot.plot :members: + +ot.stochastic +------------- + +.. automodule:: ot.stochastic + :members: diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip Binary files differindex 8102274..88e1e9b 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 d685070..120a586 100644 --- 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b/docs/source/auto_examples/index.rst index 69fb320..17a9710 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -49,6 +49,46 @@ This is a gallery of all the POT example files. .. raw:: html + <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_free_support_barycenter_thumb.png + + :ref:`sphx_glr_auto_examples_plot_free_support_barycenter.py` + +.. raw:: html + + </div> + + +.. toctree:: + :hidden: + + /auto_examples/plot_free_support_barycenter + +.. raw:: html + + <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_OT_1D_smooth_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OT_1D_smooth.py` + +.. raw:: html + + </div> + + +.. toctree:: + :hidden: + + /auto_examples/plot_OT_1D_smooth + +.. raw:: html + <div class="sphx-glr-thumbcontainer" tooltip="This example is designed to show how to use the Gromov-Wassertsein distance computation in POT...."> .. only:: html @@ -109,6 +149,26 @@ This is a gallery of all the POT example files. .. raw:: html + <div class="sphx-glr-thumbcontainer" tooltip="This example is designed to illustrate how the Convolutional Wasserstein Barycenter function of..."> + +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_convolutional_barycenter_thumb.png + + :ref:`sphx_glr_auto_examples_plot_convolutional_barycenter.py` + +.. raw:: html + + </div> + + +.. toctree:: + :hidden: + + /auto_examples/plot_convolutional_barycenter + +.. raw:: html + <div class="sphx-glr-thumbcontainer" tooltip=" "> .. only:: html @@ -149,13 +209,13 @@ 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 is designed to show how to use the stochatic optimization algorithms for descrete ..."> .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_color_images_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_color_images.py` + :ref:`sphx_glr_auto_examples_plot_stochastic.py` .. raw:: html @@ -165,17 +225,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_otda_color_images + /auto_examples/plot_stochastic .. raw:: html - <div class="sphx-glr-thumbcontainer" tooltip="OT for domain adaptation with image color adaptation [6] with mapping estimation [8]."> + <div class="sphx-glr-thumbcontainer" tooltip="This example presents a way of transferring colors between two images with Optimal Transport as..."> .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_color_images_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` + :ref:`sphx_glr_auto_examples_plot_otda_color_images.py` .. raw:: html @@ -185,7 +245,7 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_otda_mapping_colors_images + /auto_examples/plot_otda_color_images .. raw:: html @@ -209,6 +269,26 @@ This is a gallery of all the POT example files. .. raw:: html + <div class="sphx-glr-thumbcontainer" tooltip="OT for domain adaptation with image color adaptation [6] with mapping estimation [8]."> + +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png + + :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` + +.. raw:: html + + </div> + + +.. toctree:: + :hidden: + + /auto_examples/plot_otda_mapping_colors_images + +.. 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 diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.ipynb b/docs/source/auto_examples/plot_OT_1D_smooth.ipynb new file mode 100644 index 0000000..d523f6a --- /dev/null +++ b/docs/source/auto_examples/plot_OT_1D_smooth.ipynb @@ -0,0 +1,144 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# 1D smooth optimal transport\n\n\nThis example illustrates the computation of EMD, Sinkhorn and smooth OT plans\nand their visualization.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "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\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# 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 EMD\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Sinkhorn\n--------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Sinkhorn\n\nlambd = 2e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Smooth OT\n--------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Smooth OT with KL regularization\n\nlambd = 2e-3\nGsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl')\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.')\n\npl.show()\n\n\n#%% Smooth OT with KL regularization\n\nlambd = 1e-1\nGsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2')\n\npl.figure(6, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.')\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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}
\ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.py b/docs/source/auto_examples/plot_OT_1D_smooth.py new file mode 100644 index 0000000..b690751 --- /dev/null +++ b/docs/source/auto_examples/plot_OT_1D_smooth.py @@ -0,0 +1,110 @@ +# -*- coding: utf-8 -*- +""" +=========================== +1D smooth optimal transport +=========================== + +This example illustrates the computation of EMD, Sinkhorn and smooth OT plans +and their visualization. + +""" + +# Author: Remi Flamary <remi.flamary@unice.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) + +# 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 EMD +# --------- + + +#%% EMD + +G0 = ot.emd(a, b, M) + +pl.figure(3, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') + +############################################################################## +# Solve Sinkhorn +# -------------- + + +#%% Sinkhorn + +lambd = 2e-3 +Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') + +pl.show() + +############################################################################## +# Solve Smooth OT +# -------------- + + +#%% Smooth OT with KL regularization + +lambd = 2e-3 +Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl') + +pl.figure(5, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.') + +pl.show() + + +#%% Smooth OT with KL regularization + +lambd = 1e-1 +Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2') + +pl.figure(6, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.') + +pl.show() diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.rst b/docs/source/auto_examples/plot_OT_1D_smooth.rst new file mode 100644 index 0000000..5a0ebd3 --- /dev/null +++ b/docs/source/auto_examples/plot_OT_1D_smooth.rst @@ -0,0 +1,242 @@ + + +.. _sphx_glr_auto_examples_plot_OT_1D_smooth.py: + + +=========================== +1D smooth optimal transport +=========================== + +This example illustrates the computation of EMD, Sinkhorn and smooth OT plans +and their visualization. + + + + +.. code-block:: python + + + # Author: Remi Flamary <remi.flamary@unice.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) + + # 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_OT_1D_smooth_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_002.png + :scale: 47 + + + + +Solve EMD +--------- + + + +.. code-block:: python + + + + #%% EMD + + G0 = ot.emd(a, b, M) + + pl.figure(3, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') + + + + +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_005.png + :align: center + + + + +Solve Sinkhorn +-------------- + + + +.. code-block:: python + + + + #%% Sinkhorn + + lambd = 2e-3 + Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') + + pl.show() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_007.png + :align: center + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Err + ------------------- + 0|7.958844e-02| + 10|5.921715e-03| + 20|1.238266e-04| + 30|2.469780e-06| + 40|4.919966e-08| + 50|9.800197e-10| + + +Solve Smooth OT +-------------- + + + +.. code-block:: python + + + + #%% Smooth OT with KL regularization + + lambd = 2e-3 + Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl') + + pl.figure(5, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.') + + pl.show() + + + #%% Smooth OT with KL regularization + + lambd = 1e-1 + Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2') + + pl.figure(6, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.') + + pl.show() + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_009.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_010.png + :scale: 47 + + + + +**Total running time of the script:** ( 0 minutes 1.053 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_OT_1D_smooth.py <plot_OT_1D_smooth.py>` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_OT_1D_smooth.ipynb <plot_OT_1D_smooth.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_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb index 5866088..fc60e1f 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.ipynb +++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb @@ -26,7 +26,79 @@ }, "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\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection\n\n#\n# Generate data\n# -------------\n\n#%% 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# 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()\n\n#\n# Plot data\n# ---------\n\n#%% 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()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, 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()\n\n#\n# Barycentric interpolation\n# -------------------------\n\n#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_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 alpha_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('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\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()" + "# Author: Remi Flamary <remi.flamary@unice.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# 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": [ + "#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, 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_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_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 alpha_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('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\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()" ] } ], diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py index 5ed9f3f..6864301 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.py +++ b/docs/source/auto_examples/plot_barycenter_1D.py @@ -25,7 +25,7 @@ import ot from mpl_toolkits.mplot3d import Axes3D # noqa from matplotlib.collections import PolyCollection -# +############################################################################## # Generate data # ------------- @@ -48,7 +48,7 @@ n_distributions = A.shape[1] M = ot.utils.dist0(n) M /= M.max() -# +############################################################################## # Plot data # --------- @@ -60,7 +60,7 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -# +############################################################################## # Barycenter computation # ---------------------- @@ -90,7 +90,7 @@ pl.legend() pl.title('Barycenters') pl.tight_layout() -# +############################################################################## # Barycentric interpolation # ------------------------- diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst index b314dc1..66ac042 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.rst +++ b/docs/source/auto_examples/plot_barycenter_1D.rst @@ -18,52 +18,34 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. +.. code-block:: python -.. rst-class:: sphx-glr-horizontal - - - * - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png - :scale: 47 + # Author: Remi Flamary <remi.flamary@unice.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 - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_002.png - :scale: 47 - * - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png - :scale: 47 - * - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_004.png - :scale: 47 +Generate data +------------- .. code-block:: python - # Author: Remi Flamary <remi.flamary@unice.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 @@ -83,9 +65,19 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. M = ot.utils.dist0(n) M /= M.max() - # - # Plot data - # --------- + + + + + + +Plot data +--------- + + + +.. code-block:: python + #%% plot the distributions @@ -95,9 +87,22 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. pl.title('Distributions') pl.tight_layout() - # - # Barycenter computation - # ---------------------- + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png + :align: center + + + + +Barycenter computation +---------------------- + + + +.. code-block:: python + #%% barycenter computation @@ -125,9 +130,22 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. pl.title('Barycenters') pl.tight_layout() - # - # Barycentric interpolation - # ------------------------- + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png + :align: center + + + + +Barycentric interpolation +------------------------- + + + +.. code-block:: python + #%% barycenter interpolation @@ -194,7 +212,25 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. pl.show() -**Total running time of the script:** ( 0 minutes 0.363 seconds) + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_005.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_006.png + :scale: 47 + + + + +**Total running time of the script:** ( 0 minutes 0.413 seconds) diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.ipynb b/docs/source/auto_examples/plot_convolutional_barycenter.ipynb new file mode 100644 index 0000000..4981ba3 --- /dev/null +++ b/docs/source/auto_examples/plot_convolutional_barycenter.ipynb @@ -0,0 +1,90 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Convolutional Wasserstein Barycenter example\n\n\nThis example is designed to illustrate how the Convolutional Wasserstein Barycenter\nfunction of POT works.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Nicolas Courty <ncourty@irisa.fr>\n#\n# License: MIT License\n\n\nimport numpy as np\nimport pylab as pl\nimport ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data preparation\n----------------\n\nThe four distributions are constructed from 4 simple images\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]\nf2 = 1 - pl.imread('../data/duck.png')[:, :, 2]\nf3 = 1 - pl.imread('../data/heart.png')[:, :, 2]\nf4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]\n\nA = []\nf1 = f1 / np.sum(f1)\nf2 = f2 / np.sum(f2)\nf3 = f3 / np.sum(f3)\nf4 = f4 / np.sum(f4)\nA.append(f1)\nA.append(f2)\nA.append(f3)\nA.append(f4)\nA = np.array(A)\n\nnb_images = 5\n\n# those are the four corners coordinates that will be interpolated by bilinear\n# interpolation\nv1 = np.array((1, 0, 0, 0))\nv2 = np.array((0, 1, 0, 0))\nv3 = np.array((0, 0, 1, 0))\nv4 = np.array((0, 0, 0, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation and visualization\n----------------------------------------\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(figsize=(10, 10))\npl.title('Convolutional Wasserstein Barycenters in POT')\ncm = 'Blues'\n# regularization parameter\nreg = 0.004\nfor i in range(nb_images):\n for j in range(nb_images):\n pl.subplot(nb_images, nb_images, i * nb_images + j + 1)\n tx = float(i) / (nb_images - 1)\n ty = float(j) / (nb_images - 1)\n\n # weights are constructed by bilinear interpolation\n tmp1 = (1 - tx) * v1 + tx * v2\n tmp2 = (1 - tx) * v3 + tx * v4\n weights = (1 - ty) * tmp1 + ty * tmp2\n\n if i == 0 and j == 0:\n pl.imshow(f1, cmap=cm)\n pl.axis('off')\n elif i == 0 and j == (nb_images - 1):\n pl.imshow(f3, cmap=cm)\n pl.axis('off')\n elif i == (nb_images - 1) and j == 0:\n pl.imshow(f2, cmap=cm)\n pl.axis('off')\n elif i == (nb_images - 1) and j == (nb_images - 1):\n pl.imshow(f4, cmap=cm)\n pl.axis('off')\n else:\n # call to barycenter computation\n pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm)\n pl.axis('off')\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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}
\ No newline at end of file diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.py b/docs/source/auto_examples/plot_convolutional_barycenter.py new file mode 100644 index 0000000..e74db04 --- /dev/null +++ b/docs/source/auto_examples/plot_convolutional_barycenter.py @@ -0,0 +1,92 @@ + +#%% +# -*- coding: utf-8 -*- +""" +============================================ +Convolutional Wasserstein Barycenter example +============================================ + +This example is designed to illustrate how the Convolutional Wasserstein Barycenter +function of POT works. +""" + +# Author: Nicolas Courty <ncourty@irisa.fr> +# +# License: MIT License + + +import numpy as np +import pylab as pl +import ot + +############################################################################## +# Data preparation +# ---------------- +# +# The four distributions are constructed from 4 simple images + + +f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2] +f2 = 1 - pl.imread('../data/duck.png')[:, :, 2] +f3 = 1 - pl.imread('../data/heart.png')[:, :, 2] +f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2] + +A = [] +f1 = f1 / np.sum(f1) +f2 = f2 / np.sum(f2) +f3 = f3 / np.sum(f3) +f4 = f4 / np.sum(f4) +A.append(f1) +A.append(f2) +A.append(f3) +A.append(f4) +A = np.array(A) + +nb_images = 5 + +# those are the four corners coordinates that will be interpolated by bilinear +# interpolation +v1 = np.array((1, 0, 0, 0)) +v2 = np.array((0, 1, 0, 0)) +v3 = np.array((0, 0, 1, 0)) +v4 = np.array((0, 0, 0, 1)) + + +############################################################################## +# Barycenter computation and visualization +# ---------------------------------------- +# + +pl.figure(figsize=(10, 10)) +pl.title('Convolutional Wasserstein Barycenters in POT') +cm = 'Blues' +# regularization parameter +reg = 0.004 +for i in range(nb_images): + for j in range(nb_images): + pl.subplot(nb_images, nb_images, i * nb_images + j + 1) + tx = float(i) / (nb_images - 1) + ty = float(j) / (nb_images - 1) + + # weights are constructed by bilinear interpolation + tmp1 = (1 - tx) * v1 + tx * v2 + tmp2 = (1 - tx) * v3 + tx * v4 + weights = (1 - ty) * tmp1 + ty * tmp2 + + if i == 0 and j == 0: + pl.imshow(f1, cmap=cm) + pl.axis('off') + elif i == 0 and j == (nb_images - 1): + pl.imshow(f3, cmap=cm) + pl.axis('off') + elif i == (nb_images - 1) and j == 0: + pl.imshow(f2, cmap=cm) + pl.axis('off') + elif i == (nb_images - 1) and j == (nb_images - 1): + pl.imshow(f4, cmap=cm) + pl.axis('off') + else: + # call to barycenter computation + pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm) + pl.axis('off') +pl.show() diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.rst b/docs/source/auto_examples/plot_convolutional_barycenter.rst new file mode 100644 index 0000000..a28db2f --- /dev/null +++ b/docs/source/auto_examples/plot_convolutional_barycenter.rst @@ -0,0 +1,151 @@ + + +.. _sphx_glr_auto_examples_plot_convolutional_barycenter.py: + + +============================================ +Convolutional Wasserstein Barycenter example +============================================ + +This example is designed to illustrate how the Convolutional Wasserstein Barycenter +function of POT works. + + + +.. code-block:: python + + + # Author: Nicolas Courty <ncourty@irisa.fr> + # + # License: MIT License + + + import numpy as np + import pylab as pl + import ot + + + + + + + +Data preparation +---------------- + +The four distributions are constructed from 4 simple images + + + +.. code-block:: python + + + + f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2] + f2 = 1 - pl.imread('../data/duck.png')[:, :, 2] + f3 = 1 - pl.imread('../data/heart.png')[:, :, 2] + f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2] + + A = [] + f1 = f1 / np.sum(f1) + f2 = f2 / np.sum(f2) + f3 = f3 / np.sum(f3) + f4 = f4 / np.sum(f4) + A.append(f1) + A.append(f2) + A.append(f3) + A.append(f4) + A = np.array(A) + + nb_images = 5 + + # those are the four corners coordinates that will be interpolated by bilinear + # interpolation + v1 = np.array((1, 0, 0, 0)) + v2 = np.array((0, 1, 0, 0)) + v3 = np.array((0, 0, 1, 0)) + v4 = np.array((0, 0, 0, 1)) + + + + + + + + +Barycenter computation and visualization +---------------------------------------- + + + + +.. code-block:: python + + + pl.figure(figsize=(10, 10)) + pl.title('Convolutional Wasserstein Barycenters in POT') + cm = 'Blues' + # regularization parameter + reg = 0.004 + for i in range(nb_images): + for j in range(nb_images): + pl.subplot(nb_images, nb_images, i * nb_images + j + 1) + tx = float(i) / (nb_images - 1) + ty = float(j) / (nb_images - 1) + + # weights are constructed by bilinear interpolation + tmp1 = (1 - tx) * v1 + tx * v2 + tmp2 = (1 - tx) * v3 + tx * v4 + weights = (1 - ty) * tmp1 + ty * tmp2 + + if i == 0 and j == 0: + pl.imshow(f1, cmap=cm) + pl.axis('off') + elif i == 0 and j == (nb_images - 1): + pl.imshow(f3, cmap=cm) + pl.axis('off') + elif i == (nb_images - 1) and j == 0: + pl.imshow(f2, cmap=cm) + pl.axis('off') + elif i == (nb_images - 1) and j == (nb_images - 1): + pl.imshow(f4, cmap=cm) + pl.axis('off') + else: + # call to barycenter computation + pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm) + pl.axis('off') + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_convolutional_barycenter_001.png + :align: center + + + + +**Total running time of the script:** ( 1 minutes 11.608 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_convolutional_barycenter.py <plot_convolutional_barycenter.py>` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_convolutional_barycenter.ipynb <plot_convolutional_barycenter.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_free_support_barycenter.ipynb b/docs/source/auto_examples/plot_free_support_barycenter.ipynb new file mode 100644 index 0000000..05a81c8 --- /dev/null +++ b/docs/source/auto_examples/plot_free_support_barycenter.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# 2D free support Wasserstein barycenters of distributions\n\n\nIllustration of 2D Wasserstein barycenters if discributions that are weighted\nsum of diracs.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Vivien Seguy <vivien.seguy@iip.ist.i.kyoto-u.ac.jp>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n -------------\n%% parameters and data generation\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "N = 3\nd = 2\nmeasures_locations = []\nmeasures_weights = []\n\nfor i in range(N):\n\n n_i = np.random.randint(low=1, high=20) # nb samples\n\n mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean\n\n A_i = np.random.rand(d, d)\n cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix\n\n x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations\n b_i = np.random.uniform(0., 1., (n_i,))\n b_i = b_i / np.sum(b_i) # Dirac weights\n\n measures_locations.append(x_i)\n measures_weights.append(b_i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute free support barycenter\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "k = 10 # number of Diracs of the barycenter\nX_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations\nb = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized)\n\nX = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(1)\nfor (x_i, b_i) in zip(measures_locations, measures_weights):\n color = np.random.randint(low=1, high=10 * N)\n pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure')\npl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter')\npl.title('Data measures and their barycenter')\npl.legend(loc=0)\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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}
\ No newline at end of file diff --git a/docs/source/auto_examples/plot_free_support_barycenter.py b/docs/source/auto_examples/plot_free_support_barycenter.py new file mode 100644 index 0000000..b6efc59 --- /dev/null +++ b/docs/source/auto_examples/plot_free_support_barycenter.py @@ -0,0 +1,69 @@ +# -*- coding: utf-8 -*- +""" +==================================================== +2D free support Wasserstein barycenters of distributions +==================================================== + +Illustration of 2D Wasserstein barycenters if discributions that are weighted +sum of diracs. + +""" + +# Author: Vivien Seguy <vivien.seguy@iip.ist.i.kyoto-u.ac.jp> +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot + + +############################################################################## +# Generate data +# ------------- +#%% parameters and data generation +N = 3 +d = 2 +measures_locations = [] +measures_weights = [] + +for i in range(N): + + n_i = np.random.randint(low=1, high=20) # nb samples + + mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean + + A_i = np.random.rand(d, d) + cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix + + x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations + b_i = np.random.uniform(0., 1., (n_i,)) + b_i = b_i / np.sum(b_i) # Dirac weights + + measures_locations.append(x_i) + measures_weights.append(b_i) + + +############################################################################## +# Compute free support barycenter +# ------------- + +k = 10 # number of Diracs of the barycenter +X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations +b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized) + +X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b) + + +############################################################################## +# Plot data +# --------- + +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[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') +pl.title('Data measures and their barycenter') +pl.legend(loc=0) +pl.show() diff --git a/docs/source/auto_examples/plot_free_support_barycenter.rst b/docs/source/auto_examples/plot_free_support_barycenter.rst new file mode 100644 index 0000000..d1b3b80 --- /dev/null +++ b/docs/source/auto_examples/plot_free_support_barycenter.rst @@ -0,0 +1,140 @@ + + +.. _sphx_glr_auto_examples_plot_free_support_barycenter.py: + + +==================================================== +2D free support Wasserstein barycenters of distributions +==================================================== + +Illustration of 2D Wasserstein barycenters if discributions that are weighted +sum of diracs. + + + + +.. code-block:: python + + + # Author: Vivien Seguy <vivien.seguy@iip.ist.i.kyoto-u.ac.jp> + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot + + + + + + + + +Generate data + ------------- +%% parameters and data generation + + + +.. code-block:: python + + N = 3 + d = 2 + measures_locations = [] + measures_weights = [] + + for i in range(N): + + n_i = np.random.randint(low=1, high=20) # nb samples + + mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean + + A_i = np.random.rand(d, d) + cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix + + x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations + b_i = np.random.uniform(0., 1., (n_i,)) + b_i = b_i / np.sum(b_i) # Dirac weights + + measures_locations.append(x_i) + measures_weights.append(b_i) + + + + + + + + +Compute free support barycenter +------------- + + + +.. code-block:: python + + + k = 10 # number of Diracs of the barycenter + X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations + b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized) + + X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b) + + + + + + + + +Plot data +--------- + + + +.. code-block:: python + + + 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[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') + pl.title('Data measures and their barycenter') + pl.legend(loc=0) + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_free_support_barycenter_001.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 0.129 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_free_support_barycenter.py <plot_free_support_barycenter.py>` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_free_support_barycenter.ipynb <plot_free_support_barycenter.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 new file mode 100644 index 0000000..7f6ff3d --- /dev/null +++ b/docs/source/auto_examples/plot_stochastic.ipynb @@ -0,0 +1,295 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Stochastic examples\n\n\nThis example is designed to show how to use the stochatic optimization\nalgorithms for descrete and semicontinous measures from the POT library.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Kilian Fatras <kilian.fatras@gmail.com>\n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport numpy as np\nimport ot\nimport ot.plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 1000\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the \"SAG\" method to find the transportation matrix in the discrete case\n---------------------------------------------\n\nDefine the method \"SAG\", call ot.solve_semi_dual_entropic and plot the\nresults.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "method = \"SAG\"\nsag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n numItermax)\nprint(sag_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 1000\nlog = True\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the \"ASGD\" method to find the transportation matrix in the semicontinous\ncase\n---------------------------------------------\n\nDefine the method \"ASGD\", call ot.solve_semi_dual_entropic and plot the\nresults.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "method = \"ASGD\"\nasgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n numItermax, log=log)\nprint(log_asgd['alpha'], log_asgd['beta'])\nprint(asgd_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare the results with the Sinkhorn algorithm\n---------------------------------------------\n\nCall the Sinkhorn algorithm from POT\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\nprint(sinkhorn_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PLOT TRANSPORTATION MATRIX\n#############################################################################\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot SAG results\n----------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG')\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot ASGD results\n-----------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD')\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Sinkhorn results\n---------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 100000\nlr = 0.1\nbatch_size = 3\nlog = True\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the \"SGD\" dual method to find the transportation matrix in the\nsemicontinous case\n---------------------------------------------\n\nCall ot.solve_dual_entropic and plot the results.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg,\n batch_size, numItermax,\n lr, log=log)\nprint(log_sgd['alpha'], log_sgd['beta'])\nprint(sgd_dual_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare the results with the Sinkhorn algorithm\n---------------------------------------------\n\nCall the Sinkhorn algorithm from POT\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\nprint(sinkhorn_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot SGD results\n-----------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD')\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Sinkhorn results\n---------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\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": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}
\ No newline at end of file diff --git a/docs/source/auto_examples/plot_stochastic.py b/docs/source/auto_examples/plot_stochastic.py new file mode 100644 index 0000000..742f8d9 --- /dev/null +++ b/docs/source/auto_examples/plot_stochastic.py @@ -0,0 +1,208 @@ +""" +========================== +Stochastic examples +========================== + +This example is designed to show how to use the stochatic optimization +algorithms for descrete and semicontinous measures from the POT library. + +""" + +# Author: Kilian Fatras <kilian.fatras@gmail.com> +# +# License: MIT License + +import matplotlib.pylab as pl +import numpy as np +import ot +import ot.plot + + +############################################################################# +# COMPUTE TRANSPORTATION MATRIX FOR 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. + +n_source = 7 +n_target = 4 +reg = 1 +numItermax = 1000 + +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) +M = ot.dist(X_source, Y_target) + +############################################################################# +# +# Call the "SAG" method to find the transportation matrix in the discrete case +# --------------------------------------------- +# +# Define the method "SAG", call ot.solve_semi_dual_entropic and plot the +# results. + +method = "SAG" +sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, + numItermax) +print(sag_pi) + +############################################################################# +# 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. + +n_source = 7 +n_target = 4 +reg = 1 +numItermax = 1000 +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) +M = ot.dist(X_source, Y_target) + +############################################################################# +# +# Call the "ASGD" method to find the transportation matrix in the semicontinous +# case +# --------------------------------------------- +# +# Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the +# results. + +method = "ASGD" +asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, + numItermax, log=log) +print(log_asgd['alpha'], log_asgd['beta']) +print(asgd_pi) + +############################################################################# +# +# Compare the results with the Sinkhorn algorithm +# --------------------------------------------- +# +# Call the Sinkhorn algorithm from POT + +sinkhorn_pi = ot.sinkhorn(a, b, M, reg) +print(sinkhorn_pi) + + +############################################################################## +# PLOT TRANSPORTATION MATRIX +############################################################################## + +############################################################################## +# Plot SAG results +# ---------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG') +pl.show() + + +############################################################################## +# Plot ASGD results +# ----------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD') +pl.show() + + +############################################################################## +# Plot Sinkhorn results +# --------------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') +pl.show() + + +############################################################################# +# COMPUTE TRANSPORTATION MATRIX FOR 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. + +n_source = 7 +n_target = 4 +reg = 1 +numItermax = 100000 +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) +M = ot.dist(X_source, Y_target) + +############################################################################# +# +# Call the "SGD" dual method to find the transportation matrix in the +# semicontinous case +# --------------------------------------------- +# +# Call ot.solve_dual_entropic and plot the results. + +sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, + batch_size, numItermax, + lr, log=log) +print(log_sgd['alpha'], log_sgd['beta']) +print(sgd_dual_pi) + +############################################################################# +# +# Compare the results with the Sinkhorn algorithm +# --------------------------------------------- +# +# Call the Sinkhorn algorithm from POT + +sinkhorn_pi = ot.sinkhorn(a, b, M, reg) +print(sinkhorn_pi) + +############################################################################## +# Plot SGD results +# ----------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD') +pl.show() + + +############################################################################## +# Plot Sinkhorn results +# --------------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') +pl.show() diff --git a/docs/source/auto_examples/plot_stochastic.rst b/docs/source/auto_examples/plot_stochastic.rst new file mode 100644 index 0000000..d531045 --- /dev/null +++ b/docs/source/auto_examples/plot_stochastic.rst @@ -0,0 +1,446 @@ + + +.. _sphx_glr_auto_examples_plot_stochastic.py: + + +========================== +Stochastic examples +========================== + +This example is designed to show how to use the stochatic optimization +algorithms for descrete and semicontinous measures from the POT library. + + + + +.. code-block:: python + + + # Author: Kilian Fatras <kilian.fatras@gmail.com> + # + # License: MIT License + + import matplotlib.pylab as pl + import numpy as np + import ot + import ot.plot + + + + + + + + +COMPUTE TRANSPORTATION MATRIX FOR 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. + + + +.. code-block:: python + + + n_source = 7 + n_target = 4 + reg = 1 + numItermax = 1000 + + 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) + M = ot.dist(X_source, Y_target) + + + + + + + +Call the "SAG" method to find the transportation matrix in the discrete case +--------------------------------------------- + +Define the method "SAG", call ot.solve_semi_dual_entropic and plot the +results. + + + +.. code-block:: python + + + method = "SAG" + sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, + numItermax) + print(sag_pi) + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + [[2.55553509e-02 9.96395660e-02 1.76579142e-02 4.31178196e-06] + [1.21640234e-01 1.25357448e-02 1.30225078e-03 7.37891338e-03] + [3.56123975e-03 7.61451746e-02 6.31505947e-02 1.33831456e-07] + [2.61515202e-02 3.34246014e-02 8.28734709e-02 4.07550428e-04] + [9.85500870e-03 7.52288517e-04 1.08262628e-02 1.21423583e-01] + [2.16904253e-02 9.03825797e-04 1.87178503e-03 1.18391107e-01] + [4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]] + + +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. + + + +.. code-block:: python + + + n_source = 7 + n_target = 4 + reg = 1 + numItermax = 1000 + 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) + M = ot.dist(X_source, Y_target) + + + + + + + +Call the "ASGD" method to find the transportation matrix in the semicontinous +case +--------------------------------------------- + +Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the +results. + + + +.. code-block:: python + + + method = "ASGD" + asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, + numItermax, log=log) + print(log_asgd['alpha'], log_asgd['beta']) + print(asgd_pi) + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + [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 +--------------------------------------------- + +Call the Sinkhorn algorithm from POT + + + +.. code-block:: python + + + sinkhorn_pi = ot.sinkhorn(a, b, M, reg) + print(sinkhorn_pi) + + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + [[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06] + [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03] + [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07] + [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04] + [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01] + [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01] + [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]] + + +PLOT TRANSPORTATION MATRIX +############################################################################# + + +Plot SAG results +---------------- + + + +.. code-block:: python + + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG') + pl.show() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_004.png + :align: center + + + + +Plot ASGD results +----------------- + + + +.. code-block:: python + + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD') + pl.show() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_005.png + :align: center + + + + +Plot Sinkhorn results +--------------------- + + + +.. code-block:: python + + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') + pl.show() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_006.png + :align: center + + + + +COMPUTE TRANSPORTATION MATRIX FOR 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. + + + +.. code-block:: python + + + n_source = 7 + n_target = 4 + reg = 1 + numItermax = 100000 + 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) + M = ot.dist(X_source, Y_target) + + + + + + + +Call the "SGD" dual method to find the transportation matrix in the +semicontinous case +--------------------------------------------- + +Call ot.solve_dual_entropic and plot the results. + + + +.. code-block:: python + + + sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, + batch_size, numItermax, + lr, log=log) + print(log_sgd['alpha'], log_sgd['beta']) + print(sgd_dual_pi) + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + [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 +--------------------------------------------- + +Call the Sinkhorn algorithm from POT + + + +.. code-block:: python + + + sinkhorn_pi = ot.sinkhorn(a, b, M, reg) + print(sinkhorn_pi) + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + [[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06] + [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03] + [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07] + [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04] + [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01] + [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01] + [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]] + + +Plot SGD results +----------------- + + + +.. code-block:: python + + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD') + pl.show() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_007.png + :align: center + + + + +Plot Sinkhorn results +--------------------- + + + +.. code-block:: python + + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_008.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 20.889 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_stochastic.py <plot_stochastic.py>` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_stochastic.ipynb <plot_stochastic.ipynb>` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ diff --git a/docs/source/conf.py b/docs/source/conf.py index 114245d..433eca6 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -31,7 +31,7 @@ class Mock(MagicMock): @classmethod def __getattr__(cls, name): return MagicMock() -MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cudamat','autograd.numpy','pymanopt.manifolds','pymanopt.solvers'] +MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cupy','autograd.numpy','pymanopt.manifolds','pymanopt.solvers'] # 'autograd.numpy','pymanopt.manifolds','pymanopt.solvers', sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 5d37f64..e7c2bd1 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -2,7 +2,7 @@ POT: Python Optimal Transport ============================= |PyPI version| |Anaconda Cloud| |Build Status| |Documentation Status| -|Anaconda downloads| |License| +|Downloads| |Anaconda downloads| |License| This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and @@ -13,13 +13,14 @@ 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] with optional GPU implementation - (requires cudamat). + and stabilized version [9][10] and greedy SInkhorn [22] with optional + GPU implementation (requires cudamat). - 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] and unmixing [4]. +- Bregman projections for Wasserstein barycenter [3], convolutional + barycenter [21] and unmixing [4]. - Optimal transport for domain adaptation with group lasso regularization [5] - Conditional gradient [6] and Generalized conditional gradient for @@ -29,6 +30,9 @@ It provides the following solvers: pymanopt). - 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]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -104,7 +108,7 @@ Dependencies Some sub-modules require additional dependences which are discussed below -- **ot.dr** (Wasserstein dimensionality rediuction) depends on autograd +- **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: :: @@ -219,6 +223,9 @@ The contributors to this library are: - `Stanislas Chambon <https://slasnista.github.io/>`__ - `Antoine Rolet <https://arolet.github.io/>`__ - Erwan Vautier (Gromov-Wasserstein) +- `Kilian Fatras <https://kilianfatras.github.io/>`__ +- `Alain + Rakotomamonjy <https://sites.google.com/site/alainrakotomamonjy/home>`__ This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -334,6 +341,31 @@ 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 + .. |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 @@ -342,6 +374,8 @@ Statistics (AISTATS). :target: https://travis-ci.org/rflamary/POT .. |Documentation Status| image:: https://readthedocs.org/projects/pot/badge/?version=latest :target: http://pot.readthedocs.io/en/latest/?badge=latest +.. |Downloads| image:: https://pepy.tech/badge/pot + :target: https://pepy.tech/project/pot .. |Anaconda downloads| image:: https://anaconda.org/conda-forge/pot/badges/downloads.svg :target: https://anaconda.org/conda-forge/pot .. |License| image:: https://anaconda.org/conda-forge/pot/badges/license.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_barycenter_1D.py b/examples/plot_barycenter_1D.py index 5ed9f3f..6864301 100644 --- a/examples/plot_barycenter_1D.py +++ b/examples/plot_barycenter_1D.py @@ -25,7 +25,7 @@ import ot from mpl_toolkits.mplot3d import Axes3D # noqa from matplotlib.collections import PolyCollection -# +############################################################################## # Generate data # ------------- @@ -48,7 +48,7 @@ n_distributions = A.shape[1] M = ot.utils.dist0(n) M /= M.max() -# +############################################################################## # Plot data # --------- @@ -60,7 +60,7 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -# +############################################################################## # Barycenter computation # ---------------------- @@ -90,7 +90,7 @@ pl.legend() pl.title('Barycenters') pl.tight_layout() -# +############################################################################## # Barycentric interpolation # ------------------------- diff --git a/examples/plot_convolutional_barycenter.py b/examples/plot_convolutional_barycenter.py new file mode 100644 index 0000000..e74db04 --- /dev/null +++ b/examples/plot_convolutional_barycenter.py @@ -0,0 +1,92 @@ + +#%% +# -*- coding: utf-8 -*- +""" +============================================ +Convolutional Wasserstein Barycenter example +============================================ + +This example is designed to illustrate how the Convolutional Wasserstein Barycenter +function of POT works. +""" + +# Author: Nicolas Courty <ncourty@irisa.fr> +# +# License: MIT License + + +import numpy as np +import pylab as pl +import ot + +############################################################################## +# Data preparation +# ---------------- +# +# The four distributions are constructed from 4 simple images + + +f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2] +f2 = 1 - pl.imread('../data/duck.png')[:, :, 2] +f3 = 1 - pl.imread('../data/heart.png')[:, :, 2] +f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2] + +A = [] +f1 = f1 / np.sum(f1) +f2 = f2 / np.sum(f2) +f3 = f3 / np.sum(f3) +f4 = f4 / np.sum(f4) +A.append(f1) +A.append(f2) +A.append(f3) +A.append(f4) +A = np.array(A) + +nb_images = 5 + +# those are the four corners coordinates that will be interpolated by bilinear +# interpolation +v1 = np.array((1, 0, 0, 0)) +v2 = np.array((0, 1, 0, 0)) +v3 = np.array((0, 0, 1, 0)) +v4 = np.array((0, 0, 0, 1)) + + +############################################################################## +# Barycenter computation and visualization +# ---------------------------------------- +# + +pl.figure(figsize=(10, 10)) +pl.title('Convolutional Wasserstein Barycenters in POT') +cm = 'Blues' +# regularization parameter +reg = 0.004 +for i in range(nb_images): + for j in range(nb_images): + pl.subplot(nb_images, nb_images, i * nb_images + j + 1) + tx = float(i) / (nb_images - 1) + ty = float(j) / (nb_images - 1) + + # weights are constructed by bilinear interpolation + tmp1 = (1 - tx) * v1 + tx * v2 + tmp2 = (1 - tx) * v3 + tx * v4 + weights = (1 - ty) * tmp1 + ty * tmp2 + + if i == 0 and j == 0: + pl.imshow(f1, cmap=cm) + pl.axis('off') + elif i == 0 and j == (nb_images - 1): + pl.imshow(f3, cmap=cm) + pl.axis('off') + elif i == (nb_images - 1) and j == 0: + pl.imshow(f2, cmap=cm) + pl.axis('off') + elif i == (nb_images - 1) and j == (nb_images - 1): + pl.imshow(f4, cmap=cm) + pl.axis('off') + else: + # call to barycenter computation + pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm) + pl.axis('off') +pl.show() diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py new file mode 100644 index 0000000..b6efc59 --- /dev/null +++ b/examples/plot_free_support_barycenter.py @@ -0,0 +1,69 @@ +# -*- coding: utf-8 -*- +""" +==================================================== +2D free support Wasserstein barycenters of distributions +==================================================== + +Illustration of 2D Wasserstein barycenters if discributions that are weighted +sum of diracs. + +""" + +# Author: Vivien Seguy <vivien.seguy@iip.ist.i.kyoto-u.ac.jp> +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot + + +############################################################################## +# Generate data +# ------------- +#%% parameters and data generation +N = 3 +d = 2 +measures_locations = [] +measures_weights = [] + +for i in range(N): + + n_i = np.random.randint(low=1, high=20) # nb samples + + mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean + + A_i = np.random.rand(d, d) + cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix + + x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations + b_i = np.random.uniform(0., 1., (n_i,)) + b_i = b_i / np.sum(b_i) # Dirac weights + + measures_locations.append(x_i) + measures_weights.append(b_i) + + +############################################################################## +# Compute free support barycenter +# ------------- + +k = 10 # number of Diracs of the barycenter +X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations +b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized) + +X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b) + + +############################################################################## +# Plot data +# --------- + +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[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') +pl.title('Data measures and their barycenter') +pl.legend(loc=0) +pl.show() 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_stochastic.py b/examples/plot_stochastic.py new file mode 100644 index 0000000..742f8d9 --- /dev/null +++ b/examples/plot_stochastic.py @@ -0,0 +1,208 @@ +""" +========================== +Stochastic examples +========================== + +This example is designed to show how to use the stochatic optimization +algorithms for descrete and semicontinous measures from the POT library. + +""" + +# Author: Kilian Fatras <kilian.fatras@gmail.com> +# +# License: MIT License + +import matplotlib.pylab as pl +import numpy as np +import ot +import ot.plot + + +############################################################################# +# COMPUTE TRANSPORTATION MATRIX FOR 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. + +n_source = 7 +n_target = 4 +reg = 1 +numItermax = 1000 + +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) +M = ot.dist(X_source, Y_target) + +############################################################################# +# +# Call the "SAG" method to find the transportation matrix in the discrete case +# --------------------------------------------- +# +# Define the method "SAG", call ot.solve_semi_dual_entropic and plot the +# results. + +method = "SAG" +sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, + numItermax) +print(sag_pi) + +############################################################################# +# 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. + +n_source = 7 +n_target = 4 +reg = 1 +numItermax = 1000 +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) +M = ot.dist(X_source, Y_target) + +############################################################################# +# +# Call the "ASGD" method to find the transportation matrix in the semicontinous +# case +# --------------------------------------------- +# +# Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the +# results. + +method = "ASGD" +asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, + numItermax, log=log) +print(log_asgd['alpha'], log_asgd['beta']) +print(asgd_pi) + +############################################################################# +# +# Compare the results with the Sinkhorn algorithm +# --------------------------------------------- +# +# Call the Sinkhorn algorithm from POT + +sinkhorn_pi = ot.sinkhorn(a, b, M, reg) +print(sinkhorn_pi) + + +############################################################################## +# PLOT TRANSPORTATION MATRIX +############################################################################## + +############################################################################## +# Plot SAG results +# ---------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG') +pl.show() + + +############################################################################## +# Plot ASGD results +# ----------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD') +pl.show() + + +############################################################################## +# Plot Sinkhorn results +# --------------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') +pl.show() + + +############################################################################# +# COMPUTE TRANSPORTATION MATRIX FOR 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. + +n_source = 7 +n_target = 4 +reg = 1 +numItermax = 100000 +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) +M = ot.dist(X_source, Y_target) + +############################################################################# +# +# Call the "SGD" dual method to find the transportation matrix in the +# semicontinous case +# --------------------------------------------- +# +# Call ot.solve_dual_entropic and plot the results. + +sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, + batch_size, numItermax, + lr, log=log) +print(log_sgd['alpha'], log_sgd['beta']) +print(sgd_dual_pi) + +############################################################################# +# +# Compare the results with the Sinkhorn algorithm +# --------------------------------------------- +# +# Call the Sinkhorn algorithm from POT + +sinkhorn_pi = ot.sinkhorn(a, b, M, reg) +print(sinkhorn_pi) + +############################################################################## +# Plot SGD results +# ----------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD') +pl.show() + + +############################################################################## +# Plot Sinkhorn results +# --------------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') +pl.show() diff --git a/notebooks/plot_OT_1D_smooth.ipynb b/notebooks/plot_OT_1D_smooth.ipynb new file mode 100644 index 0000000..69e71da --- /dev/null +++ b/notebooks/plot_OT_1D_smooth.ipynb @@ -0,0 +1,302 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 1D smooth optimal transport\n", + "\n", + "\n", + "This example illustrates the computation of EMD, Sinkhorn and smooth OT plans\n", + "and their visualization.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary <remi.flamary@unice.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", + "# 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 EMD\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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": [ + "#%% EMD\n", + "\n", + "G0 = ot.emd(a, b, M)\n", + "\n", + "pl.figure(3, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Sinkhorn\n", + "--------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Err \n", + "-------------------\n", + " 0|7.958844e-02|\n", + " 10|5.921715e-03|\n", + " 20|1.238266e-04|\n", + " 30|2.469780e-06|\n", + " 40|4.919966e-08|\n", + " 50|9.800197e-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", + "lambd = 2e-3\n", + "Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n", + "\n", + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Smooth OT\n", + "--------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 360x360 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% Smooth OT with KL regularization\n", + "\n", + "lambd = 2e-3\n", + "Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl')\n", + "\n", + "pl.figure(5, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.')\n", + "\n", + "pl.show()\n", + "\n", + "\n", + "#%% Smooth OT with KL regularization\n", + "\n", + "lambd = 1e-1\n", + "Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2')\n", + "\n", + "pl.figure(6, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.')\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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_barycenter_1D.ipynb b/notebooks/plot_barycenter_1D.ipynb index 4a0956b..bd73a99 100644 --- a/notebooks/plot_barycenter_1D.ipynb +++ b/notebooks/plot_barycenter_1D.ipynb @@ -36,48 +36,7 @@ "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 432x288 with 2 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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gUcitYQKyhUmSJExOTmJychKrV6/G7t27YbPZqtpfPS7iZrMZq1atyhtxIUmSssYViUTg9/uVyIkQArvdDrPZrAiX4Sw0MCiOIVAGdaVQDRMACIKAiYkJTE9Po7u7G1dddVXWXKJStp8LTZ0t5sWaZVk0NTXlpSFlWYbX64UoiojH45iZmUEymQSQscTnGjTUppBKnYXGOpfBcsEQKIO6oLaKnzp1Clu3bs264KZSKYyNjSEUCuGyyy7Dvn37smzfpUCjk9yLLxWoRsBkMilOwdxBh7Q9UyKRwNzcHJLJZJYlXi1cpVriaZTK87whXAaXPIZAGdQUrRqmWCyWV8MUi8XQ39+Pyy+/PCtqKAeGYbJaHKkfbxSB0oNa4p1OJ1avXq08Ti3x1KARDoezLPG5tVzqaLMcZ2E8HkcqlcKaNWsM4TJoWAyBMqgJxWqYotEoPB4PeJ7H+vXrMTQ0VPVFUC+VdykIlB5qS7yaXEt8IBAAx3ElW+LVfwMZlyLHcQAMZ6FB42IIlEFVFKthohHA8PAwBgYG8lxx1aAnRJeyQOlRqSU+N+KilnitDh0Uw1lo0CgYAmVQEcVqmILBILxeL+x2O2w2G3bv3l3zY6BrULksR4EqhJ4lXhAExVkYCoUUSzzLsmAYBizLIhQKwel05lni1X+rKdVZSEXMEC6DajAEyqBkilnFZVnG9PQ0xsbG0NLSgm3btsHpdOKll16qy/EUiqAMAIvFomuJn5iYQCKRwMLCAqamphRLvMPhyDJoqC3xgOEsNFhcDIEyKEoxq3ita5hKhZokqHDmPm6gDcuySl1Wb2+v8rgkSUgmk8rgx1xLvHqdqxxLvOEsNKgUQ6AMdCk27oLWMAUCAXR3d+NNb3pTVqNVSj3rkmZmZhAIBCDLsuKM4zgO8/PzaGtry0pdGVxE6/fBsizcbjfcbnfW47IsI5lMKunCYDAIjuNACNGs5SrVEg9kOwuBTN9Cuj2z2ZyVjjR+jysPQ6AM8ig27iKdTmNsbAxzc3Po7e3F/v37C9YwmUwmyLJcdp2THrIsY2pqCqFQCGazGVdeeaUighzH4dy5c4jFYgiFQkrqKrd/3koXrnJuGNRtnPQs8YlEAuFwGIlEArIsl2SJV/9NmZ+fVyI89eePvtZwFq4sDIEyUChmFec4Dl6vF9FoFH19fdi4cWNJNUy1EihJkuDz+TA1NYW1a9eio6MD/f39sNls4Hle6ebgcDjQ29sLl8ul/JxeG6KVKlzUSl4Nakt8R0dH1rbT6bRyzgOBABKJBCRJyrLE0z/qqJt+TnKPzXAWrkwMgTIAIQSJREL5omvVMHm9XqRSKaxfvx5btmwp64tf7ZqQKIpKKrGrq0tph3Tq1CnN7eaaJ/TaEK1k4apnKyiGYWC322G327Ms8XQtikZcs7OzSCQSWZb4WCyGWCwGm81WtEu8erulOguNda5LC0OgVjBqq/iZM2ewfv16NDc3K8/TOUwAqqphohFUufA8j/HxcczOzqKnpyevHVK1dVArWbgWu1chkPm9FLPER6NRRKNRBINBJSrOXePKPeeGs3D5YgjUCkRr3AXLsorjSl3DtHHjxizRqoRyBSqdTsPr9WJ+fh6XXXYZ9u/fr5mOqlehbqnCFQgEkEwmL0nhWgqBKgS1xNtsNvT39yudNERRVM55riWeugnpOXc4HCULl+EsvDQwBGqFUKyGyWQyYWZmBmfOnEFzc7NSw1QL9Apqc0kmk/B6vVhYWCipT99id5IoJFzJZBLxeBzRaDRPuERRhN1uR0tLS8MIV6MJFIUQkhUlm81mNDc3590k5Vrip6enkUqlACCvlsvhcJRsiQfynYX0uXQ6jZaWFkW0DGdh/TEEaplTSg3T1NQUpqen0dbWhiuvvBJ2u72mx1AsgkokEvB4PEgkEli/fj2uuOKKkr74auHLvXNezE4ShezZHMdhYmICyWQSIyMjSKVSirnA5XLB7XbD6XTm3f3Xm0YVKEmSSjquYpZ4us5VriVe/TeFukO9Xi+uuOKKrOcMZ2F9MQRqmVLMKi4IAnw+H/x+P7q6urBu3TrlDr/W6AlULBbD6OgoeJ7HwMAA2tvba2K+aJRWRyaTCW63G83NzWBZVhm3QYUrkUhkRVwMw+SlCuslXI0qULSerVLUlng1uZb4+fl5cByXZYlXC1euJZ66C9WCZjgL648hUMsMLWFSf+HVNUw9PT1KDZPH46lb94VcIVlYWFD2NzAwkNf8tJztXoq9+Khw6UVc6rRVvYSrUQWqFvZ3LYpZ4mmzXb/fr1jirVarIlh6Y13Uf+e+D8NZWD2GQC0TSqlhGhsbU9Z3cmuYWJZVTBO1xmQyQZIkzM/Pw+PxgGVZDA4O5vWIKxe1EOUWdDayQOmxmMLVqAIFLG4vRbUlvr29XXk81xI/Pz+PeDyOo0ePKpb43PEmhrOw9hgCdYlTaNwFkEmjeTwepYZJb32nUit4KceXTCZx7tw5NDU1YdOmTXkmg0pRt1BayjWoelOOcFGjQDHhamSBagRyLfFWqxUcx6G/v18RLo7jEAqFMDExoWmJd7lcsNlshrOwCgyBukQpNO4CyPQ083g8IIQoNUyFPtAsyyKdTtfs+AghmJmZwdjYGGRZRm9vL/r6+mq2fcAYt1GNcHEch3Q6bQhViUiSpKxLWSwWtLS0oKWlJes1akt8OBzOs8Sro65yLPF027nOQmrQUK9x0T/LBUOgLiGKWcUJIZibm4PX64XVai2rhqlWEZR65EZrayt27NiB6enprAmvtaLRTRJLRTHhooMN/X4/fD4fgOIR10pHkqSiF/5ClvhcU0w5lnj13xS1QSOVSuGNN97A1q1bldc++OCD+Jd/+Zfq3nQDYAjUJQA1PkSjUaWAUS1Msiwr0UpTUxOGhobyXEzFqHYNijZwnZiYQEdHR9bIjWpbHelBhYiOQ6frACtdoPRQC9f8/Dy6u7vR3NycZc3OHbNBi2HdbrfmfKiVgiiKFdcF6tXPFbPEq9e4ClniaRRMi+0B4JlnnqnwnTYWhkA1MOpxF5Ik4fXXX8f+/fvzaph8Ph/a29urqmGqNILKbeCqNXKjXutbQMYR6PP5wDAMRFFUvqROp1NxYdUjervUUaf21NbsNWvWKK9RX0Dj8bjmfCh1HVcthKtRbyyqtb9rUcgSrx5vorbE2+32vHShKIpK+pFhGCSTyUWZx7YYGALVgBSyitML8cTEhFLDpDeHqRzKjaBEUcT4+DgCgQDWrVunNHDVgrr4agUhBIFAAF6vF06nEzt37lQWjQVBgNfrhSiKCAaDGBsbgyAIRbtorzRKWXvSu4CWIlx6Katqj2mpkCSpZuNiikHdmU6nU9cSn0gkMDU1BY7jwPM8ZFnG8PAwXn31VVgslrKMSL/85S9x7733QpIk3HXXXfj85z+f9Xw6ncYdd9yBV199Fe3t7XjssccUs8hdd92F1157DaIo4o477sAXvvCFmp0HwBCohqKYVVyWZZw/fx7BYBDr1q3Dvn37dEWhXEqNcnIbuBabBUW3nbvAWwmyLCMQCGB8fBxtbW3o6+tTbMK01sRisSjTXru7u7OOm36xZ2ZmkEgkIIqiEmWpo4FandNGphoxKEW4aLfycoSrHlFKrVhMgdJDzxIfDAYRDofR0dGBcDiM3/3udzh9+jR27tyJ1tZWbNu2Df/2b/+m+fuWJAkf+9jH8PTTT6Onpwd79+7FjTfeiC1btiiv+d73vofW1laMjIzg0Ucfxec+9zk89thjePzxx5FOp3Hy5ElwHIctW7bgAx/4APr7+2v2npf/N/ESoJhVnNYwcRwHl8uFDRs21PyLXCyCSqVSGBsbK9rAVYtqU3yyLMPv92NiYgLt7e3K+pbf79d1HuamirS6aNO1q9w7UkmSsroLUOFa6gtULalHtFKtcOVashuJRhAoPWivx9bWVtxzzz3YvXs3HnvsMXznO99BKBSCx+PRPa9Hjx7Fhg0bMDAwAAC45ZZbcOjQoSyBOnToEB544AEAwM0334yPf/zjyueH3uglk0lYrdaqG0vnYgjUElLMKh6LxeD1esFxHNavX49wOIzu7u66fIn1RIQ2cI1EIujv78emTZvK3n+pzWJzURsv1qxZgz179mStJ1XbSYJhGNhsNthstry5RepUis/ny1oDoKJF1wAa9a6/EIuZTitVuKanpxGLxfDyyy9XlSqsB40sUGoLPJBZl6UW+Pb29qxoK5epqSn09vYq/+7p6cGRI0d0X2M2m7Fq1SqEQiHcfPPNOHToELq6usBxHP71X/+14q4wehgCtQRojbtQXyzUrYDWr1+PtrY2MAwDr9db09HpanIjKHUD14GBgZIbuGpRrotPbf7QM17Q7dajDqpQdwHazy0ej2Nubk5xXWnZtBtduJY6WskVLtpQd2hoqKpUYT1oZIESBCFL/CORSF6NVj04evQoWJaF3+9HOBzGNddcg+uuu06JxmqBIVCLRDk1TBaLRbMVEBWRenxRaARVbQPXQtsuhtoR2NnZWdB4Qbe7mIW6ev3cZFlGKpVCPB7Pu6BSl5XD4cCqVasapr6oEQ0JdA2qWMTFcRzi8fiiClcjC1RuBFWOQK1bt06phQOAyclJrFu3TvM1PT09EEURkUgE7e3tOHjwIP74j/8YFosFa9aswR/8wR/glVdeMQTqUqLYuAtCiFLY2tTUhC1btuQVWFLq2S+Pjto+f/58VdNztSgmUKIoZnVWLyZMlEZpFqsenqeGFsb6fD6kUimMjo5qDjh0u92Lvv7SyAKlh1q4Vq9enfVz6siW1hMBUEZs0JRspcJVrya2tUAQhDyB2rRpU0k/u3fvXgwPD8Pr9WLdunV49NFHcfDgwazX3HjjjfjhD3+I/fv344knnsAf/dEfgWEYXHbZZXjuuedw++23I5FI4PDhw/jEJz5R0/dmCFSdKDbugq6v+Hy+kucw1VqgCCFKA1ez2QybzYbdu3fXbPsUPYGidvlSrOpaNHonCVoY29TUlDVuo9BIefX6llYT0lqh1Z17qanUxae+QdATLnUhLICstUT6s40qQMWoJoIym834xje+geuvvx6SJOEjH/kIhoaG8KUvfQl79uzBjTfeiDvvvBO33347NmzYgLa2Njz66KMAgI997GP48Ic/jKGhIRBC8OEPfxjbt2+v6XszBKrGFBt3QaMFmsbKXfgvRK0EiqYTPR4PHA4HrrjiCrjdbrz00ktVb1uLXIESBAETExOYnp5GT08P9u3bV1H6pFEiqHLR6yyg7uWW24RULVq1Kj5eLgKlR7nCpe7goC6EbXTh0oqgylmDuuGGG3DDDTdkPfaVr3xF+X+73Y7HH3887+fcbrfm47XEEKgaUayGSV0/VGkNE8uyeYPRyj3GmZkZeL1eNDU11XSseyHoWpEgCBgbG8Ps7Cx6e3vLsqprcakKlB56vdxEUcxKX9HiY7PZnCdcpRYfX4opvlqhJ1y0gwMVLrUJJpVKwePxNKRwqTtJAItnklgMDIGqEipMHMfh3Llz2L59e9YHN5lMYmxsDOFwuOz6oVzMZnNFEZS6wLW1tbUuY90LIYoiotEojh49WvU5UKMWouU8boNae3NNM4IgKMYMveJj+if3ZqgRz89SF+qqOzjkRlwvv/wympqa8oSrESKu3PWxSCRS0zXkpcQQqArJrWEym83KEDkAiMfj8Hg8ygyZzZs3V33HWm6KT5ZlTE5Owufz5TVwXQzo9N5gMAiTyVQzYaKs9HEbFosFra2tBYuPA4GAMiFWXXxMTTuN5ExbaoHSQ5ZlWCwWrF69uuSIaymFKxqNGhHUSoRaxbVqmOiCPa1hkiQJ69evr4lNm1KqQImiiMnJyYINXPWoReonlUrB6/UiHA6jv78ffX19OHnyZM2/oI1uklgKSi0+TqfTeP3117OKj9WmgaUQikYVKD2LuV7EtdTCJQiC0Sx2JVGKVTwUCiGRSMDr9WJgYKAudzDF1qDU5oPu7u6yXXHUzFDpXTXN0y8sLGD9+vVK1Kg+b7WECpEoiggEArDb7XC73StaoPTILT6emZnBnj178oqPQ6FQ1sVUvcZV76LYS02g9ChHuJLJJGRZrli4cudULbfPvSFQBVCPu6C23FxhoqYDt9uFuR0lAAAgAElEQVQNu92OK6+8sm7HYzabNXvPqQ0Yvb29FbviKi0ETiaT8Hg8iEajmmPl6zVuQ5IkxGIxHDlyBB0dHUprqHQ6rexPfYFtpHRWo1Cs+JgKV27xcT2GG8qy3JCNemtVpFtMuGgBMr1JoMJFz7fb7YbD4cg6llyLuXpfy4HG+zQ0AKXUMNHmpa2trdi5cyccDgdeeumlurqjclN81TRw1aJcIeE4Dh6PB/F4HAMDA9iyZYvme691RENHffj9fphMJuzbty8r5RqJRDA1NYX29nalCWwikVDSWVS06Be+Ee/al5pCFu3ccfJaxcd0uGE534VGrM0C6t9FotB4DbUdPncuFDW/0OsVy7JIpVLLJr0HGAKVRTGruLqGae3atXk1TNWmyIpBBYrjOHi9XkSj0YobuBbafjESiYTSFWFgYABDQ0MF91+ri466sLenpwe7du3C+fPnYTabIctylqPPZDKhra0tbx1Gq5cegKy71EourisFvXHyxYqP1edWr/i40UwblKVqc6QX3ao/x6FQCKlUCseOHcPXvvY1hMNhxONxHDx4EENDQ9i0aVNBx26ls6B+/OMfZ42UP3HiBF577TXs3LmzpufAECgUH3dBU2gzMzMFa5jMZrMy1bUe8DyPYDCopNL0IpZKKRZBxeNxjI6OIpVKYXBwsKYGkELkChNNYaZSqbJMEoXSWfTiGo1GlYsry7JZbXLcbrcxnVcHveJjSZKyIgB18XFu14zlsgZVb9SfYzrqfcOGDfjRj36E5557Dg899BAmJyfxq1/9Cj6fD88++2zNZ0HdeuutuPXWWwEAJ0+exE033VRzcQJWuEAVG3ehdqP19vbi6quvLvgFogJV6xA7Go3C4/EgmUzCbrdj7969dREGvQiKNpAVBAEDAwNKd/V6oydMlFoV6haKCtTmgfHx8bzpvPQC24hrJ40Ay7IFi49pJ4exsTHlPIdCoYaafNxoAqVGXaTLsixWrVqFyy+/HJ/97GeL/my1s6AojzzyCG655ZYavquLrMhvVbFxF/F4HF6vF/F4PMuNVgwqULViYWEBo6OjAICBgQHY7XacPXu2buKQG0FFo1GMjo5CkiRFmBaD3B59eqaPeneS0Lu4qgtkp6enEY/HlTojdbTVSN0GGg2t4uPz58+jvb0dJpOpouLjenGpCBSQPQuqGNXMglJnIB577DEcOnSomrehy4oRqGLjLoBMBbbH41EihXJTWLUQqNwGrhs2bFC+xIIg1FQAczGZTJAkCZFIBKOjoyCEYHBwcNGK/koVJvXx6glRPe22egWytM4oHo8rC9r0c2e322E2m2vqeltuSJIEq9WKpqamvHOrvinQKz6u1+RjSZKWPIrTIzdjs9htjo4cOQKn04mtW7fWZfvLXqBoDdP8/LySwsm1ilNBYFm2qhqmapq5EkIQDAbh9XqzGrjWavulwPM8hoeHYbfbNedR1Qv1uI1ShInSSL34Cg059Hq9ygU21/WWu761koVLz8XHMAysVqum6UVdfDw5OZnl1qxV8XGjR1CVDiusZhYU5dFHH8UHPvCBKt+FPstaoCRJgiAIIITg1KlT2L9/f14N09jYGJxOp6YglEslEZS6lqq5ublgA9dKR6cXY35+HqOjo0in0+jq6sLg4GDN96GF2hVZSVfzQp0kGgV6cXU4HMq4DeCi6y0ejyMcDmNychLpdFqJstTrW416915ryp25VMrkY+p0y+3kUM58qEYXKPXnY7FmQQGZG4r/+q//wm9/+9vavaEclrVAUegHkF7QaA1TS0sLduzYAYfDUZP9lCNQS93AlUaOo6OjsFqt2Lx5M+bn5+v6RaSLq7kR0/79+1fUuA2g8MgN9WTeeDyurMHkdi5v1ItmpdTKxVfInk07OZRTfNzoAqU+tsWaBQUAv/nNb9Db21vTCbp5x1i3LTcAJpMp627a6/XC7/dj9erVdWmcShvGFkKSJGVQ4erVq8uaB1ULaFum0dFROByOrAm+kUikbilEk8kEQRAwNTVVdipPD70O5peCQOlhNpvR0tKSdZFRN4CNx+NZhceVRASNSr1t5oW6lSeTScTjcc3i43g8DrfbDYvF0nD1cVoR1GLMggKAa6+9FocPHy7ziMtjWQsUkFlXmZiYUNxA5fanK4dCEZQ6aujs7CyrgWstUA8ppIua6tw1cFFEao0kSUin0zh69GhNhKkYl7JAaVGoAWwqlcqKuNQRgTriarQLqxZLVQelLiZWQ9OwZ8+eVeq4couPl3r9cDnPggKWuUARQnDs2DF0d3dj9erV6Orqqqs1VUugqm3gqkU57ZSo+cLj8cDtdhdd46plzzxJkjAxMaG0JNq9e3fN0qmFWG4CpYc6laXVjigej2d1daDFsXTcBs/zDVV43GiFujQNa7FYMDAwoNxQqouP1euH6vOr7ppRT3KbxS6nWVDAMhco2qeNEIJoNFpXizaQLVA8zyuzkKpp4JoLdfIVE7lc80Upa221cglKkqSYH6gonzhxYtHuMFeKQOmhV3hMB2vGYjFIkoTTp09nFR6rI66lKDxuxCm/QP4aVCnFx3NzczWZfFwK6nO2nGZBActcoNRYLJa6pK/U0G7jZ8+eRTgcRl9fHzZs2FDTu8JiAkUIwfT0NLxeL1paWsoyX9A6qEqhwkTtquposV4dzQkhmJ2dhcfjAcMwiqVYEISGXtxeCuhIeZfLhenpaaXzvnp9S11jtBRzohpRoEp1FxaafEzPr7r42GKx5AlXtTcGy2kWFLACBIreTVsslrpGUBzHYXR0FAsLC+jp6anJBF0t9KIcWZYxPT2NsbExtLW1YdeuXWW7AlmWrUhEciMmrV6FepbwSqFrahzHYXZ2FkNDQwCgdNnmeR7Hjh1TjAS5Hcwb8UK4WORGKlarFVarVbPwmK5vUas2AE1jxko+n8WwWCyaxpdSio8LOTZzf4/LMWuw7AWKYjab6xJB0dHuyWQS/f39iMViWfUutSZXoNS2+fb29qrcieWm+LRSeXp3gLWMoEKhEEZGRuB0OuFwOLB161alQ4jNZkNraytmZ2eVgXxqa/HMzIzi0FJfZFdSI9hSUmnqGqPcxrr0fOY63krtWm5QuPiY53lFuHJHxajPscVi0W0BtlxYMQJlsViQSCRqtj3ap04UxawGqrR3Xr2g61yyLGNqagoTExM1s6uXKiJqYerq6irJ+FELgZqfn8fIyAhsNpviQnzppZfyXpdrP9eyFhdqBKsWreVYb1TNWo9aiNasWaM8ri48Vnctp4XH6lTWSik8rgS1Y7NQ8fH8/Dzi8ThSqRROnjyJI0eOgGEYJVNUSqqw0lEbQGa8xl/8xV8gGo3CZDLh5Zdfrksd57IXKPpFrFUj13A4DI/HAyDTwHWxHTMmkwmBQABnzpzBmjVrampXLxZBVSJM6uOuVKAWFhYwMjICs9mcVbelJveCWyzdobfQrXf3upzShPUwI+gVHtP1F63mr7mNdRuRRkmbaRUfx2Ix+Hw+9Pf3Y2xsDC+88AJmZmawb98+AMDmzZvx8MMPa66fVTNqQxRF3HbbbfjRj36EHTt2IBQK1e2mY9kLFKUak4S6X5/FYsHGjRvzLmy1Yto7C0eTA6s68ufqTE5Owu/3o729vS51VHoiQvc9OTlZtjCpt13ulz0SiWBkZAQMw+Dyyy+v2zlXo5d2oYWc9EJbTpqwUS5ylMV0y+mtv6hvBHw+nzKP6+TJkw1VeNzIRhtqtHA6nXjXu96Fyy+/HJFIBI899hgEQcDY2Jjuuatm1Mavf/1rbN++HTt27ACArEiv1ix7gaJfxEoEqpQGrno/V8kFIL6QwKP/+FO0drbg9gfeB5PJlFXg29XVhb6+PjgcjrrcseQKVC2ESW/bhYjFYhgeHgYhJKubeznU8gKsThOqKTVNWA/3YjUstZ07K43V1gpAgkxYvPrqqxgcHNRsRZRbGGuz2RblPTS6QOUW6dLvCr2R1qOaURvnz58HwzC4/vrrEQwGccstt5Q0f6oSlr1AUcpJ8VGr9tjYWNEGrnr7qURA/vtff4FIKI5IKI7f/fQIeq5ci0AgkGVAmJiYqFs7Ipriq6UwUUoRqHg8jpGREQiCgA0bNpScPqURivrCuxhRS6lpwnA4rLgOGyFNuNQCBQCM7INFeAwMCQCwgmeuhsW8WrcVkbrweGpqKqswVr2+VWujy6UkUOXMgqp2v7/73e/w8ssvw+l04q1vfSt2796Nt771rTXf17IXKHUEVUyg1I64tra2ihq4VipQXCwJ7ykfyAWX1M/+z//gz//11rwCX5Zl61bPJcsyeJ7H4cOH0dnZWdO2UIVs5olEQhklT5tSlrPdRiM3Tejz+cCyLFpaWipOE9aSpRYoVnwJZvEnAOiNVhJm6Wlc1rEKILsBJvu7U6jwOHcqr1YE63Q6K/4cX0oCtVijNnp6evCWt7xFWQu74YYb8NprrxkCVQ2FugvUsoFrpWaM8TM+cIkE0uk07A4HVjWvQnpOzPtylNKQtlzUERMhpC79CrUiKFo7xnEcBgcHyx4QCVwaIzeAytOE6uigVhfKpRQok3QOZvFxADnfRQK4bFOwCAchWO4ASpxgrVUYq45g/X5/XuExPaelFB7LstzQAqW+gS5HoKoZtXH99dfjn//5n8FxHKxWK1588UV88pOfrOl7o6wYgdKiHg1cyxUonucxPj6OF5/6HUwmUyatdeHLOfKqFzv/MHtSZS2HFsqyjMnJSfh8PiViOnr0aF3a3KgFKplMwuPxIBaLYXBwEB0dHRVfMPVuPBrNmKBHpW7Caopkl0ygSAgW4YfIEycABJljMsnHYJK3QmZ3V7wbPaMLtWnH43GlyBtAnkNT3Vg3d5xFI6EVQZU6C6qaURutra341Kc+hb1794JhGNxwww14xzveUZf3uOwFSst+LIoixsfHMTMzk9eSp1pYli1JoHieh9frRSgUwmWXXQaH7II9p1fe6PExSKIE1pyd4qtWoLSEqd6910wmE9LpNM6cOYNIJIKBgQFs2bKl6gslFahGi5iqpZibMHc6bzlpwiU5X4TAIjwKgNN9nh6RWfx/4E1bAaZ2LXv0ZkQVGrVBu5vTrhqNVnhcbSfzakZt3HbbbbjtttvKPOLyWfYCpYZhGJw7dw6hUAi9vb3Yv39/zS2sZrO5oICk02l4vV7Mz8+jv78fGzduBCHA1PB03mtTSR4TZ6ewfttlymO5AkUIwTM/eRW7rrkc7WsL27CXQpiAzHuenp5GPB7H5s2bccUVV9Tsi04FKvf32EgXklpRyzThYp8fk3wCJnlY93kCKJkDhkTASs9CMt+g+/qaHVeBURs0ek2lUjh79qxSeJxbyL0UjXWB5T9qA1gBAsUwDFKpFLxeLxKJBDo7O+siTBS9FB89hnA4jPXr12PTpk3KRSLgmYHAa0dd518Z1RUoKk5Hnn8DnjcC+PBn/hhWW36KMleYiqUya3WHrY4SW1tb0drais7Ozqq3q4amDnPTMJdKiq8WFEsT5g45NJvNkGUZwWBwcXrpER5m8WeFX0IIGFw8BrP4AiT2DwGm/uNZtKDnNBaLobm5WTEQqBu/0puu3P551JhR79SgIVDLAEIIzpw5g+7ubgiCgI6OjroW/uX2/Esmk/B6vYhEIli/fr1mE9nJ8wHd7Q2/6sH1H/5D5d9qgZqbjuLI828AAIKBCH71X6/gXbfvV16rFqa1a9eWtMamd8EvB1okODs7i76+PmzcuBHBYBCxWKzibepBTRK0ZoZ26zbQTxNOT09jdnZWM6VVDzchK70IhoQLv4gAyPpa8GClw5DMf6jzA4uDJElZ56GcwuN6dyDRGve+nGZBAStAoBiGwe7du0EIQTgcXpSRG8lkEhzHwePxIB6PY/369QXTWuHpBd3thQILmJuaR8e6NmX7NELznssWtpMve3Dde3fBZreULUwUKoCVCBRd25uensZll12WFanWY9wGvTAcP34czc3NsNvt8Pv9iMfj4DgOJ0+eVFJcuYvfKxVaJOtyuZQuAkAd3YQkDbP4QvGXIT9qZ6XfQmLfAjBLZ1IQRbHoHLVC/fPUjYq1Co/pea2k8Dg3tb3cZkEBK0Cg1CzGTChRFDE9PY1QKISBgQEMDQ0V/eAtzEYKPj9+ZlIRKHUE5X0jW6BkUcZLzx2HvVUsW5golQiJKIrK5Fy9tb1aCxRtHJtKpbB161a0trZCEARlv0eOHMHAwEBe1211cSf9s1RrCEtJnhiUmSYs1U3ISr8HUEKTZpVJQjlGMg+TfBIyu7PMd1c7qskmFGpUTFs75U7kVd8I0I7lpbLcZkEBK0Sg6EJ6rRrGakHHbsRiMdjtduzevbvkO6LwTGGB8g9PY/fbtgO4eGGRRAnjwzOZF1yw0CaTSQyfnMKdf/2uiu3y5bgE1QMKe3p6sH//ft0vc60EKhKJYHh4GCzLYsuWLfB4PJrF1CaTCU6nM6/rNi3upKM3RkdH82pk6BrCco22ylljLOYm1EsTut1uuJw2uPFcaccEaNY+sdLhJRWoUqZXl4teY131Z5O2WMsdbEj/zr0BXK5rritCoCj1iKBisRhGR0fB8zwGBwdhs9mUBqelsjAbLfj81HD+GpV/PAQ+KSjCZLXZ0NLagoWZNEgRHSCEIBJJoqUlv31TKUJC17YmJiZ0BxRqbbeaL1E8Hsfw8DBkWcbGjRuV4ky9Oig9+7lWcWdujUwwGATHcfkX3Dq00lkKqr2YqSOD3JEb6jqjBfkIOldNwMQwYM1mmFkWrNkMlmU1yz+YvBgKMMnnABIBmPL7MdYCSZIWrVltKYXHNIqVJAnpdBoejwc+nw9utxsMw5R83al01MbY2BiuuOIKpd5q3759+Na3vlWbE6DBihAodbsjWpxXLep5UIODg8odZjqdLqtOKZVIIcWlC75m1jcHPsXDas9cHAkhePWl0wiHw4owMUzmSyQKEjxnAth85WWa2xJFCT/92WsYHw/hz+86gFWrsvPrhSIo9QyqtWvXliRMlEojKI7jlFTexo0b8xaBqUki94tZqHNILno1MrkXXNpKh46KUEdbS9lxu1zqVQeVlSYkBFb+p2BIC2RZhiRKECURQioFSRRBALAmU0a4LrgKTYzWOSRgpdeWzCzRCK2OtKLYVCqFM2fOoLm5GefOncMvf/lLjI+PY8+ePdi0aRO2bt2Kz3zmM5rfz2pGbQDA4OAgjh8/Xv83jhUiUJRapPgikQhGR0dBCNGcB1VqoS6lWPQEAIQAAc8sejd3K3dQU2ORLGFS88brPl2B+uWvT+HU6SkAwMFHD+Mjf3YNbLaLHwMtIZFlGYFAAGNjYxXPoCp35HsqlcLo6ChisRg2bNig2wapWARVDVrrMmrHFjUU0Jsep9OZJVyNVthJqZdAESJjQfgNIvxhSPIUXJhEO9sON+uGyWqCBarPDAEkWYIkihBFEXw6faEgNpUXbbHSKytaoLSg1vaOjg7cfffduPHGG/Hxj38cP//5zzE8PIwzZ87oHnc1ozYWmxUhUNWM3KAsLCwo03ILjYAot9NDKQIFQnDi8ClMhsexZs2azAgHPq4pTgAwcmoSkiSDZbOfl2WC06f9yr+npyN47dg49u8b1Dx+QogiTO3t7di7d2/FKa5SIyie5+HxeDA/P4/BwcGi3SbqKVB6+9NybKk7bofDYfh8PvA8D4vFAkIIHA5HzXvqVYpWYXO1SHIcU8n/i5Q0AQAwyX7EEUdcjqOTdKLdnDMziMl81liWhTXzT7AsC4vVCkmSMqLF85BEETI5jbG5Z2C29WVFrYtxHhtVoPRqoCwWC7Zs2ZIlNrlUM2oDALxeL6688ko0NzfjwQcfxDXXXFPLt5bFihAoSiUCFQ6HMTo6CpZlSxpUWO6daUEHn8r8MDUcwPW3/RGsVivmgiEshBK6+0olBQT9C+jsze4K7vOFwOWkE0+dmswSKJPJBEmSMD09DY/Hg7a2Nuzevbtqd1AxgVLXTuUWMhdisQVKD72O27RYWRTFLBecOtpyuVyLaoGv9XmRSRpTye8q4gQIAC7WvE2L0zDBhFazfo0ONUkwDAPzhbSfms0tKYS5dsTjcUxOTmadx9wBh7U8j7IsN2T6ttAsqHrS1dWFiYkJtLe349VXX8VNN92E06dP122Y6IoSqHJSfPPz8xgdHYXFYsGmTZvyHDe1QlOgVMJktdrQ0tKCdFhUohcuyoMQGUyB+pBJ71yeQJ19I99sMTkVxvx8HG1tbsWd5ff70dHRgV27dpU9bkQPPYGiFvVAIJBXO1XNdhdboPSwWq3KuIeuri4A2f3fIpEI/H4/UqmUYjNWC1c9LPC1TPERQjCd/BFS0rjyGEPmkdsQNiAG4DQ5YTPp3Oho2MzV2JgzaGt7Z2VuwirNLY2Ypq1mFlQ1ozZoBgEAdu/ejcHBQZw/fx579uypwbvKZ0UIFP2AFUu/0dHuo6OjsNlsJU/Q1dtWKR/shaAqxZclTFa0tLSAuXCxXghGkYhwcK1yIh7hi158p7xB7HnL5VnH88a5/H5/AHDi1CSGrmhXUpg9PT1ZRZy1INfFJ8syfD6f8iXInXtVKo0SQZWDuv/b2rVrlcfVbXQCgYDi1qLpQXrBrTZKqKVARYUjiIun1VsHQ/ILzwkI/KIf/ZZ+zX3r2cwpDPGDkYMgpov1RKW4CdVzoqxWa14pQSOm70qhmjZH1YzaCAaDaGtrA8uy8Hg8GB4ervm1Qs2KECiK3peSTjv1eDxwOBwYGhqqql1OOe2CIsFoQWFSMzUcwOV7BpGIpFHs2jvpncv692wwhnA4v2BSEAQ8++xraGvZiu3bt2N+fj5PxId9QTz7yghu++NdcDsqS/XRc0KHQo6Pj6Ozs7MsJ6AWl6JA6aHXRieVSimmDPWgQ3WEUE5RZ60ESpQjmEsfynqMIXFkUnz5cDKHsBRGm1ljIGWRCAoATPJJSKY/KnpcpRQd03ZE6vXBS6njSDWzoKoZtfGb3/wGX/rSl2CxWGAymfCtb32rrAGj5bIiBKqQMAWDQXg8Hrjd7rJGuxeCphKLCZQsy5j1BzN28QLCRJkansblewYRX0iDFCl2CgdjSMRScDVlPsR+f3YvNHq3Tu/m167tg9PpxMLCQtY63fHzfjz69DEQAnzv/x3F3Tftg0OjIW0ppNNpHD58GB0dHVUZLtSohUj9e74UBUoLtQVe3Y1Ar6jTZrNlpQkdDodmUWct1lVm0/8NiaSyj7dIz705aQ4tbEuepbxYBAUArPQ6JHNxgdKjkqJjnucRDofL7upQb6qZBQVUPmrjve99L9773vdWcMSVsSIESg3DMJAkSYmYmpubsWPHjqL9tsqBCpSesUCxbXvHEA8nigoTxT+aSdHFwqmSLr5T3iAu355x4kxPZ1KJoiginojDxJjQ1NSkiOj54RmsXbsqLw36vye8SrTmD0bx9JHzuPEtQ0X3TaE3AbRjw759+2rajkWvAHi5CJQeegXH6XRaiRKCwSCSyaSSCqOiJQhC1WuLSdGLuHAi59Fsc4QWAhGwIC3kRVGlRHUMGQdIDGBqtx5cKE0YjUaxsLDQkGnCldDJHFhhAkUIgSRJOHLkCFpaWrBz586aChNFz4xBhWl8fBwdHR3YsmkIz7gOl7zdmbEgCCGIzieLpvgAwOe5KFDj47OIRDKGDLcrv//c+HgI17w523QwG45jIqeR7bHzU7jhDzbDXMKXMhQKYWRkBC6XCzt37sSxY8dq3iuM1lcJgoBoNAq3261cMJazQGnBMAzsdjvsdntewTG1wIdCIczNzUGWZczMzBRtoaMFIQRz6Z/n758sQGtabi5z0hxa2dZsQSohxQcAJvkMZPaqEl5ZHbRno8PhwOWXX1zLbZQ0oSFQywy/34+xsTHIsoyhoaG6tqXPjULUwtTe3o49e/bAarViZjxY1nZj4QRmp+Yh8lJJEdekJzPi4vz58/B4AwVdYeMToQu1UxeP/dU3JvNex6UEnPbMYMfGbt39LiwsYHh4GFarFVu3bq3r+AtCCGZnZ5U0LR1zIAgCzGYz2traLpl1hXqR2/vNarXCbrejtbU162KbSGTWKIsVHHPSWSQlT85eSPGRGhcQiICIHEELq1prA4qm+ACAlU4vikAB2jVQemlC2vy1kJuwlmlCQ6CWGYIgYPfu3RgZGal7XQONoPSEiZJYKL/t0tipyZLSV5Io4o2THpw53YK1nb1wOHwFX8/zIqanI3C5qJmB4DUNgQKAl8/4NAWKiiHDMNi8eXPdrPnAxbZL4+PjaGlpwb59+yBJknJuTp06Bbvdjmg0ikAggFQqpUxDVZsLLlUXVzXQdJrWxTa34Jh22lYmybpdSDp+BmLKTskx4ADwJR9DWApnCVTpEdQbABEApv7rQaUW6TIMo7gyS3UTqiPXStKEWgK13GZBAStEoBiGQX9/v9LRvN4jN1iWRTAYxMjIiKYwUeILJYwhyGHiXABgGBCdoldJEpFIZKIIl8uJns5BxNKlvd/xiRC2bV2bKdQNRRFNaPcIHPbNIRzl0NqcMZQkEgkMDw9DEARs3LgRsykRL4xO4R3bN8Fkqm3UQgjBzMwMPB4POjo60N/fD/OFljg08mMYBhaLBa2trVlOLkEQNEdHqCOGpqamhm1RVCsKrffoFRzTcxdOnEQ06YEkSSCEXOgGYYbdMgczve8r4dRxMoeUnILdlFkLKzWCAniYZA9ktnRDQKVU20WiUKssKlzq4YbqouNiUX9uE9vlOAsKWCECBVxcNK/nTCjaGmhiYgIul0tXmCiVCFTAMwMTw0DKiaAy6wyZuhmX0wWL1QKAQWBiHvES12LGx0PYsb0LsixjbLpwuub4sB/7tqzDyMgIOI5T+uX9+tQIfnHiHABgLsbhtqt3wFKDKIUQoqxpNTc3K90tJicnSy7UpaKlvtPMLZqdmppCOp3OGtRXzvrMpUAlNnN67hK2k9tjrnQAACAASURBVHCLNDImkCQZksSDQRSiJClLULSzttJhW2N3YSmMLlPXxWMqKYYCTPLpS0KgtFC3yiolTUjXwtTCRdOE6t/hcpwFBawggaLUYyZUbs+6wcFBJZQvRLyCFF9wYg6mjlbl4itLEhIcB1EU4XI5L+zz4gd3diqMGFvaF398IqS0Ohqf1RcoWZbw0mtnYeeDGBwcxOrVq8EwDJK8gGfPjCqvO+4L4LJzq/DWLYO62yoFuqZls9mwffv2rFIAdRNa9YW3VBefXtFs7mK41voMjbYuNcoVqJTM4XziVcwLo4jwJ7HaYkab2ZwZo8GyMJtSYAgD5XJCMvsghECW5Yu/BybzezExDBjGhIgUwVrz2ouW8xIPySSfAfCeko+/UhazD1+paUJaTpBMJjEyMgK/3w+z2VzWzVOlozYoExMT2LJlCx544AH89V//ddXvvRArRqDUDWPp2OVqyRUmeldP7b3FSFQQQc0HwmjvaIUsy4jHYhBEEU6nE01Nbmh9w6d98+DcpV1Ek0kec6FM2mt8Or8Fk0wy6xMCz0OSXLhy9x7YrRfXAl4amUAqR/x/c34c125eD/bCF6ici6N6BpTemla96qBKWZ+ZmJhQxqI3NTVdMuM3yvkdjCVP41j0eYhEQFr2QZRFhEURbtaEKxwOWEym/M4RF4QoKyIiF/ctExmQRYhERCAdwCp2FQg1trBsUQMQQ+byukrUg0ZoFKuVJpQkCa+88gra2tpw5MgRPPnkkxgfH8fu3buxYcMGbNu2DZ/5zGc0SwmqHbUBAJ/61KfwJ3/yJ/V94xdYMQJFsVgsiEZL6CBeALUwaTVTLTVKqyTFl+J4xMIREJbJXBR1hIky618A3166i25ycgHJdBrh6EWBJUQGl0winU7D6XDA1doKBgzGA2Fs6svc7QmShBfPjeVtb4FL4oRvBlf2dSk1S8UujvTukOM4XH755QUXfxezk4Te+oy69kg9foOmZZLJZE0KwGtFqQI1wh3HsejzmZ8BD1G++L2JSzJOcUkMOVnYSxnpfmF3DMOAxcWLvsiIsLN2CDwPPs2Dk0SlkJhl2Uzj2Atdz9VrVCb5LKQVIFBayLKs3EDdeuuteM973oMbb7wR//u//4uRkRGcOHFCN7KvZtQGwzD42c9+hvXr19fVmatmxQlUNSm+YsJU7j7KcfGRC3fvSS4Jd0qEtcVZUs45leSRjrOwOkuLoianwhDYzHHRKvtUOgXHBVuy+q542DenCNQbgTlEkinNbb7whkcRqELdoXmex+joKBYWFrBhwwZ0dHQUL95sgFZHeuM31MMOw+EwAoFASZ0e6k0pAjWWPKOIEwAI8nzea5KyjOEkh632Ev0NGiRIAmABxmSCy33hokcy0bokihAlCRzPKwYYOidKNr0G2bGvrilWSZIaMoWr18mcZVls2rSpYEeJakZt2O12/NM//ROefvppfPWrX63xu9JmxQhUNTOhShUmSqkzoUqJoDLClATPp2G3O2AymUF4qeSLL8+L4OPpkgVqOhCB5E4hmeSRTCVht9nQ2tKqeUEb9l3s9/dGQL+mayy0gIlQRHNoYYLn8ZzXAzuXhJPjsH79emzevLnkFFSjdpIwmUxK7RG9oHR2doLnecRisbxOD/Wql9GimEBFxRCORZ9VPSJBlLXXJKOSiEnBjl6r9s1J0WMBQUSKZEVVYAATY4LJakXWWSAEopQZcigJ53DWewJpXs4ztNSqu0OjRlBLVQP1wAMP4JOf/GTFDbQrYcUIFKUcgSKEYHp6Gl6vt6y5SKVEULIsg4vpr1ORC+6ydDoNh8OB1tZWiEJG9Ph4CpaO0kJsQZCQTqThRmk1SZNTc5DbJMiyGS0t+T3T1EyHYoglUmhy2QsKFACcmJxGV85ojLlEAl98+peIJBJwOp340JW70d2tXwCshd6k3qUWKC3UDi690fLqhXC73Z4XbdXC/l5IoCQi4nDkfyCSi59fkSyAQKusQQRAMME70MYKcLGlD+pUE5EiaEMJDUdVs6JsAK7c5oLMXqFpaCGE5BUc22y2ss5fowoULUKnlDMLqppRG0eOHMETTzyBz372s1hYWIDJZILdbsfHP/7x2rwxDVaMQNEPZiniUakwUUrZRyqe0mxXpBYmWu1P8ycCn7kApONJuEpoKQNkBIpPFRfkdDoNjuMya3QRCW2dpd2RjUyG0NPdgrl44XTl6alZrOtyK66uqakpfPuVo0hJYiZ1yDD4rzdOY1NHB7rcpRf4NkKKr1r06mWKdTGnf8rtBl9IoN5IHEVEUHfCJ5rpPQBglK7lDMZ4J4Ychfvw6ZGUkxBRftrdJJ+FzF6ha2jRKh9QCo5VEaueCDWqQEmSVPEsqGpGbfz2t79VXvPAAw/A7XbXVZyAFSRQFL2UEJAtTK2trRVPki20D0oikhM90fWeVCpPmCg0ghJSPGSxtLtVnpfAp3jdixLP8+A4DqyZxapVqyATYCamfUHSYnRqDjGd8Qpq/AtRxNozDsepqSlwdhtmrWa4mIupR0KAn4+cx5/v3F3y/peDQGlRShfzmZkZpQmvw+HIEq1CRZ56n4W4uIBziVeyHpNIAjLR6hAhAypRWZAsCItmtJorW9/lTOWXXBSymxeauRWPx5FIJBAIBBCPxyHLsub5a1SB0oqgFmPUxlKwYgSqUGifK0y1nCSrh5LeUwmTzWYr2NlcHZUJXGltZQRBhCzKkHgRZtWYDDpug/Zpo1/EZIqHmCo9VTM+vYB5U/FjEXgBJyam0WS1YNeuXfj2yeOa6cPXAgFMDUaxrqm0EdKNugZVL/S6mKtHRtDWTrkTemm0oCVQhBAciz0PiWT/7kWd6AkaEc8470QLG63IMMEx5TtaK7GbaxVr643cSKUyUwNWrVpVcbRaD3IjqHLXoCodtaGGuvzqzdKf7SWCXrxo25zFEiZKIpJAKplEMpksKkwUUbi4DiAmtdsQZUPACzQtyMNssyh34AzDZAkTJc2LFwSKoJTKydn5GAIF7n5F4eL+gjYXBgYGkAJwLjSn+zP/MzKMP7+ytCiKChEhBIlEAg6HAyzLLluB0kJvZIQoikqKUB0tCIKAqakppZGuzWbDDD+G6fRY1nYJeIhEO23HaETNCdmMsGRBm7n8Ti1phgcv87CaynPN1cJurnf+jh07hrVr1yKdTmdFq3a7PW/C8WI6MXPHpZQ7C+pSYkUKlMlkUqa61lOYtO5UaQPZV4+8BkmSS54FBQCSKq2XiaAKi4goyiBy5iKdiiUhWTLuv0JdzdOCCEkgEAUJZkvxj0dalJCI8HA2ZadCJUlCIp5ZrHa5M/sLxBPg0jzOhEMFx4W8PjuDBM/DlWPxDXBRHJmbAC9L+P96h2BjzWAYBhzH4ciRI7BYLOB5XhEmu90Oq9V6yXZ8qBaz2aw5off48eNwOp3K2kwqncJo00vgrfELfQ0ztUeCbndyEdA0TQBTgr0igWIAROUoOkwdRV+rxiSfhYS3lL2/UpBlGa2trVk3cXRtUG1q4Tguq3M5/bten7lqI6hLiRUjUPSOemZmBvF4HPPz83WNmKhRgtqFc7tO9HZdBo/bX9Y26RoUAAhJHoQUrj8RBEmZgRWbj2Hdup6i9uU0NWJwPMyrin88OF5AikiKQMmSjASXgCRKcLloT8AMEiEYCc7jlWhhx58kyzg2E8Cbe/uUx4KpOP7l9ItIiZmL36nwND7cswMzo2NIp9PYs2cPzGaz4uobG8s8ru74QLtI064PTqdzWTeF1YK50J6oo6ND+ez7UucwHpYB0QZJEpFKJSFJImCdAZjMTZbJxAAXukNoRU+UqGRBTGLRVLajj0FUiqLDXK5ADQOEB5jai4FWzZ56bVDPiRkKhTA+Pg6e5/Pq3mrRZaSaNahLjRUjUIQQvPLKK3C73Whvb0d/f39d03lms1m506FpRLUjcPz3gbK3KYrqFJ++8QHI9MuLRmPK6HlWNpVUW5MWRDAMkE4IcJXgXE3yAlKClHFNcUnwPA+nywlbU765hAGDk/4gxsQFjS1l87LfrwgUL0v4v+ePKuIkSRI8swF8NxzDxzZfjbm5OTidTvB8Zi2M2l+tVit6enoAXOwiTdcZ1He+6t56haLL5YL6cyMTGafjL4FhTLBYLn5GRLKAtMReSJ9mxq8Qkkn9siYh68aIQXYz2Cnegc2OeOnHc8GRmiRJCESApaxRGiJM8jBktvQpz+VQ6g1Moc7lWl1Gis3cKsRKmQUFrCCBYhgGu3fvhslkwtmzZ2veMDYXlmUxMzMDv9+PVatW5UVrXLTMfoCEZEVQMi9B4gWY7NlCQPvFCQIPgL14wUkJkCUZJlb/7k0QJcgXUoJ8ojQTBscLSPI8FsIETpcTre4CM2kY4Lh/Fuya4ne7w+EQwskkWh0OPOsfxmRiATKRkUhwEEUBLqcLYasJYzIHl0YvPiB7oq66Bknd8UGSJOUCMj09jXg8rrjiaKS13EZwUIGKCAkcXTiKc4kg3KwJTWbThfdIIMghALQrOU1xsQDSYAijiAoIIIMA5IJGMUBItCAtMbCxJa4Bql4WlaJoN7frv1aDTHfz+ghUNeh95nJ7Ovp8PvA8rxQcq1OFWi7ClTILClhBAgVkohpZlus6E4oQgrm5OYRCIUiSpDtWvlCRrhaZO1jVN5nJ1ENZLggUIRc6TqTTcDidcLtdmJnJ7jnIczzsTfpRY1qgos0gzRU+PwSZ8QAxLgnWZILb2QyrvfDHiWEYTEVjWNfeCraAUGbeD/DKtB8H+vrxXGAECS6BdJpX7jypVPx06iz+1Lou7+dLNUmwLKvriovFYrojOJqamhq+KaweKZnHz+cO4xznwwzvg0gyv2s3a8Imtw1ONg2ZaHWGIJn0HoOLLa9o8EQy/yEX/jeQtqDHkokWGBOjdDFHTrSlcOGxqBxFO8oVqDMomu9uIPR6OtJoK5FI6M4rc7lceQK1XGdBAStMoCj1mAlFCMH8/DxGRkYUN1BnZ6emOAHlR1Dq6ImSTiThal+VKexNpZSOE0phb87PCInCAsWrXi8JEiRRBmvOvQATpC4U9cqmixFaKinA6ij8cSIAOEEExwlo0kgB5vJawI9ofA6+2WnYL7y33EtQVEzjrBTGbuSP26gUtatLbwTH+Pg4OI5b9DZF1RLkF/BrchKmhBVpmVPECcg0gD0eSWLQFUWz5q8y0zlCkwvhEz3rQeJEn5kHg4ujNyTV6A31rCii2mZSLj/Nx5AFMMQPwuTfqFTKUjhASyk49vv94DgOr732GoaHhxEIBP5/9t48SLKzPPf8fWfLtbKrunqTulst9aIFLUhYDQIvFww2Fr5wzcyEl7GxPQ7suDPjudh/TED4D4+XcJgwcWfudQC2I4xtjOeKxR7AXBuQHGaTAQkJZK0tdfXeVV37kutZvmX+OEuerMysyqruFoLy62iXqDqZJ/Pkye/53vd93ufBGNPH7BsW27XaePzxx/m1X/s1IL42v/M7v8M73/nOa3sBBsSOBKhr7QmVAlOhUOCuu+6iUqlw5syZDc+x1QxKrRvMFUBrpYk1tkKxWGR8ol8vT657TNjemJqeAlRc5IGwHVGqpUBiCMKQdquF63qMj4+z3Oq+B78dUds9GIzT6EiFwdDpbA5QQRDwnfPnuHSgwPjERLL7Hhzfbi/w87kFRRnFv7Rf4Fz7CtULVX50z32cKB+66hLdoAVkvUzR2bNnMypymmlFUfSKGPhcDNf42OVHaOJTw6Op+ll6Cs2pJtxedaitG7oVW7B0D43FsnKZdKKB1hsGg9Gx9YbRGmO6G4xlf5k93h5sy96CR9RzKOvaAdRGosYvZwwaOH788ce55557EEJw6dIllpaW+Imf+AlarRZHjx7lz//8z3vu0TSuxmrjrrvu4oknnojZuFeu8OpXv5q3v/3t171fu6MAKi8Y6/vbE7fMR2qk57our3rVq3pS9s1AsHMVGZRWCqU1YdsfCExxGKKolwq8WV8pK/ElCBV2Qkq1AlEU0kyGemu7dsULB9CJuu/P72yekbZlhDHQ3mDIOIxiO2zHcbArJZaUZGIDYBHAnOxwvrnCwUKVSEv+bvarvNA5j9KKMNL87ZWv8MbJ+3jDxLXvU2wmU5QSMqSUzM7O9hEyXq5FsKV8PjHzJXwdgIFAt4lM/2emTYQxgtOtKneN1SlY6T00nFo+LOaiApODKOdJiVAkRppGCLQx2FZMymjoBuV2GaUUArBTy43k56CxDFs9i3J+fEuvb6N4papIpCDuuvHA+3333cfnPvc5/uVf/iVjrw7T5bsaq428XYzv+y9bP3ZHAVQaV1viW1tbY2pqCiHEUCO9jfpcMpL4ndF3oxAz+LTWKKWwrJgqrPxo6I0ipe4rU4StYEPmX7fEFyOU3wygqCAZ6nXs3tvFD7vvLwrkprvOlopLREGgUEr39KG6A8RQG6th2zaX2qvQ1kxUN8i2kqb+l+fO8gtHXs0X57/F2XY/ff/LS09xQ2E3t5RvGP5c1yjWyxS5rovjOOzZs2egS+/6EuG1np/RRvPp2a+xJmNmncHQUv2GlGAwCWhJbTHVqvKqaqwMsZXsKY0V5RFqgWdtXC4zpssEFEIQElIql3CEk41JSCn7/KKcHHDZ1kUwayBGE03dLF6pABV//7vfm1SBBuJsKwWfQXE1Vht79uzhscce41d+5Ve4cOECH/vYx14WtuuOAqh8BrWdEl+j0eD06dMYYzh+/PiGCsKO4wx11e00tpC9JVTV+loDozWu44AQcRYVSmQocbz+j3F9eQ+IJY8iNfB4rQ1SpTtkg1KGVr3D7iMHcJ3+foA2mjB3DmMg6EhKlcGLq9SaMLMgMVkfSum4RKaVplKtZOfSxlAPfUQkNrWHMAaeWp7hB3ZP8q/1M/GCKujpbYDhM7OP8h+PvIOSvXV9xWsRg2R2UkZXo9HI5meiKMrmZ1Im4dUomX9z9QUuduay/61ERGh81tfPjOntMTWlw3xYYH+hDWxPqXxeFji0RSsOg6Gpm4zb44icgnnuALRWyAS4giBAac2VC58m5IFrMjLwSgWoYV5QL0e87nWv47nnnuOFF17gl37pl3jwwQevu/LOjgKoNLbK4ms2m0xNTRFFEcePHx+J0rlRiW/U/lMUhplenut6hE5/iSVsdXC8/gxuPUGie3w4EKCCSGZ+OybJhCxjYVuDbxE/kn3t8qATDQWo1rrr3e6ECBERRRGVSqUva6hHPjpuTNAOJJXi4Ka5SChkoZZ87MKjVDLsEX39/I4OeHTlWX5sz+hitNc7BjG6jDGZS2+j0ciUzPPaeukCvNkiOhss89Xlp3t+F1it3p5QfFb0gCzpUqfMbreOt81K5HxU4KDrb5lgV1d1xu0hzDQBlm3j2XbPfVMdb7Mc3DBwZGBUId00vpcAalQG39VYbeTjjjvuoFqt8uyzz3L//fdfxbvZPHYUQG3VtLDVajE1NUUQBJmy76ixIUBt0n+SiZCrELHpne041BfXqS8kNOqg6VOeGB2gonYAE73248YY6o0WkUya+ZYdN7INRJ2IwgDQyfef0vDbw7PSVhgCMWNLKcXqapNdtYmh5merYfcaNTrRUIBKgWgtarIQNbirsLG1+pNrL3Fy122Mu/F5jTEsRTMEusOYM0FtizM41yOEEBSLRYrFYo9aQV5bb2ZmJtPWK5fLWaaVautBnOX+w/w30TkB2EiHKBHi9FoBxkO4pn8DpAxc6lQ4Vtl6iQ+gY2wa2qFmb1KxWIcXTd1EGYUtRgcJhzPsqnns2tVddLcqpJvGKxmg8izRrWRQV2O1ce7cOQ4fPozjOFy4cIFTp05x8803X8u3NjB2FEClsZnjbbvd5syZM7Tb7QyYtlpe2egcwwAqBSaEiL8wuZ3SYMAxBM3BzxXJwQ3tPFHCYOi0Y+8pqcF1YyDSWmXJR9geDFD+AIAKNiBKtKIQrTVax198gYPnDS61KaNpyu7rbHQiDmyQtGoMq2EDZRSh0hQcK8us+o41ii8vPcVPHfghFsLLPNX4MqtRF/wPFG7m/tqPUbJfPtfQUWOQtl5KQ240Gj1Dn57ncbGwygV9Bcd2EgFdaKrBKh5maI9JsxiWubHYoLQZyAyJ+cjbBKBMX0ZniMkSQ7OogaGSod3urn6rQrrpzJFSKqPHv5IGtFNlmDS24gV1NVYbjz76KO9///txXRfLsvjwhz/cs3m6XrEjAWrYDdfpdDhz5gzNZpNjx46xZ8+ebd+cWynxqYQgkAm5rp+jMWYgzdyYuMQ3KAbNTUEXoDqJknqxVGR8YpzWQn6oNyWaQzgEdPIEiTSicPDsVLvj00iszS3LziwffD+iPMCKvh4FPQSPTiCRSuMMGO4VQIsAZWK5pflWyKFaERBDBWmfb17gRHOMU60vo9dlDbPBeb60/Al+eOKdjDmjZ8zfrcjTkPOx1F7l8xf+O1pp2mE7XnCFouWsxQuv1vG9LQQYlcgYrY947NYAl/wat1ZG9wnLx6IscNS0sYZ8lWLJ4/4/bljmGxK2+k4PQA2LYUK6qcLD3Nwc7XablZWVzOQwPyz73cquBmVQL4fVxrve9S7e9a53beMVX13sKIAaBja+73P27FnW1tY4evQod95551XvmkYp8elkhkapVFh1cP9Ga5NJEGUhRDyr1BrcgB5W4vObHZaXlykUCpmTLUAYrn+t8fkGKUoYYwiGGCYGnSgTjo2iiFazhY/BcVwMBq26gNDpyCEA1f+emn7EeKU/49IYmviYKFYSmG36jJkuwHU6fkxZd+xslqqjmnxu7vMcG/B8AC1V5ysrf8tbJn+BorVxyfCVGl9vPI9xoex2Z9NWogUsaSWZbMzyNICwAlKtomTMOXlE9zNeDks0Cy7VbSiVKwTLymOPs7Uy4XbKfJY+BaYDYuOZvEGRDl2nag2Tk5McPHgwMzlsNpuZwsMgS/mXQw5rfQb1/azDBzsMoPIhhMD3fc6fP8/y8jJHjx7ljjvuuGY32EYlvvpKg2ajQRRJKpVy3Ojd4Lzrs6d8yFAiwwjHy2ddpg+gtNZIpbCEoOyVKVZ62Td5Rp4Q3XZE1In6yhyBlDGBYUD4HUmh5NBsNRHE9PSO30ZEYSaHk0ZnQHamjaER9Q8UNzuyD6CkUsw3l9DGxLtKAT5QGqtgaUWnEzfngyBAtmScC9ialr3MaiQ4XHTwhuyEO6rFY6v/yI9M/A+IAcaKr+SYDZZ5pnGu53fSSHzdRFix3l5aPjZGxcOykAzQxnQJgem7JWeCMW51tpdFzUcbANQQ15jtl/meQduv3c7LzCKVRION2ZfD9PTy2da1nHWTUvbMJH0/e0HBDgOobrYQEgQBTzzxBEePHuW222675jufYfbq586d4/Tzp7MbeRR606ByXb7FEjZ9nN05WwvV1e1LZ6eEELiJHYXsRFDtAlQoVV85LP2fWhuiQOLlSAqDCBLxgwyN1SaWFzPz0lJEa0A5EMD3+8GvIf2BMjNNv/scWmuarRZKSTqOBJ3SyuMXvtCO2FewErHOIoVCfL0UioXgMuiY9n5utcF+m2Smxs68kOL5LMF8eInnW49xZ/X1g9/vKzCMMfzT4pOs77+11OpAkSJDQFZg68rrMWgodzks0fZsSonCxHoV841iRbkbz0QNeZ7tl/muDqCklBtSqDfT07tes27/lkF9H4cxhqmpKebm5vA8j1e/+tV9tfvrEVJKzp8/z9zcHEeOHGHPxF4axdFnQ+QgwkNuzidodSjv7jL5okihjUElJUbbcXqkgsJ1Sg5hHwD2rhZhO+oBqEEECaXiHpAVuT1fGKUNHZmCi1j3GE0YKgqFHG02HCzHFEYKP5SoKCAIQyqVMqHloPx4+DSSMn6PQjDfDKjIuOGdgp3BsCrn0SSDjgJWLItbd5UwJv6MlEpmapRGiLhM+53gn3HDy5QKC0RmDmM0jqhSdm5jzL2fon1o4Ov9bsTZ5jKfufwUX1o4g9YG1xbsKbnsL1u0db8zrjFySO9puGLETDjGMWelR8Ucuvss0YtyuRAsygI3DpmJGoZzTd1EGokjRl+qLP0imAaIfnbrqLFdqaNhenqDZt1Sf7KteEVdbQ/qey12FEAJIRgfH+fo0aM8//zz191ywxjDuXPnsuns17/+9ViWtbVBXWLh1v7Ildya3edTSrG2VkclO61BN3zUWg9Qg65Dd6e7nijRyWVEKTDFs1ouWpO48ca7vLbsPdf65KjTiTKA0hgaA/pPEJsYzi6usn+iwsRE/IWc68xjiEswxsT9La0UqwqigkjAJmYNBlaLQOcIJQYCpVkKJZOeg+e6kCuTah0SqHlCvcqTzSmONUxSGoszraZ9iWXnS9S817Cn8B9wre/eIhEqyd9Pv8BX5s5xsTOHTC5yoAzTzZDLzZC9VZtdxfx9ZNAM2gxohgrCAktRmcOmQcFSPVl8ulnKSr85+434h2BeekMBahhEGQx1VWf3lggrGls9gXLetIXH9MZ619qriWGzboO8olLWYX5sIA9IO8kLCnYYQAHs3bsXk/QsrhdAaa25fPlyJpf/+te/vict72xRKHaQKkQ+wlYn2aW1iCKJEPaGitphq3dhWp9BrV8qwhxRwmDwpczJLvUbIQadKAOonvLegDWo04kYH48b2s0o6OttZSVKywK7SLFUihmAKqCjYrXsNFsUloXrufHvCkWqpZio4ss2a3IRbQyCxP4BgbAEM75kj+cm5UGDQSP1IpFZAmGwbIsA6BQL3OC5SKlQUhKGAe22ZM18iRnxOGP6p9hdue9l945qy5A/Of0YF1or1GWL0PTe0yYZYr5cLxCpkD2V+O/GyAFzT4bN9PaMEcwFFW4qJazPdZlT9rZNTskjybYa0qYeQtXWmZJ5vhIwLNb0GrvZGqPSVo+j7Ddu24JDSnlddRI38idLs62FhQXOnTuHlDJTFmm1WoRhmCmLfD97QcEOBKjUJ+h6eEIZHfCn9gAAIABJREFUY5iZmeH8+fPs27eP8fFxDh8+3ANOxhja9a1lUDLaeNForTZZXVmhUo3r3PPz/eWcnucLZI95YbA+gxK9mU7YTntF0Gx3CIIwASaHQajjdySVRDu1FW3M3MoTJeo5ckQGOkLguDHotIIIndCjV6ImJlG+gP4y5pIvuaHqISyHVerYxsGGGISSf1ppFmTAnA6oeC6W08ZYyyBk8r66dPuLQcCkbePaFrZdoEAhe+tKaUL1aRba81yevpMwiJvl6S44DMPrQktuyZAPvvQNpttraGNYjvo/d2WibPmfa3nYFow54QDVCMOockbzQYWDxQa22ABYxGDPqCVdomq3YmKGSsqvxqBQCEtgpYSU3G3V1m1CE+JtwdZdmCsIcwkjbhr5MflIqwIvd9i2zdjYWI++Z15ZZHZ2lkuXLvFXf/VXfPzjH0drzUMPPcT999/PPffcs+HQ7natNh555BHe9773ZfN1H/jAB/jRH/3R63YN8vG9RU+6hnEtPaGMMczOzvKNb3yDZrPJyZMnOXHixMAsLQoiomEkgyExlMVnDFEYYaSiVhmjUIjnf4ZRzPORH9jdrAelpCbwA1ZXV2l0fFzHSb68g3enKTVdG9NDqEi0q3uOjSIVC9sSa+8ZY4ikzEosjuNkMCGVwQ8VgQpZC5tZGdNdB04Aq4EkUoZVuYDMZxVCICwLK1XHdl3qnoVVuIISsygdIKVERhEyyRSNMShjuBSm18xkdhGxcGmsLm2Pf4sbb5vi5Mn7ufPOO5mYmMD3fZaWljh37hxPPPEEL774ItPT09Tr9Q2HxTcLqTUfmfoW0+1Y9HU1aiLX9ZMMBrUuo7rS8GhJs67Wunnm1HNuY7EYbp3GjYAFVQDLwrZtHDf5fEWczWIMSsnk+kuUVGilMdqwKgcPGG8Utnps84OGxCtJSSJVFtmzZw+u63L33XfzG7/xGzzyyCNZZvXJT36Sd7zjHZw9e3bgc6RWG5///Od5/vnneeihh3j++ed7jslbbfzmb/4m733vewHYs2cPn/vc53jmmWf46Ec/+rLOQ+3IDAriBngQbOyPtFmk7rlnzpyhVqv12boPmoXastU7/Sw+rWI1cAA36ZsE7QCnGO8wo01KghB7QxVrxXUisfnI7W6VpL7cYPf+CYK2D/7GwB74seJ0K4o2Ld9AnEWZgiaQEUbrnmxo/aOX6y0it4mwBXZSWkyPyUOUMTDdXsV1W0POqjFGoom44ksOeCGW1bsgZZmWNhijmI4klTCk5rox6892chT0+NiV4GtEsske96cZHx9nYmIiA7C9e/fRarVoNBp98zR5e/nNDA+NMfztxWeYasa27MpoVqJm33FSR33XzxjDlWaZW9wIx0r9bzfuOw2K2WCMfV57yxW0yFisKpfd6TxV8nhLWL3bZZO//poFfwG35eLYsYJ5PNvmxI8b8hps9S2k8++3NRP1SgKoYVGtVhFC8O53v3vTkvLVWG3cd9992TF33nlnbJAaBJmk1vWMHQdQabiuS7PZ/6UeNVKTwmKxyD333NMzm5DGQIDaYv8Juj2ovN2G6zk9mVjY7FDZPcagGahBkWZQfeW9nvPKpBxq41nFWKF9CGU8H0pqZKg2Le+lsbbWolX0sYTAWgc6aWgdEyCavsEuG+zcatbl6XX/SxvFbLvNoV2JQndmSa4x9OrOSWOxFHrsLfS+3qxPAhAXCJm3LHY78YxbSsKwLCuWE0oJFHwHI0L2We9CSVhdXWNycnemFFKtVLjxhhuwLAudqBekDK/z589nFOe8mnle4PSbi5f4+uKF7HUuRw30ugxIG41i0GerkdrmSqPKoVodIbaXxXWUQ10W2OVufZM3FxW6ADUsEuuNvMKEV/YoUoytN6II1fHRRmMlc12Z/YZtJ72nEFt9C+X8yJZf4/cCQKUxSr/zaq020vi7v/s7XvOa17ws4AQ7EKCu1nJjbW2N06dPY9t2n0nh+hgIUGtbAyitNErG1gLpLNOgbWuqyadUXHraLKJ2ClDrFyiT6JCBbQusRM08ZvIZOiOWRf2OpKk3BqgUcP0AoqqFZXrzLUEvScJxXVoyYsz0XoKu+kE6waORRtIMXYzxEegcMA2+NnNhsQ+gBkVTa9aEzb5yN1PWWqOkRCpJp9NBSUVdfIPZ6BLB7JvZv+8Q+/btzySetNZJOSu+9sWCR6m4h/379mJZFgbRY3iYCpw6jkNYcvmblZcwSZlMGsVa1J8lSjPgvZhuptQKPRqhQ62w/TLjbFDZFkCtKJfICNx8D2uETGxVr3LQPYht2z0LZPf6K9ph2GN0iPV5WtbdVKtjW2blvZI0+KCf+p73gno54rnnnuO9730vDz/88Mt2zh0HUGlslSTRaDSYmppCa82tt97a46C60TnWA1RzbVjJqT9kFLG6UkclU+3rvzBx4zlerYNE8ijqkywaHKl5YV7iKKaMq0xYNF/yCtsRgVSoEcAPoNMO6QzZJWsdlw2FiEEniHQsgZQTa8sTINL3ro0h0ooohCE6sygUkQ5iRQQjaAYFakXVXf9ETAYwJgYsgwajaUmHprSpOpsv2OcDn92ug5N8HpZlYXkeLkmJNXEFtopXmDj2VeTa23n66aczMdJarZYNbjqOE2eHibWIUhJQOLZh90SVyd0VLEsALp1Q8YHnvkKoJDJQKKVYoUUkIiwhEMJCWAJtZF9GFQNz/DuRgNR8c4yKG2JvYig4LFajEr6yKdpbAzmDiG04tugTVVd1DjgH+qSPutc/d47M6HCFxvI3OHNmf4/1RpqZFgqFVxwQDYur8YK6WquNy5cv8853vpO//uu/5tixY9fg3YwWOxagRiVJtNttpqam8H2fEydObInSud0MKi8eW/CKOM6wx4hMJSZsdmLAGaG8B4l5YagIIonWKilVpfR00dfAjwJJszP6brnZDGDdeEa3p6BwHDfLgkKlEJHAKpBlFsaYvl5UmLi9RqHAK/QuqgZQRiJN2JOF1QObWs/8jwCcxCU2/VW8eC9FBSY8H23if8P6Z5E2nPd9jpd6extaKZqtuGw8lrgCwype9fMcr/wartiblfNWVla4ePEiYRhSLBZ75l5SlYG0/6U0YNr80+yLLMg6pYKAgoVvFNJX2MbCaJOUQTVKhJgekYfBfSapLRbbFfZXt1fqNsBcWOFIqb7psetjLipw4xZ9ojSaVbXK5AiWKHmjw2MHX+TwLQ9iILPeqNfrTE9PEwQBjuP0XP+XY3h/O3E1M1BXY7WxurrKT/7kT/L+97+fH/zBH7ym72mz2HEANaonlO/7nDlzhkajwfHjx5mcnNzyTmuQq25zrT30+EHisfXlDTKu3MtRUqHCaKT+UxqttRb1ZhtjyIBpo2hvYcC40wkxu+yE1k8GOunCAYnumzFIo7FDgXGISRK2HWvG5Z5PGY1K+kZRAOREAjQaqUPUACZaI3TQJhyqpB2HAGyWQpvj1QlKthU/qwnRpoMiB1oJU24ujNjnutQcB2NipYDUfDG1LUkj1ItcaP5nbii/i7Hq3VSrVW64Ibaej1XdfRqNBo1Gg9nZWTqdTg9NfWxsjCWt+KeFWcDN3uVC0ABjEGiEpRFoIqMQA+ebBseKX2ai1MHbYhaUxkJQ4dBmlPMB0TE2de2wa4sWHstqmd321uxvhLmApV9E27cPtN6IoohGo9EjT9RqtXj++eeHDsx+N+JqMqirsdr44Ac/yNTUFL/3e7+XKZ8//PDDPdfweoUYpHu2QWyvFvAKCq11Bkxf//rXecMb3tDz9zAMOXv2LCsrKxw9epR9+/ZtuwSwsLDA6uoqJ06cyH736f/6jzz91Rd6jjNa02q3icKoTzx2ea7O0vzgHaqSEsuyY4oucPg1J2hEhvomTMF0xqhyY401r790CHGZqm+hrVlE5dEmE5pRiLXPQdsmmymxLIsoirBTajEQKEWgIvDA3WP11Ni7GYzAV2GOBAHjeyVYGm3kQGDKx6FasE5FYXgcKXncMkTlPGbqhSjjo/EpCsVxS+P7i5RKpYTBufG9MlH4EfYW3461yUxPGIYZaK3W1/ir+edZVEFGBugQsqhy94UBjSLUiTI5EGdNm9PHxwoBB2trmx43LI6WV9hXGL7xGhZ7nIDbii1kJHHc0ffKR9wjVLfo16WtW4jc/zTS4K7WmieffJLbb7896wM2m80e8kq6cRjFnfdaxfLyMsvLyxw/fhyIQeKxxx7jj/7oj16W81/jGOmi7dgMan1EUcT58+eZn5/nlltuuSYCsoNKfK1cBmUSs7kgCGLp/kql7wu0mYqEoWv2FjQ7REMs2iHX10nKZ0gQhWHvMZ0+6v49akdQ3rwpq5PSlG5HWGNuz87TsuxkriuGm0CruBwlkx5K7nwiceD1dYhKMpcUpNp+hFsaDXTqgTMyQM34ETeVPeyBn71AiAKOKBBFJZZbLa64d/CjR34CKWYJ1DS+ukSgpgn10sDnXwm+Sit6nr2ln6Lq3DX0HvM8j8nJSSYnJ3l45iWkX2KXKSKlIpIhC+EqUsfvKWUbylS6SKST1oNlwuN7phuNoEAncii521NWmQ2q7N0G5XxJekSmParebPdxamnLAGXpc1j6WbR996bHpjN46cDs+mw3lSeam5uj0+lkw7XX2y9qp8kcwQ4EqPUhpeTixYtcuXKFm266KdPLuxYxEKBW25DYUHd8n1KxGPe1hny7ZbjBwrruMUGzQ1jqp7ubfF8np8/nNwOojFa2UFqjA73pZLfWGl/GjD9Lp8rg3aXStmOVcaVU3MhPeiVGG2RbIrxkwU1sIZSJhW8tsY5WHhZwyxKDTuwi9ND0vhnYKA0D/A77IjKGK37EodLgDEclZViI+0wNe5YlvcLB4p1U3Tu7x5lOD2D56jKhmk2khxaZbv05ZecYuwtvoeIMt3mZ7zT49PnnWesEaB0PBQd2iGVbeLaVlUljYohJSIq9/aae+bC+3yTnaY1x066VbSkDtZVLQ3nUtuj3ZBDMhQUOWFsbmG/qJr72KVrD1cYHhSM/S2jdDmLje34YxVwIQalUolQqsXfv3uz3qTvvoPm2PCFjuwrm+fP8G0DtkEhLfd/85jc5dOgQDzzwwDXf9di23QNQxhgWZhdZWVmJDQPHx2ONuQ1iswwqvyr7jQ7KLeb+FMv5qKTE5qw7V9QOMdpkJcJ8pJvwdMFS2oDUQ483OWkik5TvCLqgISDpRcmEwm4Tak2eJG5pC9u1MDpWlgiVJMopIWSLuBDIUGAnZIdYz40esMqDlgYagc34iBnXxU7IDUW3J4uK+0wdoijs6zM9tvqPvHnyf2aX250XsUWJsnOcsnM8+502EYGaIVCX8dU0gbrMTPsjOGKCmneSMffVeNb+7H2eWlrkdx/7EjONRu45NB0d4nqCSs1gO6CQaBFT14XoLemledRm0Yk82lGRipeChWH0R8OsX6FWSQBqCyB3RRbZ526dpLEgFzjsHd78wFwIs4itvoJy3rLhcVudgRrkzpv3i1peXs4UzFPlh7yC+aiVGillD8h9v3tBwQ4FqMuXL3PhwgWEENx7770bzjJdTaQZlDGG+fl5pqamaKw0GR8BmNIYZt0O/dJBfqONNTGeZShKa2zLipW6B4TSBhFKKG6eRSmdLHyhhmLuy7uODm4AnbxmIw3oGOWkiqndWS9Ka9R6YdjA4IwBloXUEiU0lrAyIdfsh9EowO8ovAIZvdrC6tFyi5dXgzGaVuiyvxIS6aCfgr0uQp3PouKyTqfjUyqVqFTGWb8CRzria8uf5i17fp6iPdyB1xIuJecIJedI7vIpQr2Ary6zFn4TZdoo7fH5Mzb/dKHFlY4PWFnBNUjUIcIQokVBYSzELvoMAxKR+0v6qofBzkK7TNmtJ5sSMeAok/vZ+7flqISv1vAsBQOUzIeBVqgtVnSB/SPqAKbR0A0CHVCwtjYH5MiH0darMdbeocdciyHdjRTM057WwsIC7XY7OzZfJhx0/kFmhf+WQX0fhjGG1772tX1aVNc6HMfB930ef/xxKpUKd5x4Ff9U/ubIjzdKZ5JGg6N3EdFSo4MQY1vYiZir2GA7q7XGCiRiIED19qBSBl0XoEyslZYI76a7wGgdPV11JMaLe0+242avJtT9/Q4dGqRWRPQ69saLXZJpJSufAZQEikm2FMXZUtqPsVLNPQQImyCyqVgTFF0LZSSRCYh0EP803R5XGhc7IXtsg99u47oe4+O7NnTWbak6X17+FG/c/T9RtEenKQthU7APULAPAPezFgT8yb9+izOr88z5ndizKSE7RMYgc7hggHbdwpMuXiXsK88NkoAa9L/TYzuRSzO0qXoRae1V9Dyqvy+ZB625qMaR0moi3NGrZL4RaF2RJfaztSzKYFiQCxzyturHFeJGf0Po/ScYYiV/vVQk8grmeXUGmYyVNJtNrly5QrPZzGbm8oSMKIp2XIlvx4nFCiG46aabcF33mgrGro96vc63v/1tgiDgrrvu4q677kIFowtywgjlPZFbHpKSpeoEmZjrhuBk4n6FCTbfuWqjuwIMQazsEEWJvYXr5koU8SAtxJmO0QYTxgBmWVY812M0vpJIoxNxIpNIE8VsvyCIhtrJr3vrREGsfu0kXlSu6yZDxvFQr4xiSZwoURm4vBbbktjCoWRXqLm7mfRu4EDhCAcKR5h0D1BzdlMQJfxAc7beyYZqR7F9X4sW+eelT9CS22PErfk+/+Vb3+RSvc6s72OMjSUK2KKEoIQyDkLYCGLH3/QqhW2XsNUt/eTpEaNW29JjlzpVwMrAziSaePE9kNwzPY+ysp/zwRjSVDCiBKIIwkNYNpaVbBhyLyYj0hhDQzusRdZWKorx9dJrdPTWpcOEuYCthqshvNwyR6ms0MGDB7n99tu5//77OXnyJLfccgulUom1tTVOnTrF3NwcU1NTPProo/zZn/0ZS0tLI89sfeELX+C2227j+PHjvP/97+/7exAE/MzP/AzHjx/nda97HefPnwdgaWmJN73pTVSrVX7913/9Wr7tkWJHZlCp5cb18IRqtVqcPn0aKSUnTpzgueeey26i5uroKhLASDNNxhhkFMWphbAQkRqppp0qQphg8PvP75WlSnfDBuVLBM7AmRBlkuc1pqulJrt6dgKBNhBplTH08rvtmPwAjNhL1kqgpMFxu69ZJK66ANjJc5oYZOfaEXttmei3Wdkgp5MAuidKyI7BlrC/EitHv2b8JIYmK9Ecq9ECDbm84RrakCs8vPgxXjv+ExwsHt/gyHWPCwP+yxOPMd9qUY986mF35swAvu66EndVB7tZTdj2sGyDU4qyv2wn/MihFblUvSiX8aRbnW6ZNe35ZR1EAcoIFoISB4pdqnt2rURKeVfx3JZQ2WdvjOBSWKIi1rIHpJlw6t017A3NyTmOuEe2PqMov4gRB9H2PX1/eyXo8AkhqFQqVCoV9u/fD8DTTz/NzTffzKVLl5iZmeGll17iF37hFyiVStx999381m/9ViYGm49UyfyRRx7h0KFDnDx5kne84x09QrF5JfOPf/zjvPe97+UTn/gExWKR3//93+fZZ5/l2Weffdnefxo7EqDSuJaeUPnB3hMnTvSYkKXR2mBId1BsxOBLmXkYE/slCUEUhehgNCaVTnpKJpDZAG1P5BBKKoXRGoTAUgzOJowhiMKMWZHtwEOTPb8BOkrmSk9pGSlXSArj3bsm2bVvsqUOA4HjDj8mXWTtpJclCyX2lNxYxV3GlhqtdoCMYuByHZdisYiVDBg/uTrNzx18c3Z9pA5ZlQusRPOsRvOsRHOsySV0bjg21AGPLn+Wm0q3cU/tR6jYG8tiBVLyJ99+gvlWC2U0M+3ufFO375T8nzEDrknMaPEbBUq2wCkkLL4UnbeYmiy2y1TctQGMvnyZNf+5mYRNCDOdArvtJrYluiAjLGKxXRtwekDLGIUxijXj4AtJ1Qnil57z7EpnNbvP1zU7bOkWdV1nlz3awGo+3OhvCMX/gbF6yRavBIAaFGkP6s477+R3f/d3efTRR/nqV7+KMYbnnnuux2Y+H1ejZF6pVPihH/ohpqamrvv7GxQ7EqBGVZMYJaIo4uzZsywtLXHs2DFe9apX9S326QK9VYAaqKuXAJM2BjtRw07Pp7UBf1SASpaJOKUBb/0XMs4yoyhCat1L6sgTJXKvRxn6GX7agDQYV+DLqNvLGhYhuHSHjw1x9hOrc/eDVuhDeQscl+lGyJ6Si2WJhBElCIJYbqhYLCb6bZJ2u4NSiifXViksa147cQe1Wo1qtcoe7yB7vK6OmTKSulxiJQGsGLwWuNh5kcv+aY6U7uBY+dXsdg/03RvaGP7ymae4sBaXBWc6dWTuGkVaIo0cAkzJFcraQgK/4VFx/fj65Zs++WM3AS0/cmhHbo7Rt1mI7FQRDnVTYdIKMEYnfloqeY0CKwdcxgiUFFi2h8HiYjjGbSUHIXyE8LHwAR8Iu9lWIumUB61pNU3B8fDcwhZHREK88E8Ivf8NY3V7WdfS7v1axnrgTFmBQgjuv//+oY+7Vkrm34145X0KL2NcjSeUlJILFy4wOzvLkSNHuPXWWweWGWzbzm74xvLWGsHrAUqp2MAtNumLezqotOcTLz46ij2VNmMJZqw84ixK5AAq1csDQNh91OUUoGJxWZ2pdPdbiMehfUMgop6Fd1gYYrKEXezSz21hYQsrEwPNQIt4h21p0FZiq7FJ1ENFPZCUbUGr1UIIQa1Wy+a1bNvC87rlS2MMz8ppjsqD1C/XaTZjJ99qtUqtVsuGOSfc/Uy4+4G7k8dpGnIlBiw5z9ONrxFqn0nvBva4Bxl391J1xvmH02d4Zn4egNXIZy30s2wp0hGhlkPeVQ6Y8nR4JfDrHsVd/aSJLuthAMnB9ILWYrtMeWAWtXlc8YtMuhv5a+lkg2QyKSytDUtG0tAeY/YYhtyuw2gQPgIfYafAFWTMTmUUV+Qsk/5krnxrZ6obtmVvUPNs44UfIvR+DWPdEj+fUi+rSvioka90bFEB6Hs2djRAbSeD0lpz+fJlLl26xMGDBzcd7E2p5o7jsLawNVHNKCnxpQaFtm1lBoWQfOeSGzXLiAzoIMIuDf+CpQSJNEwgYawAmAx0hLCwhEhmldY93pfoEjnbd3pU0defS3YiZHH0lc74wAYzmBloAQibMV1i71iRQEsCHRLoKP6p+g0TDXBmqcXN5bh8MYyCn51LCGzX5lHzIr9061upuRW01tlg5tzcHKdPn870E8fGxjLgqnmT1NxJjhCXUowxtNQaq9E8lzov8vjsZb7w4hoQC8LOdlRGvdfrGHs972AAMOVDBjbSt0dU2ujOluWjI218WabshaSK76NGWzmsSpcJt/e7FYNRDE6OYyelv64ppNaa0/U6x20L13Ezfy3LshGigjHl7qdpDIggBi3h08ZnsugybpXQRiOlRElJOwhQWscakHYOtBIyTfJu8cIPIp3/EeW84RVb4hsUo/TerlbJ/LsZOxKgtuMJZYzhypUrnDt3jv379/O6171upDJAXk1iSxmUMQR+QBTJzKCwbxsoukwunbPB0H64IUD1UdcD2WO14boeWsdA1eu2m3hN+WTvXZtYTy8uveWP7O7yREhW4hkldLC13eFKM2TvriJF26Vo57MfCHVEoCN8HdIKO3RkwJpt4ZbH8NzRF6E12eRvLj/CLx5+K1WnRK1W67FcMcZkbrmLi4ucO3eOKIoolUo9oFUp7KLqjCPkAZ652GSfV0MaydnmEoIQC400ahNw2vw6Bk0X21NY215nBYvtMkc8OxbaTRTfjUksSjKrksGPnvZLjDtRjg3YtVjpJdjE5b4UDwJAlYpUoMdfC0FiSBgbQ9q2gxAljCllm5CLsshB712UnADbmcEzMxSZRph6TCZKQMv3fZSMM9M8aDn641j6RYy+D9v+7i/O+VjfJ96KF9TVKJl/t2NHAlQao5AkUlv3qakpxsfHOXny5JYkS/IAVV9qbHJ0HFEY0mg0iSI11KBwfeQBSgUhG+UFvZ5OBtUJsRJWY8qOEMJC0/Up6rY5REzIUgaRlBmVVpn4q8mx8jI1CgUoRr7btDQYaRDOaF+QMFK0A0ll3TyXEFCwXYQC1Q7Z5+2itKuEROGqEq8b38dssMJssEywibkiwHJU5y8vfZ6fvvGN7C/0NqSFENnMSl67rdPp0Gg0WFtb49KlS3FJ2XH45PwVmlrh2A4LYZtQGWzhok2c9cUtuBwpImFGjgryRguChkdpfGvyQ/loRTatyKLqpYofdkJzz86SEB26gBWDlqElu1mUyoa0BwsTr4/zQch91VjNPztTAnBSSoIgQMoWGLBsG9eNMyLH9plX/8CN7v+ONPck3BGDJZpYZhq7cAXXm8Ez01gsgtFIpVBSEgYhbSUx5ivsK32D1uq/I4rezNjY+CvCM2p9VvdyKZkD3HzzzdTrdcIw5DOf+QwPP/xwD8HiesaOBKhRSRIrKyucPn2aUqnEvffeS2md/88okVeTqC9tnEGpRNNLCEGxUMJxRidV6FwpTm9ClEizoizD0WCb/CBmvA7mccxK6NtpH0G1QijbBLqry5AyvLvsLpESzChKC+WJmOwwQv1c+wa7OvqisNIM+wBKSjWwz+ThsBJE7HMP8WP7TmKMYU22uOIvMRssM+svcyVYoq367UXWoiZ/efELvHnPa3jN+K3YG8xHCSEye4eUKqy15sNPPk5Tx07JS+0mc1EbA0RCdUuSQmQtoVQpIw6TfW6bMRxlYCMDK2H1bS8WWgnpYuBH0QUt6A5hp+y86Y5NRa3gegJhj54V+1pzOQi5qdjNEGKbFhfHyX3GCUFHKkkURXQ6Hdb0CyzrDzBufp6x6gRjY2PY7i4Mu4jMHYTpvWcChJnBsWawvSu4ehqLK2AU9UadscrXCNV3mLt0F/OrJ7CdXl29crl8zTQ7R4mr1eF729vextve9rae36XWGQB0oiZOAAAgAElEQVTFYpFPfepTAx+bzkR9N2JHAlQag8RcIXbPfemll7Asa1Nb91HP0a53hqpC5H2gqtUqjutu6Bs18Dl6SnzBYOo4iehrAmb5vxs/QrjdL5yUilDKGGxyX8T0MY6yCIRIwClZMDOkyo7OQMsKwRvr+kDFrD/d8zO/hCnfsBXB6rVWyA27y9iWQGtDu91GRhGVajXrk62PT198ljtqe/Fsh3G3yrhb5Y6xI/FrNIaG7DAb5EFrmYZsIY3kiwuP8+21l/ihyXu4vXq4z+V1WDxy/hwvLC/jeR6RBUthiLZBGtUFniRr7V5FuvR9RO/nlvz/YaAVNFxsL9gW2QGgHVm0QovqVkDOCJSElvCIKkfZXfAwRJmnljKd5L+Hb6Smw4A9rkN5o16QENiOg+04dKtdBq1W0erzNBtvG+qvVSyWEOI4Uh8lMiZmwBuJxTwzK1/nyCGLijPPieqLnDj8PKG5h7p/B8t1h6WlJdrtdk/WnD739epdXY0X1Pdy7EiAygZH131rU/fcIAg4ceLENZERSQFqUHnPJIKS4QAfqGgjFfN1oXVKH06eV2lMJBHr2GhKKSI1eJA3JUpoHStFKDNk3imJqBMRVZ2cblv3h8lRmVPQMh2FkSLzr7KF6FnU+0ArNPFc54ibVK0Nq82QsmvwOx1K5XKiADH8MYtBi09deIafP3pf39+EENTcMjW3zK3VLkW3Jf0MtK74y/zz4rd5eP5b3Fo9zPHKQW4sTjLmDNbjO7W0yN+fPsVKvcNyo8Vyy4/npyzAFTExxOnSp7MaKV3QSj/mvHDuMNDCxKw+2XLxqnLTjGtYzLc8Kt5o7rcqmZmzExLEuXbI3oKLJVxs4WKLsRwbUyVA1UmAy8cYP7kX4KWOzz2VcuaqPFoILNvG2Bdw93+Ru2/5VWyr3OOvdeHCBVqtVp8Gnud5TJ1r4/u3c6PzKpRlgTEIVrCZoVaZYbxyGqwJjDhAZI7SbOmBEkV5MLxaFXPoB6jV1dXve5kj2KEAtT6CIODMmTPU6/Vtu+cOi7TP1VrMlYuS3oTvxwKkExP9PlADZ6CGhB6QmSk/xPJc1iuax3W7Acy8ToSOIoSwcByXMBxc+oxlb0ys+JDs6tfHQNDSgBRoR2HU+sHL2FpjPWjts6sUqw4dJemoiI6K8JUcSLHVWjO9sMbR/RXGE8HcUeIbixc4UdvDa/eMpoxdcYoccw5yrNJlQXVUwFywwhV/ieca52nIeOC2bBdxRQziC80On31smvqyRCmD1Hk1CCAwiIbAFEHUBCL9Zq7bTMXYn4JWfGGHgVY6BB21C5TLLpYTC+dqdPfnCKDVkRaNwKa2gaeW0XE/x7Ys7BwJwteGaT/i8AD7EoGNLSrYIi/Xo7NMKzQ+M2GBm4oB2mxd8aUjz3Kh+Z+5sfzLFL3Dmb9WGkqpzP797NmzrK6u4nkeu3btYn5+nkqlEmdFzl602YPUdyVvFtAtLHOJWtmiVinC/hrCPog2lazvuLKywsWLF7N5pd4MbmtGhzvRagN2OEBFUYTv+zz55JMcPXqUO+4Y7suz3UgFY+vLTTCpMnaH4iY+UKE/Ov29l/QQh/YDVLUY09MTRXODGVhmNMaALxPqrUWYKKGbdcf0nMWAiAzGG+16CQFWBFax1+69O3jZLTumoNVsBIzvKlG0XSYoJY8zBEolYBXRikJaoY8mZmRJ4W75M3zo3FPs8orcVhuucL1RlOwCN5cPcHP5QPa7QEfMBSvM+ks8Oz/Dp758iXY79v5VOn8tc9mvEIhAwCJQ0zAgEUuJKt0fohe0AHQ/aLUbgtoEIGxs4rmg9BHrLUoGOWvNt1zGCqr/djUGqeJS8DBCz/l2wF7PoTiKIRcWlihjiTIOsKTgVvcn2V8YT2xK4n+Buowym+vwhXqRC83/hz3FB9ldeBNCdJc827bxPI/FxUWKxSI//MM/jOM4tNvtHoAJw3iQOw8wrjsG3IYy3fIqUYhgmYLnUNxTZd/eXViWgyGet8yPJnQ6HRzH6cngKpXK0L7WvwHUDotz584xMzOD4zj8wA/8wHUbzEszqMvnpllZWcHzvM3tNowhHKKRNyj0ulklgyFsdbAna5miucEglc63Nnqm8TFgSQMuWRlQ5I8TCYEit55agUFtoXphfA1jcYYk0vPa6VnsLmglw5xr9Q6lZY3nxn0G13FxHJui7VCwLFqRomQcjuzaj7YFHRnhhTbH9k9yub1GMEAxfVBERvGnL36T/3jrA9y2a3sgtT4KlstNpX1cnvb5x68uIkIX17LwlcxdytxMU36EwABrFkgDY2aDIdM4ekAr+e/1oBV0DC1X4hW7mwBLWHEfRyQyRGm2S2wfb5JhaGM0oYIV32F3qXtNs3KebW94PysDZ1oBd9a2TjICeHzti7xp8qeZ9O6nRqyYYIxBmpUMrOKf00R6te/xBsWC/9+pR0+wt/gfqDh3AGRGpbfeemuPTNAgNqbv+1mJcH1fKwWYOCsqZfcvJh3p8HFswcT4GLsnxuMBZiGIoigDrUuXLmVGmOv7WvlZyjR2ghcU7GCAKhQKPPDAAzz99NOxpt11ik6nw5UrV7hyYZZdu3ZhjdBElZHaxGajN7TqLkTG6LgnEUbxBD3dxnkvey8lUfQSJULRO8QL5MBKdBcxYxAShGUlGcHmpSLjDzc8TJ6923tJ1jvbKlEqx/NqQRjQastMg9D1PMqlcjb0WfDi2/nf7T7OfXccYCFocbG1yqXWGpdaq1xqr9JRgzPTyCg+9OI3ePuhO3jLDcevOpM2xvD3T53ivz3xDKsdH19HRCk5JWHnWSIloHS17HpoDq0kzaltDlLrYxBohR2XUtnEdHBjkDrRYSQRZRXdf3GWZWdPZoBGp8jhikDqNp2wjbDpKedtFAuhZDGU7PG2vuQoI3l05TO8afKnqTlxiU4IgSt241q7GXO7gq9SNwjUTM7J+BKRXohBWs1yufVn2OpGli/fxO7KD3Dy5MlNiQ15J919+/Zlvx+lrxX3QUWsnpH0EVXuHqyNjbGrVsvWhdSxObWUP3PmTOaGnT5XGIY7pgcltiiZsb0u6yswwjDEGMMzzzzDkSNHeoYur0U0m01Onz5NFEVYlsXT/99pLr04M9Jj23Wf6QuLIx0bhhFhqLLFTQgrW8vKtx7GShlsBpqdIAEnMXABNrUCwfgWMklLIA6XSNXhlTEZjXwYaFl7XKzS6PTcQsHhppvGsy9mq93C8zwKXgGlZGylISVGx3b2juuwu1Tm//rhN1Ir99bIjDEsBW0utVcz4LrYXqUte9lkR6u7ecfhV3F8bHs6ZFJr/t9vPM0XT00x3awTqC593OgYbEYBwMzCvWwwNb1lkBoU5aqmtI4dGbeydNZfjEEr3nz0iLMamHQ1h8oW1UoVyxJEJoz/pd5aOhy6WfEswf3jZbxt0rOLdpk37f5pau7Whmi1CQjUNO3oItNz/0pHXqQ6EVF0d1PzTrLLPYln79v8iUaItK/VaDSo1+s9xInUuiXNitJqQX4NNsb0qFykNjWnTp3C8zzm5+f57d/+7UwN4k1vehP33nsvb37zm3ts6PPxhS98gfe85z0opXj3u9/N+973vp6/B0HAL/7iL/Lkk08yOTnJJz7xCW6++WYA/vAP/5CPfOQj2LbNH//xH/PWt771mlwnRrybdyxARVGE1ppTp06xd+/eaybr4fs+U1NTtFotTpw4Qblc5rnnnuNLH3yMVn0075qVhQaLs6N4Chk6nRApdZLl9H7mxZv244yV0dokvkiDqeeQlEw8G3XD1ij14sYSwhu84AwCLSoW9u6t7aJvPDiGUgGWEFQq1aF1+lToVUrJ3ZUqb6iNUyqVutJDtVpfKdcYw3LYiTOs1iqX2mtcbK3SlAFHq7s5OXmYeyZuYJe3gfZSLoJI8sdf/iZfPXOehXanC0zxBPPQ7HGzsMcs7JroE87dcggY36PZLJGPQSshVSS9QkMsUPzqySJjRS/JXHvfjzEgTdRrCKnDzMV4j+dw59jWCAL5KFhF3jDxDvYVtmb3ng7bHzx4kEOHDgGKQM8m5cFpjJG41m5Kzi2U7Jt7elVXG6n9e5ptNRqNgX0tz/MGghbA6dOnufHGG6nValiWxS//8i/znve8h1arxVNPPcWDDz7Ivffe23dupRS33nprj9XGQw891DNo++EPf5inn36aP/3TP+XjH/84n/70p/nEJz7B888/z8/93M/x+OOPMzMzw1ve8hZeeumla0WlH+kG2LElvjSulSdUFEWcO3eOxcVFjh07xp133okQIp6BanRGBicYjSCR6vNpbQaCE4DqBFCMqeva9BrG9RyXGBKKUA1l5g2NjoIhACWEwBGCPFfcNoLdtRq+lHSSf1IPLrEaE3/BFubrHDo8getsXE6ybRvbtikUCpwThn9/6wkOl8o0Gg1WV1d7Gt6pVNHY2BiTxTKThTL37r4xOa9hLfKzLOu/nX+KQEmqjsf+YpWaV6Rse1gipsa3ZMhq5HOpucZXvnOB+aU2Qerllegexpdh+ymQamgsx8Ipd0tvCZE/Aa2Y3qCS8u3QMDFhYmx8471mnEEJtLEwUmLZViYKfKEpOWZkVoa2UzWHRIrItVxcXNJBNmPIXIwDFdBWY0x6Af42zAYD7fOV5b/l3tobOV6+d1OgC4KAl156Ca019957L8ViutFwKNqHKNqHSKeJjDFEeoGWfBEhXCwKWKKAa+3GEtuniuft37fa1yoUCly+fJlWq0WhUEApRRiGvPDCC9x8883cdNNNPPjgg0PPfTVWG5/97Gf52Z/9WQqFArfccgvHjx/n8ccf5/Wvf/22r8VWY8cD1NV6QmmtuXjxItPT09x000088MADPTt827ZZm9+aSGywAUDpRIHAsi0c1yEYMi+ljUG2fdw94yilu0aCdJevfmaeQYQKUxj9tjC+QuwarQ8BoKShoC1qOSdQqXUMVlGUgFZEkGS4tm0jpYU14iBs7q3wkae/w//5ujewf//+TMkhXRjq9XqP/FC6m02Ba1ehyD0TN3DPxA3Zc66FPpfbq1xsrfHi2gIX26ushvEiq5Xh8lSTlZWAUCa9vrScdxXAlA+5qhGOwPK6vSWBiK9N7hSxl1YCXKRD0N1POvQFYWDwNqjmGkDJuDTpOE42iySEoKmgY5fYX3PjjElKpJIEfkBLxRJEtm33GEI6loND7GR8xRe8cfJBbijWMnuS1F+rpTb/rmij+fbaPzPjn+Xk+I9Ttsf6X78xzMzMcPHiRY4fPz60/JUPIQSeva+n3BdXAZpo06Er9SQQeBvOCY5yrs36WmfOnGFlZSVzXfjQhz7EwYMH+Yu/+Avuvvvuof5P+bgaq43p6WkeeOCBnsdOT09v+z1vJ3YsQOXljrZjuZEXjz1w4AAPPPDAwNRXCEFjaQtOukMYfOmciSVErIwgRGYJP8grSAgBYYRlCYJQ9zTNMypeMgiaf6Tw5ZYACl8NVa0YFs1Vn0KpC2qOZTHmeYx5HmEY0m4Z7GoRXJeOVHRkRNiSlGpb28W2wogPf/sJfuP+17Er2TnnF4Y8aAVBQL1ep16vMz09nYlx5suDtWKRO8cPcOd4l07eiAKmVhf5y689RWtVImW3PHOtgCkNYyBaVnj77A2f20Ik0lTdBdQkoKUSZl6nIXC9ftq4gWxY27ZtbMseWIs5u+YzUbTx7FjR3nWdTIE+zXyllPHn2W6jjca2uqD1qctf5n858iA3Fo9xY/FY9ryB7iRGkF1vraZcGZgTzgbn+fz8X3B79bXcVr0fR8T3VKvV4tSpU1SrVU6ePHlV3k5xFaAfALWJMEb1ynphXTW5xvM8JiYmWF1dJYoiTp48SblcZmpqioceeohPfvKTuK7L6dOn+dVf/VX+4A/+YKCL7vdL7FiASsNxnIzeOUpsRzy2uTj68wedqK9pqqQEBI6TF9s0ce8pITykbqvpgKYxBhVGdJodpLB6hme7Q5xkoJVSnkWgk5LgiO1GAwQ5A8MRorEWsPtAtefLrKSi2WpiWRa1XbUsCx1LdvkF4fCeNzzAcuBzqb7GxfoaF+t1Vjobl4nmWy0+8NjX+V9fcz8HxwYTYYQQmWFhfjeblmDq9TozMzP4vo/neT3lQdt2+Mw3XmTqygpGGQoILMeLM5Ck/Ka0GThbtJ0wCuSKxtm9tcVQkA5CJ6BlYEwW2VWzCVRsT9KRAZ0oQFgC13UHAlMaUhtOr/q8anep73UIAY5j4zg2EH+AxoDWMWhFMmLRX+K/PvVxfty9h4O79mebgGKxyP7CEfYXjuTOFbsYL0dzGXjVExdjaSTPNr7OVOs7HC/fhz2/i/pyk9tuu+26SgFZor9qMKh3FF+P0T+ntbU1Tp06xf79+7n//vtjgtXTT/Oe97yHt771rXz0ox+lUCggpeSll17aNDO8GquNUR57vWPHkiTSHd7KygpXrlwZSZ13bW2Nl156iUKhwPHjxymXB0varI//+zc+RONSv/DooFhdaLAwu5YMQMbZiWM7PWKh6Y9WO8y+FIP6UAaD2TeJVSnlvjjZ4E2v4kMSwhLYx2LCiNJxT0Nr0zuQuC7ELhcxsbXs5oabxylXPYw2tNotpJRUK1WcIbp5AD9y68288zV39PyuEQZcqte5WF/Lfi4PAC3PtnnbsRO86cjNOFch8plmWo1GgzNz8/z5v55mrp1oH1pxn2aQNE/ch4mzF5WQRkbeBAwIp2bhjF2dWKkQ/z97bx5fV13n/z/P3ZfsS5MmadKkWZoutE26IJtVFAQB/SKyiAMuIOgoRZxhGRV0BGTxJ6OiLCMjDIrKCA9B7MDIUlBEugCldEnTpmmz3aw3d1/O9vvj3HNyb3LT7E2h9/V45FFKbpNzb24+r/N+v1/v1wtqy3KxW0yEwmEkScTldiObVGJyPBFTIhJXRMb71a/Nd7DQPb35jKqC2+Tgk1lrsYRVAoGAMYdJDoPU5dXJkFUJnzjAsNSPV+ylJ3CEbl87DoeD+vxVLHavYIFtkbbrNY/QfzdH/+6MeT6yTFtbGz6fj8bGRtxuN7FYjHvuuYctW7bwwAMPpBVBTARJkqivr+ell16ivLycdevW8cQTT7B8+XLjMT//+c/ZtWuXIZJ4+umnefLJJ9m9ezef+9znDJHEmWeeSWtra0YkcSwxmdDCUChkhNI1NDRMWZIeHAyDKkxKfBAOxVKSalMVayPkIoqSFiyIkPaXUEVTzZmiMXAn3+WOEJ0uTU/+uigqakzC5LBgMZuT3iAqippo/4wiLTUiI+RP6SUh6I0imGWi0Sgul2tShrx/P3CE0+oqKc4emV9l2+wsKypmWdHInWQwHk9UWf5EpaWR1h/37+PvXR1srFzMhrJyHNNo/djtdqJ2O1s6O3nq3QOEwnG06tacqHZlZPQFaFNKxLnF+DmNiBxGDHNH1I6TgexXMNlH5lHTgaLCkV4/RS4V1yjvQqfZlvQ4NSlXSzTIC1TahmPk2My4p5CtpUMQIKxG+VNoGxeXf4TVTq3Vp89h/H4//f39hMNhzGazQVi6XLvAVkq2UEi03UxZtICPLL0EyRrBK/bRE22jLfwuLnM2xbYKFtgWYTUd+5Tc8Xw/k+H1emlpaaGsrIzm5mYEQWDHjh1885vf5MILL+S1114blaE1ecwkamP58uVcfPHFLFu2DIvFws9//vNjHuR4wlZQiqIYVke7d++mubl5zGOSPfrq6uqmJUVXVZWbz/sBTptzQveISDTK4ZZejXSS3wj6z0jfN5JkYnEJVU2n3SMxIE+07Jx2zOWT3fFIkFahEyHXDghJB6wpTcU1QlpZS3KJoRCJSylx8umgRX7LlNfm4Xa7ptQCqSzM4xtnbphyFRSKx+kIjBBWTzBIvsNBfUEhlTm5lLjd5NjsmEd9XUlR8EYjdAYCHBr28l5/H4eHhjlyeAhRlDGbLZgS1a0uJQcSP6uR3SL9Z6hHZ2hODmkqLUZMcyciLcHMhPOo8aCoiQBBBCqKsynOm5rLg6qqxBWJmCJiM8P6kly8kh9RnZ7gyCSYOLOoifV56e3GJEkyxAN+v59QKIQoioiiSHFxMRUVFYmW69iY+ZDswycNaHZMgh2LyYbbnIPNNLnVgbmCJEnGSsqyZctwOp1EIhHuvPNOtm7dykMPPXTMcpfmAZkKajJIV0FJksShQ4fo7++fsUeffzCIFJNQrCpp7z1UVVtADYU080xz8o9kNDFJRhy7qlvl6I9SdScJNeU2Qo3FpyBi0KTopriK2aoptPRDVpYl7ZskDladtEyCRqYuyUTpghxARZQVIqJINC4REbUPWUn+OgIWswU5JiBMIfMJ4MjgMP/33gHOPal+Sv/ObbOxtLCIpYUjy7fJpPV6ZwedAT8hMY7NpNkuiYpMaJRprnc4SE93QHNbsFgQ4ipKWEaNKahi0gtvEhBsAoJDk4YLFiHxemq7RXr7FFJJK51pbjJppcSTyJqyz5I/+XmUSrLjuKbO6x2OkuO2YZ9CFSQIAnazFXsiwTgey+ebdWfjl0N4okN4YoP0RIcmHQapqAp/6d9OW6iHc0o2kGdNragtFgv5+fnk5+cTiUTYt28fbrebhQsXEolE6O7uNpZi9b0iQ65tzSPLMuK6oKoqUSVMSPZjxpxYbhewCLZj1hIcHByktbWVRYsW0dDQgCAI/P3vf+fGG2/k85//PK+88sqMxB0fFJzwFRTA3//+d0455RQURaGjo4OOjg4qKyupqKiYcSjZ3jf288itv8HpdGIZVaZLCS8us9mM2+0mMByhr3uYpNtwQDN4VWQFs9mEyWwmGhWRdSlzourRSEo1yCoZ5ooSBMfk5wSC2YS5piDNoaemkJaWQaWRlsNto6SuAIvFOuY1UxQFfzBIOB5HsFiJyyoRUUQwC1TWT905XkDg8g+dRHNV2ZT+3WQQFsXEPEtrEXb4ffSHw4iSiKfHRyAgal5qEQXVL6Omz2YffcGYsswIOWOrnWTSSh6y6w7vBnGN+pLJpOUqtKE61XGd3nXo6jyT2aTFniR9zmm3ULMwe4rRFqlYV7iIK2qaUn6eqqoyLAbpiQ0amVqe2BCRNGGQOiyChdMKVrI+fyk208jvjP77mc4/L/kxoVDIqLQCgQCSJOF2u1NahOmETXKSY7pgiPjTu65MF6Io0traSiwWo7GxEYfDQSgU4vvf/z67d+/moYceor5+ajdf71NknCSOBjVRuQC8/vrr1NTU0NbWRklJCYsXL561u5eXfvNXnn/sJex2uxFhLcsyoWAQVVVTQs662gcIB6PoPztFHjlQzCbN2FOWFKITLPKOJi1TYR5C3lip7NFgqcpHmJTcXDUO2aK6XFRkFEXFbDZhsViQEzcCbpcr4eIwMgOLywobVlXizLXTOeSjY8hPZJI7aXNJUskQRZE3dr3H/7x7gAFRJRISCQ5EkOPTcHEwC5jyzJhcR69UxictIZW4Eo8XBIGqqnysNhMxWRoTT6IkVhRAc3wf78AtznVQWjA54c94OGNBDZ+tWnnUQ10LgwzTExtKSTEOyqkhnS6zg5Pzl7Emtw4xFGPfvn0UFBRQXV09pVmIqqqGk4NOWskL28kuI2OdMcZpr06DtPr7+zlw4ACLFy+mtFRbVXjttde45ZZbuPrqq7n22muP+YxnHpEhqKNBJ6jBwUHeeustysvLWbJkyay7mj/+/f9h19/3JnZFrIneuYQ7y40tUVFprS+F9n2elD0UQRCwmM0jUnBVszaaqsONJceNrbxYkzsrCdlzmoiOZJiL3JimeFgVLMolp9iNSiJWJBw2ZmnJXnkWswWL1YJJMLEg3831l55uKJ0Gg2E6vH46hnx0Dvnp8PqIiumdPgQEPtpYzSdW1s1ImZcOqqrS0dnJczveoyUgogpmBnoCBH0x4/O6uEGfFU1WlWfKMiPkjU8Uaa8HDOuhsaQl4HRaWVSZnzJDU9Fyx4LRCILdhiSoCeIav9KqKskixzWzgL2Tiyq5rHr1iKR9kghKEYOsPAny8sYDxMMxyuRczqzZQH1B1axUNMlODjppJa8R6NWWyzV2RpquZX60Nno8HqelpQVVVVm6dCk2mw2/3893vvMdjhw5wsMPP2x4351AyBDU0aAoCm+88QYWi4VgMMgpp5wy43beaKiqyj1X/Jyh/iEkSXPhdjpdOBx24/M6/N4wvV3exM4T2nxjVKskGhUN5/KpQLCacdUtSv0FUvVBeWKmMYq0BKcVy6KpuSU7sm0U1eQRCgYxmc24Xcn5NhoJS5KY2IUZMXg9e301p65aQk5Ozhi1kqqqDATDGmF5/RwZ9NHl9RNNsqcqy8vmnJV1LC9bMCuH1+CQlz/9YwfvDYWQTFZCgRgD3QHkCV77qZCWYDdhKrLMaJl3NGm53WZyc63aeweBuBjHYbfjdLlSVhBUIK5ojh06YUVlEUVVMZsFlizMmdI8Kh2W55VyZU0zrgnsqY6G/v5+dh/Yh6M0GyXHgifuJSLHKLHnUeuuYJGzOGVWNxuIxWIppBUOh7FYLCmVlsvlGnNWjEdafX19tLW1sWTJEhYsWICqqrz44ot897vf5brrruNLX/rSrJ877xNkCGoiDA8P43Q62b59OytXrpz16mmw28v/d/UvCAaDWKxWcpPk6aOXcdtbPUTD8TTSci3OPBabHjnpcNVVYLJNcFiojFRZqop1SSGTGbGAXgXK5FW7ycvPwTKpg0n7NyZB5eLTa1DEqNYOdLtTlmHTkVZ/IEyH12dUWp1eP/luB81VZaysKGFB9tjdmaNBUhQO9PTz4lu72NPnxWJ3AkJK1TQdqKq2pCsrY0lLsAqYiqwIltmbcZSWZqGqcRRFwWIxI8taaq7FnLAcsmp/ptN/xhStPWizmFhRUYgn5h83nmQyKLZncXXdespcU1vLiMVitLS0ANDQ0DDm9zImx+mNefGKQWwmCw6zDZfZQbEtd05EDqIoppCWHquRItpp6nAAACAASURBVMRIatXrz2Hfvn2YzWYaGhqwWq14vV5uueUWvF4vDzzwQMK09oRFhqAmgh65sXPnTpYsWTKpXZzJYnBwkBf/Zwvbn96FxWIxLPf111ufM4TDYULBCN7esJbflPixqfrOkaQgSdOYd4yCvaIYa+7Unl9xfQnOwiyicU2RF41pf4op15MQcSiaNU5eaTYFFVPPqVlRU8Lnzl4DQDgcNmyH/H4/siwbcQX6x+gZoaKq9AdCdCRmWcPhKCZBINdlJ9/lJNthw261YDGZkBSFuCTjj8TwhiN0DvnZ3+UhEArjcrmw2W2EAzH6uwKz8tqPhToSSWIG8wILojDzXy09N6iyKpcs90h7VkVbTdCd3iVJSiWtxEcyoS8tLOKaNU34xdiE8SRHg0Uwc255A2curJ2w5aeqKl1dXXR0dEzaP0+HqEgMi0HMggmLYMYsmLGazCkii9mEJElGXHwgECAYDALgdrtRVRWfz0ddXR0lJSWoqsqf//xnfvCDH3DjjTdy+eWXz3rV9KUvfYnnnnuOBQsW8N577435vKqqbNq0ic2bN+NyuXj00Udpamqa1WuYIjIENRF0gtq9ezfl5eWzEgAWCAQMS/q217rYtWWvISO32+1YrFYsZgvxeIxwJILDbsc/GCUwHNaWXxMVjCJPPCeaCiz5WTjKppYW6y7OZkFDyZj/L8sKkZhEMBQhGI4gKfoSsoDJLFCxogTTpOK9U3HuKUs5fXX1mP+vqiqhUMggrEAggCzLYyqtdKTV50+Qlnek0hKTAipFUSQU1DKmnC4niqIy6AkS8E7O+WM2YLGaKF2ch2RSiUqiJs2XRGKTDNJUlMROk2DCbDHjsFtYtCjP2M9KBzVRvUqiZJi9aq4lZiO9uGlhGVevaU6da6njx5McDWXOXC6sXM7S3PQ7ecFgkH379pGdnc2SJUtmRaQkqzKSqqAnpOmKvKnOxiaLUCjEnj17DL/HF154wbAmMplM3HrrrZx55pnk509xq30SeO2118jKyuKKK65IS1CbN2/mZz/7GZs3b+bNN99k06ZNY0xjjzEyBDUR9Eyo/fv3k5+fP6U7ttGIRqO0trYSiUSor68nJyeH+65+iMCQdmclyzKiJBGPxRBFEUHQ/M5URaD3iC/h6znauTOVsGRFy+WZDtLOoSaAyWKmcv3iMXMSWZINebzL5cJkNiFJilFlldUWIjtMhCJTbw9ddtZqTqpdOOHj9Iyd5EpL34FJnheMVkXJikKfP8RBTz9v7T9IXzBCxGRFVlVC/hiDniCSOBdV09FhtZkpq87DkjT7UVQ1EUuik5ZETB6ZvanqSNVktqRaLGVn2yktzZ6iEEMdydQSNdKqc7r5VOVi8nNzjdc0Xcs1OZ7kSHiYztAwPnEsyTfkLOCssjrqs4vQk2YPHTrE4OAgS5cunfXg0DHPMdFy1UXkOmYyu1RVlc7OTrq6ugz5u6qqPP3009x7771ceeWVFBcX88477/DWW2/x2GOPUVVVNfEXniLa29s577zz0hLUNddcw8aNG7nssssArXW6ZcsWI/5jHpBZ1J0sZpIJJUkSbW1tDAwMUFtbS1GRtgjaub/bICf9zR+PxRAEgfz8fEwmE5Ik0d0+iKwk9mkEDIcBbXkTzGYTZrMJEmeCvn+kyIpBXpNR9amijBoTp7QPpUgyUX8EZ57WLlIVrZLRq5dk3zyLxUSWxUaWy0YOVq75pzMIRuJ09vno6vfR2e+jq99PZAKJ/O9f3Ek4GmfD8sqjHhrJGTtlZZrUXN+B8fv9eDweWltbU0hLH3BHhvqxDPdx+alNFBYW0jPo5w9bdrEn0EuOzU5EkIiKIlO7d5sZxLhMd/sw5dX5mC3aHb5JEHBZrbisVkgYPeik5QuHCUSjKBYLUpr7xkAght1uoWAKSkwhsUBtMVt0j1f6gdciIT5dWMjAwACHDh1CFMWUlmt2djZ5Nid5Nue48SQdoWGOhIdp8ffR4u+jwpXLalcRzl4/VQvLDWPUuYYWZz9WzDBdk9dwOMzevXsN53Sz2YzH4+GGG27A7Xbz8ssvG2fCFVdcMTtPYhpIF7vR1dU1nwQ1KZzQBKW/AaeTCaUoCp2dnXR0dLBo0SJOPvlkBEEw7mhbd7Rpf1cUwsGgcagn330OD4SJx+SRdoY6ErmtJJGW4dyQiOA2mwXMZpPOWdrjZRVZUQzySne4SqEItikQFECwL4Az10UkEiEWi2kzGpvtqPc/A4MB3n6ng3VrF5OX7WTFklLjOof8Ebr6NdLq6tNIKxofuTlQFJVnXttDR+8w55yylCzn5IUryYNr3XVZURRjVtDW1obX68VqtVJQUEB33xAvbG3jvfYBVBXyXE70Jq+KSkyUiCacMCJxkagkzSlpiTGZnvZhFlbnaTclaaDKCmI4TLbZTGnxgkSooDoSAJnI1IrJEgMDIaxWM9nZMxP/7PcN83tUrl7dRIPDacxO/X4/g4ODKaSVkqllc5BrGxtPcsg3wLa2FrYOHkTJcVIh9rHaa2FZbsmMVH/TRToimqizpKoqR44cwePx0NDQQF5eHoqi8Jvf/Iaf/vSn3H777VxwwQWzuuR7IuKEJigd+n7SZKBLRw8ePEhxcTEbNmzAbDYbu0ugveFbth0kFAoRj8eNQ11/s6qqirc/wPBgMPWLp/NnSyYtSSO/EdIascgxWwTMScm1qpJMWNp/y8EIFE4tgiDQF8BcYMXpcmozukn+vm15tYWVK8pxOEYOHEEQKMx1UZjrMtp4qqoy4AslKi2/RloDft5q6WbPoT5OW13NusYKctzT800zJRzG+/r6sNvtnHrqqXT0B/jbOwfZdaCVuCimSPstFgtWi5YO67BacVitY0hLs29KCEdmmbRiUYneIz5Kq1JnSDopiKJIVlZWyowmtdLSSi2dtOIhmbqyfIKqiCcUnPa1Hvb5uOuN1/niSatZWliE2+02rIb064tEIvj9frxeL4cPHyYej+N0Og3Sys7OJjQ8TOjQET5ZvYySkhIEQSAkxekIDfOPgSNYBK0Sz7c7WeTKm/X9tsniaMQSDAbZu3cv+fn5rFu3DpPJRFdXF5s2baK0tJTXXnttTuZMM8HxEJ0xHZzQMyi93z4wMMDg4CANDQ1HfbzX62X//v243W5qa2ux2+0J49ORPSIxJtLy7n5+fdtTOBMZQwYxKSrhUIzBPj+xacxndOgtCVVVEnswmkZBIyuTsbyZDgvXLSUmysSiItGohDJOf1BVVGNZuLi+hNyyqQtIPrRhCWd9fPnEDxwFRVHpHw4apNUz4MfpsFJTVkB1WQElBVnaAvME0Nuvvf2DOHIX4BmOsbutF19w7GxEU7pp+1mSJKWQljVBXOY08uy5Ii13to2SylwEQSAeixMKh3A6nUmR5ZOHw2rhnz+6geJsF52BAB1+Hx0BzcqpJxiY8rVurFrMp+oasE3wM0hOLx4aGqK3txdVVcnJySEvL++oDg5hSaQ/GsRiMmEzmbEIZpwWKw7z/N1TK4rC4cOH6e/vN+ZliqLw2GOP8fDDD3P33Xdz9tlnz1vVdLQZ1J///Gfuv/9+QyRx3XXXsXXr1nm4SgMZkcRE0AlKj/5esWJF2seFQiH279+Poig0NDTgdrsNYoKRu62BgQEOHjzI9v/ZTc/efhRZJR4XiUVExLhEPCbNqjIvGaqqGkubhpN5kpeblrAqULG6FnfRyCA6HpeIRkWiMZFYVCIajSNJ+uDdgiCAPdtB2aqp72wICFx2yXrq6sYqAacKSVbo9wbp6vfRMxggEhOJxSVcDhtupw2rxYzZJCArKqIk0+3pp9PTjyzYiMvaSHyy0MIeZSRJRopLSIqMigKCouVl6aRltWI2m9OSlt4ajMY1gUNMTDcpOjrcOTacOQIms4ksd9aMlnrdNhvXfmQdFfmpIoS4LNMZ8Bv+gx1+Pz3B4ISuGPkOBxc2NLKmpPSoB7KiKGNaYckODn6/n1gsZqQX65VW8o2djpgsGao8kzDyMVeqvGQEAgH27t1LUVERixcvxmQy0d7ezje+8Q3q6+u55557yM6emp3YbOKyyy5jy5YtDAwMUFJSwve//31jbHHttdeiqipf//rXef7553G5XPzqV79i7dq183a9ZAhqYugEFQ6HaWlpYc2aNSmfj8fjHDhwwIjbKCgo0EQJalJIoCDg9/tpbW3FbrdTmFXMIzc+keoormox7tFInFhYJBqJE4+Kc/5iaqSVHPkAOeUFFNdXjNl90Vs0sVgMi8WOokA0ppFXPC5RtmYRtmlY4DjsVq7+8hkUFLgnfvAUIckynsFg0jzLR0evl0AwiMVsSUR5THx4qYpKNBgnGogRDcaIR6Rx1ZI2pwWr24Ity4pg0VzJBUi8nlYsVsvRSSuuCTAmIi09EyyvyE1JRe6s3JW7bFauPmMti4uOXg2LBmmNENd4pFWVm8s5S+pYUVQ85hr9fj/79u2jsLCQ6urqcUUQqqqmBEH6/X6i0Sh2uz1lppWOtGRVGekgGHLymanykqEoijG7bGxsJCsrC1mW+eUvf8ljjz3Gfffdx8aNGzOzpqkjQ1ATQXc0j8fj7Ny5k3Xr1gHa4dDe3o7H46G6utros+sCCJ2YotEoBw4cIBaLUVdXR05ODs/c/zy7Xt07ie+tEouKxCJxohHtz3hsekrCqcBsNVO+vt4gZ1QVwaSJO2w2e9r0UlVVqWwopeHkGrq7h+nuGWZgIGjEfUyEgnw3n//cyeTnzz5J6dBvJgLBEDlFZQyHJE052Oejzzt29qKqKrFgnOBQhPBwZFouHTanhZySLFx59oSNk6RZOclygrSshnPDeKSVTFgagWkVrD47A8grclFQMjVnjPFgNZu54pRVrCifWlWrk1ZHwM8Rn9Ye9IQCyAkiL83KYmNlFWsXlmFF4ODBgwQCAZYuXTrtBfjRpBWJRLDZbCmk5XSOjZyfLYPX4eFh9u3bx8KFC6ms1FSlra2tXHfddTQ1NXH77bfjds/de/oDjgxBTQSdoFRV5R//+Acnn3wyXV1dHD58mLKyMqqqqoxdjeR2niRJtLe3MzQ0RE1NDUVF2k7Hu6/u5dn7n5/29ciyos2GwnGDuCRxcsuaU8GipjpcBdnGNrzJJCSk9vJIXpMl2WVAMza9+t8+SVGpJrKIxSR6PD66u4fp6dFIa8g7vtDE7bJz2aUbKJ/GLOtoUBSFrq4uOjs7qa6uNgbvyYiJEj0Dfjr7fBw8Msju3V30HPEixWfntbXazeRX5OLKHZkPqaqitQgT3oOSYf47YjekzdESzvWKTDAUQlFULHYHcVk25loxUSa/2EX+gtkhKQGBT55Uz0cbq2f09URZpisY0EIgfT46An66vF6KZIXTqms4Y2kjtlnONIrH4ykL2+FwOCUiXl8lOJqZa7LRbjrIssyBAwcIBoM0NjbicrmQJImf//zn/OEPf+CnP/0pp5566qw+rxMQGYKaCMmRG3qscn5+vrHJPpqYdCuWzs5OFi1aRFlZmdG26DnYy2O3PokUn90qSJJkYhExpT0oyzNbJM1ZWEBWZSGKomj7TKMOET0cUUxUBLIkgyBQu2Ih519x8riHQDgcx+Px0dXtpadHIy9fIGJ83iQIbNhQw8YzGrDZZn5w6aIVvYU0XlSBKMq07Pewc2cHB9v6UdGUjdG4lPgQicYk4tLMCMuZa6dwUR4WW/rr0EgrSYiRIC0AJWHnpIkgUl9XRVWIijK1NUXkF7vp9Prp84cmXcGOhxXlJVy6fgVu+8zcy2HEP09SFLLLy/BEI/SGQritNvIdDuoKCih0zizKYzwkR8T7/f4Ug1eduNJ1BtJhaGiI/fv3U15eTkVFBYIgsGfPHjZt2sTpp5/O9773vWkJVTIYgwxBTQRVVRkYGGD//v0MDw9zyimn4HQ6U+Iu9Dd1f38/bW1tFBcXU1VVZRzqqqqy4//e5S+Pvqod5MfgmiVxhLT09uCkxBeqJjdHgJrTV+BwTj7mWz9cz71iDTa3tqCoRxMcbUYQDEbp7vHR1eU1Ki5VVWlaU8Wa1ZXTmk3prh2yLFNfX4/LNfbgkySZ9vZBdu/pYl+Lh2hsYtWkLKsT+A5ODJNZoKAiF3fB2NbTaIiSFlhpMZsxm81IsjwSs5KsHkyqtE5fXc05H2ogLst0eQOGy3vHkG9apJXjsHPxuhUsL09vQTQRJuOfJyoy3YEAEUnCbjZjN1twWCzkp3m/zBaSDV510jKbzSmSd7d7xG1fkiTDCaaxsRGn04koitx3331s3ryZX/ziF/MtKvigIUNQE0GSJLZv305NTQ27d+9m/fr1Kf1rWVIYGhhi354WBNlESVEJLrcLQYDAUBBPez+7X29huNc3j89COyTEuEQ0rM+04sSiqU4IKeGHZhNlK2vILpn6rkZRaQ5fvPETWK0W4vE4Pp/POAT0wXZubq5BWqOdqFVVxe+P0N3to7tnGEmScblsFBVls7A0l9zc8Q92Xebb29ub4toBWnu0r8/PkSNDHDo8wKFDA8THyZGaCmRZs3CKxEYqLWkSFaw730nholxMlrHCAEXVHC8UWRnjgg3azYBeZUliotIyCQZhrV9eyUVnrh4jtY+KEp1eP11eP0eGfHQO+egLTG6/b0V5CZ9a3UBR9uRvGGbinycqMsF4HIvJhFkwaWo8k4DVNHeBfTpp6cSlu5JbrVYCgQDl5eWUl5fjcDjYuXMnmzZt4pxzzuHb3/522gTe2cDzzz/Ppk2bkGWZq666iptvvjnl80eOHOHKK69keHgYWZa56667OPfcc+fkWo4xMgQ1GUSj2k7Mjh07sFgs5OXlkZubi8lkoq2tDVmWqaurIysri3g0jqetj64DHroP9NJ90IOvzz/PzyA91ERERzgYxe8LIsV1SyTtfeEuyqFide20vnbz6XWcffG6sd8zSY2lE5e+qJxcaaXzchsaCtHdM8zgYIhoVCQSjWO3WXG77djtZsLhEB5PD/n5+RQWFhGPy4RCMXy+CEPeEAMDQa06PAZI9h3UyEs0xALJsNjNLKguwOYyPD+0IMdIFJfbhd1mY7Lyd0VVEsauIpIkU5pn4+NrKigsyDtqBRuJi3R5/XR4/VpqsddP/zikZTGZ2FBTwceW1ZDnGr+6lmWZQ4cOMTQ0NKv+ebKSWJEQ9LD1EReVuYAoiuzdu5dYLEZBQQH9/f189atfRVEU/H4/1157LZ/+9KdZvnz5nBCU3gH4y1/+QkVFBevWreO3v/0ty5YtMx7zla98hTVr1vDVr36VPXv2cO6559Le3j7r1zIPyHjxTQSPx8Nzzz1HU1MTy5cvJxqN0tnZaRCTw+GgoKCAQCCAIAi4XC4ql1VQuWxkJyjkC9N9sJeeAx66D3joOtBLJGnuMl9QVAVRimGyKlQsXpCYqakpqkGH3UQ0NvVDfcdfWylbXMTK9anO44Ig4EgsJy9YoLWMdPm6z+djYGDAeG1dLpdRaWVnZ1NYmEVh4YjaS5YVBgeDtB3qZefOfQx5Y4iiBRUP4JnRazNTJPsOAqBqe1rRmKip8hLVlhST6Wnpp7AyD0eejWAwiNViIS8vd1Ly92SYBBM2m804KMMKvNEW4fyShYRCIXp6egyV2+i2a21JIbUlhcbXisRFoy2o/zkQDCMpCq8fOMKbbZ2srlzIGfVVLCpIdR7RZzQLFy6cdf88s8nE6PppJj55R4PuBlNTU8OCBVrQpdfrJSsri0996lNs3LiRnTt38pOf/IRTTz2Vq6++ekbfLx22bt1KbW0tNTU1AFx66aU888wzKQSlr7EA+Hw+w3fyRMEJXUH19vbyyCOPsGPHDlpaWpBlmXA4zLXXXstnP/tZiouLjXaAz+cz5i7JLazRA1NVVfH1B+hOEFb3Qa3amm3xxHjQLHEixOOxMRZLo7HyjEbO+vJH8BwZovvwID1Hhug5MkhgeGKCFQT45OUnc9KGmmldo27q6vP5CAQCKIqSIh92uVy0t7fj9Xqpq6sjPz8fWVbo7fNrUveE3L2/PzDpqPVjChVESSYSE/EFQthzLOQtKkBhdpdKnQ4rl3xsFQ2V2uxHr2BHt12TSSutc0NcTMSRaHlaHUM+BkNhyvNzWLe4nBULi/B0aPZFS5cuxTmF+eVsY6qR68mIx+Ps27cPQRBoaGjAZrMRiUS444472L59Ow8++GAKQcwl/vCHP/D888/zy1/+EoDHH3+cN998k/vvv994TE9PD2eddRZer5dQKMSLL75Ic3PzMbm+OUamxTdZvPrqq2zatIlzzz2X1atX884777Bt2zY8Hg81NTU0NzfT3NxMU1MTdrudQCBgtLB0A9WjtbBkWWGgc5Ceg710tWrE1d8xiDJDNV4ytPZanHAkrMV8p9kPGQ3BJPDPP/0ieSWpd8kBX5iewxpZ6cQVDacPqTv17OWc9okVmC0zmx3opq4+n4/e3l58Ph82m43CwkLjhiB5qK1DFCU8ngRpeYbp6hpmcCg4znc5hlA1sgiHtRBEu91OeVkeHz1rGf5IPOHu7qO73098FlYJTl21mLM31GNN83PQ7YaSl2AdDseYJdjRCMXidA752HXoCHsOd7KgqIj6ioUsK1tASc7sSN5nCxMRlKqqeDwe2tvbDTGHqqq88cYb3HjjjXz+85/nuuuum5UcqsliMgT14x//GFVV+da3vsUbb7zBl7/8Zd57770PQkx8hqAmi/b2dlwul9GW0qEoCq2trWzdupWtW7eyY8cOIpEIy5Yto7m5mbVr17JixQoURUkRC8iybEQ85CZydMYcrHGJ3kPaPKvnYC/drR6GPMPTun5RFAmFQpjN5rSH+NGw5mMr+eQ1HzvqY1RVZXggSPeRQYO4PB1DiIk9opKKfM78f2uoqhu7gzQV6GGPLpeLJUuWYDabU5RYegZVdna2QVrp5O7RqEiPx6ftZ3UP09U9zLAvPO3rmiqS87LcbneKRZHbZefC/9dETbVW8SiKysBwyCCszj4fPQP+KasHAYrz3Xz6wyuoKSs46uOSZ4XJdkMOhyPlRktRFPbu3YvD4aCurg6r1UowFqdryE8gFsNps2K3WCjKch51ZjXfiEaj7N27F7vdPvI8gkG+//3vs3fvXh566CHq6uqO+XW98cYbfO973+OFF14A4Ic//CEAt9xyi/GY5cuX8/zzzxtRGTU1NfzjH/8Yc1a9D5EhqLlAPB7n3Xff5c0332Tr1q3s2rULq9XKmjVraGpqYu3atSxZsoRoNGqQlj7DSj5Y0+1lRIJReto0stLmWR5Cw+MfrLIsEwqFUdX0+0yTggBfuP1SKuqnlgsjywqDHp/RFuw5MoTVZmHlhmoaTlqEYwq2SKIocvDgQYLBoBH2eLTHjm67Wq3WMW3XsTtaMUM52J0grkAa09gZQdXk9/F4XHMct6b/eQgIfGRjA6edWpeW0GVFoc8bNOJIOvt89Az6kSfpdtHUUMbHN9STlzWVNYIRY1efz0d/fz/RaJScnBwKCwuN9246sUAwGiMUF7GaNT9EsyDgsFnnzYlcR/LeYl1dHYWFhaiqyquvvsott9zCNddcw7XXXjtv1YgkSdTX1/PSSy9RXl7OunXreOKJJ1i+fMRg+ZxzzuGSSy7hC1/4Anv37uXMM8+kq6vruKpep4kMQR0LqKpKIBBgx44dvPnmm2zbto3W1laKioqM1uDatWtZsGBBysEaCoVSDtbc3NwxswFVVQkMBo0qq6vVQ89BD7FIfNJzpsmgsCyfq+79PNYZLs+KokR/1zB93cOYzSbsTit2p43SinzszrEHW/IOzeLFiyktPbrx6HjQ3QX0G4LkuYv++o6WuwMEAlFjlqX/GY6kb2VOeA2xOOFw2BCJTObXb0n1Aj79qTVkZU2c1yTJCr1DgaRYkmE8Q8Fx99+sFhMfWlnFaauqyXZNPg/K5/PR0tJCYWEhixcvTnFu0FWZTqczpdJKR1pRUQLUkQBONBHEsTpYI5EIe/fuxeVyUVtbi8Viwe/3853vfIeOjg4eeughFi9efEyu5WjYvHkz119/PbIs86UvfYlvf/vb3Hrrraxdu5YLLriAPXv2cPXVVxMMBhEEgXvuuYezzjprvi97NpAhqPmC3u/eunWrQVq6r59OWE1NTTgcjpR5VjQaNX759YM1eZ6lqio9PT3s2r4bU9SK6JPxtPXhOdQ343nWyec387ErzpjpUx+DWFSkt2OISFjzmItHJcwWEyarwoC3h4XlC1hSO7UdmmRIokw4GCUUiBIOxhIfUUKBCH5fgGAgRCgURlEVnE47ufnZFBbnUVpexIKyAnILRipZVVUZ9kVSRBg9PcPEjiJwUWTFODzcbjemcYIGx0OW286nzl9Dbe3UWzaiJOMZDGgmuQmz3L6hVN9Bq8VE89IKPrSyigX543viSZJk+Oc1NjaO6zGXnPukf4xO2E03hwWQEsa6gpDqSjibpKWqKh0dHXR3d9PQ0EB+fj6qqvKXv/yFW2+9lU2bNvHFL37xgzDDeb8jQ1DHExRF4cCBA0ZrMHmepbcG9bgPnbB8Pp+RxGuz2RgaGiIvL48lS5ak3LVKokTf4QG6D/ZqysFWDwPdQ1P+aX3iyx9h7SdWz+bTHoNYLMaunXvo7fTiMOXg7QvhGwqhquBw2XA4rVjtmv+ffthrS8aKFlkS1Vzho+E44WCM2AQR8snQDXJ1fzxFUXE4bZRVFVK9tIxlq6upqClOObxUVWVwMJggLK1F6PH4EEXZcH93u91YbTNLgl3btJiPnbkMu31mVWxMlPAMBFJmWgPD2utbU15A89IKlleXYE+qlvv7+zlw4ACVlZWUlZVNmTCSE3b1LoEoirjd7hQhRjrSmk0JeSgUYu/eveTm5lJTU4PZbGZoaIhbbrkFn8/HAw888L4I6TtBkCGo4x3J86xt27axa9cuLBZLyjzLZDLxpz/9iQ996EO43W5jsdiI1c7NTTvPioVj9LT10tXaS89BbZ4VMQDq9AAAHV5JREFUGJ3gmwbnfe0sVn9k6iGDEyE5F2jJkiWGwS4k3CW8YWOW1X14EM+RoSmRz3Qhy5pbg+7cYHOYqVleysp11dQtryQ7O3uM08PAwADbtu8GnEiSDY/HT2+ff8aLwrk5Ls4796RpVVNHQzQu0j0QMCJJeoeCFOW5qavIh8gQTruV+vr6tG3Q6SKZtPQPSZJwu90pHnnplranSlD6e6u3t5elS5eSm5uLqqo899xz3H777dx8881cdtllmarp+EKGoN5vSJ5n/fWvf+V3v/sd/f39rF69mlWrVtHc3My6detYsGCBIcnWLVt0c0y9NZjWF88bSuxleQziioZiY66j+ayT+NiVH57xTErH4OAgra2tLFiwgKqqqnFNXUe/FkN9gYTMXSOu3k7vnLi7j/rOhgu5O8dGeX0uixuLKCjKw+VyMTQ0hCAIY3aBRFGmr8+vVVndXrp7fPT3B6Zl6Nq4dCFnfWw5eXlzY66qqioH2trZte8Q9uxC8vNycTqsFOa6KS/OwTxHB3ny/ptebekdgmQ38qm0e/UgweTMqf7+fv71X/8VVVW5//77KSmZeWBmBrOODEG9XyHLMh/+8Ie55JJLuOaaaxgcHDSk7tu2baOnpydlnrVmzRqcTmeKCEPfdUkmrdHDbFVV8fb6NNVgYqHY09aHJEoULMznI5edwtKT0yvNJoNIJML+/fsRBIH6+voZu0DLkky/x0fP4RG5e3+Pb85SinVYbWYWLsmmsNJKcWk+kiSlGI/m5uamlbvH4xIej9YW7Ooapsfjm/SOltViZsP6Gk750BKcaQQm00UwGDTaYLqUX0coEmdgOITZLGCzWDCbBXLcjrS7VbMFRVHGVFq6y35ypTWatBRF4dChQwwODtLY2Eh2djaqqvLUU09x7733cuutt3LRRRd9ENRuH1RkCOr9DEmSxr2TTDfPCofDY/azAOOX3ufzIUmSYTGk72eNrmZkSaa/YzBRZXmQRImK+jKWnVKPO3dyd/R64OPAwICRRDxXEOMSvZ3ekUrr8BBD/YFZ+/qSJBIMhrBZrbiz3axcV82HPr6c3EJXyqGqqzInqmIjkbjh6q6pB334/OOvEjjsVtavq2bD+hpc00g01jFd/7xwNI6sqJhNmieeySRgS+SDzRUURRlTaSmKYuwWmkwmOjs7KS0tpbKyEpPJhMfj4YYbbiArK4v/+I//SDESnm1MZPAK8OSTT/K9730PQRBYtWoVTzzxxJxdz/sUGYI6kRCPx9m1a1fKfpbFYmH16tXGPKuuri5l1yUQCKCqakolkG7RNx6N09s+gMlswpXjwOaw4chyYB6lWFNVlb6+Ptra2ow8nfno+0fCMTxHhkZ2tA4P4T/KPlk6qKpCKBQ2lq6TiVwQoLGpitM+scIIcATGSLIjkUiKzZC+SjAaoVBsjNw9OKr1arWYWb1qEevX11BUOLWE2mT/vEWLFs3oZ6KqKpKsoPGTYEStm0xzW6noBq5tbW0EAgFsNhtvvfUWr776Kvn5+bzxxhv88Ic/nPOqaTIGr62trVx88cW8/PLL5Ofn09fX90FYrJ1tZAjqRMbo/azt27cb4X76fpY+zwqFQsY8S3dASK4E0tkmiXEJQcCwOAoGg7S2tmrmpLW1cxZPMF0k2zfpxBUJpd95ikWjhCMRXC4ndvv4bUlBgKWrKzn1EytYME5SsH5DkOzYMNEekaqq+PUdLUPu7iMS1a63qrKQNasrWdqw8Kiqv3g8Tmtr65z7581WxPrR4PV6aWlpoaysjEWLFiEIWqz8TTfdhCRJlJWVsXfvXhRF4cEHH5wzv7rJuD/ceOON1NfXc9VVV83JNXxAkCGoDFKhqiq9vb0p+1nd3d1j9rNcLlfKPEuvBJKXivVDVRRF2tra8Pv91C6pTWkdCSbhuFVOqarK8GBQI6uE32DnoT68Q8OYzRbcbteUHMeXrl7EKWcvp7RiYpuhdHtEyTOXdEIBVVXxesMpVdbAQJDFVYU0NpZRu2SBQVbJvnPJbt3HEsnnSrJac6rXIUkSBw4cIBQKsWzZMiNQ9NFHH+Xhhx/m3nvv5ayzzjK+biymVZ6zqUhMxmT88z796U9TX1/P66+/jizLfO973+MTn/jEnFzP+xgZghqNyfSOTzQkz7O2bdvG9u3bU+ZZzc3NnHTSSQBjcp5MJhPRaJSysjIWL16cVjI8eoHYZD52bgKThSRJtLW1Mewdpii/jMBQzCCuvq5h5CksQdcuL+NDH1/GoiWTb+mMVrfpQgF95qILBUbPCxVF29Hq6h6mr8+P1WrGahUQ40MsWJBDfX192t2j+UI60joadPXnokWLjP2s9vZ2vvGNb9DQ0MDdd99Ndnb2XF7yGEyGoM477zysVitPPvkknZ2dnHHGGezatYu8vPRV9gmKTB5UMmRZ5p//+Z9TescXXHDBMbPWP15hMpmor6+nvr6ef/qnfwJS51mPPvoo7777bsp+lsVi4emnn+bWW2+lrKyMUCjE22+/jaqqZGVlGZVWVlbWGJdzRVHGkNZMndCni+SZ2aJFi6irG1Esrjp5CaA5VfR1DydmWZq7+2Cvn/Hu6w7s7ubA7m7KFxey4cxG6lZWjJnVjYYgCGRlZZGVlWXk/eju7n6/n+7ubgIBTfiRvPialZVFcXE2xcXZRtpwT4+HsrJyBJOdri4fVpsFp8NKfv5YleGxxmS/vyiKtLa2EovFWL16NQ6HA1mW+c///E8ef/xxfvzjH7Nx48Z5eT7l5eV0dHQYf+/s7Byz/FtRUcGGDRuwWq1UV1dTX19Pa2sr69aNDfnM4Og4YSqoyfSOM0gPfZ710ksvceedd+LxeIxo7OR5VmlpqXGo+ny+lHmW3hpMN8+SZYWUE18QJjzUZ4pwOMy+ffsMh+upzMx0+6YeI0drkOHB9Cm1ufkumk6v46STl+DOnqHMXpbHuLubTCbsdjt+v5/i4mLq6urGVFqiKBMIRDFbTJgTbVet2pqfG4OjQXe1SPZm3L9/P5s2baKpqYk77rgDl2tu9sMmg8kYvD7//PP89re/5bHHHmNgYIA1a9bwzjvvUFhYeJSvfMIhU0Elo6ury7CsB+0u580335zHK3r/QBAEcnJyeOaZZ7j55pu58MILAVLmWY8++ig9PT0sXrw4ZZ7ldrsNv8G+vj7C4fCkjFxlSQadyFR11uZZyRL4hoaGabVd7A4rlXUlVNaNLICGg1GjLahnaIUCUXzeMK88u5PX/ryLhlUVnHRyDYsbSqf1XMxmM3l5ecY1S5LE/v378fv9lJSUEI1G2bp1qyF31z+cTicFBaneeqIoE094DAqCpsYzz2P7NR6P09LSgqqqNDc3Y7PZkCSJ+++/n6eeeoqf/vSnnHrqqfNybcmwWCzcf//9nH322YbB6/Lly1MMXs8++2z+7//+j2XLlmE2m7n33nsz5DRNnDAV1GR6xxnMDIqicPDgQUPqvn37dmO4rZNW8jxLr7Ti8XhKBPx4IoHRC7mmxG7OZDEwMMCBAwdmRW49EVRVJTAcTkkq9hwZIhoRyc510thUybKmKhZWFU6LFPTI8nT+eRPJ3ccLKEyetemkpf/3XCG5zbpkyRJDjr1nzx6uu+46PvzhD3PbbbfNeMk7g+MOGZFEMjItvvlB8jxr27ZtvPvuu5jNZtasWcOaNWtYu3Yt9fX1RoCeLsIYHQGfLvQxhbRUFRKLpGMDDKO0tLTMmqPFdKHbN6UkFYdi1Cwro25lOYtqiiecx+nPxWQyGZHlk8F05O76NesQBGFaSrzxEIvF2LdvHxaLxRB0xONx7rvvPv73f/+XX/ziF6xdu3ZWvlcGxx0yBJWMyfSOM5h7qKpKMBhMyc/av38/BQUFY+ZZyftZgUAAk8mUMs9KZy+kKIoxzlIUhc6ODnr7eo3AuuMNI/ZNQwz1+XE4bWTlOqmsW0B+0YhCTVVVOjs76erqora2dsZOCXpAYXIS9GTl7jpRJWMqpKXHxhw+fJi6ujrjuezcuZNNmzZx7rnn8m//9m/H3S5dBrOKDEGNRrpwsAzmH8n7WbrfYHd3N1VVVSnzrKysrDFpujabbYy9EGiLnfv376e4uDjRztMqE0HQ21fHl9Q9GaKo2TdFgjGsdguiGKfLc4SFFcXU1tZOymx3Opiu3H0qpBWJRNi3b58RI2+xWIjFYtx999289tprPPjgg0YbOIMPNDIEdbyho6ODK664gt7eXgRB4Ctf+QqbNm1iaGiISy65hPb2dhYvXsyTTz5Jfn7+fF/uvGL0PGvHjh2EQiEaGxtT5lmCIIxJ05VlGZPJRHV1NcXFxWl3gZSkaAxVnfo861gg2T+vZnEtgmrBYjFhspgwmUw4nNY5l+jrvnjJlSyMlbtPNM9LrgDr6+sNf8Zt27Zxww038NnPfpZvfetbx9XeVgZzigxBHW/o6emhp6eHpqYmAoEAzc3N/PGPf+TRRx+loKCAm2++mbvuuguv18vdd98935d73EEUxRS/wXfffReTycSaNWtYvXo1bW1tDAwMcMsttxgR336/3/DT0yut8eZZ2gcp4oD5Ii19STXZ2mc0YpG4Rq5mAcEkICBgOQbS8fHk7smtweSMsnA4zN69e8nKyjIqwHA4zJ133smOHTt48MEHaWxsnPPrzuC4Qoagjnd86lOf4utf/zpf//rX2bJlCwsXLqSnp4eNGzfS0tIy35d33EOfZ/32t7/lhz/8IYWFhciyTG5uLs3NzTQ1NRnzrEgkklIFCIJAdna20RpMF/qok1Yy5tq6KR6Ps3//fkRRnLJ/XopzR0KFd6xIVpKkMe7uFosFQRCIRqNUV1ezcOFCBEHg73//OzfeeCNXXHEF11133Zy1LHVM1kHmqaee4qKLLmLbtm0ZccbcI0NQxzPa29s544wzeO+996isrGR4eBjQDpn8/Hzj7xkcHZFIhMsvv5x///d/Z8WKFYZsOdlvsKura8x+VnZ2dso8S4/LSPYbtNvt45JW8v+fDQJIFg7Mpn/esTByTYdgMMju3btxOp243W62bdvGHXfcgc1mIxKJcNNNN3HBBRfMeQT7ZNzHQQs+/OQnP0k8Huf++++fFYLy+Xz09vZSX18/46/1AURmUfd4RTAY5DOf+Qz/8R//MSaX53gf4B9vcDqdPP3008bfBUGgpKSE888/n/PPPx9InWe9+OKL3HXXXQSDwZR51urVqzGbzUaV1d3dTTQaNaTYOnFZrda0pDUaU/kZ6q4WTqeTtWvXzuocJt11jL5e/e+z8b7TLZf6+/tpbGwkJycHVVXp6OggKyuLyy67jMbGRnbs2MFVV13FFVdcwWWXXTbj7zsetm7dSm1tLTU1NQBceumlPPPMM2MI6rvf/S433XQT995776x83+3bt/PEE0+wdOlS6uvrZ1WefyIhQ1DHGKIo8pnPfIbLL7/ccGQoKSmhp6fHaPFlsmNmFyaTibq6Ourq6vj85z8PpM6z/vu//ztlnqXnZ61cuRJRFPH5fAwODtLW1mZElOuElU7VBmNJIN3hpB/mfX1903a1mA5GX0s6Bd50oMevFxcXs3btWkwmEz6fj+9+97t0dnby7LPPUlVVBWjt7WOByTjIvPXWW3R0dPDJT35yxgQ1MDDApz/9aYqKitixYweXXnopMPcV6wcVGYI6hlBVlS9/+cs0NjZyww03GP//ggsu4LHHHuPmm2/mscceO2a/vCcyrFYrTU1NNDU18dWvfjVlP2vr1q3cfffdtLS0kJ+fP2Y/S4/LGG3iqicVp5tnQSppDQ8PGzL4devWzXssyXgH6GTu/BVFoa2tDa/Xy7Jly8jKykJVVV544QVuu+02rr/+er7whS/M+3NMB0VRuOGGG3j00Udn5esdPHiQj3/849x222187Wtf4+DBg8b3OR6f//GOzAzqGOJvf/sbp59+OitXrjTerHfeeScbNmzg4osv5siRI1RVVfHkk0/OaUx6BpPDRPMsXYiRk5NDMBg02oO6QCBd/HtyvlFDQwNud6pH3vvtTnt4eJiWlhYjfl0QBIaGhrj55pvx+/088MADcz5nOhomcpDx+XwsWbKErCwtpdjj8VBQUMCzzz47rTnUnXfeya5du/jtb39LJBKhqamJp556ymgpZlp9BjIiiQwmD1mWWbt2LeXl5Tz33HMcOnSISy+9lMHBQZqbm3n88cczm/2MVAvJfoOj51knnXQSZrM5xW8wGo0iCAKxWIzS0lIWL16c1iR3pvOsYwVZljlw4IDx3F0uF6qq8txzz3H77bdz8803c9lll8171TBVB5mNGzfyox/9aNoiibfffpuHH36Y6667jsbGRk466SQcDgennXYad9xxx5ylGr8PkRFJZDB5/OQnP6GxsRG/3w/ATTfdxDe/+U0uvfRSrr32Wh555BG++tWvzvNVzj9MJhO1tbXU1tZy+eWXA2PnWbt27UIQBFavXk1zczPl5eU8+OCD3HDDDdTW1hIOh9m1a5dhLZRskjvdedaxxNDQEPv376e8vJz6+noEQaC/v59/+Zd/QRAEXnzxRUpKSib+QscAk3Efn024XC6cTid//etfAfj4xz9OU1MTH/3oRzPkNA1kKqgM6Ozs5Morr+Tb3/42P/7xj/nTn/5EcXExHo8Hi8Uypk2SwdGhz7O2bt3Kz372M/72t7/R0NCAxWIxWoNr166lrKzMmGf5fD4CgQCqqhouDfo8K10VktwqOlZtI0mSaG1tJRKJ0NjYaMSvP/XUU/zoRz/itttu4zOf+cy8E+h84/e//z0vvPACf/rTn/jRj37ElVdeCWTae6OQqaAymByuv/567rnnHmPgPzg4SF5enmEUWlFRQVdX13xe4vsK+hLwW2+9RWNjI0888QROp5O+vj62bdvGm2++yeOPP05nZydVVVWsXbs2ZZ6lWwsdPnw4JfRRr7RGhz4ei0NvYGCA1tZWqqqqWLp0KYIg4PF4+OY3v0lOTg6vvPLKjA1sPyi45JJLOP/887nnnnuM1yRDTtNDhqBOcDz33HMsWLCA5uZmtmzZMt+X84GC3vLSUVJSwnnnncd5550HpM6zXnrpJe6++26CwSBLly41qqxVq1ZhNpuNpWKPx2PkOyUvFc/VfFAURVpaWpBlmaamJux2O4qi8Jvf/Ib777+fO+64g/POOy9z+I6Cy+XC5XIhyzJmsznz+kwTGYI6wfH666/z7LPPsnnzZiMzaNOmTQwPDyNJEhaLhc7OznlVYr1fMdGhNN4867333uPNN9/kN7/5Df/6r/+KyWQy5llr165lxYoVhrXQ8PAwR44cIR6PG1EZut/g6KiMZExmOVcPRUx2tujs7OS6666joqKC11577Zjtbr1fMdc2Th90ZGZQGRjYsmULP/rRj3juuef47Gc/y2c+8xlDJHHSSSfxta99bb4v8YTD6P2sbdu20dLSQl5enkFY+jxrdL6THvqoV1qTcR0HzQ9w3759CIJghCIqisKjjz7Kf/7nf3LPPfdw1llnZaqCDGaCjMw8g6khmaDa2tq49NJLGRoaYs2aNfz6179OK4vO4NhDVVX6+/tT9rOS51lNTU00NzeTm5tLMBg0RBjJ8yz9Izn0UVVVPB4P7e3t1NbWUlxcDMChQ4f4xje+QWNjI3fddRfZ2dlHu7wMMpgMMgSVwfsbw8PDXHXVVbz33nsIgsB//dd/0dDQkMnOSoPkeda2bdvYvn07gUBgzDzLYrEQCASMSiscDmO323G5XAwPD+N2u1m6dClWqxVZlnn44Yf59a9/zX333ceHP/zhOa+aJnIe//GPf8wvf/lLLBYLxcXF/Nd//Zdhn5TB+woZgsrg/Y0rr7yS008/nauuuop4PG5kCGWysyaH5HnWtm3beOeddzCZTKxatcogrbq6Oh555BFqa2spLS0lHo9z2223IYoig4ODrFixgp/97GfHZK9pMs7jr7zyChs2bMDlcvHAAw+wZcsWfv/738/5tWUw68gQVAbvX/h8PiOEMPmuvaGhIZOdNU0kz7O2bdvGK6+8wj/+8Q/q6urYsGEDGzZsYNWqVTzzzDNs3ryZM888E5/Px44dO7Barbz88stzWkFNZEs0Gm+//TZf//rXef311+fsmjKYM2T2oDJ4/+LQoUMUFxfzxS9+kZ07d9Lc3MxPfvITent7WbhwIQClpaX09vbO85W+f6DvZ23cuBFJkvjd737HM888Q0NDgzHPuvvuu2lsbOSll17C4XAY/1aW5Tlv703GeTwZjzzyCOecc86cXlMG84sMQWVwXEKSJN566y1+9rOfsWHDBjZt2sRdd92V8phMdtb0sX79ev72t78Z9jv6ftYPfvCDtI8/3uTSv/71r9m+fTuvvvrqfF9KBnOIjP97BsclKioqqKioYMOGDQBcdNFFvPXWW0Z2FpDJzpoBdEeK4wnl5eV0dHQYfx9v/+7FF1/kjjvu4Nlnn80oSz/gyBBUBsclSktLWbRokTFfeumll1i2bJmRnQVksrM+YFi3bh2tra0cOnSIeDzO7373uzFmrm+//TbXXHMNzz77bObm5ARARiSRwXGLd955x1Dw1dTU8Ktf/QpFUTLZWR9gbN68meuvv95wHv/2t7+d4jz+sY99jF27dhlzyMrKSp599tl5vuoMpoGMii+DucG2bdv48pe/zNatW5FlmfXr1/P73/+eFStWzPelHRPcd999/PKXv0QQBFauXMmvfvUrenp6MvlZGWQweWQIKoO5w3e+8x2i0SiRSISKiopxpcAfNHR1dXHaaaexZ88enE4nF198Meeeey6bN2/mwgsvNKyhVq1alcnPyiCD8TEpgsrMoDKYFm699Vb+8pe/sH37dm688cb5vpxjCkmSiEQiSJJEOBxm4cKFvPzyy1x00UWAtmD8xz/+cZ6vMoMM3v/IEFQG08Lg4CDBYJBAIEA0Gp3vyzlmKC8v51/+5V+orKxk4cKF5Obm0tzcnMnPyiCDOUCGoDKYFq655hp+8IMfcPnll3PTTTfN9+UcM3i9Xp555hkOHTpEd3c3oVCI559/fr4vK4MMPpDILOpmMGX893//N1arlc997nPIsswpp5zCyy+/zEc/+tH5vrQ5x4svvkh1dbXh9H3hhRfy+v/f3t28RBWFcRz/PiRtRShTeqHgipt04WZylwyD/QGWrdQU3BiCyzYlbdKNqzYJtmkxEAol4egm2khK4a6BuC6Mil5kRtpIgvq0mCluFuSV1HHm91mde3iYc1bzcM99zjnz87o/S2Qf6A1KYuvq6mJqagoonDCwuLhYEckJCmXNCwsLrK+v4+6/9me1tbUxOTkJVM7+rNnZWRobGwmC4I9TPgA2Njbo7OwkCAISiQQrKysHP0k50pSgRGJIJBJ0dHTQ0tJCU1MT29vb9Pf3Mzo6ytjYGEEQkMvl6OvrO+yp7qutrS0GBgbIZDJks1nS6TTZbPa3mImJCWpqalheXmZoaKiiloLl/1CZuYjEtpuTx9vb2xkeHqa1tZXNzU3q6upYXV3V+YkCKjMXqQy9vb3U1tb+tlE6n8+TSqVoaGgglUqxtrYGFK7cGBwcJAgCmpubWVpa2tOYfzt5fGflYjSmqqqK6upqcrncnsaTyqQEJXLE9fT0/FFJODIyQjKZJAxDksnkr29EmUyGMAwJw5Dx8XFtJpaSFneJT0RKkJmdB565+8Xi81vgsrt/MrN64IW7N5rZg2I7vTMu5nitwLC7txefbwG4+71IzFwx5qWZVQGfgZOuPx3ZJb1BiZSnU5Gk8xn4eWf7aeB9JO5DsS+uV0CDmV0ws+PAdWDnqa3TQHex3QE8V3KSOLQPSqTMubub2X9NDO6+aWY3gTngGPDQ3d+Y2V3gtbtPAxPAIzNbBvIUkpjIrilBiZSnL2ZWH1ni+1rs/wicjcSdKfbF5u4zwMyOvtuR9nfg6l5+WwS0xCdSrqLLa93A00h/lxVcAr7F/f4kclBUJCFyxJlZGrgMnAC+AHeAJ8Bj4BzwDrjm7nkrbEK6D1wB1oEb7v76MOYt8i9KUCIiUpK0xCciIiVJCUpEREqSEpSIiJQkJSgRESlJPwDFktDSR/aUIwAAAABJRU5ErkJggg==\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" - } - ], + "outputs": [], "source": [ "# Author: Remi Flamary <remi.flamary@unice.fr>\n", "#\n", @@ -88,12 +47,26 @@ "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\n", - "\n", - "#\n", - "# Generate data\n", - "# -------------\n", - "\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", @@ -111,24 +84,74 @@ "\n", "# loss matrix + normalization\n", "M = ot.utils.dist0(n)\n", - "M /= M.max()\n", - "\n", - "#\n", - "# Plot data\n", - "# ---------\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()\n", - "\n", - "#\n", - "# Barycenter computation\n", - "# ----------------------\n", - "\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation\n", + "----------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "#%% barycenter computation\n", "\n", "alpha = 0.2 # 0<=alpha<=1\n", @@ -153,12 +176,47 @@ "pl.plot(x, bary_wass, 'g', label='Wasserstein')\n", "pl.legend()\n", "pl.title('Barycenters')\n", - "pl.tight_layout()\n", - "\n", - "#\n", - "# Barycentric interpolation\n", - "# -------------------------\n", - "\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycentric interpolation\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" + }, + { + "data": { + "image/png": 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FjY2N+MxnPoN//ud/xuWXXx6zjdtuuw0tLS1YuHAhVq5ciY0bN0bc/7WvfQ3Hjx9HRUUFbrjhhpjnP/DAA1i/fj3WrFmD1atX4/zzz4+pm0qVkpISnH/++ejs7ERRURGA8MVLS0sL6urqFJ+jdCzTZenSpaitrUV9fX2EeFxwwQVwOp3YtGmT9Ngf/ehH2L9/P8rLy3HNNdfgs5/9rHRfIBDAvffei5qaGtTX12NiYgI//elPAQCvvvoqOjs7YTQaceedd+K5556DwWBAc3MzduzYgZ/85Ceora1Fc3MzHnnkkbiCK4oifvGLX6CxsRFVVVXYtWsXfvOb3wAAPvnJT6KzsxP19fWoqakBAPzbv/0bFi9ejI0bN6KsrAyXX365WvtUADDRaaUkqOM2ZpmnnnoKv/3tb6VUoUpinnzySfzud79Tj5eKSmGRUs55divqVNLC6/Xi17/+Nf7+7/9+tnelYCCEIBgMQhRFaDQasCwLlmVzuhamoqIyO6gpvnOE1157DbW1tViwYAFuueWW2d6dWUcURXg8Hvj9fgSDQfj9fng8HrhcLhw7dgxOpxMejwehUAiEEAiCEGNCUVFRKWzUFJ/KOQOtjQiFQhBFEe+//z42bdoEnuchiqIUNe3btw8bNmyIqKdgGCZcV8Gy4DguItqiEZcadamozBhqik9lbkDO9FajQgRAEhSGYaToiOO4mPuitwOEa5qi+/QxDKMoXqpwqajMHqpAqRQsNDUnCIIUIckFQxRFnDx5EpOTk9JtBoMBgUAA4+PjKCkpQXFxsWQvpo9REhwqgoIgSPZjCsdxEX9U4VJRmRlUgVIpOKgw8TwvpefkguDxeGA2m+Hz+VBRUYGOjg6IoghCCPx+Pw4ePAifzwebzQav1wtRFKHX6yXBKikpQUlJiRRxAYgrOHLhovtCYVlWiraocKkGDRWV3KEKlErBQAgBz/MRYkCjHwCYnp6GyWRCKBRCW1sbpqenUV9fL6XrWJZFcXExtFqt1IqIbtfv98Pr9cLj8WB4eBherxeCIECn00WIFn0+JZFwUQehknBFR1yqcKmopI8qUCqzDhUmKjRyYSKEwG63w2w2g2VZtLe3S0WiPT09EduIJwAMw8BgMMBgMEgFuvQ5wWAQHo8HHo8HY2Nj8Hg84HkeWq02QrRKSkqkoli6zXjCBYRnUYVCIQCAw+EAy7KorKxUhUtFJQ1UgVKZNWjqTC5M9GRNCMHExATMZjOKi4uxbNmyiKauuYBhGOh0Ouh0OlRVVUXcR4XL6/XCarXCYrEgFApBo9FERFxUuKLXt+Si4/f7JcGlwqXkLFQSLlW8VOYzqkCpzDjUkScIAoBIYRJFEaOjo+jv70dFRQXWrl0Lg8Ew4/tYVFSEoqIiVFZWRtzO87wUcdntdgwODiIQCIBl2ZiIS6/XRwiW6ixUUUkPVaBUZoToGiYgUpgEQcDQ0BCGhoZQW1uLrq4uaXRDpq+Xj5O4RqNBeXk5ysvLI24XBEFa45qensbIyIgUORFCoNfrodFoJOFSnYUqKslRBUolrySqYQKAUCiEgYEBjI2NobGxERdeeGHEXKRUth8NTZ3N5Mma4ziUlpbGpCFFUYTZbAbP83C73RgfH4fP5wMQtsRHGzTkppBMnYXqOpfKXEEVKJW8ILeKHz16FKtWrYo44fr9flgsFthsNixatAgbN26MsH2nAo1Ook++VKAKAZZlJadgQ0ODdLsoilJ7Jo/Hg8nJSfh8vghLvFy4UrXE0yg1GAyqwqVyzqMKlEpOUaphcrlcMTVMLpcLra2tWLp0aUTUkA4Mw0S0OJLfXigCFQ9qiS8uLkZtba10O7XEU4OGw+GIsMRH13LJo810nIVutxt+vx91dXWqcKkULKpAqeSEZDVMTqcTJpMJwWAQbW1t6OzszPokGC+Vdy4IVDzklng50Zb40dFReL3elC3x8r+BsEvR6/UCUJ2FKoWLKlAqWZGsholGAD09PWhvb49xxWVDPCE6lwUqHpla4qMjLmqJV+rQQVGdhSqFgipQKhmRrIbJarXCbDZDr9dDp9Ohq6sr5/tA16CimYsClYh4lvhQKCQ5C202m2SJ5zgODMOA4zjYbDYUFxfHWOLlf8tJ1VlIRUwVLpVsUAVKJWWSWcVFUcTY2BgsFgsqKiqwevVqFBcX47333svL/iSKoFQArVYb1xI/MDAAj8eDqakpDA8PS5Z4g8EQYdCQW+IB1VmoMrOoAqWSlGRW8VzXMKUKNUlQ4Yy+XUUZjuOkuqzm5mbpdkEQ4PP5pMGP0ZZ4+TpXOpZ41VmokimqQKnEJdm4C1rDNDo6isbGRlxwwQURjVYp+axLGh8fx+joKERRlJxxXq8XdrsdVVVVEakrlbMofR4cx8FoNMJoNEbcLooifD6flC60Wq3wer0ghCjWcqVqiQcinYVAuG8h3Z5Go4lIR6qf4/xDFSiVGJKNuwgEArBYLJicnERzczO6u7sT1jCxLAtRFNOuc4qHKIoYHh6GzWaDRqPBeeedJ4mg1+vFqVOn4HK5YLPZpNRVdP+8+S5c6VwwyNs4xbPEezweOBwOeDweiKKYkiVe/jfFbrdLEZ78+0cfqzoL5xeqQKlIJLOKe71emM1mOJ1OtLS0YMmSJSnVMOVKoARBwODgIIaHh7FgwQLU1NSgtbUVOp0OwWBQ6uZgMBjQ3NyMkpIS6Xnx2hDNV+GiVvJskFvia2pqIrYdCASkYz46OgqPxwNBECIs8fSPPOqm35PofVOdhfMTVaBUQAiBx+ORfuhKNUxmsxl+vx9tbW1YuXJlWj/8bNeEeJ6XUokNDQ1SO6SjR48qbjfaPBGvDdF8Fq58toJiGAZ6vR56vT7CEk/XomjENTExAY/HE2GJd7lccLlc0Ol0SbvEy7ebqrNQXec6t1AFah4jt4ofP34cbW1tKCsrk+6nc5gAZFXDRCOodAkGg+jv78fExASamppi2iFlWwc1n4VrpnsVAuHPJZkl3ul0wul0wmq1SlFx9BpX9DFXnYVzF1Wg5iFK4y44jpMcV/IapiVLlkSIViakK1CBQABmsxl2ux2LFi1Cd3e3YjoqX4W6qQrX6OgofD7fOSlcsyFQiaCWeJ1Oh9bWVqmTBs/z0jGPtsRTNyE95gaDIWXhUp2F5waqQM0TktUwsSyL8fFxHD9+HGVlZVINUy6IV1Abjc/ng9lsxtTUVEp9+ma6k0Qi4fL5fHC73XA6nTHCxfM89Ho9KioqCka4Ck2gKISQiChZo9GgrKws5iIp2hI/NjYGv98PADG1XAaDIWVLPBDrLKT3BQIBVFRUSKKlOgvzjypQc5xUapiGh4cxNjaGqqoqnHfeedDr9Tndh2QRlMfjgclkgsfjQVtbG1asWJHSD18ufNFXzjPZSSKRPdvr9WJgYAA+nw+9vb3w+/2SuaCkpARGoxHFxcUxV//5plAFShCElPYrmSWernOla4mX/02h7lCz2YwVK1ZE3Kc6C/OLKlBzlGRW8VAohMHBQYyMjKChoQELFy6UrvBzTTyBcrlc6OvrQzAYRHt7O6qrq3NiviiUVkcsy8JoNKKsrAwcx0njNqhweTyeiIiLYZiYVGG+hKtQBYrWs2WK3BIvJ9oSb7fb4fV6IyzxcuGKtsRTd6Fc0FRnYf5RBWqOoSRM8h+8vIapqalJqmEymUx5674QLSRTU1PS67W3t8c0P01nu+diLz4qXPEiLnnaKl/CVagClQv7uxLJLPG02e7IyIhkiS8qKpIEK95YF/nf0e9DdRZmjypQc4RUapgsFou0vhNdw8RxnGSayDUsy0IQBNjtdphMJnAch46OjpgecekiF6Logs5CFqh4zKRwFapAATPbS1Fuia+urpZuj7bE2+12uN1u7Nu3T7LER483UZ2FuUcVqHOcROMugHAazWQySTVM8dZ3MrWCp7J/Pp8Pp06dQmlpKZYtWxZjMsgUeQul2VyDyjfpCBc1CiQTrkIWqEIg2hJfVFQEr9eL1tZWSbi8Xi9sNhsGBgYULfElJSXQ6XSqszALVIE6R0k07gII9zQzmUwghEg1TIm+0BzHIRAI5Gz/CCEYHx+HxWKBKIpobm5GS0tLzrYPqOM2shEur9eLQCCgClWKCIIgrUtptVpUVFSgoqIi4jFyS7zD4YixxMujrnQs8XTb0c5CatCQr3HRP3MFVaDOIZJZxQkhmJychNlsRlFRUVo1TLmKoOQjNyorK7F27VqMjY1FTHjNFYVukpgtkgkXHWw4MjKCwcFBAMkjrvmOIAhJT/yJLPHRpph0LPHyvylyg4bf78fJkyexatUq6bEPP/wwHnnkkezedAGgCtQ5ADU+OJ1OqYBRLkyiKErRSmlpKTo7O2NcTMnIdg2KNnAdGBhATU1NxMiNbFsdxYMKER2HTtcB5rtAxUMuXHa7HY2NjSgrK4uwZkeP2aDFsEajUXE+1HyB5/mM6wLj1c8ls8TL17gSWeJpFEyL7QHgb3/7W4bvtLBQBaqAkY+7EAQBhw4dQnd3d0wN0+DgIKqrq7OqYco0gopu4Ko0ciNf61tA2BE4ODgIhmHA87z0Iy0uLpZcWPmI3s515Kk9uTW7rq5Oeoz8BOp2uxXnQ8nruHIhXIV6YZGt/V2JRJZ4+XgTuSVer9fHpAt5npfSjwzDwOfzzcg8tplAFagCJJFVnJ6IBwYGpBqmeHOY0iHdCIrnefT392N0dBQLFy6UGrgqQV18uYIQgtHRUZjNZhQXF2PdunXSonEoFILZbAbP87BarbBYLAiFQkm7aM83Ull7incCTUW44qWsst2n2UIQhJyNi0kGdWcWFxfHtcR7PB4MDw/D6/UiGAxCFEX09PTg448/hlarTcuI9Oqrr+LOO++EIAi4/fbbce+990bcHwgEcNttt+Hjjz9GdXU1tm/fLplFbr/9duzfvx88z+O2227DD37wg5wdB0AVqIIimVVcFEWcPn0aVqsVCxcuxMaNG+OKQrqkGuVEN3BNNguKbjt6gTcTRFHE6Ogo+vv7UVVVhZaWFskmTGtNtFqtNO21sbExYr/pD3t8fBwejwc8z0tRljwayNUxLWSyEYNUhIt2K09HuPIRpeSKmRSoeMSzxFutVjgcDtTU1MDhcGDPnj04duwY1q1bh8rKSqxevRr/+Z//qfh5C4KAb3/723jjjTfQ1NSEDRs24Prrr8fKlSulxzz++OOorKxEb28vnnvuOdxzzz3Yvn07nn/+eQQCARw5cgRerxcrV67EF7/4RbS2tubsPc/9X+I5QDKrOK1h8nq9KCkpweLFi3P+Q04WQfn9flgslqQNXJXINsUniiJGRkYwMDCA6upqaX1rZGQkrvMwOlWk1EWbrl1FX5EKghDRXYAK12yfoHJJPqKVbIUr2pJdSBSCQMWD9nqsrKzEt771LXR1dWH79u347W9/C5vNBpPJFPe47tu3D4sXL0Z7ezsA4Oabb8aOHTsiBGrHjh146KGHAAA33ngjvvOd70jfH3qh5/P5UFRUlHVj6WhUgZpFklnFXS4XzGYzvF4v2tra4HA40NjYmJcfcTwRoQ1cp6en0draimXLlqX9+qk2i41Gbryoq6vD+vXrI9aTsu0kwTAMdDoddDpdzNwieSplcHAwYg2AihZdAyjUq/5EzGQ6LVXhGhsbg8vlwocffphVqjAfFLJAyS3wQHhdllrgq6urI6KtaIaHh9Hc3Cz9u6mpCXv37o37GI1Gg/LycthsNtx4443YsWMHGhoa4PV68R//8R8Zd4WJhypQs4DSuAv5yULeCqitrQ1VVVVgGAZmszmno9PlREdQ8gau7e3tKTdwVSJdF5/c/BHPeEG3m486qETdBWg/N7fbjcnJScl1pWTTLnThmu1oJVq4aEPdzs7OrFKF+aCQBSoUCkWI//T0dEyNVj7Yt28fOI7DyMgIHA4HNm/ejMsvv1yKxnKBKlAzRDo1TFqtVrEVEBWRfPxQaASVbQPXRNtOhtwRWF9fn9B4Qbc7k4W68fq5iaIIv98Pt9sdc0KlLiuDwYDy8vKCqS8qREMCXYNKFnF5vV643e4ZFa5CFqjoCCodgVq4cKFUCwcAQ0NDWLhwoeJjmpqawPM8pqenUV1djW3btuHTn/40tFot6urqcNFFF+G1HJk3AAAgAElEQVSjjz5SBepcItm4C0KIVNhaWlqKlStXxhRYUvLZL4+O2j59+nRW03OVSCZQPM9HdFZPJkyUQmkWKx+eJ4cWxg4ODsLv96Ovr09xwKHRaJzx9ZdCFqh4yIWrtrY24nnyyJbWEwGQRmzQlGymwpWvJra5IBQKxQjUsmXLUnruhg0b0NPTA7PZjIULF+K5557Dtm3bIh5z/fXX4/e//z26u7vxwgsv4JOf/CQYhsGiRYuwc+dO3HrrrfB4PPjggw9w11135fS9qQKVJ5KNu6DrK4ODgynPYcq1QBFCpAauGo0GOp0OXV1dOds+JZ5AUbt8KlZ1JQq9kwQtjC0tLY0Yt5FopLx8fUupCWmuUOrOPdtk6uKTXyDEEy55ISyAiLVE+txCFaBkZBNBaTQa/OpXv8KVV14JQRDw1a9+FZ2dnXjwwQexfv16XH/99fja176GW2+9FYsXL0ZVVRWee+45AMC3v/1tfOUrX0FnZycIIfjKV76CNWvW5PS9qQKVY5KNu6DRAk1jRS/8JyJXAkXTiSaTCQaDAStWrIDRaMR7772X9baViBaoUCiEgYEBjI2NoampCRs3bswofVIoEVS6xOssIO/lFt2EVC5auSo+nisCFY90hUvewUFeCFvowqUUQaWzBnX11Vfj6quvjrjtxz/+sfT/er0ezz//fMzzjEaj4u25RBWoHJGshkleP5RpDRPHcTGD0dLdx/HxcZjNZpSWluZ0rHsi6FpRKBSCxWLBxMQEmpub07KqK3GuClQ84vVy43k+In1Fi481Gk2McKVafHwupvhyRTzhoh0cqHDJTTB+vx8mk6kghUveSQKYOZPETKAKVJZQYfJ6vTh16hTWrFkT8cX1+XywWCxwOBxp1w9Fo9FoMoqg5AWulZWVeRnrngie5+F0OrFv376sj4EcuRDN5XEb1NobbZoJhUKSMSNe8TH9E30xVIjHZ7YLdeUdHKIjrg8//BClpaUxwlUIEVf0+tj09HRO15BnE1WgMiS6hkmj0UhD5ADA7XbDZDJJM2SWL1+e9RVruik+URQxNDSEwcHBmAauMwGd3mu1WsGybM6EiTLfx21otVpUVlYmLD4eHR2VJsTKi4+paaeQnGmzLVDxEEURWq0WtbW1KUdcsylcTqdTjaDmI9QqrlTDRBfsaQ2TIAhoa2vLiU2bkqpA8TyPoaGhhA1c45GL1I/f74fZbIbD4UBraytaWlpw5MiRnP9AC90kMRukWnwcCARw6NChiOJjuWlgNoSiUAUqnsU8XsQ128IVCoXUZrHziVSs4jabDR6PB2azGe3t7Xm5gkm2BiU3HzQ2NqbtiqNmhkyvqmmefmpqCm1tbVLUKD9uuYQKEc/zGB0dhV6vh9FonNcCFY/o4uPx8XGsX78+pvjYZrNFnEzla1z5Loo91wQqHukIl8/ngyiKGQtX9Jyqufa9VwUqAfJxF9SWGy1M1HRgNBqh1+tx3nnn5W1/NBqNYu85uQGjubk5Y1dcpoXAPp8PJpMJTqdTcax8vsZtCIIAl8uFvXv3oqamRmoNFQgEpNeTn2ALKZ1VKCQrPqbCFV18nI/hhqIoFmSj3lwV6SYTLlqATC8SqHDR4200GmEwGCL2JdpiLn+tuUDhfRsKgFRqmGjz0srKSqxbtw4GgwHvvfdeXt1R0Sm+bBq4KpGukHi9XphMJrjdbrS3t2PlypWK7z3XEQ0d9TEyMgKWZbFx48aIlOv09DSGh4dRXV0tNYH1eDxSOouKFv3BF+JV+2yTyKIdPU5eqfiYDjdM57dQiLVZQP67SCQaryG3w0fPhaLmF3q+4jgOfr9/zqT3AFWgIkhmFZfXMC1YsCCmhinbFFkyqEB5vV6YzWY4nc6MG7gm2n4yPB6P1BWhvb0dnZ2dCV8/VycdeWFvU1MTzj//fJw+fRoajQaiKEY4+liWRVVVVcw6jFIvPQARV6mZnFznC/HGyScrPpYf23jFx4Vm2qDMVpujeNGt/Htss9ng9/tx4MAB/OIXv4DD4YDb7ca2bdvQ2dmJZcuWJXTsZjoL6g9/+EPESPnDhw9j//79WLduXU6PgSpQSD7ugqbQxsfHE9YwaTQaaaprPggGg7BarVIqLV7EkinJIii3242+vj74/X50dHTk1ACSiGhhoilMv9+flkkiUTqLnlydTqd0cuU4LqJNjtFoVKfzxiFe8bEgCBERgLz4OLprxlxZg8o38u8xHfW+ePFiPP3009i5cyceffRRDA0N4bXXXsPg4CDefPPNnM+C+tKXvoQvfelLAIAjR47ghhtuyLk4AfNcoJKNu5C70Zqbm7Fp06aEPyAqULkOsZ1OJ0wmE3w+H/R6PTZs2JAXYYgXQdEGsqFQCO3t7VJ39XwTT5gouSrUTRQVyM0D/f39MdN56Qm2ENdOCgGO4xIWH9NODhaLRTrONputoCYfF5pAyZEX6XIch/LycixduhT/9E//lPS52c6Cojz77LO4+eabc/iuzjIvf1XJxl243W6YzWa43e4IN1oyqEDliqmpKfT19QEA2tvbodfrceLEibyJQ3QE5XQ60dfXB0EQJGGaCaJ79MUzfeS7k0S8k6u8QHZsbAxut1uqM5JHW4XUbaDQUCo+Pn36NKqrq8GybEbFx/niXBEoIHIWVDKymQUlz0Bs374dO3bsyOZtxGXeCFSycRdAuALbZDJJkUK6KaxcCFR0A9fFixdLP+JQKJRTAYyGZVkIgoDp6Wn09fWBEIKOjo4ZK/pLVZjk+xtPiPJpt41XIEvrjNxut7SgTb93er0eGo0mp663uYYgCCgqKkJpaWnMsZVfFMQrPs7X5GNBEGY9iotHdMZmptsc7d27F8XFxVi1alVetj/nBYrWMNntdimFE20Vp4LAcVxWNUzZNHMlhMBqtcJsNkc0cM3V9lMhGAyip6cHer1ecR5VvpCP20hFmCiF1Isv0ZBDs9ksnWCjXW/R61vzWbjiufgYhkFRUZGi6UVefDw0NBTh1sxV8XGhR1CZDivMZhYU5bnnnsMXv/jFLN9FfOa0QAmCgFAoBEIIjh49iu7u7pgaJovFguLiYkVBSJdMIih5LVVZWVnCBq6Zjk5Pht1uR19fHwKBABoaGtDR0ZHz11BC7orMpKt5ok4ShQI9uRoMBmncBnDW9eZ2u+FwODA0NIRAICBFWfL1rUK9es816c5cSmXyMXW6RXdySGc+VKELlPz7MVOzoIDwBcUf//hHvPPOO7l7Q1HMaYGi0C8gPaHRGqaKigqsXbsWBoMhJ6+TjkDNdgNXGjn29fWhqKgIy5cvh91uz+sPkS6uRkdM3d3d82rcBpB45IZ8Mq/b7ZbWYKI7lxfqSTNTcuXiS2TPpp0c0ik+LnSBku/bTM2CAoDdu3ejubk5pxN0Y/Yxb1suAFiWjbiaNpvNGBkZQW1tbV4ap9KGsYkQBEEaVFhbW5vWPKhcQNsy9fX1wWAwREzwnZ6ezlsKkWVZhEIhDA8Pp53Ki0e8DubngkDFQ6PRoKKiIuIkI28A63a7IwqPM4kICpV828wTdSv3+Xxwu92KxcdutxtGoxFarbbg6uOUIqiZmAUFAJdeeik++OCDNPc4Pea0QAHhdZWBgQHJDZRuf7p0SBRByaOG+vr6tBq45gL5kEK6qCnPXQNnRSTXCIKAQCCAffv25USYknEuC5QSiRrA+v3+iIhLHhHII65CO7EqMVt1UPJiYjk0DXvixAmpjiu6+Hi21w/n8iwoYI4LFCEEBw4cQGNjI2pra9HQ0JBXa6qSQGXbwFWJdNopUfOFyWSC0WhMusaVy555giBgYGBAaknU1dWVs3RqIuaaQMVDnspSakfkdrsjujrQ4lg6biMYDBZU4XGhFerSNKxWq0V7e7t0QSkvPpavH8qPr7xrRj6JbhY7l2ZBAXNcoGifNkIInE5nXi3aQKRABYNBaRZSNg1co6FOvmQiF22+SGWtLVcuQUEQJPMDFeXDhw/P2BXmfBGoeMQrPKaDNV0uFwRBwLFjxyIKj+UR12wUHhfilF8gdg0qleLjycnJnEw+TgX5MZtLs6CAOS5QcrRabV7SV3Jot/ETJ07A4XCgpaUFixcvzulVYTKBIoRgbGwMZrMZFRUVaZkvaB1UplBhonZVebSYr47mhBBMTEzAZDKBYRjJUhwKhQp6cXs2oCPlS0pKMDY2JnXel69vyWuMZmNOVCEKVKruwkSTj+nxlRcfa7XaGOHK9sJgLs2CAuaBQNGraa1Wm9cIyuv1oq+vD1NTU2hqasrJBF0l4kU5oihibGwMFosFVVVVOP/889N2BXIcl5GIREdMSr0K41nCM4WuqXm9XkxMTKCzsxMApC7bwWAQBw4ckIwE0R3MC/FEOFNERypFRUUoKipSLDym61vUqg1A0Zgxn49nMrRaraLxJZXi40SOzejPcS5mDea8QFE0Gk1eIig62t3n86G1tRUulyui3iXXRAuU3DZfXV2dlTsx3RSfUiov3hVgLiMom82G3t5eFBcXw2AwYNWqVVKHEJ1Oh8rKSkxMTEgD+eTW4vHxccmhJT/JzqdGsKmk0uQ1RtGNdenxjHa8pdq1XCVx8XEwGJSEK3pUjPwYa7XauC3A5grzRqC0Wi08Hk/Otkf71PE8H9FAlfbOyxd0nUsURQwPD2NgYCBndvVURUQuTA0NDSkZP3IhUHa7Hb29vdDpdJIL8b333ot5XLT9XMlanKgRrFy05mK9UTZrPXIhqqurk26XFx7Lu5bTwmN5Kmu+FB5ngtyxmaj42G63w+12w+/348iRI9i7dy8YhpEyRamkCjMdtQGEx2vccccdcDqdYFkWH374YV7qOOe8QNEfYq4auTocDphMJgDhBq4z7ZhhWRajo6M4fvw46urqcmpXTxZBZSJM8v3OVKCmpqbQ29sLjUYTUbclJ/qEmyzdEW+hO97V61xKE+bDjBCv8Jiuvyg1f41urFuIFEraTKn42OVyYXBwEK2trbBYLHj77bcxPj6OjRs3AgCWL1+Op556SnH9LJtRGzzPY+vWrXj66aexdu1a2Gy2vF10zHmBomRjkpD369NqtViyZEnMiS3fCIKAoaEhjIyMoLq6Oi91VPFEhL720NBQ2sIk33a6P/bp6Wn09vaCYRgsXbp0Ro55vLQLLeSkJ9p00oSFcpKjzKRbTqvVorLMgWrjaTB1IQjchRCZmogLgcHBQWke15EjRwqq8LiQjTbUaFFcXIzrrrsOS5cuxfT0NLZv345QKASLxRL32GUzauP111/HmjVrsHbtWgCIiPRyzZwXKPpDzESgUmngGu95uZwiSwt8Gxoa0NLSAoPBkJcrlmiByoUwxdt2IlwuF3p6ekAIiejmng65PAHL04RyUk0T5sO9mA0zJlAkBA3/EjhhN4CwSHPCW+A1V4HRXR6RxhJFER9//DE6OjoUWxFFF8bqdLoZeQ+FLlDRRbr0t0IvpOORzaiN06dPg2EYXHnllbBarbj55ptTmj+VCXNeoCjppPioVdtisSRt4BrvdbIVEPnoCbkBYWBgIG/tiGiKL5fCRElFoNxuN3p7exEKhbB48eKU06c0QpGfeGciakk1TehwOCTXYSGkCTMRqLSfQwg0/PPghH1RdwjQ8C+BMGUQuQukW+m493itiOSFx8PDwxGFsfL1rVwbXc4lgUpnFlS2r7tnzx58+OGHKC4uxmWXXYauri5cdtllOX+tOS9Q8ggqmUDJHXFVVVUZNXDNVqB4nkd/fz/GxsYU2wJxHJe3ei5RFBEMBvHBBx+gvr4+p22hEtnMPR6PNEqeNqVMZ7uFRnSacHBwEBzHoaKiIuM0YS5JR2yCwgSsgR3w8ifAsaWoKvoUyrUXJX0+J+xSEKezaEPbEWQWgLAtABJ3kUhUeBw9lVcpgi0uLs74e3wuCdRMjdpoamrCJZdcIq2FXX311di/f78qUNmQqLtALhu4ZmrGCIVC6O/vx/j4eMLOE6k0pE0XecRECMlLv0KlCIrWjnm9XnR0dKQ9IBI4N0ZuAJmnCeXRQa5OlKkKlE+wYMj7XyAk/H3mRScm/H9CQBhCnf4LcbfBiEPQ8MkmrArQhv6EYNF3gTOfYbprTfEKY+UR7MjISEzhMT2mqRQe08iuEOF5PuICOh2BymbUxpVXXol///d/h9frRVFREXbt2oXvfve7OX1vlHkjUErko4FrugIVDAbR39+PiYkJLFq0CN3d3Ql/NLkcWiiKIoaGhjA4OChFTPv27ctLmxu5QPl8PphMJrhcLnR0dKCmpiZjQYl34VFoxoR4ZOomTLVINiD6YPEdg09wYaF+CWq0C1MSqJDowKj3cUmc5EyH9kLPLUJ50abYJxICDf8n0DWnRDBkAKx4ECJ3Xk778MUzulCbttvtloq8AcQ4NOWNdaPHWRQSShFUqrOgshm1UVlZie9973vYsGEDGIbB1VdfjWuuuSYv73HOC5SS/Zim0cbHx2Na8mQLx3EpCVQwGITZbIbNZktJmOTbz1aglIQp373XWJZFIBDA8ePHMT09jfb2dqxcuTLrSIcKVKFFTNmSzE0YPZ1XKU1oD41hl/0F8CScEu7xHsQK4wVYRFYnPF6EiBj1PQGeuOM+xhrYgWLNUmjZmojbWfEjsKI55fep4V9EkF01I6M2lGZEJRq1Qbub064ahVZ4nG0n82xGbWzduhVbt25Nc4/TZ84LlByGYXDq1CnYbDY0NzenLArpoNFoEgpIIBCA2WyG3W5Ha2srlixZktY+ZCNQsyFMQPg9j42Nwe12Y/ny5VixYkXOfuhUoKKPYSGdSDKFEAJHyAVrcBqLDHUwcLqU04RuYQqm0vdBNCI0Gg4cp4GG43DCvQ+ChkDP1MZ5VcAZ2ge/MBj3fgAQSRAT/j9jYfE3ZDscgoZ/Ma33yBA7WPEARHF5wY3aoNGr3+/HiRMnpMLj6ELu2WisC8z9URvAPBAohmHg9/thNpvh8XhQX1+fF2GixEvx0X1wOBxoa2vDsmXLMjqJZiJQ0cKULJWZq4hEHiVWVlaisrIS9fX1WW9XDk0dRqdhzpUUXzwIIXht8kPsnz4NACjm9Ph07QVYblwU89joNKFIBLxhewaGkA48z4PnBYRCPgiCAEKAj5g3scxzCaqsVbHTY4kfk4GXU9pHD38CfmEIeq4pvB/C+2CIM+33quH3QBSXFtyojbKyMrhcLpSVlUkGAnnjV3rRFd0/jxoz8p0aVAVqDkAIwfHjx9HY2IhQKISampq8/hCie/75fD6YzWZMT0+jra0t6yay6QiUXJgWLFiQ0hpbvBN+OtAiwYmJCbS0tGDJkiWwWq1wuVwZbzMe1CRBa2Zot+5CYNTngp7TpC2WhBC8Yt2Hg84e6Tav4Mefx97BbQuvQJMhfvQDAH2+w3DydjAMC622CJEfeThdNSaeQqOrJSalxZbvA6+zQ8NpwKTwO3EE/4YGw98BhAcn7EzrfVIYMgCWDIBlC6+bhCAIEYapeI1flQqP892BRGnc+1yaBQXMA4FiGAZdXV3hdInDMSMjN3w+H7xeL0wmE9xuN9ra2nKW1krFhJGJMFGoAGYiUHKLfPS6Wj7GbdATw8GDB1FWVga9Xo+RkRG43W54vV4cOXJEMhREL37nE14U8dLwCewcD/dlrBI1+LuFa1J+/knPQIQ4nYXgxYn38LXmq1HEKn+eAdGH4+73E2ydAcuy8OgmYWzSoL1oNYDwidjltmIw8DH4YAg+wSetC3Gc5kyaMJwqlB9Dd+gQgkUT0MMEhkyl/B6j0eEDsOzlGT8/X/A8n3SOWqL+efJGxUqFx1S8Mik8jk5tz7VZUMA8ECg5MzETiud5jI2NwWazob29HZ2dnTk9KSaKoOQNZNMVJkomQkKLikdGRuKu7eVaoGjjWL/fj1WrVqGyshKhUEh63b1796K9vT2m67a8uJP+iV5DCPA8Ttgm0VFZhdIMyg2e6z+EfbazazjDATeeHTuOf2xogJZNLPwhkcebk/vj3u8IufC27RCuqF2veP8J914ExUBK+3nM/T7qqsIpQ47jIOqPQMdw0OFsBEpEEbxA04T+M2lCIomVRsNhgryJdr0ppdeMh449Ao69JKtt5INssgmJGhXT1k7RE3nlZQW0Y3mqzLVZUMA8ESi6kJ6rhrFK0LEbLpcLer0eXV1deblaV9pmLoSJkk4KUT6gsKmpCd3d3XF/zLkSqOnpafT09IDjOKxcuRImk0mxmJplWRQXF8d03abFnXT0Rl9fX0SNzCAfxJ/7LSAMA2NREb64chXW1NbFbD8eJ53WCHGimH3TeGXkNK5vWpHw+e85jsHJJ+66f8DZg4sqO1GiibyyD4g+mH1HUtxTBpPBEUyFrKjQ1kIkIUwFd8c+imWhZWPThIIgQhB48DwPW+A1VPkIWDDgNGEzBqfRgOO4NNLpPEqKegEsT/HxM0Mq06vTJV5jXfl3k7ZYix5sSP+OPq7n+pprPOaFQFHyEUG5XC709fUhGAyio6MDOp1OanCab+TClKvO5qkICU0hDgwMxB1QqLTdbH5EbrcbPT09EEURS5YskYoz5XVQR4cncHLMhsV1YWu2ktlDqbiT1sgM2Cbxx5NH4Q0EIQgCphngP999B3/fuRrtNbVJW+kEBB7bLYfi3r97woRLF7ShTKvcncQvBLFv6mTSYyEQAR9Nn8aW6rURt5t9R8Er1C1FI/8YTL7DOF97GVyhjxPayiNhzkRQHIqKdGDFSXDaCpSypRAEATzPIxgMQuB5iISAZWKFS6n8o6ToGIBrU9yHmUEQhBkzb6RSeExr4gRBQCAQgMlkwuDgIIxGIxiGSfm8k+moDYvFghUrVkj1Vhs3bsRjjz2WmwOgwLwQKHm7I1qcly3yeVAdHR1SvUogEMhbrzwKIQSDg4M5FSZKOinEVISJkkj4QrwA65QHDdWlMT8wr9crpfKWLFkSswhMTRK7Tlvw14+PgwDYax7C8hIO56coiAzDQKfX469Dg+B0OpSeSZMQQiDwAl4ZHsTnCaRWOnRUBP1DOxK8a+2HLRj/+xUUBbw+2osbF61SvP+Qqw8hktoF1P7p0+iuXCmtRYlEQJ/3YErPBQjoYe73ncCqkosxFXonxedGEwTgxpQAlHFl0Gg0Md8JURQh8AJ4gUfI74fA8yAAOJYNC5dGEx7Ix5kB4gKYUsVXmg0KodWRUk2c3+/H8ePHUVZWhlOnTuHVV19Ff38/1q9fj2XLlmHVqlX4/ve/r/j7zGbUBgB0dHTg4MFUv2vZMS8EipKLFN/09DT6+vpACFGcB5VqoW4mUIHweDzw+/0zNnJDFEWMjo7CYrFIgqjRaOCY8qKqMrWvULyWRB8eH8Sf3z4MUQQu7erApzcuk0oD+vr64HK5sHjx4rhtkBiGwZDDiR37T4YjgzMP+XDUgS0TdixtSOx4oxyaGMeQO9IizTAMNFoNxvkQ7MZibFy6NMKxReuOvF4vRELwv/4B+CCcOelyYBlW2h/Ku1YLLqvvQGVRZHpOJCI+mjqV0r4CgE8M4IjLjK7ypQCAIX8PvEKqEdBZeBKCybcHrDiS9nMBgCV2AARu0Q2e8NAwsd8HlmXBFrHQQvZdJYAgChD4cJowGAiAEILx8T/CTzZFdMuYzUnHhSBQSlBre01NDb7xjW/g+uuvx3e+8x289NJL6OnpwfHjx+PudzajNmaaeSFQ2YzcoExNTUnTchONgMhlKyIKbWLb39+Puro6GI1GdHR0pJ16GOybQGlFMSqq448Mke8/IUQSpurqamzYsAFFRUWYnvZhx4sfwmy2Yssly7D54qXguMT7oiR8tmkP/vedY6A3v/1xH0BEtFWxsNvt6OjoSNptgmEYvH4itnMBIcCzHx7F/ddugSbJcSKE4M3+xN0PXjH14cKGhXEdW4fsIwieHgbLE4RCIfh8PhBRPGPVJuBYDjzHgWg0+GByAFc1Rrak6fUOY5pPT2COuEySQJl9R1N/IgHkytnn3YMlGQ1DFQHiOLNJApfgQqUmRZszg7NpwjN7w3EcllQ5YQ8ulNoRyaNWubllJuqMgMIVqHg1UFqtFitXrowQm2iyGbUBAGazGeeddx7Kysrw8MMPY/Pmzbl8axHMC4GiZCJQDocDfX194R9PCoMKc7n29P9+9yZ6D5tQtqgYF/2fCySBcDgcaefGhy2T+MP/fRMCL2D9lmW48qYNio9jWRaCIGBsbAwmkwlVVVXo6uqKcAe98OePMDAQ/rK+9fZJ8LyIyy+L/4Og25ULFCEEz795GMFQWAxFIsLn9eKvOz/GXV+4GN3d3Skdy2GnF6fH7bGRJANMewM4NDiGrpbGhNvom3Kg3zmd8DF2vw8nbJNYWaMcke22WqDRcNBoOMh9VKJI4PV6pXUuXhDwsnM/mmxBlJaWSlHCR1Onk77XaEb8k7CHXNCxBBPBxJ0f5EReBwuYDA2jpciAojQveBjiAnD2YswpOlGJzOpwwprJgMMAykoZlJVFfmbyqHVoaEjqTUiNMKn2JkyXfLdgypREs6DySUNDAwYGBlBdXY2PP/4YN9xwA44dO5a3YaLzSqDSSfHZ7Xb09fVBq9Vi2bJlMY6bfCKKIg68ewivb9uJIq0OxUPFuPqmSinVQaOcVNN77mkf/vibt8CfEYMP3z6FVetbsbAt8mRL6zZGRkZQU1OD888/P8Yh198/KYkTZe8+E7o3dqCkJL7FNVqg+sccMI/YQUi4F5o/EIDBYEB5SSVOj3qwYklqJ5m9Q1aQOI1JCQje6RnA+YsaEp603hqwpPRa7w4PKgrUuN+NHtek4nNYlpFMAXo9XdsCsKACBlIUThkPW3AwdBJgAI1Uc6QBp+HAJjnZHndZUFWU2PUXy9k1KJ44QYgIG8+jIc1UGnMmeqJ4RA8EIoBjMog4CDkT0xGw4jGI3MaIu5P1JlTqo5erNGEhts3KZhZUNqM2aAYBALq6utDR0YHTp09j/XrlsodsmRcCRbLjlYQAACAASURBVL9gydJvdLR7X18fdDpdyhN0420r3S82TeVZzBa8/9QBVJRXSNX8bzy9C1964HMpvY9ojuwzweOOrI15868HcOtdn5JccJOTk1IKs6mpScpPR/POntgC0mCQx553e3DlFcqL/0Csi++jE4Pw+bzw+fzQG/SorKiUjteHxwdx2YYlMBoS13S4fAH0O1zQKAg1c+Z0N2ifhsU2hbYa5St7byiEY5PWhK9DOWq1wuH3ozJKtPfbh1N6vrRvDHDIM4mtbedhwYIFsE2dQMVkOUSRSC64QMAP3num5ojlwGk4SbxYlpME5qjLjEWG1PZfCV4Mi4wtlK5ABQFECiMBgUt0oYJLv1iURlAAwAlHYgRKCXmdkbyUQN6bUD4narbShPkgmzZH2YzasFqtqKqqAsdxMJlM6OnpiXuuyAXzQqAo8QSDnqBNJhMMBgM6OzuzapeTbrsg+RpTbW0tFpQ2wmvfG9FqpveABaZD/Whf25K2QB37yBJz20DvBE4fHkRNUwl6e3tRXFyMNWvWwG63x932+Pg0enrHFe/78CMzNnUvRmmp8mIGPSaiKKJ/YBA7PzgKVqNFZWUFGCYyhRLiRbxz0IyruhPXxBwcHA2f2JQCKObs7e+cHogrUIcmxiGkuPgrguCDkSFc1b5Yuo0Qgv329A0GBx0juGnRaug4DY65LADC0RbLaqDVnv1ZEhL+fvA8L1mLBVEAAwYaDQcnNw2WuFCh08Ycx3iQM2tQBEEIJNzZwCkICIpiymm+cPQUe9xcQmYCdTaCAljxFEACAJNZ0WmyESZut1tqR0QIgcFgiBCumeo4kg3ZzILKZtTG7t278eCDD0Kr1YJlWTz22GNpDRhNl3khUImEyWq1wmQywWg0pjXaPRE0lZhMoKKFia4x7dy2R/Hxu1/4AO1rW9JKVVpHpzE25Ii5PRQK4i9P78RVt56HVatWSYI8NTUVd53u2PH4J+JQSMDH+/tx6Zb482gCgQA++OADTHpZlJSWhV1ucfj45BCuvHAZWDb+iWJ//ygAnE3xRT2U3n5sZAL+EA+9NvbrfmBiLO72lTgwPhYhUCM+J8b96fcYDIoCTjitaC4pwWjAFvdxDANwHAuOi4xuxDMW+GneiglfAGzAL7W+0ZyJtrgz7YliDgzC0T0vRrYmsvM86lOKoggYorxm5xbdEImY8LNV3iKkCArgwYonIXJrEzwjfTJJEwaDQTgcjrS7OuSbbGZBAZmP2vjc5z6Hz33ucxnscWbMC4GSwzAMBEGQIqaysjKsXbs2ab+tdKACEq/tCLVt9/f3o6amRhImiulQv+LzBk4MwT3lSSuCOvZRpDuNdmNmWQYsU4K2RYsjosVE244XPVGOHx+OESh6EUA7NmzcuBHPvHYw6QnM5QnAMmpH+8JqxfsdHh9MVnv8qa6ykzIvijgxasV5ixoiHuMJBXHKFl8clBj1uGH1elF75kImk+iJcnRqDC6SWYqJZRgwGg14MQg3o0F5eTEAcibaEiAIPALeIEQx/FnKWxPR9DMfJTJTvID6FPSJIR6EU3yxiBDhET0o5dJbs41OibPi8ZwLlBKJ0oROpxNTU1MFmSacD53MgXkmUISEc/x79+5FRUUF1q1bl1NhosSLcKKFSWm0vM/tx0if8lU9IcDJvb2oXGxMWaBO7B8AAPB8WJgAJqL/3IkDA7jwk2fb78QrqHW5/RgZSdwMdHzCCavVhdra8MnJZrOht7cXJSUlWLduHQ4cOACW08A0bE9p34/2jcUVqIMDo9L/E0IgknAaTMNpzgYMsgzUkaGJGIE6NDEBMYXJr9Ecto7jspa2cHrPkY1AjcPLZt7ZJEh8ECHCLQABQYSOY8GyHIqKOACy79WZ7z0v8AgGQwgGgwDrB6vzgGFYMAzAMCymhLOdHxLBIPH3wC260xYoeYoPAFjxRPgLP0upNtqz0WAwYOnSpdLthZImVAVqjjEyMgKLxQJRFNHZ2ZnXtvTRUYhcmKqrqxWFiWI5MoBESyIn3j+Nzcs2pCRQbqcP4yP2M8JEzgxXi0xTnNjfHyFQ8SKo3t6JpK8HAMeOD2Pd2nr09PSgqKgoIn0IAOYRO3ghtZ58R/pGcd1m5TqoE6NnjQHBQBBerxcaLjwskoCAiOErcm2RFhpOgxOjVgR5AUWas1e7h62JI8J4HJ4IC9So3wVbIF0H3VmmQj70uZ0o12V2Be4Tzr62LSSgMV4t2plWQ5xGA50uvNZFODcIGx4FIooEIhEg8ATDU1OoOFNorNFowHGaM2lW+hkIQJKZT27RnbZJKDLFBzDECYYMgTDNcZ+Tb5RqoOKlCWnz10RuwlymCVWBmmOEQiF0dXWht7c373UNNIJKR5gofXHSexTLsUFc6F0H1pj4Pbjdbrz1+l643W6UlJTE/WEMmScxbfegvCosIvEiqGTpPQDgBR5vvX0QZaVLsXz5ckVrfs9g6o4zpycAy6gDbY2Ri7AhXkDfhB1+nx8+rw9arRaVlZURLkGXywWWY8OOOH8ATpeA/931LtY018NoNEJXbMDpNNN7FPP0FJyBAI5NZSZwFI/gA+/nMxIoQgC/eFag7EEejfpUT34EIlxgEO7dJp2DOSBUpEWxNvz9DQVD8Al+iKIAhgmvbem0Hmi5sEkjZmnrDEESRJAEoUvH5BAVQQHhNJ/AFpZAKcEwjNSBPFU3obz5ayZpQiWBmmuzoIB5IlAMw6C1tVXqaJ7vkRscx8FqtaK3tzdlYaIMnUqcMhJFgv4jw1h8QYvi/R6PB319ffD7/SCBopSuqk4c6MfGM4W2ShGUKBL09cWPoOgPUSQijCVGNDUtjls31jOoXC8UjyO9oxECRQjBvpM9sE7aUFRUBEOxASzDRjSNBcKfuVarjfgR+wxlqK2thdvtxr7eXkw67CAk/J41Gu5MQ1NN+AImwcU/AXDEOoGj7iwFivfBI/JoL0/frRY4k96jOEICBELApRC1iPCBQACD2IscBy+gTaeDTqeBvOKYkPDaFktGIQqidKwZJvwf+hmEbwRcogs6NvX3FR1BAQAnHIeguTLlbeSabLtIKLkJaassKlzy4YbyouNkacLoQv25OAsKmCcCBZztep3PmVC0NdDAwABKSkrSEiYAEHgBkymsz5j296P1/MjCOq/Xi76+Pni9Xska+tSe11N63ZMHBiSBUoqgJidd8Ptjj5koilJnZXmUduLkKOrqYivLfUEeo5PpOd5O9E/genSCECKtaR2e9KC8ohwsy8Lv8ysW6jIKCtMz4UBF91pUVlbiI58H5RUVZ9Znzsw8CvHw+/wQRREMy5ypO9KcqUHiIk6gh6zjsAix7shU4UURPjEIiICfF6HXpBfV+4XI1KIIYDokoKoo+U+aMPE/g4Aowi8SGLjI48cwLIq0PBgxAODMSfvMYSdEhEhEEJFItzl4B4wao9TBPFm6jxAS85kxZGBWm8fmo82RvFVWKmlCuhYmFy76O5Mf07k4CwqYRwJFycdMqOiedR0dHVIonw62YQeEFNZnRk6PI+APu6h8Pp80h6qjowM1NTVhpyIvYHQgNTPCsGUSfm8Q+uIixQhqaDjyRCyKIrxeL0KhkGKVPu3RF82o3ZfS/sixT3thHhzF5NgQdDod1qxZg91vfwyW9YcfwABSICFrFiuvg6J4gkEMT7nQVFmGE7YzkRzDhO3YGi4iYojowO0LghfOuuE0nAZ7/QMw1rJJexDGwyOcPRaOAI8GTerflej0nrSdlASKQGQ8SODex5TAw8Ap7E/0xFwaMDEsIk7jJJzmE4mIQIAHf8ZIELbAy8ZusFxkpBqzTwSseBoi15XkPeWHmezDl2qakM6I8vl86O3txcjICDQaTVrLFpmO2qAMDAxg5cqVeOihh/CP//iPWb/3RMwbgZI3jKVjl7MlWphozzqr1ZrRa4wPpLY+Iwoihk+PguUYTE9Po729Paap6tiQQ2ptlPx9AAN9E1i6ukkxgho6U0dFr/KCwSCKi4vjdtkYGLQjGORRFHWyHJ/yI3z1ndriOS+EB7i9+/Fx/J/LLkBpaSlcvgCGpyIX6ZPVQck5PWZDiaEIo57EjVmVO3DTTg8CJn1O+B0EOi481C/ixMtxSc1nHsEv/f9UQEBDGnXhQeKPSO9RHCl83gJxI6zo8XdwmhfQEKNPJPWR7mc8FYJWOOvmI4AoCuClThkBCKIYbhKr0YCIIkKhEDQcF1Ggzoon5oVAxUMpTSgIAj766CNUVVVh7969ePHFF9Hf34+uri4sXrwYq1evxve//33FQZ7ZjtoAgO9973u46qqr8vvGzzBvBIqi1WrhdCZ2ISVDLkxKzVQzjdIm+pMLlCiEe9f1HDBh7UWrsGLFCsX0ybA5vfY3llNjWLq6STGCGhwMj5QInOmXl2wxVhBEDAzYsHjxgojbrc4AiKY4qTwJoiClDo0lJYC+QlrT6pmINDYopfIS3X563AZDWYZOKuqG4zQIBAhKNTpUVBjO1h7xPII+n3T86NqWIER2FREJgVc423pqKiCk5XpTip4AwCOISbtB8CmIzLQgxNjNGXgRr/bp/7P3rjGSnfd55+89l7p0Vd/mQg45HIqXGVISSYkSNRK1iRebtRHb8FpZOwbixHFsGLaBBQw4BhZWEHgDJUEQBfAXB04+Jd7Y3oUsbbKwI2CjFROvbVmmREm2LGkoitNzn+6ZnulrXc85720/vOecOnXtqq4eklL7kYYz6K46tzr1Pud/e55x6JuHEuD5PiXf74u4s9EPmSSuG1OrPNryfR/P/ws6wd9iYWHYRfZB451AUKNgjMm7CX/qp36KH//xH+djH/sYX/ziF10K/BvfGJu9mcdqQwjB7//+7/Pkk0/OpbQzC44dQc2T4juImObdx72b47vKbJpWS5KESqVCe7PLmTNnxm9rfcqn3RTX33SzV8UISmvNlSvX+M6bN6lUKjN1CV25er+PoGKp2G1LlpfGz7YYa+i02wOpQ8GV29sYY/E8wdX7A3WfEam8ST+/trVL+cR8t31XS7S1tCLFQ7hrVip5UOoRn7XkunpaJ0gpiaMIz/eQnkFbnTugKmNpScNi6eDF0Nrh+lMRu1LzcHncQm7Qpkl/LnQY2lpa2rBUaMmfOnoqoGUOtg8RQhAEAcLzqNXTRc+6e0ErhdIt7q5/hd2GI7rBtu0H6RWltX5bvajGYZySue/7PPvssxMVJeax2qhUKvyrf/WveOWVV/j1X//1Iz6r0Tg2BDWPJ9S0xJThsJ5Qm9eHO+UcMXVJkl70Yq3l/o0dZKIIx9Qc7h0wVDv0+vU92s2I2mIFay03b97k1q1bGFtjZXVlbEQyDtcGIrhbm3tgRy+N1ho63S5xHBdSh71XdSLJ+v19zj28wrX7/XU1gZioZj4IZQxfX9+EOcZRWspFEp1YoY3FH1HQEYLcfiN7+qyUyxhruBvtYJVTfci64e7stQgWy7mS+TiJJ2ljNOPvrd1E83B59Mlp2xx7rQaxp1WBoDSMkTaahNjGJDahJGZc5AV4wsMrlQiBZ59W6OBi3pTTarXY2dnh5s2bJElCGIZDDsdHEfm8UyOot2sG6hOf+AS/8iu/cmgB7cPg2BBUhlkIylrL3bt3uXbt2lTElOEwEVTUjtjf7j1xWuNSeX1ptZRkBa7j7/Z3NnjyhcdHHvf9O7M/8V7/zh1WHinRbreJ45gPf/jDfOnL12YmJ4A7d/dpt+PcguPW5p47fGtz7rE4HbSoG+XnOG5fa7e3OLVaY313oANtTKQ0bjuJ1tzf63L69OFTFC3l0nPWQjuSLC1MuQAL8PCIGdZp7KQzSXGcoLVz6B2lYj4uvZdhV45PFyrjSGYaitpXOm8aEbYBI2pe06Ct25RmaAAZBc98G83fxPM8FhcXh0YYBtUd2u021tr8YSf7Uy6XZxoefqcSlJTy0F5Q81htfPnLX+Y//sf/yK/+6q+yt7eH53lUKhV+6Zd+6WhObASODUFlN+Y05HFYYspwGIK6f8ul94rElKfVRnyprLVc/9bNkQS1t91CJrNEcJY4Tvj/PvclfuAnPsDCwgIXLlwA4M4hiC7DtWv3ef75xwDn/4QQqUqGM+/rdLuUy+WRiuaDuHxrizOPLE8dAeB2M4S2lHTmGDPQ1tJRvfe3IzU9QQGxlSg7/Nm0lSUsl/o8o0apmLf9Xaxn8vSgcDpF+XYSa+kay4I/eM9olC2m3CYv1E2tUdYSCDHk+zQLWqZ1sInhAR+pZ66D7YAYLeQ8St3BpN+jVqvF/v4+6+vrxHFMEAR9Q7L1en0sCb1TCUprfWgvqHmsNr7whS/kr/nEJz5BvV5/oOQEx4igMgz6EhVRJKbV1dWZiWmafYzD1voO3U6HKIomEhOQ//z6t26P/PX9qdN72dBghyAIMNEK7373u/mzP/uz/BV3Nw/fUHLt+hbPP/+YSxne3UUASRITRRFhyQ0RT6t6ffPuLmubw0O+Y1N8Yy5dWyYksUobF2YvundU0re3djTbg0hbRSN/biw0E81K2X0lR6mYS5PQTXaw1kslilyK0Kavz3T1duKEhYUyxYugbIPpYqceGkpzMtRAZ6b3FdE27QMbQCwHNYhYPPMdjP+BqfebyQzVajUefrhXC5VS0mq1aLfb3Llzh1arhTGGarXaF21VKpV3LEGNiqDeCquNtwPHhqAmfkEGiGmUk+yDgjGGW7du8doXv4q1lpWVlb4220lYv3wHGUvCgZrDNPUnKR0xeZ7H0tISvu/T2O3S2OstRkmi2Nk5vNbcrVuuXrTT6LDbaJNIiQWWl5fxvNm++FIZvnF9hHLDRMWH4QW5nbhjiCJFrTZ76qkl+zvZokSjjcGf8jMrtpcPYj/uEdQoRKYDWeTU9xuLta6WZy3c70TUkyitg7muQ+PvjhTRnYR9rTgVHD6CBtBourbLwpjoxx3PsMzRIFy7+fQENQ6ZLFax4Wec5UYUOQuT5eXlnLiKxPB2YTCCmrUGdVirjSKyLr8Hjbf/ar9NyCKczc1Nrl69+rYQ0/r6Ojdv3uTMmTOcrJ3ibm26wdoMWhtuv3lnKM13f2N8QVspSbvVRgjBYr2OP/CFW7/qmhustdy715g5Eixi816Dzc0t/vgr3yTqdimFIdWFhZnJKTueK+tbLJ4YoT5v3R+t0ideMboGlWiNtK6W0u3IwxGU6ncmtkA70iwtHExQ2hoiM75Vez+enJYdX38SaQTlrmtXwPKyq7FprZAqQpomWJsLEWf6esITY+t1e0pBaT6CAhdFLXjjCWqUzNEgPPPGA1M3H2e58Rd/8Rc8/PDDxHHM5uZmbhlTqVT6UoTVavUtbYGXUg6ZFc7iBfXdhGNJUJ7n5UaBD5KYRqU2MgHZ69ev89BDD/HhD3+YMAz5o/tfHrOViTtg/fLdYYIaUTfSyg29Wiy1+rCqeYbbV++zdM51Ic6T3ssm4L/0pW8SLK6wtNSh1WrCIQkvUopWFA8RlMD5e+3u7eKJ/iFjz/fwhJe2MQvahdpTtzt7HUoaTWyGSaQTSZYWDm4LnBQ9gUvxjdPTU1Yh7XRzSNK6mah64BMEIdbbx5jeV10pmUt/GWXS+5S+upYQHl2TkBhNac61t23anOb0+BdMEUE5dfN1rHhsvoOZAcYYVldX+9J81rr6aVHdodPp9CmXZ38/qBb1eSOo7yYcG4LKvpCbm5t5m+qDjJiyRolMN2tQdWLQpHDn7mxPqhnx3X6zX1xWK812gVhyIVdjJqqaZ7h9bYvn33USYwybm7O3Fg/q89XqD3O9mXXeiUO4Lzl0E0nUkX2kr6Si2WpijHGp0Wxht9DpOgFOKSXdbhdjDdtSorVGeIJuV2K0xRtqJhiPrL18EO14ujpUZ0z9KYOhvw5VxKTZp1HYk5p62iY+aEwIAuF5eH20YF09y1q0MWA1nojZSXweCuO0vtUTg50FHdNBW40vRkfO00RQkKmbv7UENRgZCSGoVqtUq1VOnTqV/7woSbS9vc2NGzdIkoRyuTzUAj9vtDVPDeq7DceGoKy1fPWrX6Ver3Py5EmeeOKJB5rOC4Igf9LJ0ojjOgKjdkSnOZs0Uka462/e6Vu097bbaG0wRtNud1BKpUOvIdOsLHdv7fC8PTVzBNUng1RboFxy53jz1ja3VTc9Zg4dQXUSiVYGGWv8ULho0FgWqgvESYzv+73oSYDv+XkbbIbN7W086yIGpQ337+9QqfRUzINMpmjMDFJbjiaobqzHzkP10K8eMQ7j6lAHtZcPYk9qHquCsTHGTiZGhyxyItU4NwhradgyDwmnq2d1QS0+TQ3mKcIDaoEd0xlrYjitioavL6GDvznFuRwdpm1Ln6RcnrXAb287RRZgqAW+VCpNva/j4gUFx4ighBC89NJLeJ7Ht7/97SMXjB2E7/tsbm6ysbHB8vLyxGht9xCRCilBtfY77N9vsPKQm4PY3Nih1WohZcLCQo3Fxf6h14OgtWHvfgetNffuHUxQWYF5nAzS9RvbRKsuxdZrM58dnUQClt3tBqUFkStNaKWJkzELf2FfUhukNQjPxQ2eD0FYZWm56kRhlSKKI7Ryc0TDFhxibARlcUO7i9Xx0WlsFXqKWaJRdShtFYk9mNz6tqOcXJEa2SI+RUqNBATsmxAhPDyPXMQcSx5t9VtvCPrb33vbm+iyO0WKD95+dfNZUVQuP3my5wydiS23Wi12d3e5detW38BxMVU4qovwuHhBwTEiKHBRjTHmgXpCWWvZ2tpie3sbrfVUtvI7h5g1EvT8j26/eYfa6gLXr1/ntT/7ZjrrUWPmXEyK7bttdnfbIy02iuh2u3S73Yn6fK1OjC5bKvVSejSzM5TUmm6cuLmgJOThswUDwwlSR8UGj7YaPpduRyJOLhCEAUFY+CpYpweoVM+CI9KSSMcuYkjTXcUn3k40maA6ZjqCGVWH6s6Y3gNQFtpa4x9gzz4aFnAPcIn16FqPBVEg16xeVby/0vk25847HG3tm31Oc8otrAORwrQpPtdu/m2M/+FDnNM7B57n5ZFTEVm01W63WV9fz1PzxWirVqsNEdT3qhcUHDOCyvAgPKGstezs7LC2tpZ3A505c+ZAcoLDRVAiXYCtMXz9i99gX2zz+OOPc3r1EW5XDj+3ArB9pzkxeorjmE6nkw7Zrk5MTUSxxLQdQSFmq0FZLFE3YqfZQgj3uamkfwvTqly0k+HoJ4rU6PSScBGw7/csOJKoQ9CVA3WaXuSw34o4UQ/GyhR1TcwIf8AhGKCVaJYLab5Z03sZtpM2p0uzS26Bi1Yz7OuQBe8AghXuP3lNbyDaklbSiJp42l2ETPk98AOMnV6lwtevvyUENU/36mExzcDxxsYGnU6HP//zP+fy5cvcueNS/IOdfeNwWKuN1157jV/8xV8E3LX5xCc+wY/92I8d7QUYgWNJUEftCZURU7lc5vnnn6dWq3HlypWp97E7Y4MEuEUxiiKklNy7Uefv/q9/G9/3+aN7b8y8rUFsbTTZHEFQmRNoGIb9TQkT0E0UtmVYfjglk6m++DYnwVKpRFCp4Em3iMlEo6QmCAudVeMMCws/7oyIoIy1xLGmUjn4a9BScd8MUpFrrDFE0hBFMUan3ke+MzkMgoDESKTVeNMwFC7NlxGUtnrm9F6GXRlxemwj2fjPTtB/rfZUyCPh4Y6hL9oqw0qwkiuYa6VIZIJMEkz6s5y4RvlFkbWbKxAPduka1SDxdmDUwPFrr73G+973PoQQ3Lp1i+3tbX7oh36IdrvNU089xb/7d/+uj+QyzGO18fzzz/PVr36VIAi4c+cO73//+/nRH/3RBz4XdqwIqigYG0XTFI4nY29vj8uXLxOGIe9973v7QvZZSHDv/gwRVNrmGscxYejUGDpbkXMz9WFnirrRQYg6kls3esrqUkra7Ta+76dDttN/ceNEYlWajpyCnxLpSDBTUfY8n/vb/XWUqCOpL6cENS7FV4AyhniMeG/UlQcSlB2QNxpENljthRUWF4M0RZiqcStFM+mg0b3ZI5E2F4wh+P2CTFVXH6wIPhqGfakxlonmhMNQDOru7evwSEaQWqbFCU7kCuZBEFAGkiBAaU2lXEFphVaKTpK4jktII60s4jJ45grGf7BzP+9UFYks4g/DkA9+8IN84AMf4LOf/Sxf/OIXMcZw/fr1sbp881htLCz05tiiKJpJ03AeHCuCyjBvim9/f5+1tTWEELz73e8eEq8EZqpz7d+fglRsf1RRqVQJAmfuprXh7rV7nH78NM39+c0YhRCsX91Gl0Pa7XSod3Fx5i+s0galLWiLljo1vx3NJkop2u0WQgiWFhfx/ezWtHST/uvoCMqlM6aROmpP+By6XcnK6uQ0bFvJscddRCdW1CpBmiJ0MkWlcon7poFv/VQCy6QRRH9zgZdq6nnCo5H0/Ji6U1hWjIK1CmMFbR2wGAw8KE04FcFwpKQRtIzPon+YdGEPbdPGWDMkb5Utup7vUfJLMMIvSimV+0Vtrf9ndro/2NcFV61Wj3TRfKcSlNa67wExiqK8K9jzvJx8RmEeq41Tp07x5S9/mZ/7uZ/jxo0b/O7v/u5boqpxrAiqGEEdJsXXbDa5fPky1lrOnz8/UUE4CIKpXHWttezfb056Qa6XF4YBy8sreL5HN7XRznD7O3fwK7PrBo6CMZaNG/epnV2iXju8vEtUIJa4LfEXBAy49WbmhEYbavUa4cAAcaxcC3ffdjsFwpkQQWXENUkctjuuDlVAe0z33tDrIsXpgVtCW0tkZX6sQnjDKUJrscam7e8Ki+XuboN6WRB73Vxjb/qmF4tJ03QNNYKgxmI4esqwp8O5Ccpg6JgOdb+/OcBZsIw+t2K0lWFpucVJc55WOne0ublJt9vF9/28A25xcZFarXboe/edSlDjvKDeCnzkIx/h0qVLfPvb3+ZnfuZn+OEf/uEHrrxzrAgqw6xdfK1Wi7W1NaSUnD9/fqqWzmlTgOP7+QAAIABJREFUfK3dNkqN/uLLtObjUmtLeIUvTDYHlWFj7S6Lj5wctZmpkc1OdbsRKrKsLM/XGRQlvfOP2wm1hUrOJc6csIOUSZ854SA6yfDnFHcV1th8XmlsDSrFpAhKKYNShjAcvxg11XT1l048THYdHXGQQaAQAjHQXCD9EO03sQaMUblLyXAr9yilew1p40FDhZxlunS2mOCYu6tKnCvNnxZvmuYQQR1weYYg2Gehcp/qwrs4fbqnUKFStZRWq8Xdu3dptVporUcKwR4UbX03EdS0HXzzWG0U8Z73vId6vc63vvUtPvShD81xNgfjWBHUrKaF7XabtbU14jjOlX2nxbQEtb81nN5Tac1HCOd/M6iXB+5cirI+G1fucurZc0OvmwbW9tx6FxZqJIlBddt9JHAYRHE/QdUfcmaI7Y7zm1qoVqnXV5m0Og2m99zxWuJIUVkIx3fxpZGVtpZYT/4coq4aS1DKGKID3p9BG0ssNZWCiWRLHS7l2pSGajVO1RfSY7M9tYd+JXORpwmFENhCk0NTBVPWoRRMMEJsGh9lBYGYr7utZVpDJG6xU6vaZ/D1N1Heu/p+lqWkihHFoBDsnTt3iKII3/f7SGvQduOdTFBFNZhZIqh5rDauXbvGuXPnCIKAGzdu8MYbb/DEE08c5amNxLEiqAwHOd52Oh2uXLlCp9PJiWnW/Pa0rrp7haaGjJgQwn1hJqQnBiOo7Tt7bN6aTWw2H7KNIqoLVVZXnMXH3l4HayxJJ6FcP3zasC+C6iTEcUKcONfc1ZXJ7ekZRhEUuDRfZSE8MMXXSRXUJ+4jkiwujT7PccO549COegRlrD1Qf28c9mLJqpH9AdJYJXOXIjTWYI3BiiR9jWvrb0iflQPazUfVngZfsa8DTgbzjWckNiGxCWVRuN5TDuoW4Zm/BPsjB3ZujBOCVUrlCg9F241s5khrnT8MvFUNAdNAqX6zy1m8oOax2vjTP/1TPvnJTxKGIZ7n8W//7b/tk3p6UDiWBDXuhut2u1y5coVWq8XTTz/NqVOnDn1zTh1B3W/0hFytdXnzA/TyYJigANbfvDPdwVlLN4rodrtUKhVWBmaZEukWs7gZHZqgtLHINHVpjEFL5QhvocxCdYL1QgHGGiI5+hpGnQTo346UEk94Tq4oXfLGyRMV0e2O/5xacrb26k6sOJkOT3V0PJvBYgGJVXSVx0J40IxQSlrpmmVshLUeGWtbC/vSpyac/UY2i2atST9zgZt7OngWaU+FcxMUuDRf2evdV9MP6vYg7H2EvY0Vh8saBEHAyspK3+KeyXVlda1Op8Pu7m5uclgcln27oqtREdRbYbXx0z/90/z0T//0IY54PhwrghpHNlEUcfXqVfb393nqqad47rnn5n5qmoagut0ur3/9DZrNphNynUH9eBRB3bm2yeIjk55qip2A451sZWKwWOJWBByuABsnEmsNSmnXFhuECDvb7dZN1NjlvdgoYa1ld3fXLRrWpWcsFizsa+lSlQPSO0UkscIYOzxgaw8TQfUEbdv68B2Vxio6yTQE1fcubNaQkcVQAlqmQhi687DWoIzOU4RY8P0I0tRdHp+NuFa7R9Ru3jRNTtG7T621Uw9cF+Hrv0B5hyOoURBC5DNHSilOnjzJ2bNnc5PDVquVKzyMspSfRU/vsBiMoL6XdfjgmBFUEdmg6/Xr19nZ2eGpp57iPe95z5HdYJNSfHEcc+XKFfb390EKd4PN+gQ5oGtnlKGx3R5DUCM6AcfMMrmpdA0W4ubhhjO11uzsNXKx3OyayrYkmMGDaVx6D0BJQ9xNiJKus0VYWe1bXKWUtDtd4qSXqsl+6Xn9TQYWNw+1MHBsXS1RM6gcAEhtkcoQBj6tA9TLx0FbR8xt6XOK6btNe+TUj5YK8vqR6yLUhTb+CGF7WVKTXacCEWXpwhifyHpUxWzXZBAd00FaSSjCfF+HUeXyzF+A/dH5GXMEMkk0GG1yeJCeXjHaOsqBX6VU30zS97IXFBwzgsoWyiRJiOOYr371qzz11FM8++yzR/7kM2p7SZJw7do1tre3c0L8+qe/c6gvmCuG9xgqSRRRY1gSJxuyLTrnToKUOt+q7CQYbfCmtEYvWm1Y4Q95TsUdSdUenL7MMKqDz8GilWZ3u8HJh5dp6ZabMTIFIrIQWzNkjthrMrCu2w0Awd5emyCw+AUFg+aM6b0M7VgRComZIm02Cto6UupIf4aIxWLGEBS4ZonVcPD32qlGiF7klO/L9rojbfZvC1txwNlSNFIQdhY0dZMTwYl0V4er8wi7i7DXseLJwx3EBCilJrZQH6Sn12q1uHXrlqspQ5/B4TxeUX8VQX0Pw1rL2toam5ublEol3v/+91Or1R74fpVSXL9+nc3NTd71rndx4cKFdGjT9jVJzAIh+mUZZKKQUYJKJEEpzFtugZmsqpOk/4k9bsVUlycPshatNrKW8Z317aHXqVhj9LSLth1JUFq79JTv+wRemSAIsFiajSZ+4BP4LrUaJ/GAYE+/4rZDTzBOKovSmjhOFQyEYNdEGMyQMOxB6EQKPzgcuVlrcmIzlinrUGBtwqQJ3D0ZDhCURUxqPxf96T7h3sKeLXOWqJcizH6fDhlP6xnVNE1OcCI7+MPyHL7+Cso7eoI6rNTROD29TqdDs9nMvaKklJRKpZm9ouatQX234VgRlBAunfbUU0/x+uuvP3DLDWst165dy6ezP/rRj/bdgN1WRBIfrujs2swLBJW2dHf2WlDxpzYoHEQcq77GuLgZjSUoS9YF2G+1YazNGy36jhlIOhqmcAZIlEYV2uiNMWnrr5efU9SVYGFlZQWtNVEU0ew2c0JpxDEam6s0DNc5erklmViq+XyMQBpFst+BAWFYCu3c4+aQWpHCrx4uvacGUnrT1aEM5gC33Ybqvw9c196MEZ6Apgkwnt9rN8/+slkXYS9fmM9rjfCMapt2bmJ4mCaJDL7+c1TwP4M4WvfaQdfaeTAq2hrnFZV1HS4uLubvKX6Hj5MXFBwzggI4ffo01tpDq0lMA2MMt2/fzuXyP/rRj45MrTW2JihIHATR31+dxBKtFLub2zzy7Lmxg68HIY+g0gU5bo5eaKO0C7BcLrOyutK3+MfjmhsEyGhK99k0erLGoFJ5l0GyjVN1caVVrt93YvUEwhMoYzBb9/FSNW0XuWVRVM8uI1NoMMYJx5bLAWBoyiRdXL1h1YfCHFK6wb45pE4iWTCW2R/ALcb2X59p6lAHkRNApH0i7VHxDcJTMENtq/8IBXs65FSQ7jMLmITAxy++8EDPqH21z4nwxKGbJNIzwzN/ifEvHvL9o6GUeqBiseO8orTWebR1//59rl27hlIqd+Ztt9skSZJLO30ve0HBVAYA31vIUhAPwhPKWsv6+jqvvvoqcRyzsrLCuXPnxtZ9ptLgO2ifxtButWk22wjPw9dQKpU5bHEgTlTfW+NWf6oqSRJ2d3fRWrOyssLCwsLQ4hIl4xc/2ZluYWx2I5SSGGMIU6fbQRhj2d7ao9vt5k+c2WBxW0pHGp5rOw/DgDAMCYIw7dazGKNRSqKURGtFo9FNG1uEU4/I0qiFPwLwhHDCpWFIEIaurV0IZxCoFYmWRB2dpyOnbTTPmiOK6EgfM2kDVo9tjhjEvgoBiSfmu+931BRRuQDhOX09P/Cd51YQ4Kf1TGMNW9F99vb2UErlppda6wPFfwfh6y8f/KIZkaWR32r4vs/i4iKPPvoozzzzDB/84Ae5ePEizz77LMvLyyiluHXrFr/xG7/Byy+/zPr6Op/61Kf4whe+4JquJuBzn/sczz77LOfPn+eTn/zk0O/jOObv/J2/w/nz5/nIRz7C9evXAXjllVd46aWXeOGFF3jppZf4wz/8wwdx6iNx7Agqw1F6QllruXv3Lq+++iqtVouLFy9y4cKFA6O0vXkIKn2K393bc4uACPA8j6hxeC8ol3boP14dK7R0Yp17e3vEcczy8jK1Wm182/7YtKVARXqi147WmkZjn2Y3wvcDN6w8uB9rc6VwYf2RzR+tEf5P4DaVkVYQpKQVhnieTxxrojhir7HPbreNUmooUioewxBp+T6+H2A9sDrIRwGy9KSUEq00Ro++BsoO3yvGQleO+5pa9JQyRuDmoSbWnabErgonk+Y4pHNYnu+uvwo1taUavucRhAFGazrtNnv7e+zv79NutYiiCCXlxHvGM2sIc+/Q5zMK7yQlCSEElUqFU6dOEYYhL7zwAv/wH/5DXnnllTyy+sxnPsPHPvYxrl69OnIbmdXGf/kv/4XXX3+dT33qU7z++ut9rylabfzKr/wKH//4xwE4deoUn/3sZ/nmN7/Jb//2b7+l81DHLsVXjKDi+JAeNyky99wrV66wtLQ0ZOt+0CzU/mFSfKndRiZEe2J1FaOdOjaAiiUqTgjKs+fks/ZyQc9Y0FrLzt1tSktl6ot1Av/gW2ZSBGW0QcWacMDiwmnztZFSUl2oYUU0IgZ055k93YaeN2RgmGEcQY2CwD3tK2Wp1+o0VEzQjnspKmux2chAIZ2X/dsdmiMrZRVYkAnU0hRR1qbt+VmnocmVCtwmBVYYLAYGajUA7cSnVhquF1krc829g2FoqAOisSmh8GjogJWpRWhHw2JpmAYBPuVSqe8BwOYPIZoojtHtNhYXYWQ+W34QOIkkAb7+E5T3E3OeWQ/vJIIah3q9jhCCn//5nz+wiWceq40PfOAD+Wuee+65PNrNVNQfJI4dQWUIw5BW67BeOz2Twkqlwvve976+2YQMBxLUvdl8oIp2GysrK+ztOaPDZNCOotGhPt6pbiziuHisFqVS8VPJ1Hpf1lriMeoP+X7aSU5QmdxSFEcsLCxQq9dpRfFQlieLQgZrUX3K5tn2lUaa2ZW3M+HYhoxyYdZ80fTTYn6RtIquup6ra0mrQYDWrq4lhE2tM0hniwTC84fqWrFJ8n9nvRvZktNOMmWIYgSnMVMZGVpcM4RFW0FTlVn054+itlRpboIC2Nf7aTdf/wIrhMhTqDmsU79XSpFIie5GqX2HwPP/kB31ErX66am64Q7CdwNBZZimw3Req40M/+k//Sc++MEPviXkBMeQoOa13Njf3+fy5cv4vj9kUjiIo4qgMlXz3MQv/eJkKSQZ9+/DEdTsnT1xIrHY/Ole+B6B56EmSAENIkoUE7Ixbj+dhPrJKlFKuJVyhdXUoddaS7uQIsybJIQgHJHuU9IMOezOKk9URLub0Biz8Lt+ijSCKvzcZsdpdZ+Ab9xRBCWL53tuHqswY1R8t8kbFgS5sIftvbAjBVGsCLxek4EVB6lUWHrk1MOurBwJQe3oEtZ25p6R7douCcl0JVNBmkb1+xZIk5pDhvI1btx4Pu+Gq9Vqfd1ws3blvZM0+GC49b3oBfVW4NKlS3z84x/n85///Fu2z2NHUBlmbZJoNpusra1hjOGZZ55haWlpqn1MJKgDalB9quYj6iyZmkQygqBmhrW0W12kVPi+hxAefvpliFvx1KKZ4+tPPXSaEd6uya3jvfQ8TFrP6SQyN6oD+tQoRu6z6LALNGdI7w1irxVhF2bLg7kUoYcxKp9vs9ailEe5arEWZOrIO2hOaAUoU7hmBWLq7UCQEFIJTBq9RVhrCq8ThYjLFv4MY1dWOHcEa5q0Hg0TsOzPH0W1vBYP8dDBLxwDz/PwSiXOlF/nxJkfBxGitfMZazabbG5ucuXKlT7rjYy4yuXyO46IxmEeL6h5rTZu377Nj/3Yj/E7v/M7PP3000dwNtPh2BLUtE0SnU6HtbU1oijiwoULM7V0TiIomSha+6OJZFrx2ExNQibDBDW9CrMlimK6nQ5JognDAIHIa1oARmpUrAgrB3dvdSfUn9zC7QjvkfopgtDPiQncAqu0ptnpYq1TdZiKFAsOu9baif5PB6HZjhALYmb1HWMtypiUONI5NeXj+xlR+KlIa88yQ2mFFhIjioOqmYBrARZacchSqYMlAqELZDQbYhPQNQH1Oc0HwaX5joKg2mK00+6sEHYfX7+KDv57fN81zxQfJIvWG41Gg/X1deI4JgiCvkjrrRjePwzmmYGax2pjb2+PH/mRH+GTn/wkf+2v/bUjPaeDcOwIalpPqCiKuHLlCs1mk/Pnz3Py5MmZn7QmueqOmoEy6VOf1noq8dhMTWIwglKJRMWSsDL5/UmaOgzDgKWlZe5v3R+7KMeteCqCGhlBWafS4EgTfD9AxRov8Ppe0+l22e90Xbv8DDWEYh2qdUDH1yRYC7HUlLUPvhi5/I+6PhaIdZIrhGf3iVakIrS99xbrWsY3KGMRtjfTlkkKQX82s5X4KJuk7SuD12Zy1DSIPVWhHrTHn9CU2JIlnix1pvCamgyDoWEarPjzD5z6+r+i/Y+CGL5Xx1lvSClpNpt98kTtdpvXX3997MDs24F5Iqh5rDZ+8zd/k7W1Nf7ZP/tnufL55z//+b5r+KBw7AgqQ5aKGUSSJFy9epXd3V2eeuop3vve9x46BRAEwVjB2GJ6zxpDu9NBJpJabcEN2U6xz0xNYjCCAhdFjSMopSStVr8+XxRNToslzQhOja+3Qc+wrwdbkCZybfBSuoU8aseUa6kiRGb9Ua3ilyp4yWwpyiTqqZE35ujMlKk2n0ksfnVUD+EwBRjj5p20MK5RYnCbMZRHCHFYQJr0WEX+n94/C6k+iyXRhlhaygGFdF62t/6h7eGj7f/dnqxyttzKd1HY/ZDiwyQoPHb10Vhw7Kgdlr3ludNtwjbw9RfQwf849XvCMOyTJzLG8LWvfY1z587RarX6BmYrlUpOWIuLi1O58x4VBglqFi8oOLzVxq/92q/xa7/2a4c44vlx7Ahq3M0kpeT69evcu3ePJ5988kgEZCel+Hbv7WONyVs2FxYWqNdqM0m+CCHSWZ1hoo0abRYf6r95XV6+hTV2qGgcD0RhbsnrTfgPDuyOQlToJjRao43G93zCMCNKi+85lfe9+w1MKDHWOr+dxUWCIGCztTfl2fdgrROirdRK8xFU2vlnE2AEqRQ/mayrUHge1hdgh5NuApCJoFzt/3wsIG3CxBHevKHC5E3/HVmmEnbTVCH0GiCKNajim0dJO0FTlUkIKHturCAXHba4Y5qBtO7J8pEQVNd26dgONTF/ei1Qn0f7F0EsHur9mczR4uIii4uLPPLII4BLEUZRlMsTbW5u0u128+HaB+0XddxkjuAYEtQglFLcvHmTO3fu8Pjjjw/p5c2DcQRljOHK61fY3dujWqm4utYhFc0H03sZio0SxqQzRkpRH5M6HCSood+3ogPrWt1Ypgu3KrSD9z/de76PsAIda/wgYKFUQqdELZVyKT5S/bai+OgBiLoSUxboGe0xMmhr0WlEbeLxxGHTdCW4z9cKiLUcSQUWSxK7GS8hUnt2BMrqXLF8+F2k7DMcr7VkiRNEBfLotQX2SMv2PVgMRlvuNT7bySqPVLogDI7oNEIYBGZ60sJ5REkrCA9rBV9425baolY6ivpPRKD+Myr8qUO9e1yLuRCCarVKtVrl9OnT+c8zd95msznSLyojr8MqmBf381cEdUxgjEFKyZe+9CUee+wxXn755SN/6vF9v4+grLVsbGxw/fp19u41XGv1HGQohBhqMc8QNTqYdOFPUpv1+mKdcY/C8ajmhkKngNUW2Uko1Ua3gEkp2d1rptJEYYFweyuQtaC1xFoIAp+Fco2gVOi+i2KCVF/PGCdFNKjh5nlerp/Xd75tSVQ95CIJJIW5KSudhbooFFcsqRFiOiScPcRk80uDyEjBGrDaw0u/adomadfebHUjgI4M0QaG3U9EH2kVY7lsrMqm0VYWDW0nJR6pxLh6lgXC3pEIR1hgEELnpOW2mpJ4wdbkbhxwthT3mhwOmXhomRaRiah4420upoWvv4L2P4z1Lsz83llnoEa58xb9onZ2dnIF80z5oahgPu1DmFKqj+S+172g4JgS1O3bt7lx4wZCCF588cWJs0zzIIugrLXcu3ePK1eucPLkSS5evMgbn70xFzkBrv14DEHJKGZ78z71lUVWVw6K0CxxNJCmGfH6qBkNEVTWzmutxeARBH2DPPk/tdYYm9WiXKda3E4ISr1cWitOAEdE/euDTYUaDEbrXtdfTlqCqCOJpxSiHTp7C6pIULgoyq+65Joxbr+e7+OHYb7+amumithkAl4A2kqkVem1LV7frDHiANKy0JYllsrTtNGnFClsLsaai17gTAzbsabs9xo73B8PZ0PiU4ij+iMtDEKY/Lg31QKPBjGa4QeKWX2j7ql7PF56fLoXH4BQ/p8kpV8FMTxEPwlHMaQ7ScE8a8i4f/8+nU4nf20xTThq/6PMCv8qgvoehLWWD3/4w0NaVEeNIAiIoojXXnuNWq3WJ4U0lw5fCoEYapAoKi6URUi1evCXU0o9VMfKEnPFdSVuxnDG/dtaS7vt9OpqtRrGCsxuzODi6qSJ0lpUEPZtMW4n1FYHCWrMmQoQwgev4OJUUMtOpCJuA6FXcMz1JvNyisToIUqwMZhyr84UFIiJ9CwjM13tJYnBq8ZoO661W8BQVDg63deMpyUoW3AI6SfEbFf7tsajQZReR5PqDqZirWLAeTgnraAYE4PQRBgaCFaDDpA43hqh+j6WtAqn3TRNOqbDgjcbqYyCsHuE8jPI8GdmSqE/KBWJooJ5UZ0h825rtVrcuXOHVquFMabPUn5xcREp5V+l+L7XIYTg8ccfzy03jlrRPEOj0eDNN98kjmM++MEP9s1WyETR2ht2v50VQmQ+UGKk4kLU6LB05sSB24nGRR7W9n2x41aExdLtFBo76nUslt1GlyI59UkT9aX8eojbvWufaE2iZpvNyRc6z0Mqha8FourlKUJrFRnNCm/Ax6lwiokZPn8Vafy6GDsknORpuvFwdSFDHBsCo2csM2bFnv43daSHc8HVLm03IoKzKakNEtMgtpIyj5Qzd9zJzsPuVhAMR1suRbihFlgKHwWrQUQIESOI8IiAOCdaY1PfKF2kOScbJdIU4abc5InSE0fSHeeZr+Prx2fq6nurZY4yhZhiy3hmAtpqtdjf32d9fZ39/X2azSZRFHHp0iW2t7enntn63Oc+xy//8i+jtebnf/7n+Uf/6B/1/T6OY/7BP/gHfO1rX+PkyZN8+tOf5oknnmB7e5uf+Imf4Ctf+Qo/+7M/y2/+5m8e6bkfhGNHUEAuqfMgPKHa7TaXL19GKcWFCxe4dOnS0E20tzmDBt8k2Ezg1RHJ4GI6raJEPKVpYtSM2N3aoVKr5h5Q2dNxN5aAwFqDUs6RNgjCiYtM0knyWk/rgDb3ybAuRRd7eIt+mvYr/DZNDzp1ikyBwS2yaqCXLtPCExb8VIi0z7rdgrI6b0kfjnl6tvO9RgOBlh7BCMHXWaGtR0dWqJfTbQkLuMjHWO1IWYCYwqigq33a2qcejDCX7CPynvNwrz5o8rk2ITy2jaQdBtSCEKhjqfWuq7WOtIgRIiUtP4L8M9GptqET2m3QYFNushqsOFsT3z9UE1GGQP1nrDiB8V+c6vXvBB2+TKqpVqvx8MMPA/CNb3yDJ554glu3brGxscGbb77J3//7f59qtcoLL7zAP/7H/zgXgy0iUzJ/5ZVXeOyxx7h48SIf+9jH+oRii0rmv/d7v8fHP/5xPv3pT1OpVPjn//yf861vfYtvfetbb9n5ZziWBJXhKD2hioO9Fy5c6DMhG8RR+EBprWnst7DGuPTTqJpRoz2VokQ0WH+CvpXXFKKzSlBlobrgnnoLc2SdboJSEotrgBBTqAJYC0lXUq6VaEbztIenJBPbkeebLaJAXttyChaGROnCyFFGXO5NJoGg2u/Eq9AkVvWR0DRtDir2j4SgABpJQD1P8wmwHloZhAgJ/Er62TlVC5s2O1hG+yzdT8rUg2nnzkbVB3vkf73d4Vx6SEEQEPgBfuAsSIRYwNqFvu5ARIwxHZRpUQ0tEAGOpHbZpWZqiK7s65rMtnmQ/NUgQvm7SHyM/8KBr30nENQoZDWo5557jn/6T/8pf/qnf8qf/MmfYK3l0qVLfTbzRcyjZF6r1fjrf/2vs7a29sDPbxSOJUFNqyYxDaSUXL16le3tbZ5++umRg72Di+buLCrmA7DG0G63kVIh8HMV7VEw2pC0I8r10Zbt6RZHtphn0ZGUEtK0oRCCuNVvAW+tpdFs0Y3jfBh3FkSthKAa0B5bfzoYMpNlMhaUhfDghUsIR2xAXmwTwsv6sF07eUdjAxcNCs/DYEisShsO+jv8RkZOBejYw9bnCgRyNCM/3VYq7GusM03su/Y+QvgICuoHIq0xpYRlMWwlZR6vdvDnOK6s1tcUUK7VqHgeWju/rjiOUaoN1o0YhGGQWmYEKO3TaftUKg+jRSm9kAohIiwRu0GVs9WH8MVOHmkppUhSkWFr7bD9xtj7TxPK/x3J38P4H5p4Pkdp936UGCTOrCtQCMGHPjT+nI5KyfztwDvvU3gLMY8nlFKKGzducPfuXd71rnfxzDPPjCQK3/eHbvjdw6T40px0sfZz/87BQ61RozORoKQ0fbp7blcWbVwqLAhCJ+aa/i9pxJBqTHa7XWcoh1+YeZoNcSvBLvkzNFv3Y7CTzsZ2lMrNEJQxxNoRc7GdPGMQAQgJfuCDtSRWIQdqVfnnLcRY0spmiqz2sNpDBPNHUcoKGhEsBArf8/DDab/GXh5J9o7U0NI1TpcTjI0wtouZ0qF3ENbCrTjhmYUqQeDci4u/1Fo7x+EkoRm3wFqCMECnIx+BHyC8EEuIpc6egYD/gSXvRYRdJ/DuEpTWKZkNPO6CNWPsNzyCwHfpwSDAz5XkDaH8P9BmHRX8CIjR101r/ZaqhE+L4oPuYeW8vttwrAnqMBGUMYbbt29z69Ytzp49e+Bgb9ZqXiSonTu70+/Q9gwKKwNDvUms8if+cejut1l+dHy6sVh/yqw2jLFpQ4F7Mi5uH6pEAAAgAElEQVRGBHErIoqiXOp/dXWVja0Ghx1+iVox3c7hH9+TQXKNDbbee8ocGqBNW967Jh2unRDSWA1aGqSvsNj8c7bZhrJ/F2zdB0nL/T8lPVmhXEo75tIUnMFMmSTsnYC1sB8FLK8O9lkeBh4bkcejleWcqC0qJStHWNpGWDvs0TUK96XkEV1icTBFJgR+4COlRErJ4mKdUlhK08e9aCtrrMlSeffN/02ldo5q+CzKXEBam3bAKzw28fwN/GCDCuv4dgNsjLEGpRRaKTpxjDaulT7wM9J6hUCvoUt/F+s9OnQO79QU3yhMk+acV8n87cSxJKjDeEJZa7lz5w7Xrl3j4Ycf5iMf+chUaYBRahJTEVQ6M9Fut3ODwsG5KRmrAxeNqDG5W7DbdQSltUYbg+97hKHvZn+MRRQGVq2xRO2IuBOxvJouaNZ5KPUKHLMtmFoZOq0Ywtlnwqy1ffNL4CIo0idN10XXI1htnC6gAuwBc2HZ+3RkYKBRqkhs2b8nkZYjK5CRoFoDT/j4uKf67B3GOkddM460bO+4hBC0VQlt1Iih3dnR1oYdqTlZCtJzCvBFHV8U5wMNxkZo2+2RF9HIJ/mr3Yj31foHUJ3+Yyu1WFnNf+f5PiXfp1Qa9ndSWtHptnh9/9cRe3+bxerZfFaoVqshxOMYew6Vp2ot2C0CsYEf3KFk16mwgbANd6+kpBVFEVp9E2sv0ZQfIhF/g1r9TC4I+04kqMEywSxeUPMomb/dOJYElWGaJonM1n1tbY2VlRUuXrw4k2TJIEEZY9i9OznFN86gsAijjevgOwBxsztkdNaDpdXqkkiJ73m51YbFplbaBm0MVpu8TuN5HiQ9ko8ShTbZk3xxyHTK6XhjsBGIQxBUPEqIV/fqUNm8jzU2n2cSno82LiLC9ghiLNHHAmoHxw7TkFaSuIFoP+i1amfeUL5I54wKw7QGjU3TWCZ16s0eFoyFRhywWj2aLtTb3SQnqNHw8MQCXt/Qq8HYxEVZRDlxtbRmU0rOlEpYa2i3O2itWKwv4k/xUJf5O4X0vmfBqS+wan+ObksMzQotLS1Rq9WcvmR4BmMfRpn3u8/eWjzRxLMb+OU7hKV1SnYdjy2wlrp+HaXeYKf1Xt64+SzduIaUMleaWVxcfEd4Rg2S5lulZA7wxBNP0Gg0SJKE3//93+fzn/98X4PFg8SxJKhpmyR2d3e5fPky1WqVF198kWp1UrPBaAwSVGOriRoz76NTTS8hBIuLk7/MQ8oPY2CtJW52qS73hwFSSlrNFt0oyRsgsjoTkJOR1RoQeIHvKMhaWttNbKpG04o1RutUO2/U3M24xd29Tirtmrdm1PU01o61dbeRq0O5p2add5VJa4iyulOefusdS4+0CoSVCMcGh4hUhkgLgdEh5bLrILS5h1Sq9OANzBml3Xm+F1IK3AUvpgYbEZyoHhxFT4NdqWlIzVI4S+Tg4YkKnqgUFhKLtQmbSnFCnaXduEp9pURYl0z70DIKyu6z6/0Wjz3yizz2mFscMzmhZrPJ7u4uN2/eJEkSKpVKHmktLi7ihytYVpD2PSS5RFOEsHcIvA2C0gZnKus8eur/wYgnuHzjBOHCQzSbTTY2NojjmDAM+9QejsJWfqbzn1OH77BK5gDXr1+f7WCPEMeSoDKME3NtNpu8+eabeJ53oK37rPvYGdHYUPSBck+BB1f54+70tbNov50TVGaGCBCWKgR+3E9M+TGZPOXne8WajgAJK6srrh14Y9s97euiWZ9HJkE0nrTcPI22FmI989KVNTiMgo0Mquran30/QHiCWKs+vb1RGKwZuSO1eNLHVjIx2fnoQEaChZp1M1bFY8ZFei5iMthU2SMjrSzF4+OR+cJrXWJRnKIcaqRJkDZ2f0wyW10rxdVOzPuXqnNGCwJjfFqtLm/4Xf7WU/8b5VIFZRpE+jaxvk2kbxHrdRKzPdOWldnnZus3OFP9eyyVPtAnJzSoON5sNmk2m9y9e5dut0sYhn2kVaksIMR5lHlqoK51D2u/yJnVrxOGiwhvCeM9TixP00odere3t3Nb+aLSwziJoqPAPF5Q3804lgSVfQEHv4iZe24cx1y4cOFIZEQGCWq7UH+y6RNgMqMPFEAyZQQFrg6VKZor7aSJwjBke7s1TEypAoSzyRh9eyTtBKOMU/KWZuBL2Rvm1CNIK1N0AEGSpeiUxSqDGKXjN4K6lDG9usMArLXYrkac8AiCEG0MsZK5UvmsEAi8RBAuuHM09NQQDMYNmM5ABkoKtLZDs0QCwBNY7c4hCFzbuLVOZUEXJIPyBhZPsNGWPHuiSskrk4Wh1oKyCdImSNMjrb6B4xHYk7qvFjUrMvUDKROnMBLEvN59lQ+U/gaBt0Tdey/1sJca0rZLrNdzwor0bRJ9d+L1NDZho/MfaKlv8nDlb+N7/ZmBouJ40VAv08BrNpvcuHGDdrs9pIFXKpVYu9Yhit7Do8F7McIDYxFmh0BcZ2WxzOpyGeE9AtTRxs+tNwbTjkUynFfFHOb3gvpuxbEkqEHEccyVK1doNBqHds8dh8E61/bGLlibt2hXq1VWV2fzgQIONBgsorm1T2Vvj4VajXqpnjcPdAtRmEnrNE4qKTwgG2OJmxHxyOGZcWKvTiHcaNcWbq2bX7Kkpx5pqHv5NgrvLNSKXGqvq4bJ2WYnlRaeTGKIQ31oYirCdC122UUwHlndqBcB2ZS0tDUpgU3uzEu6UB0IynX+YOBsSrIrIIQA3y9oOdB3LTcaHVZtRCV0LdXZn9BLazh+Pb8+Tqy2n7Q0/VHl1XbMauinDxHTImvo6VCtVqjVVsg+wzfbf04tWOKZ2ktD7/JFlYXgPAvB+fxnxkpivZETVqxvE5t1zIA9SSP5Gm15iZOVH2Sl9H14B8wWlEolTp482deZprXO7d+vXr3K3t4epVKJ5eVl7t27l9e1/OAhrD2NMcbp5RrAdrFWU6+FLNZXeeTMSTwvxFhBt9vtSztm80r9EdxsRofH0WoDjjlBSSmJooivfe1rPPXUU7znPe858mJoJhgLbmG5/p0b7O7uDrWMzwRjSaZQ7jZao7XB15rFhTpBOUxr9q4bLIqc9YVOIzw3nT/dIUSNiGZ52hx8WlPxe6WcWCn3dJq2TZu2xJZ7Eju5UGlfrcgSKd07B3oLdo70d7pj0UtH81laAzYBMaZpSiDwhehL21lcnclY40ZiC6QVR4JKzckEFf2lwiA88Po7kQtBb7LWpxuUObHgo5QmSWI6nTYmHWINs1mgICDwQgJCqr6LOqwFg+qlB01CZGJudyWPL0z31G+MzuumK8vLIxX6v77/R4SizJMLzx+4PU+EVIN3UQ3elf/MWkNi7hVShO5vbbvc6/4BO9F/Y6X8fayUPkrgTZ/28n2fUqnE1tYWlUqF7/u+7yMIgqnqWmGYXUObCy1r7fQZyyWfyqkTPHT6FJ7nZvziOM49ozKjwyAI+iK4Wq02tq71VwR1zHDt2jU2NjYIgoCXXnrpgQ3mZRHUvXv3WFtbY2t9Z2TL+CxIYtW/KGctdilyoVYhCEvOI7yz32axnH15Bd1uTJLIfBq/b1h1CnT3u3SWDpe6sNYis/Re2oItEhDpFzBTKB+01YiNRhu3sDsHW+uk6EQvNZiRlhdbZq9sjYfpWrzy9NsTOC0/X3i5lkNOWsZSNgJFTGwUfjBrxNKP9XbC2cU6lUoAuPs4m/fKhlhVp+vsTjy/L9LyvICKH1Ch150XG3hh8SU80WFXbrIr79FSuwMxoU3T0wm1Wj0d1B4NC7y29/8Smy7vrl+c+fyE8Cj7Zyj7Z4APpednUXa3QFo3udH6EmX/ERbDD1APX8AX45uarLW5UekzzzzTJxM0a10rI5gsKspU3N1wsov2A99ndWWFE6urjoSEcI1KKWndunUrrw0P1rVGzVIeBy8oOMYEVS6Xefnll/nGN76BHtWufETodrvcuXMHKSXPP/cC/zV6dW4fqKjbS+8JyPkpm/UQoqdo7mCJ9lssPrSCtZZut83WVgvhefgzElOG9l4Hu1jUQ5t+O4nOoqACtAVpEaVUusnz8rSWsYauVE4xwvYiJpG2aGft5MX2Bl8LSiLEeDZ3y3VRzOGgI4NvxVwRtgDXvm8M8Z7l3EMnKVfKJEYRm4TYSPe3ljPVtbSxrLcS3rXUe8gSwmkiBoFPkbSMcUOsKpsHMtopL4RO5y4InBTRqzs3+JlzP0iQNshIk7Cn7rMrN1nfu8bNnSt4C376FD/dNfnLxp+wr7Z4aen7Cbz56jJCCEJxgtA7wWL4vvznyjSJ9Tr7yZcQBATeIoFYoeI/hkiVIxqNBm+88QYnTpzg4sWLBzY2zFPXcvNaws0VOtXivvVmaXGR5aWlfJQk81fLLOWvXLni5KxSXTwhBEmS/FUN6nsZQgjOnj2bFqNHd/LNi1arxeXLl/NZihdeeIGNK3cPEn6YCt1OQZ4pfWLLbuJ+Ec3eTFJ3r00Udel2I6qVChDgeYfXIUwShRdrqIT0zz/19lxssM6QCbSORKSh1E/eyhgilc4tpbNW/Z2Bqe9R3/hVGpVFlrDuF9Xo0lpRWi9K/57mI7EarAQxx7pqrEWnDxCxDQhLJSfA64dU/N5RWuvsPDLCioxMSWt8k8N6K+HReolwwgOHK2d5+H6Jcrl3IjobjFWKdifGaMO+2OO39xN+6OTFfM5o0ZzkzpVtTtjzfPTZHyEsB+zLLXblPfbUPfe3vD/Gzt7heud1tpM7vLT8AzxcPhpjwiICb5HAezc13p3/zNiY2NzFaMHNG+s0Gh3e/Z7nWFo8PWFLB2NSXavZbE6e1woCip5ZOm36yYioXq9z5swZN+phLW+88QalUonr16/zT/7JP+H27du8+uqr/PEf/zEvvvgi3//9399nQ1/EYa02AP7lv/yX/Pt//+/xfZ9//a//NT/4gz841zWbFceSoIA8FD9qT6goilhbW6PdbnPhwgUWFha4dOkSAPdubh3NPtpZBOWK5cpolybqi8x6FGGMobm9zwmpWFlZxhiI4sML1mrt8u6iIxGV0Rp8IiOOfBTW/bc7ylo+PzENS2E+H5SY1Iah0AAx7M4qilzVG7y1oNsKXbZ9BoZeOhwbFgabTCHCyqKtkYKvHYNXmr2N2AJaKyfqGgQunWdht5VwennY3lwIKPshZT+ENPVmLUiriHVKWCl5mbRLUhvLjUbM+ZXZ7dJ9z8Mvlfq6zYyx3FDb/HlrjSd2TrC7u4uUktXVVR566KF8NuhE6QwnSmd677OGptphV95jV26yJ++xq+4hTS/qb6pd/mj7/+KxygWeX/zvWA4frCCpJ8q0diusra1x9uxZnj3/GKDRtouzJcn0CUUeZR0Wvu8PeTtNO69VKpX6SAvIoy1rLSdPnuTJJ5/klVde4Wd/9mf55V/+ZdrtNl//+tdZX18fSVDzWG28/vrr/N7v/R6XLl1iY2ODH/iBH+DNN998a72y3rI9vUNxVJ5QUkquXbvG1tYWTz/9NM899xxCiDydArB5/f7c+9FKkyQqb4BAuPx2L22YEZNw5KVV2lXn4xsnFtppd+c6BplGQKYrJ8yv9izGSY8oURnhFEmroOPQscRx7MpL6QuyLj5E//bG7LIXtQlApsoXgiEDw1GkxUjS6kVbxW6+aWAZtIv3+rh1qxFxcqk8Vf1JCCiJgJIX5DPNrp1cE6XpwUY3QdYDwmD++9nzBKVSyFflFZrdBh85+17OnTuXL7S3b9+m1WoBUKvVWFpayusxy+EplsNTPMF70+O0tPQee/J+j7TkJrejy9yOLnO28jRPL7yfM+UnjrxJKY5j3nzzTYwxvPjii7mjNQT4A8ufIwc3mO4wehxlVhxmXisjrnK5zO3bt2m325TLZbTWJEnCt7/9bZ544gkef/xxfviHf3jsvuex2viDP/gDfvInf5JyucyTTz7J+fPnee211/joRz861/WYBceeoOb1hDLGcPPmTdbX13n88cd5+eWX+yKZTM0cjiCCspbGXguZSDzfIyyFaOUaJoQTaQMGUn4F8urutagu12i1D++9ZC2oVKDVduWQRtg4SK2JU2LL59Dy34p0cYBAgQqFiwwyYjpsyc4CscVb8AcMDNOnVGuxepTrbpG0IDPsMxZWqSAqgq6WdLUa0gLMkKfzRtjFZ1DKsNuKObk4e9QD7uMOhU/oVVnENQRU9DL/y4WL3I/3uBPvcDfa5k68Q1PN5uBsjKXdbmOM5s264MnViKfCkJWVlb7ahzEmT2ndvXuXy5cv96W0ssV2sbTKYrDKueozQLpAm3behHGl8w2+3foyJ0pneKxygZPhI0zjKTYO1lo2Nja4efMm58+fH5v+KqJnbd+/nVH3+LT3/aR9HVTXunLlCru7u7nrwr/5N/+Gs2fP8lu/9Vu88MILY/2fipjHamN9fZ2XX365773r6+uHPufD4NgSVFHu6DCWG0Xx2DNnzvDyyy+PDH2LN/E8EVSmz9dpJm6ANhPb9Dy0NmitCqku15nnBT5FGujut9HazEVQiSw8nRsLkYLq5BkUqTWRnPxUL9Jmh1IMfkkgvAA8r5d2M7MNxGawXQML/Z9L3soO+Xrk6tdmAmm5v2Xb8NhqL32jrHFkpSSRVnRUQiQTLPTSeROwtR9xon50Wm/r3X3+5O5NPnbuvVyoP5b/vK0iNuMd7sQ73Im2uRvvsCebQ++3FqLYqefXFhYoleoIAf9t62vciXf4nx5+mZLX+7w9z2NpaYmlpaVcIdtaR27NZpOtrS2uXbuGlJJqtdpHWpVyjUcrT/No5el8e7Hpsivvca17ibJXJRRlqn6dur8y9TVqt9u88cYb1Ot1Ll68OJe306R99jXrHBFKpRKrq6vs7e0hpeTixYssLCywtrbGpz71KT7zmc8QhiGXL1/mF37hF/gX/+JfjHTR/V7BsSWoDEEQ5O2d0+Cw4rGtvTbtxuyptUF9vub2TkpOmRSOR+AJNwCbC6J66eCtI60sKujuNmk2uyMVqKeBMRap+gv1piPxxxCUtZZY6V5L+QGw1kBHEa4uFDoQC7NFg7WiKUjLRqOfgAfhGgK9kaRVtIrf31csLnpUqqX/v703D4+qsPe4P2e2TCaTDQiEJBAIWQg7SRBcqliv2tqW9rpi9WKrtmoXUNsqXlu1rVpRX62V1qVatbZqfWvfarmorVq1bhDABVmyEALZ98y+neX9Y3IOZ7KQyR7gfJ4nDySZZM5MZs7v/LbvNzr1ZjaTbEnAabERCAQIKQoJySlIZoGgJPYErsiAMkvhiEyXN8yU5NFbc/hnUyV5yVNYlHakN5RksZNnySIv6Yi9REAK0RLq0gJWnaeFw64mLBYLaalpmHoNXOz1HKQh2MZXpq+K+T290UsA6Uta6gJrd3c3dXV1hEIhEhIStKCVkpKC3W4nMyE35vdF5DBeqVtTgRcEExbBirlXv0iWZQ4ePEhHRwdFRUVjKgUUd5l3iJmWy+Vi//79zJgxg7KyMkwmE5999hkbNmzg3HPP5ZlnniEhIQFRFKmsrBw0MxyJ1UY8PzvWCEM8WY2GLuWkQN0R6erqoqmpKS51XpfLRWVlJQkJCeTn5+NwOAb9GYAPPviAzKRsnv3FX+M+PlmS8fm8SJLUs2diQRJlavY30XsAQhIlTGahx5it15tBLWUpUckcy+xMJMGM6hJr6jN0oP/Z2D94MBTpUS4/guCwYsmJHXeVe5ZPw5IcVzBUesZv1VKkkJOokz0a/GcHC1qmqRZMjtFp7CoKpKbaSEmJ9i4lKSpGK0syVpsVh8OBxdz3uk9SZAKSSFCK9GRcIuEeA0SL2URhTgrmURQfTTRbub74NLIdg5+kRVHUlFTyCvPx2yJa0GoOdtIW7u7znBYmzeL0qUvJtA9eZhoIRYn2HD0eD263G4/Ho/Vh9JmWOl6tR1YkZGR6rCLp6uqmsrKSmZkzmT179rgKuR6NeAOUJEnU1NTgcrkoLi4mKSmJUCjEvffey9tvv80jjzzCsmXLhnz/oihSWFjIm2++SXZ2NitWrOC5555j4cKF2m1++9vfsnv3bh599FFeeOEF/va3v/Hiiy+yZ88evvnNb7J9+3YaGxs566yzqKqqGq0hibii9gmfQcUzxefz+aiqqkKSJIqKikhJSRnSfQiCQH1lY1y3jerzBQiHo8656gKxoij4PEH1N6LIMqIUtSO3WC0Dvwl0vRRZMCF6Q1jTU3qClowoRU/oR9S0dUFLF7siEann5A9q5FIA/GGkcARFXVDs+YjrsaqBqWfvScMvQUp8JxhBELD0GnDoHbQIAPFdS8Rxf+D1Rpg+PRkFBZ83mn07EhORZBm/z9+TyQpRySGLBYvFitlsxmmx4bToxrt71NUDUoQk0UFqipXWYF99xOEQkCI8vP8Dri8+jczE/qXiFUWhpaWFgwcPMnv2bM0VeiowK/FIXyQii7SFu2kOdvb0tTqp9jdQ6atjriOLsrRC8pOyeyxD4kcQBOx2O3a7PSYTUPswbrebtrY2/H4/ZrNZC1jquLbFFH3vVlVVEgwGWbpkqeY4MBblt+EQz/13dXVRUVFBVlYWpaWlCILAzp07ueGGGzj//PN59913j7oIfTRGYrWxcOFCLr74YhYsWIDFYuG3v/3tuPtknbAZlOr3EgwG2bNnD6WlfbXC9Bp9BQUFw3aYLC8vZ/+rtdR8enjgGymxzrm932gALfVduLv9SKIEREeWh/IGDIVEcNix50zv+00FLWgpcmzQUhQIho9SppuZjJwYte7WxsKPhjrJJwyw+JpoxjRjeIMD/SEIkF0whTDRhd+AGCEoigPadQyGAqSkWEi0M6CKgurqqn5I6sWETsVBn2mZTQI3rzqVaQ4H9X4Xdb5u6vwuDvu6aQ54hh20Uqx2vluwkjnO9Jiv+3w+KioqsNvt5OfnD1nQVFIk2sMumoKdNIc6cUd8pFmdzHXMZI4jE6tpdK99RVHUhgfcbjc+n09z583IyCAnJydqrTHACVQfsEY64DBaiKKoraQsWLCAxMREAoEAd999N9u3b+exxx4bN9+lCSCuP8AJH6AkSaK8vDxmWkUURQ4ePEhbWxt5eXnMmDFjRC/oXbt28dr/8x8i/ennKbHOuQ7HESdS/d9GVhSq9zQgiVHjsqGWMBRZwR8II5hNOApnx/d4FBAliWBY1O00qaO3aP83pSdiznBqPyT3lNqi/8YGrai6+SCKDAIIsxxDll86GtNmOkmdGptGibJMQBQJitGgFYgMHrRUGSmbzcK8/GlRZYg4URQZUZSIiJHYoGW2YLFayE5O5X9POx17r4AXlkQaAm7qfGrg6qbR7xlUnVzFIpj55txlrJiao/VpOjs7KSwsHFU1AlmR6Qi76Qi7sZrMJJhsJJisTLElDzm7OhqBQID9+/djtVqZOXMmgUAAt9utLcWqe0Xqx3Czj7Gko6ODqqoqZs2aRVZWFoIg8MEHH3DTTTdx+eWXs379+hENdxwDGAHqaKgBCqI9olNOOQVZlqmrq6Ouro7Zs2eTk5MzKrXs/7zxPv/8zX/6+DyJPVpcZrM5Riiy998kEAjgcfnobg30664bD+GwSKQnC3LMy8ZkH/yKWRRlQhGxn4xInyUpYDVjzk3T9or6G8kVxR7nXZMQLQPKR88JhIwEhKTRe4MmJFrIzksfNDBLclQtPdAraEXHxqUeNYaoqO706U7S0oZuYqlHfW6iHxHmJybxXxkztFKWWs7q/TqMyBKNfreWZdX5umkMuKNyUAOQb0tlsd9CQU7uqL22B0NRFDxiIPq8CWbMPRcnVmFo2T+gvT/708/T30adIFT7WqIYtZjRlwhHwwJjOERLklWEQiGKi4ux2+34fD5+/vOfs2fPHh577DEKCwsn5NjGGaMHdTT0bw51ZLympoYZM2awatWqUb166azrjgk6kiTh83pRFCWmLNE7MIVCYfwBP/aEBATZEn9w6r0IqxBjDy/6g9iOEqBkWSEckbR9p77ol2aFqMV6REaxmHpkbhRtlFudhDObzVitJvSyD30yLX3Q8oowigEqFBAJBUTsjqNfTZtNAk6bDWfPCUyRFTw+L75wGCHRQbhHFzAiS3R0+ElOTsBsHv6JXhAErFZrz1V+Ig2AJ2MqBWlTNI03r9erLXuqQcvpdJLrTCdXV7oTZZkmNdPyd3PY102D301IjODzetkleKhNSeUsYSrTZBHHCPXw4n18KdbYzDUq9CpxZG25R5rqKJl1vPp5JpNJC0RZWVna/akLxp2dnRw6dEhTctBPEI61tXtbWxvV1dXMmTOHzMzohOU777zDLbfcwne+8x0eeuihce/xTHZO2AxK6SmtdXR0sGvXLrKzs5k3b96YqJo/ecefqNlxGKvV2lM7F0lyJmHryaj0fwNBEAhHIvh9PiwWS8+koMDB/U2arP9QCQTCSNIRwTpTsoOErOmo7rGKovSoHhwJFEPFnOHElK5mE9GxbFmSdFOFSkyGpX7EEhu0rLlJhGQ57qGLwUhKSSBzdvyjx2pP0JHoIMEe+7pQM615WVPIyHBy2O2i3e8fleMEuGLxUk7KOjLSK0lSTFagqjjos4Lejq6yLHPwUC37Gg9jy5xGt1mm3t9NvT8qc3XStNmcmpFLjiN1UvRk+jsXiaJITU0NHo+H+fPnj8jduvd9qUoO6nMaDAax2WwxE4T6kvtwCYfDVFRUoCgK8+fPx2az4Xa7+elPf8rhw4d5/PHHNe27EwijxHc0ZFnmww8/xGKx4PV6OeWUU8ak5KEoCndd/iBdLd1IkkRiogO7/chknooqi6TuZCUlJWlZnLvLT0tDV99fHgeRiEQ41Kv3ZTGRVDAbuUfdWuopuQ03AMKRcXO1z6LKK+mFa7W9Ilmd9IsNWtHpwSOv2ymzUknOcBAWJQIRkWA4WnILRsRhB63ZhVOxDqKnJ0ZEvD4vVkt0bPxovTCLycSPzj2FzFtTGqYAACAASURBVNRk/JEIh90u6twuDrvd1LldtA0zaJkEgYuKF3D6rNwBb6M33FN7MIAWqDo6OsjMzGTu3Lkxr21ZUWgNerXSYFiWmJrgoCBlGrlJ6SOy/hhN2traqKquYlbOLLKzs2MCxVgF1N5j736/H4vFEpNpORyOuM4ViqLQ2tpKTU0N8+bNY/r06SiKwhtvvMHPfvYz1q9fz5VXXjlpRuLHGSNADUZ3dzeJiYns2LGDxYsXj3r2pCgKn+/cy+9/9CcsViupuvF0/fOuliBEUcSRdCSzUqmvaSPgj99BV0USZYIDWMMn5mVhTox9vIpCT6CSkeSenaJ4Xx8CkJsW1Qa0mOOUqVF04pjqfR0JWnanjaz5GX1+l4KiBa1AOEJwCEErdWoi02YOMHYtK3h9PY32JCdmS3zlluy0FK4/exWWfsoz/kiEOrdbF7iGFrRW587hG4VFWE3xHUsgEGDfvn0Eg0GSk5MJBALa4IBaHuxv2k1RFFqDPtpDPhLNVuxmCwlmC2k2e4wR43gQCoWoqKgAoKioqM/7cqDX5FgFrUgkEhO0VFsN/SBG7+w1FAqxf/9+zGYzRUVFWK1Wurq6uOWWW+jq6uKRRx4hJyfnKPd63GMEqMEIh8MoisKnn37KvHnzRq18ANEpncrKSg7vaOKTV/dq+mSxgSl6QgmFgtrOU+83WcAfpr5m6BJJoihFx8oH+IvZpqdhy0jv/5s61KCllv76C1pqYDFnJmNOTSTO195A9xgTtNLnJmGymjCbzbq9IstRglaEQFgkEBEJ9RO0BJPA7IIpWKxm/Q8TCAYIBaO7Z7aEofdmzpw/lzXL5g9+Q44ErTqPqydwuWk9iprJTKeT/1m0hNzUgSfuFEWhvr6e+vp67WpdRdXLU0+wHo8nZtpNDVq9+67RAYcQZsGkuQabBAFLP4Mwo4GiKDQ0NFBXVxe3fp7+Z/WMdclS7FF46V1yTUpKQlEUXC4XBQUFzJgxA0VR+L//+z9++ctfctNNN3HZZZeNetZ05ZVXsmXLFqZPn87nn3/e5/uKorBhwwa2bt2Kw+Hg6aefpqSkZFSPYYgYAWow1AC1Z88esrOzR2Xk1uPxaJL0hYWFPP/Lv1Ozu1ZTI7ZYrVjMlqg1dyCAPSFB23lS0f9JGmvbh5Q9RXtrImLk6CPIpkQbjrzhyZYoPaVBUYqW8xR13Nxpw5w1uvIyKdOTmJKTgtij/CFGRERJ1IRwLT2LsP1lbQpRqSW1NBgtD0ZwptmZnh3NZiORCD5vdMQ/0ZE4ohPbZauWUDZneM+pPxKh3hPNtAYKWqUzZ/KVeQXMSIq9kHK5XFRUVJCenk5eXl5cjXZ12k0ftKKqJUkxQav3iLa6BC309C+1MYcRBgSv18v+/ftJTk5m3rx5oz5i3bucPhb4fD727t2rCcG+/vrrmjSRyWTitttu46yzziI9ffALw6Hy7rvv4nQ6WbduXb8BauvWrTz88MNs3bqVbdu2sWHDhj6iseOMMcU3GKPpCRUMBqmqqiIQCFBYWEhqairuTg/1FY2ahH5EFAn4/UQiEW16S+096Y0G1feP1x3A7wtpqg36ZUNda0fLNkRJjmrGxXEZIQfCyKLUIyg7dBRZxmISSEhMIOrmC7KokJxkJ9yzO9VbFmk4eDv8pM1M7tG9s6jmsCgomlxVKBTC54ugQE/QskYDl9WC3RL9UC891EzrjLmzqG1upEUKY0tLRRmFk9YL2z7HmWBj/syhG+E5rFYKp0ylcMqRZfCAGKHe7eaw+0jguvP9/7BgWgan5cwiPzWN2poa/H4/CxYsGFIFQF+iUlF9i9xuN62trZqbq16ZPCUlpd+gNdyym14/b/78+UNWaYmX3ruFo7m4q2avDQ0N2vi7ajKYmJjIFVdcQUZGBh988AGbN2/mmWeeITd34N7icDj99NOpra0d8Psvv/wy69atQxAEVq1aRXd3N01NTZpW4mTlhA5QKiPxhFKnjNrb28nPz2fatKj5miRJ7PuwMubFHw6FEASB9PR0TCZTjPV29P6FqE231YpJMNHe5EI16FMNKhRFQZaihn6yrCBL8uDKDQMgef2Y0vrvx/SLgmbjYTabY4YHonsuAg5JYUZmGihR36hgWCQYipbdgmFxyIMYsqTg7QyQkpEU83UBYeCgFREJhYL4fGJs0LJG7cylcIhPdlXx/Yu+wLRp0xBlmWaXl/pOF4c7XTR0uWns9iDK8S3CqkiKzB/e+5jLVi1h6azMwX9gEBItVgqmTKWgV9Cqc7n57HAtW8vLycuaSWFONhb7yJU39L5F+hHt/pTJ1aClBq7+9ooGq86oEj+ZmZmaMOpYc+QicHSyKL/fz759+zTldLPZTHNzMzfeeCNJSUm89dZb2jlh3bp1o3Kfw6E/242GhgYjQE1m1BfpcDyhZFmmvr6euro6Zs2axapVqxAEQTuBA+z8525kRcHv9WrlE/3V55H9lyiKohARRcRIhMb6TgK+sE6NPCruKggCZouAWa89p+sRyVL0//FkUaLHjzXOACVLUVtqs9mM+ShTbd42D8mZKSCA1WrGajWTnKRFEMKiRDAU6Qlc0SGHwQKsp9VL8rTBx31jghb2nrtUNDtzv68nezUJtElW3v+sltWlVpxOJznpKeSkp7BqXvRNLMoyzd0eDne6qO9yU9fpotnlHTRoRSSJZ97/hPOWFHBWcd6ol5OkYAhPbS2LkpL4+jnnYrVaCYoiTV4PJkEgwWLBZjKTaLWQaBm5gsJAyuRqptXR0RETtPQLxgMtw+qXVZcuXdqnxD3eDOdvpCgKhw8fprm5maKiItLS0pBlmT//+c/85je/4c4772TNmjWTYnz/WOaEDlAq6n5SPKijowcOHCAjI4OVK1diNps1CRyIvuAPfFrLoYqotbPD4cBms8Vh+SBgtVjobvMhhhWsNit6NXJZ7Al+OgsNwRTVy7P0mvJSp/HULEuSlT5BS/IFBi1vRG07JMwmU9SHahCCriBiSMSS0M9tBbBZzdisZrRCjgLhiBSdxgtFR8mPSCtFiYQk/N1BktKHfiJTlz9DoTAms4kpyVMQTAKSKPLe7nqmJpmwEr046T3pljMllZwpOu8nSaKx20t9l4u6Tjd1XS6au7191BsUFP7vs0r2NbVz6UmLmJYcm/0NB1V+q7u7u49gsd1iYW5abF8jLEn4IxFMQnS4AQHMqhDwCBEEgaSkJJKSkvrYabjdbrq6urRl2MTExJieVnd3NwcPHmTu3LmahNhk0caLF6/Xy759+0hPT2fFihWYTCYaGhrYsGEDmZmZvPvuu2PSZxoJk8E6Yzic0AFKn0HFU+Lr6uqisrKSpKQkSkpKSEhIiA4LqK62goAkybS0NPPKH7ZiMplIS4vfaE2MSLQ0dOH36gwFdWrkR3yK1Ek3GVmM7hcJApoauSBELc1N/QUtSY6dyvMFsDj7Sn0rPYFJDZrxD+YpeFvdpM2K04ZBAJvNjM1mJtWpPr6oMaKaYQVDIq4mD45U+5D0+RQl2lOJRCIkOZ1YdRmFpef/22u8XPvfq0iwmrWpLL2duT4jcDqdzJ6ayuypR4JWRJJo6vZS1+mirstFfaebJpcHWVGoaetk06vvcfK8WZy1II/UxKGX4RRFoa2tjQMHDjBr1izy8/Pjej3ZzGZsvYYlVMUO1RxSZTSCgyAIOBwOHA6HppKgLsO63W7a29vZs2cPiqKQkpKCz+ejra1NU3CIh4kOZLIsc+jQIdra2rR+mSzLPPXUUzz++ONs2rSJc889d1IG2zVr1rB582bWrl3Ltm3bSE1NnfTlPTjBA5TKYEMSPp+PyspKZFlm4cKFJCUlaYEJjji0tre3c+DAAUS3gqc+gM1qQ4xIKApYrOY+BnAQfdOFAhE8rgCuTl9ce0dHVBhMsUFLjno+SUo0W4qWBY+4wfYJWoqCw6SQMj2VUChCIBghFIog9WRqUbX0IT2VAHia3KRmpw9b7FUQIMFmIcFmIVUt1Smwcn4uU2Ym09DqoqHNRXOnd4CelkIwFCLgD5CYaCcpKYmBImxbl48/bt3JVWtOIjU1NcbkTr8IW1dXpxlHqplWamoqSUlJ/Qathi4PDV1uDne6ONDWyUdb6lmSM4NV82YxL2NwTUCI9jcqKiqw2WyUlpaOWD+u9yI0HLnY6S39NVpBKyEhgUAggMvlYunSpaSlpcUoONTX18cYF6qZlt1un1Qneo/Hw759+5g2bZrWL6utreWHP/whhYWFvPfeezEDJ+PNpZdeyttvv017ezs5OTn8/Oc/185p1157Leeddx5bt27VfOyeeuqpCTvWoXBCj5mrU2DqiWD58uUx3w+Hw1RXV2t2G1OmTEGW5Zg3tSAIuN1uqqqqSEhIIC8vj+d/8XeaDrRov0eRFUKhCGJYIhKRiIREwqEIkhgd0x6JgsPRUE0KVZ8mFLSApQUti5l5py9BMAnRnaxgELMlAVkWCIUiBIMRwmFpyHYPGUUzcGaM7hs2yZHAD773Rez2aPYTESWaOjw0tLqob4sGrcbWbjxeLxazhaQkR5wLwzAvZyqXnbucxISj9230kkOqeoM6EXdUcdeeoFXX6cITDJHmsDPV6WDutHRslt6ZrkxtbS1tbW0UFhaOe7lotAKUqp83derUPmoWve8vFAppI+9ut5tgMEhCQkLM8zpY0BoLDyhZlqmpqaGrq4vi4mKcTieSJPHEE0/wzDPP8OCDD7J69epJFUyPEYw9qMFQFc3D4TCffvopK1asAKInodraWpqbm5k7d66WCqsDEOoJPhgMUl1dTSgUoqCggJSUFMpf/YTX//DvuO47FIgQDIS1fyNH81waJdSgpfa1UGBq0UwS0pOw2RL6dS9VZCU62BCMlttCwQjhyNFLognJdrKWjv6m/JLFOfz31/suGGoXEx4fKVNn0uUXaWhzUd/qor3bF9ek4/T0JNadV8bU1KG5G/b2KlKDlnpiHUgeJySKtLp9mE0mbBYzVrMJv9vNoYM1ZGZmTipn2N4cLYipDr0j1c/rHbQCgQA2my0maCUmjmx37Wh0d3ezf/9+Zs6MuvQKgkBVVRXr16+npKSEO++8syc7NxgGRoAaDDVAKYrCRx99xKpVq2hoaODQoUNkZWWRm5uLIAg9wwbRRri6t1RbW0tnZyd5eXlMmzYt+uLdWcP/e98/kAdUAT86kijHBKxQIIwoDu93xYMiK4iSSOIUJ1lL5iKKEpKkjrvrjPUsZnq/nmRJjg419JQFg8EIETE2wM5cko09ZfQntC48v4yFC6Jj0LIs09DQQH19fUzjXU8wHKGx3aOVButbXXS4+pcbslnNfPnkIlYujNMzawDUoKVmWj6fT3OF1Wda6n0Eg0EqKyuJSBLz8gtwJCZG+4o9Qw7HyhW6qtg9a1Zf/bzRIBwOa8+pqpWnt4hXLwZGcr+SJFFdXY3X66W4uBiHw4Eoivz2t7/lr3/9K7/5zW849dRTR/FRnZAYAWowVEVzQLNVTk9P1zbZewcmVYqlvr5eMxpTr3Aryg/w/z24FXGQzGKoiBGJYCBMMBAh5A8TDIRHXBJUFEXLBi1mCyaziXmnL8bcM6WnKNHR7EiPR5EkRlXJrT3Lr6qFeW8kSe7JsiKEghFMiTamFE0f9ZNUot3GVd8+DZMpQmVlpVZCGopVgT8YobE9Gqwa2tw0tLro8gS072dlpHDOykIKZ00btePXa7q53W7NyhyiAWru3Ln9ntRVuabeRzGZgtZg+nljid4iXn1eVYFXNXD1Vxnoj87OTiorK8nOziYnJwdBENi7dy8bNmzgC1/4AnfccQf2Udg5MzAC1KAoikJ7ezuVlZV0d3dzyimnkJiYqA1AmExHNMfa2tqoqakhIyOD3NxcTYqlo7GTd178iL3vV4zbMUfCEqFA+EjgCkTiE3XtmeKSJRmzJdaVN6Mghym5/VjBaz8afU4ikSMW5iaTScuyrJb+/arOu3wVCamJNDZ209jUTVOTi0Bw6MK3emRJRhAinPNfc1i2bGGPJcnI8QXCNLQdybIa2twkJlhZuXAWS/Jn4ojD5HEoqCUkp9OJw+HA6/VqJ1d9ptVfRjAe0j3xMBL9vLFkoIsB/ci7vlcoiqKmBFNcXExiYiKRSIQHH3yQrVu38rvf/Y6ysrIJflTHFUaAGgxRFNmxYwd5eXns2bOHk046KeaNHwlGaG1uY//nFcghheSEVMwmM2JYpKvFRVNNCy21QxdyHW0URSEcEqNlQX+0PBgORmL+WLIU3dMymU09Bnuxrw+r3cbcUxcO6WSnyLKWZYkREUmWMfXsS6lyQ9Oz0rhq43maqZ+iKHR1+WlsigasxsZo0BqspxX9YVVcN4QjycGsnGlc/s1VOJ1jd0Xr8YdoaHXR1BENVs7EBLIyUpiSMvygGA6HtUXV+fPn9wmwkUhEO7GqJ1e1jDXU3stYjmaPtX7eaKMGLTVwqarkVqsVj8dDdnY22dnZ2O12Pv30UzZs2MCXv/xlbr311jFz4H3ttdfYsGEDkiRx9dVXs3HjxpjvHz58mCuuuILu7qhdzz333MN55503JscyzhgBKh6CwSAAO3fuxGKxkJaWRmpqKiaTiZqaGiRJoqCgIDq9I0q0Hm6nsbqZxuoWGqubaavvmJTPiiwrhIIR/N6oXbwYkVEk+owZ68laPJfkGSObGJNlKSbTkmWZsi/mcdKZ87WTa28tN1lW6Ojw0tDYTVNP4GpudiNKR3pa4VAYv99Pgiqu2/MwUpMTWbv2JDJnjK5I7UAoioLbF8IfCmOzWLBaTFjMJhITrIMGAn22kZeXx/Tp8Zc/++u9qOZ6R5tyG4tMS5IkDh48SGdn55jq5401kUiEffv2EQqFmDJlCm1tbVx33XXIsozb7ebaa6/lG9/4BgsXLhyTACVJEoWFhfzrX/8iJyeHFStW8Pzzz7NgwQLtNt/97ndZvnw51113HXv37uW88847qubeMYQhFjsYzc3NbNmyhZKSEhYuXEgwGKS+vl4LTHa7nSlTotbb6iLizLwZzMybQek50d8RCoRprmml8cCRoOVqc0/sA6OnJCeFMNsUcuZOx2KxIEkyoWCEoD/cUyKMIOqs4LsOt444QJlM5p7F2yM9iAMfd7JgeZhwuF17bh0OB6mpqVq5JSMj+rFsaVRqSJJk2to81Bxs4dNPq+jqNmGzpfYJsC5PgD889R6rz5jPypPmjsh+PR4EQSDVaSdVl7UpikI4Imkivuq/Zl0J1ePxsH//flJTU1mxYsWQsw2bzca0adM0XTeIDVpNTU3alNtgQUt/3OpjijfTUns0M2fOHDf9vLFAVYPRXyh0dXXhdDr5+te/zurVq/n000956KGHOPXUU/nOd74z6sewfft28vPzycvLA2Dt2rW8/PLLMQFKXWOBqGq9qpF4onBCZ1AtLS08+eST7Ny5k4qKCiRJwu/3c+2113LRRReRkZGhlQNcLpd21aqeWNUTQG98Lj+N1c00VDfT1BO0At7guDymqE5agHA4FJfEkihKR6YG/RFySgvAOnINt95My0zh2z/5ElabRRMgVZ9X1Z+od9+ltraWrq4uCgoKSE9PJxKRaGlxa6XBxqZu2tu92o5W5vRUVp9RRGFh30m+8UbdPZNEiZqa6Mh1b4misUAdzVY/1H0ifdDqz3dssEwrEokOpITDYebPnz/h+nnDJRwOs3//fgRBoKioCJvNRiAQ4K677mLHjh08+uijMQFiLPnrX//Ka6+9xhNPPAHAs88+y7Zt29i8ebN2m6amJs455xy6urrw+Xy88cYblJaWjsvxjTFGiS9e3nnnHTZs2MB5553HsmXL+OSTTygvL6e5uZm8vDxKS0spLS3V5I08Hg8ulwu32x3th+iUnQeyI+huddFY1RO0DrTQdKB1VCf+osuOYfwBv+YxNZyT9Kz5WXz9+q/QXNdJ0+FOGg910Hy4k9AAzrxDoXBxNv995Wn9OtWqpnoul4uWlhZcLhc2m42pU6dqFwT9LcCGQiJNza6eXlY0aFmtZkqW57J4UTaJiWPTOxgMRVFoaWnh4MGD5ObmHlVWZqyDqSo3pF+CtdvtfZZg+0NRFJqbm6mtrR1wjP9YQP841GEORVH48MMPuemmm7j88stZv379uPbR4glQDzzwAIqi8KMf/YgPP/yQq666is8///yYzVx1GAEqXmpra3E4HDEupBA9aVZVVbF9+3a2b9/Ozp07CQQCLFiwgNLSUsrKyli0aBGyLGsBy+12I0lSjBxOcnJynxeUJMm013X0ZFnNNFS30FbXjjKMEfJIJKLt2fR3Eh8qF9z4FYpPLtQ+VxSFzlY3jYc6aTrcQdOhDlrqu4a1o7WwLJc1607p9ySnmj06HA7mzZuH2Wzuo9qgTmKpQau/CTe/P0xzs4vmFjcJCRaSk+1kTHOSnj4+S5U+n4/9+/fjcDjIz8/vc8Gip7/331gHAL1yg/oRCoWw2+0xF1qyLLNv3z7sdjsFBQUDPo6J1sgbjGAwyL59+0hISNAeh9fr5ec//zn79u3jscceo6CgYNyP68MPP+SOO+7g9ddfB+BXv/oVALfccot2m4ULF/Laa69pVhl5eXl89NFHfc5VxyBGgBoLwuEwn332Gdu2bWP79u3s3r0bq9XK8uXLKSkpoaysjHnz5hEMBrWgpfaw9CfW/vYyIqEIzQfbtPJgY3Uz3S2uAY9FkiR8Pj+KIpOUlDRqV3+pGSlc++srsNoG/n2SKNHW7KLpUAdNPYGrrckV145W/sIs1qw7Bbsjmt1EIhEOHDiA1+ulsLDwqGUw/fiwWna1Wq19yq69n1uvN0ggEMFqNWOxmDGbBez2wQcbhoJ+eKCoqChG12+4jNc4uV7Y1eVy0dbWRjAYJCUlhalTp2qv3aH4Pk100NLvLRYUFDB16lQUReGdd97hlltu4ZprruHaa6+dsGxEFEUKCwt58803yc7OZsWKFTz33HMsXLhQu82Xv/xlLrnkEr71rW+xb98+zjrrLBoaGib8uR0FjAA1HiiKgsfjYefOnWzbto3y8nKqqqqYNm2aVhosKytj+vTpMSdWn88Xc2JNTU3ttzfgdwdiBjAaq5vxufxD6jMNh5O/XsZZl39hSD8TCYu01HfRdLiDxp7A1dnm6fe2UzKS+erlq8AWoq6ujjlz5pCZmTmsx6EOC6gXBPq+i/r89l4cVRSFSJ/BBqFfQd94UBXH9Queo4mapfR+v472/aj28VOnTmXOnDkxgxhut1uz0NBnWgMFrd4utmNxvAMRCATYt2+flsVaLBbcbjc//elPqaur47HHHmPOnDnjcixHY+vWrVx//fVIksSVV17Jrbfeym233UZZWRlr1qxh7969fOc739GEiu+9917OOeeciT7s0cAIUBOFWu/evn27FrRUXT81YJWUlGC322P6WcFgUHvzqyfW3oaGTU1N7P1kP+aQlYhbormmlaYDrURCI+8R9eaSjV+noDRvRL8j6A9Hy4KH1fJgJ+5uP2Ikgtfno7h0Fmu+eTpTp4/emHjvEpbL5dJOrPpMq79eYX8c7aQaCASoqKjAbDZTWFg4rgoKo1la0+vnFRcXD6gxp/d9Uj96O+z299yqPwtjnwnW1dXR2NhIUVER6enpKIrCv/71L2677TY2bNjAt7/97eOhh3OsYwSoyYQsy1RXV2ulQX0/Sy0NLlq0CEALWC6XS3PitdlsdHZ2kpaWxrx582KuWmVZpr2+k8YDLTRWRbOs1sPtw9YEVLEnJXD1pstIG8Udo1AoxGef7KG5rgu7KYXOFi8t9V3kFs5g6cnzyC0Ymyb8QCfWpKSkmF5h7zLpQO8PRVE4dOgQLS0tFBYWMmVKnP5XkxBVP2/27NlkZWUN+fnXO+yqVQL1udUPYhytFzca+Hw+9u3bR2pqKnl5eZjNZjo7O7nllltwuVw88sgjx4RJ3wmCEaAmO/p+Vnl5Obt378ZiscT0s0wmE//4xz84+eSTSUpK0haL1Te96knUp58VFmmpbdPKgo3VLXQ2dQ35GFMzUvifOy4ibfrIxqNlWdYssufNm6cJ7ELPlGOHl6bDnfjcQZJTE3GmJjIjJ/2ofbCRoh93Vz9kWY4ZcHE6nX00/jo6OqisrGTGjBnMmTMHk8k06QcF+iMUCrF//35MJtOoZ3/6oKV+iKKoXRCo+2/xDJAM9ryqr62Wlhbmz59PamoqiqKwZcsW7rzzTjZu3Mill15qZE2TCyNAHWvo+1n/+c9/eOGFF2hra2PZsmUsXbqU0tJSVqxYwfTp07WRbFWyRRXHVEtY/Q0KBLxBmmqOZFkN1c34uvtX9daTMi2Zb/70fKZlDy9L6OjooKqqiunTp5ObmxuXqKssy7g6fJhMAharGbPFjNkan+38SFDH3fWqDRB11nU4HHR2diIIQp9doN4n0/EoZw0XRVGor6/XhgemTZs2buU3/QWBx+PRKgR6NfKhDPuoRoJ6z6m2tjZ+8pOfoCgKmzdvZsaMGWP2mAyGjRGgjlUkSeKMM87gkksu4ZprrqGjo0MbdS8vL6epqSmmn7V8+XISExNjhjDUXRd90OrdzFYUBU+HV9vNaqhqpulAM+F+dp4sNgtfuvqLLF29IO6TWCAQoLKyEkEQKCwsHLEKtCRF1eVVZ1j9cMNYovZnmpubSUpKQhTFGOHR1NTUAQVd9YMNkyFYeb1erQymjvKrTEQWKMtyn0xLluU+mVbvoCXLMgcPHqSjo4Pi4mKSk5NRFIWXXnqJ++67j9tuu40LL7xwUjznBv1iBKhjGVEUB7yS7K+f5ff7++xnATGDAqIoahJDas+ldzajKArtDZ00Vbdoo+4ttW1aP2vu4tl88fLTmJk38FWpavjY3t6uORGPFWM9IaZOtU2ZMiXG0kMUxZiTqjqVOVgW2/uYx/LY9YxEP2+sJwd7I8tyn0xLX3o1mUzU19fHmDo2Nzdz44034nQ6+fWvfx0jCTXaZB9KQgAAGEtJREFUDCbwCvDiiy9yxx13IAgCS5cu5bnnnhuz4zlGMQLUiUQ4HGb37t0x+1kWi4Vly5Zp/ayCgoKYXRePx4OiKDGZQH+LvmJEpPVQe4x8U9r0FErPXUresjkxSuWtra3U1NRo49YTXfcfbvYSiUSorq7G7/czf/78uJxTe49kBwKBGJkhdZVgtI4xXvT6ebNmzRrR36S/YDUemZcq4FpTU4PH48Fms7Fr1y7eeecd0tPT+fDDD/nVr3415llTPAKvVVVVXHzxxbz11lukp6fT2tp6PCzWjjZGgDqR6b2ftWPHDs3cT93PUvtZPp9P62epag36TKA/2aSgL0TzwRa6W92kTE0Gi0Kbu5XkVCf5+fljZk8wXOI9iaqj/IcOHRrRbpaKekGgV2yId49Iz3D3w6qqqsZcP288FnW7urqoqKggKyuLWbNmIQgCBw4c4Oabb0YURbKysti3bx+yLPPoo4+OmV5dPOoPN910E4WFhVx99dVjcgzHCYaa+YmMIAikpKRw5plncuaZZwJH9OHU/aynnnqKxsbGPvtZDodD62c1NzdrmYB+qdielMCcRbOJRCLU1NTgdrvJnTWblJRUBEVAkuQxVxYfCvFYYagSRU6nk7KyslEZi7bb7djtdu0KWj/u3tnZSW1tbcy4u/oxUHk3nsCl150bqq3HcBjoGHqrpQ9026MhiiLV1dX4fD6WLl2qGYo+9dRTPP7449x3332cc8452u8NhUIjfDRHp6GhQZMdAsjJyWHbtm0xt6msrATg1FNPRZIk7rjjDr70pS+N6XEdr5xQASqe2vHxjCAIZGZmsmbNGtasWQPE9rP++c9/cvfdd8f0s0pLS1m+fDkQ7Wd1d3dz+PBhwuEwJpOJYDBIVlYWy5Yt6/eELolSz8kq+vkRw8TJgyiK1NTU4HK5YqSWxqJ0pdq2OBwOMjMztftRey6tra1UV1fH9FzUQQGz2RxzPPogoOL3+6moqMBut49akB0OvZ83NUgNJVCp05+zZs2iqKgIQRCora3lhz/8IUVFRbz//vskJyfH/Mx4LkoPhOrO+/bbb1NfX8/pp5/O7t27SUtLm+hDO+Y4YQKUJEl8//vfj6kdr1mzZtyk9Scr6g5MYWEh//M//wPE9rOefvppPvvss5j9LIvFwt/+9jduu+02srKy8Pl8fPzxxyiKgtPp1DItp9PZR7lcEiXCwXDMOLY1DrO/sUDfM5s1axYFBQUTchyCIOB0OnE6nZrfj37cvbGxMWbcXQ1aTqdT6yfJssyhQ4dobm7WFBTUx6jex0QT7zFEIhHNcXjZsmXY7XYkSeL3v/89zz77LA888ACrV6+ekMeUnZ1NXV2d9nl9fX2f5d+cnBxWrlyJ1Wpl7ty5FBYWUlVVxYoVK8b7cI95TpgeVDy1Y4P+UftZb775JnfffTfNzc2aNba+n5WZmamdVF0uV0w/Sy0N9tfPioRF1BRLzVqsCWN75e/3+9m/f7+mcD3Untl4T7ZB9CKrt7q7yWQiISEBt9tNRkYGBQUF/U5m9j7WybpYrKpa6Pt/lZWVbNiwgZKSEu666y4cDseEHV88Aq+vvfYazz//PM888wzt7e0sX76cTz75hKlTp07YcU9CjB6Unnhqxwb9o/azXn75ZTZu3Mj5558PENPPevrpp2lqamLOnDkx/aykpCRNb7C1tVWzbT+akCvQR1tQMAlYRmFJVz8CX1RUNOyyy0B9l7E86ZvNZtLS0rRjFkWRyspK3G43M2bMIBgMsn37dm3cXf2IxxtsojOtcDhMRUUFiqJQWlqKzWZDFEU2b97MSy+9xG9+8xtOPfXUCTk2PRaLhc2bN3PuuedqAq8LFy6MEXg999xz+ec//8mCBQswm83cd999RnAaJidMBhWPOZjByJBlmQMHDmij7jt27MDn88XsZy1ZsgSI3c8Kh8MxFvD9DQkoioIYFo9YvitKtJ/Vj/nhQLS3t1NdXT0q49YTjWpZ3p9+3mDj7gMZFPa3UzbWQVdfZp03b542TLJ3717Wr1/PGWecwe233z7iJW+DSYeRQemJp3ZsMDJMJhMFBQUUFBRw+eWXA7H9rGeeeYbPPvsMs9nM8uXLWb58OWVlZSxdulRTH9cPCej7LcnJyX3KfpIkR8uDEC0RClFZpD4j8cEgFRUVCIKg9TQmkpEsF6uPxWQyaZlGb2w2G9OmTYtZVtWPu9fX18c97t77eIdzzAOhagFaLBZtoCMcDvPggw/y6quv8rvf/Y6ysrJRuS+DY5MTJoOKp3ZsMPYoioLX643xz6qsrGTKlCl9+ln6/SyPx4PJZIrpZ/UnLySJkmaaKMsy9XV1tHW0aYZ1xyqqfl5DQwP5+fkjVkpQDQr1TtDxjLuPhgqGftdM1QIE+PTTT9mwYQPnnXce//u//zvpdukMRhVjUbc3/ZmDGUw8+v0sVW+wsbGR3NzcmH6W0+ns46Zrs9n6yAtBdLGzsrKSjIwMsrOyMZmOlAIFgVHpZ40XR9PPG00GU3fXj7sP9PN6+gtcgUCA/fv3azbyFouFUCjEpk2bePfdd3n00Ue1MrDBcY0RoCYbdXV1rFu3jpaWFgRB4Lvf/S4bNmygs7OTSy65hNraWubMmcOLL76ojQmfqPTuZ+3cuROfz0dxcXFMP0sQhD5uupIkYTKZmDt3LhkZGf0aE4oRSd/OmpT7WXr9vOLiYpxO54SIufbOZGHgcfeB0GeAev+s8vJybrzxRi666CJ+9KMfTdjelsG4YwSoyUZTUxNNTU2UlJTg8XgoLS3l73//O08//TRTpkxh48aN3HPPPXR1dbFp06aJPtxJRyQSidEb/OyzzzCZTCxfvpxly5ZRU1NDe3s7t9xyi2bx7Xa7kSQpxuMpOTm5zwlVURQkUYr5mtnSt581XqhLqnppn8nCQOPu+tKg3qPM7/ezb98+nM6oDJbZbMbv93P33Xezc+dOHn30UYqLiyf4URmMM0aAmux8/etf5wc/+AE/+MEPePvtt5k5cyZNTU2sXr2aioqKiT68SY/az3r++ef51a9+xdSpU5EkidTUVEpLSykpKdH6WYFAICYLEASB5ORkrTTYn+mjJMkoclTFXf2eyWwa02ARDoeprKwkEomMWD9vPHed+lN3t1gsCIJAMBhk7ty5zJw5E0EQ+OCDD7jppptYt24d69evH7OSpUq8CjIvvfQSF154IeXl5cZwxthjBKjJTG1tLaeffjqff/45s2fPpru7G4ieVNLT07XPDY5OIBDgsssu4xe/+AWLFi3SxpbV/azy8nIaGhr67GclJyfH9LNUuwy93mBCQkK/Qxi9Gcqo+0DoBwdGWz9PH6jGK2h5vV727NlDYmIiSUlJlJeXc9ddd2Gz2QgEAtx8882sWbNmzCdp41Efh6jx4Ve+8hXC4TCbN28elQDlcrloaWmhsLBwxL/rOMQYM5+seL1eLrjgAn7961/38eUZDwO+44nExET+9re/aZ8LgsCMGTP42te+xte+9jUgtp/1xhtvcM899+D1emP6WcuWLcNsNmtZVmNjI8FgUBvFVgPXQHqDegSTMKQdK1XVIjExcUz08/Svp/GwxTh06BBtbW0UFxeTkpKCoijU1dXhdDq59NJLKS4uZufOnVx99dWsW7eOSy+9dMyOZ/v27eTn55OXlwfA2rVrefnll/sEqJ/97GfcfPPN3HfffaNyvzt27OC5555j/vz5FBYWTlrljsmOEaDGmUgkwgUXXMBll12mKTLMmDGDpqYmrcRneMeMLv3tZ+n7WX/84x9j+lmqf9bixYuJRCK4XC46OjqoqanRLMrVgJWcnNwng+rTzxIETKa+Fx7qyby1tXVEqhaTBdV+PSMjg7KyMkwmEy6Xi5/97GfU19fzyiuvkJubC0TL2+NBPAoyu3btoq6ujq985SsjDlDt7e184xvfYNq0aezcuZO1a9cCk0ML8VjECFDjiKIoXHXVVRQXF3PjjTdqX1+zZg3PPPMMGzdu5Jlnnhm3N++JjNVqpaSkhJKSEq677rqY/azt27ezadMmKioqSE9P77Ofpdpl9BZxVZ2Kk5KS+g9aktrPgu7ubqqqqsjIyGDFihXHtKqFLMvU1NTQ1dXFggULcDqdKIrC66+/zu23387111/Pt771rUn5GGVZ5sYbb+Tpp58eld934MABzj77bG6//Xa+973vceDAAe1+JuPjn+wYPahx5L333uMLX/gCixcv1l6sd999NytXruTiiy/m8OHD5Obm8uKLL46pTbpBfAzWz1IHMVJSUvB6vVp5UB0Q6M/+Xe9vVFRU1Ef49Fg7iXV3d1NRUaHZrwuCQGdnJxs3bsTtdvPII49MqGLLYCLRLpeLefPm4XQ6AWhubmbKlCm88sorw+pD3X333ezevZvnn3+eQCBASUkJL730klZSNEp9GsaQhEH8SJJEWVkZ2dnZbNmyhYMHD7J27Vo6OjooLS3l2WefNTb7OZIt6PUGe/ezlixZgtlsjtEbDAaDCIJAKBQiMzOTOXPmDGj/3lsKaTKe0CRJorq6WnvsDocDRVHYsmULd955Jxs3buTSSy+d8IA7VAWZ1atXc//99w97SOLjjz/m8ccfZ/369RQXF7NkyRLsdjunnXYad91115i5Gh+DGEMSBvHz0EMPUVxcjNvtBuDmm2/mhhtuYO3atVx77bU8+eSTXHfddRN8lBOPyWQiPz+f/Px8LrvsMqBvP2v37t2a7l9paSnZ2dk8+uij3HjjjeTn5+P3+9m9e7cmLaQXye1tSghHgtZ4Djscjc7OTiorK8nOzqawsBBBEGhra+PHP/4xgiDwxhtvMGPGjAk7Pj3xqI+PJg6Hg8TERP7zn/8AcPbZZ1NSUsIXv/hFIzgNAyODMqC+vp4rrriCW2+9lQceeIB//OMfZGRk0NzcjMVi6VMmMTg6aj9r+/btPPzww7z33nsUFRVhsVi00mBZWRlZWVlaP8vlcuHxeFAURVNpUPtZ/S0V92Y8ApbqFBsIBCguLtbs11966SXuv/9+br/9di644IJJmfGNJ3/5y194/fXX+cc//sH999/PFVdcARjlvV4YGZRBfFx//fXce++9WsO/o6ODtLQ0TSg0JyeHhoaGiTzEYwp1CXjXrl0UFxfz3HPPkZiYSGtrK+Xl5Wzbto1nn32W+vp6cnNzKSsri+lnqdJChw4dijF9VDOtgfydxtJEsb29naqqKnJzc5k/fz6CINDc3MwNN9xASkoK//73v0csYHu8cMkll/C1r32Ne++9V3tOjOA0PIwAdYKzZcsWpk+fTmlpKW+//fZEH85xhVryUpkxYwZf/epX+epXvwrE9rPefPNNNm3ahNfrZf78+VqWtXTpUsxms7ZU3NzcrPk76ZeKbTZbv6VBleGeHCORCBUVFUiSRElJCQkJCciyzJ///Gc2b97MXXfdxVe/+lXj5NsLh8OBw+FAkqR+y7YG8WEEqBOc999/n1deeYWtW7dqnkEbNmygu7sbURSxWCyGd9YwGeykNFA/6/PPP2fbtm38+c9/5ic/+Qkmk0nrZ5WVlbFo0SJNWqi7u5vDhw8TDoc1qwxVb7C3VQYcuZKPx0FXNUXUK1vU19ezfv16cnJyePfdd4/53a2xZqxlnI53jB6Ugcbbb7/N/fffz5YtW7jooou44IILtCGJJUuW8L3vfW+iD/GEo/d+Vnl5ORUVFaSlpWkBS+1n9fZ3Uk0f1UwrHtVxiOoB7t+/H0EQKCoqwmazIcsyTz/9NL///e+59957Oeecc4yswGAkGGPmBkNDH6BqampYu3YtnZ2dLF++nD/96U/9jkUbjD+KotDW1hazn6XvZ5WUlFBaWkpqaiper1cbwtD3s9QPvemjoig0NzdTW1tLfn4+GRkZABw8eJAf/vCHFBcXc88995CcnDyRD9/g+MAIUAbHNt3d3Vx99dV8/vnnCILAH/7wB4qKigzvrH7Q97PKy8vZsWMHHo+nTz/LYrHg8Xi0TMvv95OQkIDD4aC7u5ukpCTmz5+P1WpFkiQef/xx/vSnP/Hggw9yxhlnjHnWNJjy+AMPPMATTzyBxWIhIyODP/zhD5p8ksExhRGgDI5trrjiCr7whS9w9dVXEw6HNQ8hwzsrPvT9rPLycj755BNMJhNLly7VglZBQQFPPvkk+fn5ZGZmEg6Huf3224lEInR0dLBo0SIefvjhcdlrikd5/N///jcrV67E4XDwyCOP8Pbbb/OXv/xlzI/NYNQxApTBsYvL5dJMCPVX7UVFRYZ31jDR97PKy8v597//zUcffURBQQErV65k5cqVLF26lJdffpmtW7dy1lln4XK52LlzJ1arlbfeemtMM6jBZIl68/HHH/ODH/yA999/f8yOyWDMMPagDI5dDh48SEZGBt/+9rf59NNPKS0t5aGHHqKlpYWZM2cCkJmZSUtLywQf6bGDup+1evVqRFHkhRde4OWXX6aoqEjrZ23atIni4mLefPNN7Ha79rOSJI15eS8e5XE9Tz75JF/+8pfH9JgMJhYjQBlMSkRRZNeuXTz88MOsXLmSDRs2cM8998TcZrLq1B0LnHTSSbz33nua/I66n/XLX/6y39tPtnHpP/3pT+zYsYN33nlnog/FYAw5tqSTDU4YcnJyyMnJYeXKlQBceOGF7Nq1S/POAgzvrBGgKlJMJrKzs6mrq9M+H2j/7o033uCuu+7ilVdeMSZLj3OMAGUwKcnMzGTWrFlaf+nNN99kwYIFmncWYHhnHWesWLGCqqoqDh48SDgc5oUXXugj5vrxxx9zzTXX8MorrxgXJycAxpCEwaTlk08+0Sb48vLyeOqpp5Bl2fDOOo7ZunUr119/vaY8fuutt8Yoj//Xf/0Xu3fv1vqQs2fP5pVXXpngozYYBsYUn8HYUF5ezlVXXcX27duRJImTTjqJv/zlLyxatGiiD21cePDBB3niiScQBIHFixfz1FNP0dTUZPhnGRjEjxGgDMaOn/70pwSDQQKBADk5OQOOAh9vNDQ0cNppp7F3714SExO5+OKLOe+889i6dSvnn3++Jg21dOlSwz/LwGBg4gpQRg/KYFjcdttt/Otf/2LHjh3cdNNNE30444ooigQCAURRxO/3M3PmTN566y0uvPBCILpg/Pe//32Cj9LA4NjHCFAGw6KjowOv14vH4yEYDE704Ywb2dnZ/PjHP2b27NnMnDmT1NRUSktLDf8sA4MxwAhQBsPimmuu4Ze//CWXXXYZN99880QfzrjR1dXFyy+/zMGDB2lsbMTn8/Haa69N9GEZGByXGIu6BkPmj3/8I1arlW9+85tIksQpp5zCW2+9xRe/+MWJPrQx54033mDu3Lma0vf555/P+++/b/hnGRiMAUYGZTBk1q1bx0svvQREFQa2bdt2QgQniI41f/TRR/j9fhRF0fazzjzzTP76178CJ85+1muvvUZRURH5+fl9VD4AQqEQl1xyCfn5+axcuZLa2trxP0iDYxojQBkYDIGVK1dy4YUXUlJSwuLFi5Flme9+97ts2rSJBx54gPz8fDo6Orjqqqsm+lDHFEmS+P73v8+rr77K3r17ef7559m7d2/MbZ588knS09Oprq7mhhtuOKFKwQajgzFmbmBgMGTiUR4/99xzueOOOzj55JMRRZHMzEza2toM/UQDMMbMDQxODK688kqmT58esyjd2dnJ2WefTUFBAWeffTZdXV1A1HJj/fr15Ofns2TJEnbt2jWs++xPebz35KL+NhaLhdTUVDo6OoZ1fwYnJkaAMjA4xvnWt77VZ5Lwnnvu4ayzzqKqqoqzzjpL6xG9+uqrVFVVUVVVxeOPP24sExtMaoZa4jMwMJiECIIwB9iiKMqins8rgNWKojQJgjATeFtRlCJBEB7r+f/zvW83xPs7GbhDUZRzez6/BUBRlF/pbvN6z20+FATBAjQDGYpx0jGIEyODMjA4PpmhCzrNgOrZng3U6W5X3/O1oVIOFAiCMFcQBBuwFuit2voKcEXP/y8E3jKCk8FQMPagDAyOcxRFUQRBGNXAoCiKKAjCD4DXATPwB0VR9giC8Atgh6IorwBPAs8KglANdBINYgYGcWMEKAOD45MWQRBm6kp8rT1fbwBm6W6X0/O1IaMoylZga6+v3ab7fxC4aDi/28AAjBKfgcHxir68dgXwsu7r64QoqwDXUPtPBgbjhTEkYWBwjCMIwvPAamAa0ALcDvwdeBGYDRwCLlYUpVOILiFtBr4E+IFvK4qyYyKO28BgMIwAZWBgYGAwKTFKfAYGBgYGkxIjQBkYGBgYTEqMAGVgYGBgMCkxApSBgYGBwaTk/wd1sUcynvMIGwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "#%% barycenter interpolation\n", "\n", "n_alpha = 11\n", diff --git a/notebooks/plot_convolutional_barycenter.ipynb b/notebooks/plot_convolutional_barycenter.ipynb new file mode 100644 index 0000000..d0df486 --- /dev/null +++ b/notebooks/plot_convolutional_barycenter.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Convolutional Wasserstein Barycenter example\n", + "\n", + "\n", + "This example is designed to illustrate how the Convolutional Wasserstein Barycenter\n", + "function of POT works.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Nicolas Courty <ncourty@irisa.fr>\n", + "#\n", + "# License: MIT License\n", + "\n", + "\n", + "import numpy as np\n", + "import pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data preparation\n", + "----------------\n", + "\n", + "The four distributions are constructed from 4 simple images\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]\n", + "f2 = 1 - pl.imread('../data/duck.png')[:, :, 2]\n", + "f3 = 1 - pl.imread('../data/heart.png')[:, :, 2]\n", + "f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]\n", + "\n", + "A = []\n", + "f1 = f1 / np.sum(f1)\n", + "f2 = f2 / np.sum(f2)\n", + "f3 = f3 / np.sum(f3)\n", + "f4 = f4 / np.sum(f4)\n", + "A.append(f1)\n", + "A.append(f2)\n", + "A.append(f3)\n", + "A.append(f4)\n", + "A = np.array(A)\n", + "\n", + "nb_images = 5\n", + "\n", + "# those are the four corners coordinates that will be interpolated by bilinear\n", + "# interpolation\n", + "v1 = np.array((1, 0, 0, 0))\n", + "v2 = np.array((0, 1, 0, 0))\n", + "v3 = np.array((0, 0, 1, 0))\n", + "v4 = np.array((0, 0, 0, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation and visualization\n", + "----------------------------------------\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 720x720 with 25 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(figsize=(10, 10))\n", + "pl.title('Convolutional Wasserstein Barycenters in POT')\n", + "cm = 'Blues'\n", + "# regularization parameter\n", + "reg = 0.004\n", + "for i in range(nb_images):\n", + " for j in range(nb_images):\n", + " pl.subplot(nb_images, nb_images, i * nb_images + j + 1)\n", + " tx = float(i) / (nb_images - 1)\n", + " ty = float(j) / (nb_images - 1)\n", + "\n", + " # weights are constructed by bilinear interpolation\n", + " tmp1 = (1 - tx) * v1 + tx * v2\n", + " tmp2 = (1 - tx) * v3 + tx * v4\n", + " weights = (1 - ty) * tmp1 + ty * tmp2\n", + "\n", + " if i == 0 and j == 0:\n", + " pl.imshow(f1, cmap=cm)\n", + " pl.axis('off')\n", + " elif i == 0 and j == (nb_images - 1):\n", + " pl.imshow(f3, cmap=cm)\n", + " pl.axis('off')\n", + " elif i == (nb_images - 1) and j == 0:\n", + " pl.imshow(f2, cmap=cm)\n", + " pl.axis('off')\n", + " elif i == (nb_images - 1) and j == (nb_images - 1):\n", + " pl.imshow(f4, cmap=cm)\n", + " pl.axis('off')\n", + " else:\n", + " # call to barycenter computation\n", + " pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm)\n", + " pl.axis('off')\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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_free_support_barycenter.ipynb b/notebooks/plot_free_support_barycenter.ipynb new file mode 100644 index 0000000..b8df589 --- /dev/null +++ b/notebooks/plot_free_support_barycenter.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 2D free support Wasserstein barycenters of distributions\n", + "\n", + "\n", + "Illustration of 2D Wasserstein barycenters if discributions that are weighted\n", + "sum of diracs.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Vivien Seguy <vivien.seguy@iip.ist.i.kyoto-u.ac.jp>\n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + " -------------\n", + "%% parameters and data generation\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "N = 3\n", + "d = 2\n", + "measures_locations = []\n", + "measures_weights = []\n", + "\n", + "for i in range(N):\n", + "\n", + " n_i = np.random.randint(low=1, high=20) # nb samples\n", + "\n", + " mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean\n", + "\n", + " A_i = np.random.rand(d, d)\n", + " cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix\n", + "\n", + " x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations\n", + " b_i = np.random.uniform(0., 1., (n_i,))\n", + " b_i = b_i / np.sum(b_i) # Dirac weights\n", + "\n", + " measures_locations.append(x_i)\n", + " measures_weights.append(b_i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute free support barycenter\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "k = 10 # number of Diracs of the barycenter\n", + "X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations\n", + "b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized)\n", + "\n", + "X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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": [ + "pl.figure(1)\n", + "for (x_i, b_i) in zip(measures_locations, measures_weights):\n", + " color = np.random.randint(low=1, high=10 * N)\n", + " pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure')\n", + "pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter')\n", + "pl.title('Data measures and their barycenter')\n", + "pl.legend(loc=0)\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": "3.6.5" + } + }, + "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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\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 new file mode 100644 index 0000000..0911c28 --- /dev/null +++ b/notebooks/plot_stochastic.ipynb @@ -0,0 +1,563 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Stochastic examples\n", + "\n", + "\n", + "This example is designed to show how to use the stochatic optimization\n", + "algorithms for descrete and semicontinous measures from the POT library.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Kilian Fatras <kilian.fatras@gmail.com>\n", + "#\n", + "# License: MIT License\n", + "\n", + "import matplotlib.pylab as pl\n", + "import numpy as np\n", + "import ot\n", + "import ot.plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source = 7\n", + "n_target = 4\n", + "reg = 1\n", + "numItermax = 1000\n", + "\n", + "a = ot.utils.unif(n_source)\n", + "b = ot.utils.unif(n_target)\n", + "\n", + "rng = np.random.RandomState(0)\n", + "X_source = rng.randn(n_source, 2)\n", + "Y_target = rng.randn(n_target, 2)\n", + "M = ot.dist(X_source, Y_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the \"SAG\" method to find the transportation matrix in the discrete case\n", + "---------------------------------------------\n", + "\n", + "Define the method \"SAG\", call ot.solve_semi_dual_entropic and plot the\n", + "results.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2.55553509e-02 9.96395660e-02 1.76579142e-02 4.31178196e-06]\n", + " [1.21640234e-01 1.25357448e-02 1.30225078e-03 7.37891338e-03]\n", + " [3.56123975e-03 7.61451746e-02 6.31505947e-02 1.33831456e-07]\n", + " [2.61515202e-02 3.34246014e-02 8.28734709e-02 4.07550428e-04]\n", + " [9.85500870e-03 7.52288517e-04 1.08262628e-02 1.21423583e-01]\n", + " [2.16904253e-02 9.03825797e-04 1.87178503e-03 1.18391107e-01]\n", + " [4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]]\n" + ] + } + ], + "source": [ + "method = \"SAG\"\n", + "sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n", + " numItermax)\n", + "print(sag_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source = 7\n", + "n_target = 4\n", + "reg = 1\n", + "numItermax = 1000\n", + "log = True\n", + "\n", + "a = ot.utils.unif(n_source)\n", + "b = ot.utils.unif(n_target)\n", + "\n", + "rng = np.random.RandomState(0)\n", + "X_source = rng.randn(n_source, 2)\n", + "Y_target = rng.randn(n_target, 2)\n", + "M = ot.dist(X_source, Y_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the \"ASGD\" method to find the transportation matrix in the semicontinous\n", + "case\n", + "---------------------------------------------\n", + "\n", + "Define the method \"ASGD\", call ot.solve_semi_dual_entropic and plot the\n", + "results.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[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" + ] + } + ], + "source": [ + "method = \"ASGD\"\n", + "asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n", + " numItermax, log=log)\n", + "print(log_asgd['alpha'], log_asgd['beta'])\n", + "print(asgd_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare the results with the Sinkhorn algorithm\n", + "---------------------------------------------\n", + "\n", + "Call the Sinkhorn algorithm from POT\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06]\n", + " [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03]\n", + " [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07]\n", + " [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04]\n", + " [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01]\n", + " [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01]\n", + " [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]]\n" + ] + } + ], + "source": [ + "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\n", + "print(sinkhorn_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PLOT TRANSPORTATION MATRIX\n", + "#############################################################################\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot SAG results\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 360x360 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot ASGD results\n", + "-----------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 360x360 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Sinkhorn results\n", + "---------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "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": [ + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source = 7\n", + "n_target = 4\n", + "reg = 1\n", + "numItermax = 100000\n", + "lr = 0.1\n", + "batch_size = 3\n", + "log = True\n", + "\n", + "a = ot.utils.unif(n_source)\n", + "b = ot.utils.unif(n_target)\n", + "\n", + "rng = np.random.RandomState(0)\n", + "X_source = rng.randn(n_source, 2)\n", + "Y_target = rng.randn(n_target, 2)\n", + "M = ot.dist(X_source, Y_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the \"SGD\" dual method to find the transportation matrix in the\n", + "semicontinous case\n", + "---------------------------------------------\n", + "\n", + "Call ot.solve_dual_entropic and plot the results.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[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" + ] + } + ], + "source": [ + "sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg,\n", + " batch_size, numItermax,\n", + " lr, log=log)\n", + "print(log_sgd['alpha'], log_sgd['beta'])\n", + "print(sgd_dual_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare the results with the Sinkhorn algorithm\n", + "---------------------------------------------\n", + "\n", + "Call the Sinkhorn algorithm from POT\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06]\n", + " [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03]\n", + " [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07]\n", + " [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04]\n", + " [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01]\n", + " [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01]\n", + " [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]]\n" + ] + } + ], + "source": [ + "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\n", + "print(sinkhorn_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot SGD results\n", + "-----------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 360x360 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Sinkhorn results\n", + "---------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 360x360 with 3 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\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": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ot/__init__.py b/ot/__init__.py index 995ce33..b74b924 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -19,7 +19,7 @@ from . import datasets from . import da from . import gromov from . import smooth - +from . import stochastic # OT functions from .lp import emd, emd2 @@ -29,7 +29,7 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif, tic, toc, toq -__version__ = "0.4.0" +__version__ = "0.5.1" __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', diff --git a/ot/bregman.py b/ot/bregman.py index ffa6202..321712b 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -11,6 +11,7 @@ Bregman projections for regularized OT # License: MIT License import numpy as np +from .utils import unif, dist def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, @@ -49,7 +50,7 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, reg : float Regularization term >0 method : str - method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or + method used for the solver either 'sinkhorn', 'greenkhorn', 'sinkhorn_stabilized' or 'sinkhorn_epsilon_scaling', see those function for specific parameters numItermax : int, optional Max number of iterations @@ -105,6 +106,10 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, def sink(): 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) elif method.lower() == 'sinkhorn_stabilized': def sink(): return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, @@ -118,7 +123,8 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, print('Warning : unknown method using classic Sinkhorn Knopp') def sink(): - return sinkhorn_knopp(a, b, M, reg, **kwargs) + return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) return sink() @@ -199,6 +205,8 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + [21] Altschuler J., Weed J., Rigollet P. : Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration, Advances in Neural Information Processing Systems (NIPS) 31, 2017 + See Also @@ -206,6 +214,7 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, ot.lp.emd : Unregularized OT ot.optim.cg : General regularized OT ot.bregman.sinkhorn_knopp : Classic Sinkhorn [2] + ot.bregman.greenkhorn : Greenkhorn [21] ot.bregman.sinkhorn_stabilized: Stabilized sinkhorn [9][10] ot.bregman.sinkhorn_epsilon_scaling: Sinkhorn with epslilon scaling [9][10] @@ -346,8 +355,13 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, # print(reg) - K = np.exp(-M / 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) + # print(np.min(K)) + tmp2 = np.empty(b.shape, dtype=M.dtype) Kp = (1 / a).reshape(-1, 1) * K cpt = 0 @@ -355,13 +369,14 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, while (err > stopThr and cpt < numItermax): uprev = u vprev = v + KtransposeU = np.dot(K.T, u) 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) @@ -375,8 +390,9 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ np.sum((v - vprev)**2) / np.sum((v)**2) else: - transp = u.reshape(-1, 1) * (K * v) - err = np.linalg.norm((np.sum(transp, axis=0) - b))**2 + # 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 if log: log['err'].append(err) @@ -391,10 +407,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, log['v'] = v if nbb: # return only loss - res = np.zeros((nbb)) - for i in range(nbb): - res[i] = np.sum( - u[:, i].reshape((-1, 1)) * K * v[:, i].reshape((1, -1)) * M) + res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) if log: return res, log else: @@ -408,6 +421,159 @@ 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): + """ + Solve the entropic regularization optimal transport problem and return the OT matrix + + The algorithm used is based on the paper + + Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration + by Jason Altschuler, Jonathan Weed, Philippe Rigollet + appeared at NIPS 2017 + + which is a stochastic version of the Sinkhorn-Knopp algorithm [2]. + + 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 : + + - M is the (ns,nt) 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) + + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) or np.ndarray (nt,nbb) + 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) + loss matrix + reg : float + Regularization term >0 + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + 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.bregman.greenkhorn(a,b,M,1) + array([[ 0.36552929, 0.13447071], + [ 0.13447071, 0.36552929]]) + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + [22] J. Altschuler, J.Weed, P. Rigollet : Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration, Advances in Neural Information Processing Systems (NIPS) 31, 2017 + + + 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) + + 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] + + n = a.shape[0] + m = 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) + 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 + + for i in range(numItermax): + i_1 = np.argmax(np.abs(viol)) + i_2 = np.argmax(np.abs(viol_2)) + m_viol_1 = np.abs(viol[i_1]) + m_viol_2 = np.abs(viol_2[i_2]) + stopThr_val = np.maximum(m_viol_1, m_viol_2) + + if m_viol_1 > m_viol_2: + old_u = u[i_1] + u[i_1] = a[i_1] / (K[i_1, :].dot(v)) + G[i_1, :] = u[i_1] * K[i_1, :] * v + + viol[i_1] = u[i_1] * K[i_1, :].dot(v) - a[i_1] + viol_2 += (K[i_1, :].T * (u[i_1] - old_u) * v) + + else: + old_v = v[i_2] + v[i_2] = b[i_2] / (K[:, i_2].T.dot(u)) + G[:, i_2] = u * K[:, i_2] * v[i_2] + #aviol = (G@one_m - a) + #aviol_2 = (G.T@one_n - b) + viol += (-old_v + v[i_2]) * K[:, i_2] * u + viol_2[i_2] = v[i_2] * K[:, i_2].dot(u) - b[i_2] + + #print('b',np.max(abs(aviol -viol)),np.max(abs(aviol_2 - viol_2))) + + if stopThr_val <= stopThr: + break + else: + print('Warning: Algorithm did not converge') + + if log: + log['u'] = u + log['v'] = v + + if log: + return G, log + else: + return G + + def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=20, log=False, **kwargs): """ @@ -532,13 +698,13 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, 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((na, 1)) + - beta.reshape((1, nb))) / 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((na, 1)) - beta.reshape((1, nb))) + / reg + np.log(u.reshape((na, 1))) + np.log(v.reshape((1, nb)))) # print(np.min(K)) @@ -748,8 +914,8 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne 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((na, 1)) + - beta.reshape((1, nb))) / reg) # print(np.min(K)) def get_reg(n): # exponential decreasing @@ -917,6 +1083,116 @@ 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 + where A is a collection of 2D images. + + 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 (2D images) in the mast two dimensions of matrix :math:`\mathbf{A}` + - reg is the regularization strength scalar value + + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [21]_ + + Parameters + ---------- + A : np.ndarray (n,w,h) + n distributions (2D images) of size w x h + reg : float + Regularization term >0 + weights : np.ndarray (n,) + Weights of each image on the simplex (barycentric coodinates) + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + stabThr : float, optional + Stabilization threshold to avoid numerical precision issue + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + a : (w,h) ndarray + 2D Wasserstein barycenter + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [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 + ACM Transactions on Graphics (TOG), 34(4), 66 + + + """ + + if weights is None: + weights = np.ones(A.shape[0]) / A.shape[0] + else: + assert(len(weights) == A.shape[0]) + + if log: + log = {'err': []} + + b = np.zeros_like(A[0, :, :]) + U = np.ones_like(A) + KV = np.ones_like(A) + + cpt = 0 + err = 1 + + # build the convolution operator + t = np.linspace(0, 1, A.shape[1]) + [Y, X] = np.meshgrid(t, t) + xi1 = np.exp(-(X - Y)**2 / reg) + + def K(x): + return np.dot(np.dot(xi1, x), xi1) + + while (err > stopThr and cpt < numItermax): + + bold = b + cpt = cpt + 1 + + b = np.zeros_like(A[0, :, :]) + for r in range(A.shape[0]): + KV[r, :, :] = K(A[r, :, :] / np.maximum(stabThr, K(U[r, :, :]))) + b += weights[r] * np.log(np.maximum(stabThr, U[r, :, :] * KV[r, :, :])) + b = np.exp(b) + for r in range(A.shape[0]): + U[r, :, :] = b / np.maximum(stabThr, KV[r, :, :]) + + if cpt % 10 == 1: + err = np.sum(np.abs(bold - b)) + # log and verbose print + 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)) + + if log: + log['niter'] = cpt + log['U'] = U + return b, log + else: + return b + + def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, stopThr=1e-3, verbose=False, log=False): """ @@ -1023,3 +1299,302 @@ 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): + ''' + 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 (ns,nt) 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 : np.ndarray (ns, d) + samples in the source domain + X_t : np.ndarray (nt, d) + samples in the target domain + reg : float + Regularization term >0 + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + 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 : (ns x nt) ndarray + Regularized optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> n_s = 2 + >>> n_t = 2 + >>> reg = 0.1 + >>> X_s = np.reshape(np.arange(n_s), (n_s, 1)) + >>> X_t = np.reshape(np.arange(0, n_t), (n_t, 1)) + >>> emp_sinkhorn = empirical_sinkhorn(X_s, X_t, reg, verbose=False) + >>> print(emp_sinkhorn) + >>> [[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): + ''' + 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 (ns,nt) 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 : np.ndarray (ns, d) + samples in the source domain + X_t : np.ndarray (nt, d) + samples in the target domain + reg : float + Regularization term >0 + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + 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 : (ns x nt) ndarray + Regularized optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> n_s = 2 + >>> n_t = 2 + >>> reg = 0.1 + >>> X_s = np.reshape(np.arange(n_s), (n_s, 1)) + >>> X_t = np.reshape(np.arange(0, n_t), (n_t, 1)) + >>> loss_sinkhorn = empirical_sinkhorn2(X_s, X_t, reg, verbose=False) + >>> print(loss_sinkhorn) + >>> [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): + ''' + 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 (ns,nt) metric cost matrix (resp (ns, ns) and (nt, nt)) + - :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 : np.ndarray (ns, d) + samples in the source domain + X_t : np.ndarray (nt, d) + samples in the target domain + reg : float + Regularization term >0 + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + 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 : (ns x nt) ndarray + Regularized optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> n_s = 2 + >>> n_t = 4 + >>> reg = 0.1 + >>> X_s = np.reshape(np.arange(n_s), (n_s, 1)) + >>> X_t = np.reshape(np.arange(0, n_t), (n_t, 1)) + >>> emp_sinkhorn_div = empirical_sinkhorn_divergence(X_s, X_t, reg) + >>> print(emp_sinkhorn_div) + >>> [2.99977435] + + + 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) @@ -15,7 +15,7 @@ import scipy.linalg as linalg from .bregman import sinkhorn from .lp import emd from .utils import unif, dist, kernel, cost_normalization -from .utils import check_params, deprecated, BaseEstimator +from .utils import check_params, BaseEstimator from .optim import cg from .optim import gcg @@ -473,22 +473,24 @@ 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 +645,8 @@ 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` @@ -740,288 +743,6 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, return A, b -@deprecated("The class OTDA is deprecated in 0.3.1 and will be " - "removed in 0.5" - "\n\tfor standard transport use class EMDTransport instead.") -class OTDA(object): - - """Class for domain adaptation with optimal transport as proposed in [5] - - - 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 - - """ - - def __init__(self, metric='sqeuclidean', norm=None): - """ Class initialization""" - self.xs = 0 - self.xt = 0 - self.G = 0 - self.metric = metric - self.norm = norm - self.computed = False - - def fit(self, xs, xt, ws=None, wt=None, max_iter=100000): - """Fit domain adaptation between samples is xs and xt - (with optional weights)""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = emd(ws, wt, self.M, max_iter) - self.computed = True - - def interp(self, direction=1): - """Barycentric interpolation for the source (1) or target (-1) samples - - This Barycentric interpolation solves for each source (resp target) - sample xs (resp xt) the following optimization problem: - - .. math:: - arg\min_x \sum_i \gamma_{k,i} c(x,x_i^t) - - where k is the index of the sample in xs - - For the moment only squared euclidean distance is provided but more - metric could be used in the future. - - """ - if direction > 0: # >0 then source to target - G = self.G - w = self.ws.reshape((self.xs.shape[0], 1)) - x = self.xt - else: - G = self.G.T - w = self.wt.reshape((self.xt.shape[0], 1)) - x = self.xs - - if self.computed: - if self.metric == 'sqeuclidean': - return np.dot(G / w, x) # weighted mean - else: - print( - "Warning, metric not handled yet, using weighted average") - return np.dot(G / w, x) # weighted mean - return None - else: - print("Warning, model not fitted yet, returning None") - return None - - def predict(self, x, direction=1): - """ Out of sample mapping using the formulation from [6] - - For each sample x to map, it finds the nearest source sample xs and - map the samle x to the position xst+(x-xs) wher xst is the barycentric - interpolation of source sample xs. - - References - ---------- - - .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). - Regularized discrete optimal transport. SIAM Journal on Imaging - Sciences, 7(3), 1853-1882. - - """ - if direction > 0: # >0 then source to target - xf = self.xt - x0 = self.xs - else: - xf = self.xs - x0 = self.xt - - D0 = dist(x, x0) # dist netween new samples an source - idx = np.argmin(D0, 1) # closest one - xf = self.interp(direction) # interp the source samples - # aply the delta to the interpolation - return xf[idx, :] + x - x0[idx, :] - - -@deprecated("The class OTDA_sinkhorn is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class SinkhornTransport instead.") -class OTDA_sinkhorn(OTDA): - - """Class for domain adaptation with optimal transport with entropic - regularization - - - """ - - def fit(self, xs, xt, reg=1, ws=None, wt=None, **kwargs): - """Fit regularized domain adaptation between samples is xs and xt - (with optional weights)""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = sinkhorn(ws, wt, self.M, reg, **kwargs) - self.computed = True - - -@deprecated("The class OTDA_lpl1 is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class SinkhornLpl1Transport instead.") -class OTDA_lpl1(OTDA): - - """Class for domain adaptation with optimal transport with entropic and - group regularization""" - - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, **kwargs): - """Fit regularized domain adaptation between samples is xs and xt - (with optional weights), See ot.da.sinkhorn_lpl1_mm for fit - parameters""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M, reg, eta, **kwargs) - self.computed = True - - -@deprecated("The class OTDA_l1L2 is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class SinkhornL1l2Transport instead.") -class OTDA_l1l2(OTDA): - - """Class for domain adaptation with optimal transport with entropic - and group lasso regularization""" - - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, **kwargs): - """Fit regularized domain adaptation between samples is xs and xt - (with optional weights), See ot.da.sinkhorn_lpl1_gl for fit - parameters""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = sinkhorn_l1l2_gl(ws, ys, wt, self.M, reg, eta, **kwargs) - self.computed = True - - -@deprecated("The class OTDA_mapping_linear is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class MappingTransport instead.") -class OTDA_mapping_linear(OTDA): - - """Class for optimal transport with joint linear mapping estimation as in - [8] - """ - - def __init__(self): - """ Class initialization""" - - self.xs = 0 - self.xt = 0 - self.G = 0 - self.L = 0 - self.bias = False - self.computed = False - self.metric = 'sqeuclidean' - - def fit(self, xs, xt, mu=1, eta=1, bias=False, **kwargs): - """ Fit domain adaptation between samples is xs and xt (with optional - weights)""" - self.xs = xs - self.xt = xt - self.bias = bias - - self.ws = unif(xs.shape[0]) - self.wt = unif(xt.shape[0]) - - self.G, self.L = joint_OT_mapping_linear( - xs, xt, mu=mu, eta=eta, bias=bias, **kwargs) - self.computed = True - - def mapping(self): - return lambda x: self.predict(x) - - def predict(self, x): - """ Out of sample mapping estimated during the call to fit""" - if self.computed: - if self.bias: - x = np.hstack((x, np.ones((x.shape[0], 1)))) - return x.dot(self.L) # aply the delta to the interpolation - else: - print("Warning, model not fitted yet, returning None") - return None - - -@deprecated("The class OTDA_mapping_kernel is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class MappingTransport instead.") -class OTDA_mapping_kernel(OTDA_mapping_linear): - - """Class for optimal transport with joint nonlinear mapping - estimation as in [8]""" - - def fit(self, xs, xt, mu=1, eta=1, bias=False, kerneltype='gaussian', - sigma=1, **kwargs): - """ Fit domain adaptation between samples is xs and xt """ - self.xs = xs - self.xt = xt - self.bias = bias - - self.ws = unif(xs.shape[0]) - self.wt = unif(xt.shape[0]) - self.kernel = kerneltype - self.sigma = sigma - self.kwargs = kwargs - - self.G, self.L = joint_OT_mapping_kernel( - xs, xt, mu=mu, eta=eta, bias=bias, **kwargs) - self.computed = True - - def predict(self, x): - """ Out of sample mapping estimated during the call to fit""" - - if self.computed: - K = kernel( - x, self.xs, method=self.kernel, sigma=self.sigma, - **self.kwargs) - if self.bias: - K = np.hstack((K, np.ones((x.shape[0], 1)))) - return K.dot(self.L) - else: - print("Warning, model not fitted yet, returning None") - return None - - def distribution_estimation_uniform(X): """estimates a uniform distribution from an array of samples X @@ -1466,25 +1187,25 @@ 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 ---------- @@ -1569,22 +1290,19 @@ 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 @@ -1669,28 +1387,32 @@ 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 ---------- @@ -1786,27 +1508,28 @@ 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 @@ -1928,10 +1651,12 @@ 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 ---------- diff --git a/ot/datasets.py b/ot/datasets.py index 362a89b..e76e75d 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -38,9 +38,9 @@ def make_1D_gauss(n, m, s): @deprecated() -def get_1D_gauss(n, m, sigma, random_state=None): +def get_1D_gauss(n, m, sigma): """ Deprecated see make_1D_gauss """ - return make_1D_gauss(n, m, sigma, random_state=None) + return make_1D_gauss(n, m, sigma) def make_2D_samples_gauss(n, m, sigma, random_state=None): 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 a2fdd3d..deda6b1 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -1,12 +1,37 @@ # -*- coding: utf-8 -*- +""" -from . import bregman -from . import da -from .bregman import sinkhorn +This module provides GPU implementation for several OT solvers and utility +functions. The GPU backend in handled by `cupy +<https://cupy.chainer.org/>`_. + +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 +arrays to the functions and desactivate the conversion to numpy of the +result of the function with parameter ``to_numpy=False``. + +""" # Author: Remi Flamary <remi.flamary@unice.fr> # Leo Gautheron <https://github.com/aje> # # License: MIT License -__all__ = ["bregman", "da", "sinkhorn"] +from . import bregman +from . import da +from .bregman import sinkhorn +from .da import sinkhorn_lpl1_mm + +from . import utils +from .utils import dist, to_gpu, to_np + + + + + +__all__ = ["utils", "dist", "sinkhorn", + "sinkhorn_lpl1_mm", 'bregman', 'da', 'to_gpu', 'to_np'] + diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 47939c4..978b307 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -8,14 +8,18 @@ Bregman projections for regularized OT with GPU # # License: MIT License -import numpy as np -import cudamat +import cupy as np # np used for matrix computation +import cupy as cp # cp used for cupy specific operations +from . import utils -def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, - log=False, returnAsGPU=False): - r""" - Solve the entropic regularization optimal transport problem on GPU +def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, + verbose=False, log=False, to_numpy=True, **kwargs): + """ + Solve the entropic regularization optimal transport on GPU + + If the input matrix are in numpy format, they will be uploaded to the + GPU first which can incur significant time overhead. The function solves the following optimization problem: @@ -40,9 +44,10 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, ---------- a : np.ndarray (ns,) samples weights in the source domain - b : np.ndarray (nt,) - samples in the target domain - M_GPU : cudamat.CUDAMatrix (ns,nt) + b : np.ndarray (nt,) or np.ndarray (nt,nbb) + 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) loss matrix reg : float Regularization term >0 @@ -54,8 +59,9 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, Print information along iterations log : bool, optional record log if True - returnAsGPU : bool, optional - return the OT matrix as a cudamat.CUDAMatrix + to_numpy : boolean, optional (default True) + If true convert back the GPU array result to numpy format. + Returns ------- @@ -88,60 +94,78 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, ot.optim.cg : General regularized OT """ + + a = cp.asarray(a) + b = cp.asarray(b) + M = cp.asarray(M) + + if len(a) == 0: + a = np.ones((M.shape[0],)) / M.shape[0] + if len(b) == 0: + b = np.ones((M.shape[1],)) / M.shape[1] + # init data Nini = len(a) Nfin = len(b) + if len(b.shape) > 1: + nbb = b.shape[1] + else: + nbb = 0 + if log: log = {'err': []} # we assume that no distances are null except those of the diagonal of # distances - u = (np.ones(Nini) / Nini).reshape((Nini, 1)) - u_GPU = cudamat.CUDAMatrix(u) - a_GPU = cudamat.CUDAMatrix(a.reshape((Nini, 1))) - ones_GPU = cudamat.empty(u_GPU.shape).assign(1) - v = (np.ones(Nfin) / Nfin).reshape((Nfin, 1)) - v_GPU = cudamat.CUDAMatrix(v) - b_GPU = cudamat.CUDAMatrix(b.reshape((Nfin, 1))) - - M_GPU.divide(-reg) + if nbb: + u = np.ones((Nini, nbb)) / Nini + v = np.ones((Nfin, nbb)) / Nfin + else: + u = np.ones(Nini) / Nini + v = np.ones(Nfin) / Nfin - K_GPU = cudamat.exp(M_GPU) + # print(reg) - ones_GPU.divide(a_GPU, target=a_GPU) - Kp_GPU = cudamat.empty(K_GPU.shape) - K_GPU.mult_by_col(a_GPU, target=Kp_GPU) + # 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) - tmp_GPU = cudamat.empty(K_GPU.shape) + # print(np.min(K)) + tmp2 = np.empty(b.shape, dtype=M.dtype) + Kp = (1 / a).reshape(-1, 1) * K cpt = 0 err = 1 while (err > stopThr and cpt < numItermax): - uprev_GPU = u_GPU.copy() - vprev_GPU = v_GPU.copy() + uprev = u + vprev = v - KtransposeU_GPU = K_GPU.transpose().dot(u_GPU) - b_GPU.divide(KtransposeU_GPU, target=v_GPU) - ones_GPU.divide(Kp_GPU.dot(v_GPU), target=u_GPU) + KtransposeU = np.dot(K.T, u) + v = np.divide(b, KtransposeU) + u = 1. / np.dot(Kp, v) - if (np.any(KtransposeU_GPU.asarray() == 0) or - not u_GPU.allfinite() or not v_GPU.allfinite()): + 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) - u_GPU = uprev_GPU.copy() - v_GPU = vprev_GPU.copy() + u = uprev + v = vprev break if cpt % 10 == 0: # we can speed up the process by checking for the error only all # the 10th iterations - K_GPU.mult_by_col(u_GPU, target=tmp_GPU) - tmp_GPU.mult_by_row(v_GPU.transpose(), target=tmp_GPU) - - bcopy_GPU = b_GPU.copy().transpose() - bcopy_GPU.add_sums(tmp_GPU, axis=0, beta=-1) - err = bcopy_GPU.euclid_norm()**2 + if nbb: + err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ + np.sum((v - vprev)**2) / np.sum((v)**2) + else: + # compute right marginal tmp2= (diag(u)Kdiag(v))^T1 + tmp2 = np.sum(u[:, None] * K * v[None, :], 0) + #tmp2=np.einsum('i,ij,j->j', u, K, v) + err = np.linalg.norm(tmp2 - b)**2 # violation of marginal if log: log['err'].append(err) @@ -150,20 +174,32 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, print( '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) print('{:5d}|{:8e}|'.format(cpt, err)) - cpt += 1 - if log: - log['u'] = u_GPU.asarray() - log['v'] = v_GPU.asarray() - - K_GPU.mult_by_col(u_GPU, target=K_GPU) - K_GPU.mult_by_row(v_GPU.transpose(), target=K_GPU) - - if returnAsGPU: - res = K_GPU - else: - res = K_GPU.asarray() - + cpt = cpt + 1 if log: - return res, log - else: - return res + log['u'] = u + log['v'] = v + + if nbb: # return only loss + #res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) (explodes cupy memory) + res = np.empty(nbb) + for i in range(nbb): + res[i] = np.sum(u[:, None, i] * (K * M) * v[None, :, i]) + if to_numpy: + res = utils.to_np(res) + if log: + return res, log + else: + return res + + else: # return OT matrix + res = u.reshape((-1, 1)) * K * v.reshape((1, -1)) + if to_numpy: + res = utils.to_np(res) + if log: + return res, log + else: + return res + + +# define sinkhorn as sinkhorn_knopp +sinkhorn = sinkhorn_knopp diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 71a485a..4a98038 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -11,80 +11,30 @@ Domain adaptation with optimal transport with GPU implementation # License: MIT License -import numpy as np -from ..utils import unif -from ..da import OTDA +import cupy as np # np used for matrix computation +import cupy as cp # cp used for cupy specific operations +import numpy as npp +from . import utils + from .bregman import sinkhorn -import cudamat -def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False): +def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, + numInnerItermax=200, stopInnerThr=1e-9, verbose=False, + log=False, to_numpy=True): """ - Compute the pairwise euclidean distance between matrices a and b. - - - Parameters - ---------- - a : np.ndarray (n, f) - first matrice - b : np.ndarray (m, f) - second matrice - returnAsGPU : boolean, optional (default False) - if True, returns cudamat matrix still on GPU, else return np.ndarray - squared : boolean, optional (default False) - if True, return squared euclidean distance matrice + Solve the entropic regularization optimal transport problem with nonconvex + group lasso regularization on GPU + If the input matrix are in numpy format, they will be uploaded to the + GPU first which can incur significant time overhead. - Returns - ------- - c : (n x m) np.ndarray or cudamat.CUDAMatrix - pairwise euclidean distance distance matrix - """ - # a is shape (n, f) and b shape (m, f). Return matrix c of shape (n, m). - # First compute in c_GPU the squared euclidean distance. And return its - # square root. At each cell [i,j] of c, we want to have - # sum{k in range(f)} ( (a[i,k] - b[j,k])^2 ). We know that - # (a-b)^2 = a^2 -2ab +b^2. Thus we want to have in each cell of c: - # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] +b[j,k]^2). - - a_GPU = cudamat.CUDAMatrix(a) - b_GPU = cudamat.CUDAMatrix(b) - - # Multiply a by b transpose to obtain in each cell [i,j] of c the - # value sum{k in range(f)} ( a[i,k]b[j,k] ) - c_GPU = cudamat.dot(a_GPU, b_GPU.transpose()) - # multiply by -2 to have sum{k in range(f)} ( -2a[i,k]b[j,k] ) - c_GPU.mult(-2) - - # Compute the vectors of the sum of squared elements. - a_GPU = cudamat.pow(a_GPU, 2).sum(axis=1) - b_GPU = cudamat.pow(b_GPU, 2).sum(axis=1) - - # Add the vectors in each columns (respectivly rows) of c. - # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] ) - c_GPU.add_col_vec(a_GPU) - # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] +b[j,k]^2) - c_GPU.add_row_vec(b_GPU.transpose()) - - if not squared: - c_GPU = cudamat.sqrt(c_GPU) - - if returnAsGPU: - return c_GPU - else: - return c_GPU.asarray() - - -def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, - numInnerItermax=200, stopInnerThr=1e-9, - verbose=False, log=False): - """ - Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization The function solves the following optimization problem: .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma) + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma) + + \eta \Omega_g(\gamma) s.t. \gamma 1 = a @@ -94,11 +44,16 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, 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_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. + - :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_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 gradient as proposed in [5]_ [7]_ + The algorithm used for solving the problem is the generalised conditional + gradient as proposed in [5]_ [7]_ Parameters @@ -109,7 +64,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, labels of samples in the source domain b : np.ndarray (nt,) samples weights in the target domain - M_GPU : cudamat.CUDAMatrix (ns,nt) + M : np.ndarray (ns,nt) loss matrix reg : float Regularization term for entropic regularization >0 @@ -125,6 +80,8 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, Print information along iterations log : bool, optional record log if True + to_numpy : boolean, optional (default True) + If true convert back the GPU array result to numpy format. Returns @@ -138,8 +95,13 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, 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. + .. [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. See Also -------- @@ -148,108 +110,35 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, ot.optim.cg : General regularized OT """ + + a, labels_a, b, M = utils.to_gpu(a, labels_a, b, M) + p = 0.5 epsilon = 1e-3 - Nfin = len(b) indices_labels = [] - classes = np.unique(labels_a) + labels_a2 = cp.asnumpy(labels_a) + classes = npp.unique(labels_a2) for c in classes: - idxc, = np.where(labels_a == c) - indices_labels.append(cudamat.CUDAMatrix(idxc.reshape(1, -1))) + idxc, = utils.to_gpu(npp.where(labels_a2 == c)) + indices_labels.append(idxc) - Mreg_GPU = cudamat.empty(M_GPU.shape) - W_GPU = cudamat.empty(M_GPU.shape).assign(0) + W = np.zeros(M.shape) for cpt in range(numItermax): - Mreg_GPU.assign(M_GPU) - Mreg_GPU.add_mult(W_GPU, eta) - transp_GPU = sinkhorn(a, b, Mreg_GPU, reg, numItermax=numInnerItermax, - stopThr=stopInnerThr, returnAsGPU=True) + Mreg = M + eta * W + transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax, + stopThr=stopInnerThr, to_numpy=False) # the transport has been computed. Check if classes are really # separated - W_GPU.assign(1) - W_GPU = W_GPU.transpose() + W = np.ones(M.shape) for (i, c) in enumerate(classes): - (_, nbRow) = indices_labels[i].shape - tmpC_GPU = cudamat.empty((Nfin, nbRow)).assign(0) - transp_GPU.transpose().select_columns(indices_labels[i], tmpC_GPU) - majs_GPU = tmpC_GPU.sum(axis=1).add(epsilon) - cudamat.pow(majs_GPU, (p - 1)) - majs_GPU.mult(p) - - tmpC_GPU.assign(0) - tmpC_GPU.add_col_vec(majs_GPU) - W_GPU.set_selected_columns(indices_labels[i], tmpC_GPU) - - W_GPU = W_GPU.transpose() - - return transp_GPU.asarray() + majs = np.sum(transp[indices_labels[i]], axis=0) + majs = p * ((majs + epsilon)**(p - 1)) + W[indices_labels[i]] = majs -class OTDA_GPU(OTDA): - - def normalizeM(self, norm): - if norm == "median": - self.M_GPU.divide(float(np.median(self.M_GPU.asarray()))) - elif norm == "max": - self.M_GPU.divide(float(np.max(self.M_GPU.asarray()))) - elif norm == "log": - self.M_GPU.add(1) - cudamat.log(self.M_GPU) - elif norm == "loglog": - self.M_GPU.add(1) - cudamat.log(self.M_GPU) - self.M_GPU.add(1) - cudamat.log(self.M_GPU) - - -class OTDA_sinkhorn(OTDA_GPU): - - def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): - cudamat.init() - xs = np.asarray(xs, dtype=np.float64) - xt = np.asarray(xt, dtype=np.float64) - - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M_GPU = pairwiseEuclideanGPU(xs, xt, returnAsGPU=True, - squared=True) - self.normalizeM(norm) - self.G = sinkhorn(ws, wt, self.M_GPU, reg, **kwargs) - self.computed = True - - -class OTDA_lpl1(OTDA_GPU): - - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, - **kwargs): - cudamat.init() - xs = np.asarray(xs, dtype=np.float64) - xt = np.asarray(xt, dtype=np.float64) - - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M_GPU = pairwiseEuclideanGPU(xs, xt, returnAsGPU=True, - squared=True) - self.normalizeM(norm) - self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M_GPU, reg, eta, **kwargs) - self.computed = True + if to_numpy: + return utils.to_np(transp) + else: + return transp diff --git a/ot/gpu/utils.py b/ot/gpu/utils.py new file mode 100644 index 0000000..41e168a --- /dev/null +++ b/ot/gpu/utils.py @@ -0,0 +1,101 @@ +# -*- coding: utf-8 -*- +""" +Utility functions for GPU +""" + +# Author: Remi Flamary <remi.flamary@unice.fr> +# Nicolas Courty <ncourty@irisa.fr> +# Leo Gautheron <https://github.com/aje> +# +# License: MIT License + +import cupy as np # np used for matrix computation +import cupy as cp # cp used for cupy specific operations + + +def euclidean_distances(a, b, squared=False, to_numpy=True): + """ + Compute the pairwise euclidean distance between matrices a and b. + + If the input matrix are in numpy format, they will be uploaded to the + GPU first which can incur significant time overhead. + + Parameters + ---------- + a : np.ndarray (n, f) + first matrix + b : np.ndarray (m, f) + second matrix + to_numpy : boolean, optional (default True) + If true convert back the GPU array result to numpy format. + squared : boolean, optional (default False) + if True, return squared euclidean distance matrix + + Returns + ------- + c : (n x m) np.ndarray or cupy.ndarray + pairwise euclidean distance distance matrix + """ + + a, b = to_gpu(a, b) + + a2 = np.sum(np.square(a), 1) + b2 = np.sum(np.square(b), 1) + + c = -2 * np.dot(a, b.T) + c += a2[:, None] + c += b2[None, :] + + if not squared: + np.sqrt(c, out=c) + if to_numpy: + return to_np(c) + else: + return c + + +def dist(x1, x2=None, metric='sqeuclidean', to_numpy=True): + """Compute distance between samples in x1 and x2 on gpu + + Parameters + ---------- + + x1 : np.array (n1,d) + matrix with n1 samples of size d + x2 : np.array (n2,d), optional + matrix with n2 samples of size d (if None then x2=x1) + metric : str + Metric from 'sqeuclidean', 'euclidean', + + + Returns + ------- + + M : np.array (n1,n2) + distance matrix computed with given metric + + """ + if x2 is None: + x2 = x1 + if metric == "sqeuclidean": + return euclidean_distances(x1, x2, squared=True, to_numpy=to_numpy) + elif metric == "euclidean": + return euclidean_distances(x1, x2, squared=False, to_numpy=to_numpy) + else: + raise NotImplementedError + + +def to_gpu(*args): + """ Upload numpy arrays to GPU and return them""" + if len(args) > 1: + return (cp.asarray(x) for x in args) + else: + return cp.asarray(args[0]) + + +def to_np(*args): + """ convert GPU arras to numpy and return them""" + if len(args) > 1: + return (cp.asnumpy(x) for x in args) + else: + return cp.asnumpy(args[0]) diff --git a/ot/gromov.py b/ot/gromov.py index fe4fc15..33134a2 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -40,7 +40,7 @@ def init_matrix(C1, C2, p, q, loss_fun='square_loss'): * h1(a)=a
* h2(b)=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
diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 4c0d170..02cbd8c 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -17,8 +17,9 @@ from .import cvx from .emd_wrap import emd_c, check_result from ..utils import parmap from .cvx import barycenter +from ..utils import dist -__all__=['emd', 'emd2', 'barycenter', 'cvx'] +__all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx'] def emd(a, b, M, numItermax=100000, log=False): @@ -216,3 +217,95 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), res = parmap(f, [b[:, i] for i in range(nb)], processes) 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): + """ + Solves the free support (locations of the barycenters are optimized, not the weights) Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Wasserstein distance) + + The function solves the Wasserstein barycenter problem when the barycenter measure is constrained to be supported on k atoms. + This problem is considered in [1] (Algorithm 2). There are two differences with the following codes: + - we do not optimize over the weights + - we do not do line search for the locations updates, we use i.e. theta = 1 in [1] (Algorithm 2). This can be seen as a discrete implementation of the fixed-point algorithm of [2] proposed in the continuous setting. + + Parameters + ---------- + measures_locations : list of (k_i,d) np.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 + 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 + Initialization of the support locations (on k atoms) of the barycenter + b : (k,) np.ndarray + Initialization of the weights of the barycenter (non-negatives, sum to 1) + weights : (k,) np.ndarray + Initialization of the coefficients of the barycenter (non-negatives, sum to 1) + + 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 + ------- + X : (k,d) np.ndarray + Support locations (on k atoms) of the barycenter + + References + ---------- + + .. [1] Cuturi, Marco, and Arnaud Doucet. "Fast computation of Wasserstein barycenters." International Conference on Machine Learning. 2014. + + .. [2] Álvarez-Esteban, Pedro C., et al. "A fixed-point approach to barycenters in Wasserstein space." Journal of Mathematical Analysis and Applications 441.2 (2016): 744-762. + + """ + + iter_count = 0 + + N = len(measures_locations) + k = X_init.shape[0] + d = X_init.shape[1] + if b is None: + b = np.ones((k,))/k + if weights is None: + weights = np.ones((N,)) / N + + X = X_init + + log_dict = {} + displacement_square_norms = [] + + displacement_square_norm = stopThr + 1. + + while ( displacement_square_norm > stopThr and iter_count < numItermax ): + + T_sum = np.zeros((k, d)) + + for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights, weights.tolist()): + + M_i = dist(X, measure_locations_i) + 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)) + if log: + displacement_square_norms.append(displacement_square_norm) + + X = T_sum + + if verbose: + print('iteration %d, displacement_square_norm=%f\n', iter_count, displacement_square_norm) + + iter_count += 1 + + if log: + log_dict['displacement_square_norms'] = displacement_square_norms + return X, log_dict + else: + return X
\ No newline at end of file diff --git a/ot/lp/cvx.py b/ot/lp/cvx.py index c8c75bc..8e763be 100644 --- a/ot/lp/cvx.py +++ b/ot/lp/cvx.py @@ -11,6 +11,7 @@ import numpy as np import scipy as sp import scipy.sparse as sps + try: import cvxopt from cvxopt import solvers, matrix, spmatrix @@ -26,7 +27,7 @@ def scipy_sparse_to_spmatrix(A): def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-point'): - """Compute the entropic regularized wasserstein barycenter of distributions A + """Compute the Wasserstein barycenter of distributions A The function solves the following optimization problem [16]: diff --git a/ot/stochastic.py b/ot/stochastic.py new file mode 100644 index 0000000..85c4230 --- /dev/null +++ b/ot/stochastic.py @@ -0,0 +1,736 @@ +# Author: Kilian Fatras <kilian.fatras@gmail.com> +# +# License: MIT License + +import numpy as np + + +############################################################################## +# Optimization toolbox for SEMI - DUAL problems +############################################################################## + + +def coordinate_grad_semi_dual(b, M, reg, beta, i): + ''' + Compute the coordinate gradient update for regularized discrete distributions for (i, :) + + The function computes the gradient of the semi dual problem: + + .. math:: + \max_v \sum_i (\sum_j v_j * b_j - reg * log(\sum_j exp((v_j - M_{i,j})/reg) * b_j)) * a_i + + Where : + + - M is the (ns,nt) metric cost matrix + - v is a dual variable in R^J + - reg is the regularization term + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the ASGD & SAG algorithms + as proposed in [18]_ [alg.1 & alg.2] + + + 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 + + Returns + ------- + + coordinate gradient : np.ndarray(nt,) + + Examples + -------- + + >>> 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) + >>> 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) + + References + ---------- + + [Genevay et al., 2016] : + 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)) + return b - khi + + +def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): + ''' + Compute the SAG algorithm to solve the regularized discrete measures + optimal transport max problem + + 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 : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term with :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the SAG algorithm + as proposed in [18]_ [alg.1] + + + Parameters + ---------- + + a : np.ndarray(ns,), + source measure + b : np.ndarray(nt,), + target measure + M : np.ndarray(ns, nt), + cost matrix + reg : float number, + Regularization term > 0 + numItermax : int number + number of iteration + lr : float number + learning rate + + Returns + ------- + + v : np.ndarray(nt,) + dual variable + + Examples + -------- + + >>> 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) + >>> 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) + + References + ---------- + + [Genevay et al., 2016] : + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. + ''' + + if lr is None: + lr = 1. / max(a / reg) + n_source = np.shape(M)[0] + n_target = np.shape(M)[1] + cur_beta = np.zeros(n_target) + stored_gradient = np.zeros((n_source, n_target)) + sum_stored_gradient = np.zeros(n_target) + for _ in range(numItermax): + i = np.random.randint(n_source) + cur_coord_grad = a[i] * coordinate_grad_semi_dual(b, M, reg, + cur_beta, i) + sum_stored_gradient += (cur_coord_grad - stored_gradient[i]) + stored_gradient[i] = cur_coord_grad + cur_beta += lr * (1. / n_source) * sum_stored_gradient + return cur_beta + + +def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): + ''' + Compute the ASGD algorithm to solve the regularized semi continous measures optimal transport max problem + + 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 : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term with :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the ASGD algorithm + as proposed in [18]_ [alg.2] + + + Parameters + ---------- + + b : np.ndarray(nt,) + target measure + M : np.ndarray(ns, nt) + cost matrix + reg : float number + Regularization term > 0 + numItermax : int number + number of iteration + lr : float number + learning rate + + + Returns + ------- + + ave_v : np.ndarray(nt,) + dual variable + + Examples + -------- + + >>> 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) + >>> 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) + + References + ---------- + + [Genevay et al., 2016] : + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. + ''' + + if lr is None: + lr = 1. / max(a / reg) + n_source = np.shape(M)[0] + n_target = np.shape(M)[1] + cur_beta = np.zeros(n_target) + ave_beta = np.zeros(n_target) + for cur_iter in range(numItermax): + k = cur_iter + 1 + i = np.random.randint(n_source) + cur_coord_grad = coordinate_grad_semi_dual(b, M, reg, cur_beta, i) + cur_beta += (lr / np.sqrt(k)) * cur_coord_grad + ave_beta = (1. / k) * cur_beta + (1 - 1. / k) * ave_beta + return ave_beta + + +def c_transform_entropic(b, M, reg, beta): + ''' + The goal is to recover u from the c-transform. + + The function computes the c_transform of a dual variable from the other + dual variable: + + .. math:: + u = v^{c,reg} = -reg \sum_j exp((v - M)/reg) b_j + + Where : + + - M is the (ns,nt) metric cost matrix + - u, v are dual variables in R^IxR^J + - reg is the regularization term + + It is used to recover an optimal u from optimal v solving the semi dual + problem, see Proposition 2.1 of [18]_ + + + Parameters + ---------- + + b : np.ndarray(nt,) + target measure + M : np.ndarray(ns, nt) + cost matrix + reg : float + regularization term > 0 + v : np.ndarray(nt,) + dual variable + + Returns + ------- + + u : np.ndarray(ns,) + dual variable + + Examples + -------- + + >>> 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) + >>> 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) + + References + ---------- + + [Genevay et al., 2016] : + 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] + alpha = np.zeros(n_source) + for i in range(n_source): + r = M[i, :] - beta + min_r = np.min(r) + exp_beta = np.exp(-(r - min_r) / reg) * b + alpha[i] = min_r - reg * np.log(np.sum(exp_beta)) + return alpha + + +def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, + log=False): + ''' + Compute the transportation matrix to solve the regularized discrete + measures optimal transport max problem + + 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 : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term with :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + The algorithm used for solving the problem is the SAG or ASGD algorithms + as proposed in [18]_ + + + Parameters + ---------- + + a : np.ndarray(ns,) + source measure + b : np.ndarray(nt,) + target measure + M : np.ndarray(ns, nt) + cost matrix + reg : float number + Regularization term > 0 + methode : str + used method (SAG or ASGD) + numItermax : int number + number of iteration + lr : float number + learning rate + n_source : int number + size of the source measure + n_target : int number + size of the target measure + log : bool, optional + record log if True + + Returns + ------- + + pi : np.ndarray(ns, nt) + transportation matrix + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> 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) + >>> 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) + + References + ---------- + + [Genevay et al., 2016] : + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. + ''' + + if method.lower() == "sag": + opt_beta = sag_entropic_transport(a, b, M, reg, numItermax, lr) + elif method.lower() == "asgd": + opt_beta = averaged_sgd_entropic_transport(a, b, M, reg, numItermax, lr) + else: + print("Please, select your method between SAG and ASGD") + return None + + opt_alpha = c_transform_entropic(b, M, reg, opt_beta) + pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) * + a[:, None] * b[None, :]) + + if log: + log = {} + log['alpha'] = opt_alpha + log['beta'] = opt_beta + return pi, log + else: + return pi + + +############################################################################## +# Optimization toolbox for DUAL problems +############################################################################## + + +def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, + batch_beta): + ''' + Computes the partial gradient of the dual optimal transport problem. + + For each (i,j) in a batch of coordinates, the partial gradients are : + + .. math:: + \partial_{u_i} F = u_i * b_s/l_{v} - \sum_{j \in B_v} exp((u_i + v_j - M_{i,j})/reg) * a_i * b_j + + \partial_{v_j} F = v_j * b_s/l_{u} - \sum_{i \in B_u} exp((u_i + v_j - M_{i,j})/reg) * a_i * b_j + + Where : + + - M is the (ns,nt) metric cost matrix + - u, v are dual variables in R^ixR^J + - reg is the regularization term + - :math:`B_u` and :math:`B_v` are lists of index + - :math:`b_s` is the size of the batchs :math:`B_u` and :math:`B_v` + - :math:`l_u` and :math:`l_v` are the lenghts of :math:`B_u` and :math:`B_v` + - a and b are source and target weights (sum to 1) + + + The algorithm used for solving the dual problem is the SGD algorithm + as proposed in [19]_ [alg.1] + + + Parameters + ---------- + + a : np.ndarray(ns,) + source measure + b : np.ndarray(nt,) + target measure + M : np.ndarray(ns, nt) + cost matrix + reg : float number + Regularization term > 0 + alpha : np.ndarray(ns,) + dual variable + beta : np.ndarray(nt,) + dual variable + batch_size : int number + size of the batch + batch_alpha : np.ndarray(bs,) + batch of index of alpha + batch_beta : np.ndarray(bs,) + batch of index of beta + + Returns + ------- + + grad : np.ndarray(ns,) + partial grad F + + Examples + -------- + + >>> 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) + >>> 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) + + References + ---------- + + [Seguy et al., 2018] : + 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)) + + return grad_alpha, grad_beta + + +def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): + ''' + Compute the sgd algorithm to solve the regularized discrete measures + optimal transport dual problem + + 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 : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term with :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + Parameters + ---------- + + a : np.ndarray(ns,) + source measure + b : np.ndarray(nt,) + target measure + M : np.ndarray(ns, nt) + cost matrix + reg : float number + Regularization term > 0 + batch_size : int number + size of the batch + numItermax : int number + number of iteration + lr : float number + learning rate + + Returns + ------- + + alpha : np.ndarray(ns,) + dual variable + beta : np.ndarray(nt,) + dual variable + + Examples + -------- + + >>> 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) + >>> 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) + + References + ---------- + + [Seguy et al., 2018] : + International Conference on Learning Representation (2018), + arXiv preprint arxiv:1711.02283. + ''' + + n_source = np.shape(M)[0] + n_target = np.shape(M)[1] + cur_alpha = np.zeros(n_source) + cur_beta = np.zeros(n_target) + for cur_iter in range(numItermax): + k = np.sqrt(cur_iter + 1) + batch_alpha = np.random.choice(n_source, batch_size, replace=False) + batch_beta = np.random.choice(n_target, batch_size, replace=False) + update_alpha, update_beta = batch_grad_dual(a, b, M, reg, cur_alpha, + cur_beta, batch_size, + batch_alpha, batch_beta) + cur_alpha[batch_alpha] += (lr / k) * update_alpha[batch_alpha] + cur_beta[batch_beta] += (lr / k) * update_beta[batch_beta] + + return cur_alpha, cur_beta + + +def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, + log=False): + ''' + Compute the transportation matrix to solve the regularized discrete measures + optimal transport dual problem + + 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 : + + - M is the (ns,nt) 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) + + Parameters + ---------- + + a : np.ndarray(ns,) + source measure + b : np.ndarray(nt,) + target measure + M : np.ndarray(ns, nt) + cost matrix + reg : float number + Regularization term > 0 + batch_size : int number + size of the batch + numItermax : int number + number of iteration + lr : float number + learning rate + log : bool, optional + record log if True + + Returns + ------- + + pi : np.ndarray(ns, nt) + transportation matrix + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> 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) + >>> 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) + + References + ---------- + + [Seguy et al., 2018] : + International Conference on Learning Representation (2018), + arXiv preprint arxiv:1711.02283. + ''' + + opt_alpha, opt_beta = sgd_entropic_regularization(a, b, M, reg, batch_size, + numItermax, lr) + pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) * + a[:, None] * b[None, :]) + if log: + log = {} + log['alpha'] = opt_alpha + log['beta'] = opt_beta + return pi, log + else: + return pi diff --git a/ot/utils.py b/ot/utils.py index 7dac283..bb21b38 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -77,6 +77,34 @@ def clean_zeros(a, b, M): return a2, b2, M2 +def euclidean_distances(X, Y, squared=False): + """ + Considering the rows of X (and Y=X) as vectors, compute the + distance matrix between each pair of vectors. + Parameters + ---------- + X : {array-like}, shape (n_samples_1, n_features) + Y : {array-like}, shape (n_samples_2, n_features) + squared : boolean, optional + Return squared Euclidean distances. + Returns + ------- + distances : {array}, shape (n_samples_1, n_samples_2) + """ + XX = np.einsum('ij,ij->i', X, X)[:, np.newaxis] + YY = np.einsum('ij,ij->i', Y, Y)[np.newaxis, :] + distances = np.dot(X, Y.T) + distances *= -2 + distances += XX + distances += YY + np.maximum(distances, 0, out=distances) + if X is Y: + # Ensure that distances between vectors and themselves are set to 0.0. + # This may not be the case due to floating point rounding errors. + distances.flat[::distances.shape[0] + 1] = 0.0 + return distances if squared else np.sqrt(distances, out=distances) + + def dist(x1, x2=None, metric='sqeuclidean'): """Compute distance between samples in x1 and x2 using function scipy.spatial.distance.cdist @@ -104,7 +132,8 @@ def dist(x1, x2=None, metric='sqeuclidean'): """ if x2 is None: x2 = x1 - + if metric == "sqeuclidean": + return euclidean_distances(x1, x2, squared=True) return cdist(x1, x2, metric=metric) @@ -3,4 +3,4 @@ description-file = README.md [flake8] exclude = __init__.py -ignore = E265,E501 +ignore = E265,E501,W605,W503,W504 @@ -24,6 +24,12 @@ ROOT = os.path.abspath(os.path.dirname(__file__)) with open(os.path.join(ROOT, 'README.md'), encoding="utf-8") as f: README = f.read() +# add platform dependant optional compilation argument +opt_arg=["-O3"] +import platform +if platform.system()=='Darwin': + if platform.release()=='18.0.0': + opt_arg.append("-stdlib=libc++") # correspond to a compilation problem with Mojave and XCode 10 setup(name='POT', version=__version__, @@ -39,7 +45,9 @@ setup(name='POT', sources=["ot/lp/emd_wrap.pyx", "ot/lp/EMD_wrapper.cpp"], # the Cython source and # additional C++ source files language="c++", # generate and compile C++ code, - include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/lp')])), + include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/lp')], + extra_compile_args=opt_arg + )), platforms=['linux','macosx','windows'], download_url='https://github.com/rflamary/POT/archive/{}.tar.gz'.format(__version__), license = 'MIT', @@ -55,7 +63,7 @@ setup(name='POT', 'Operating System :: MacOS', 'Operating System :: POSIX', 'Programming Language :: Python', - 'Topic :: Utilities' + 'Topic :: Utilities', '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 c8e9179..7f4972c 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -1,6 +1,7 @@ """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 @@ -71,11 +72,39 @@ def test_sinkhorn_variants(): 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_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) + Gerr, logerr = ot.sinkhorn(u, u, M, 1, method='do_not_exists', 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, Gerr) + np.testing.assert_allclose(G0, G_green, atol=1e-5) + print(G0, G_green) def test_bary(): @@ -105,6 +134,30 @@ def test_bary(): ot.bregman.barycenter(A, M, reg, log=True, verbose=True) +def test_wasserstein_bary_2d(): + + size = 100 # size of a square image + a1 = np.random.randn(size, size) + a1 += a1.min() + a1 = a1 / np.sum(a1) + a2 = np.random.randn(size, size) + a2 += a2.min() + a2 = a2 / np.sum(a2) + # creating matrix A containing all distributions + A = np.zeros((2, size, size)) + A[0, :, :] = a1 + A[1, :, :] = a2 + + # wasserstein + reg = 1e-2 + bary_wass = ot.bregman.convolutional_barycenter2d(A, reg) + + np.testing.assert_allclose(1, np.sum(bary_wass)) + + # help in checking if log and verbose do not bug the function + ot.bregman.convolutional_barycenter2d(A, reg, log=True, verbose=True) + + def test_unmix(): n_bins = 50 # nb bins @@ -135,3 +188,69 @@ def test_unmix(): 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 diff --git a/test/test_da.py b/test/test_da.py index 97e23da..f7f3a9d 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -484,66 +484,3 @@ def test_linear_mapping_class(): Cst = np.cov(Xst.T) np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2) - - -def test_otda(): - - n_samples = 150 # nb samples - np.random.seed(0) - - xs, ys = ot.datasets.make_data_classif('3gauss', n_samples) - xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples) - - a, b = ot.unif(n_samples), ot.unif(n_samples) - - # LP problem - da_emd = ot.da.OTDA() # init class - da_emd.fit(xs, xt) # fit distributions - da_emd.interp() # interpolation of source samples - da_emd.predict(xs) # interpolation of source samples - - np.testing.assert_allclose(a, np.sum(da_emd.G, 1)) - np.testing.assert_allclose(b, np.sum(da_emd.G, 0)) - - # sinkhorn regularization - lambd = 1e-1 - da_entrop = ot.da.OTDA_sinkhorn() - da_entrop.fit(xs, xt, reg=lambd) - da_entrop.interp() - da_entrop.predict(xs) - - np.testing.assert_allclose( - a, np.sum(da_entrop.G, 1), rtol=1e-3, atol=1e-3) - np.testing.assert_allclose(b, np.sum(da_entrop.G, 0), rtol=1e-3, atol=1e-3) - - # non-convex Group lasso regularization - reg = 1e-1 - eta = 1e0 - da_lpl1 = ot.da.OTDA_lpl1() - da_lpl1.fit(xs, ys, xt, reg=reg, eta=eta) - da_lpl1.interp() - da_lpl1.predict(xs) - - np.testing.assert_allclose(a, np.sum(da_lpl1.G, 1), rtol=1e-3, atol=1e-3) - np.testing.assert_allclose(b, np.sum(da_lpl1.G, 0), rtol=1e-3, atol=1e-3) - - # True Group lasso regularization - reg = 1e-1 - eta = 2e0 - da_l1l2 = ot.da.OTDA_l1l2() - da_l1l2.fit(xs, ys, xt, reg=reg, eta=eta, numItermax=20, verbose=True) - da_l1l2.interp() - da_l1l2.predict(xs) - - np.testing.assert_allclose(a, np.sum(da_l1l2.G, 1), rtol=1e-3, atol=1e-3) - np.testing.assert_allclose(b, np.sum(da_l1l2.G, 0), rtol=1e-3, atol=1e-3) - - # linear mapping - da_emd = ot.da.OTDA_mapping_linear() # init class - da_emd.fit(xs, xt, numItermax=10) # fit distributions - da_emd.predict(xs) # interpolation of source samples - - # nonlinear mapping - da_emd = ot.da.OTDA_mapping_kernel() # init class - da_emd.fit(xs, xt, numItermax=10) # fit distributions - da_emd.predict(xs) # interpolation of source samples diff --git a/test/test_gpu.py b/test/test_gpu.py index 1e97c45..6b7fdd4 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -6,7 +6,6 @@ import numpy as np import ot -import time import pytest try: # test if cudamat installed @@ -17,63 +16,81 @@ except ImportError: @pytest.mark.skipif(nogpu, reason="No GPU available") -def test_gpu_sinkhorn(): +def test_gpu_dist(): rng = np.random.RandomState(0) - def describe_res(r): - print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format( - np.min(r), np.max(r), np.mean(r), np.std(r))) - for n_samples in [50, 100, 500, 1000]: print(n_samples) a = rng.rand(n_samples // 4, 100) b = rng.rand(n_samples, 100) - time1 = time.time() - transport = ot.da.OTDA_sinkhorn() - transport.fit(a, b) - G1 = transport.G - time2 = time.time() - transport = ot.gpu.da.OTDA_sinkhorn() - transport.fit(a, b) - G2 = transport.G - time3 = time.time() - print("Normal sinkhorn, time: {:6.2f} sec ".format(time2 - time1)) - describe_res(G1) - print(" GPU sinkhorn, time: {:6.2f} sec ".format(time3 - time2)) - describe_res(G2) - - np.testing.assert_allclose(G1, G2, rtol=1e-5, atol=1e-5) + + M = ot.dist(a.copy(), b.copy()) + M2 = ot.gpu.dist(a.copy(), b.copy()) + + np.testing.assert_allclose(M, M2, rtol=1e-10) + + M2 = ot.gpu.dist(a.copy(), b.copy(), metric='euclidean', to_numpy=False) + + # check raise not implemented wrong metric + with pytest.raises(NotImplementedError): + M2 = ot.gpu.dist(a.copy(), b.copy(), metric='cityblock', to_numpy=False) @pytest.mark.skipif(nogpu, reason="No GPU available") -def test_gpu_sinkhorn_lpl1(): +def test_gpu_sinkhorn(): rng = np.random.RandomState(0) - def describe_res(r): - print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}" - .format(np.min(r), np.max(r), np.mean(r), np.std(r))) + for n_samples in [50, 100, 500, 1000]: + a = rng.rand(n_samples // 4, 100) + b = rng.rand(n_samples, 100) + + wa = ot.unif(n_samples // 4) + wb = ot.unif(n_samples) + + wb2 = np.random.rand(n_samples, 20) + wb2 /= wb2.sum(0, keepdims=True) + + M = ot.dist(a.copy(), b.copy()) + M2 = ot.gpu.dist(a.copy(), b.copy(), to_numpy=False) + + reg = 1 + + G = ot.sinkhorn(wa, wb, M, reg) + G1 = ot.gpu.sinkhorn(wa, wb, M, reg) + + np.testing.assert_allclose(G1, G, rtol=1e-10) + + # run all on gpu + ot.gpu.sinkhorn(wa, wb, M2, reg, to_numpy=False, log=True) + + # run sinkhorn for multiple targets + ot.gpu.sinkhorn(wa, wb2, M2, reg, to_numpy=False, log=True) + + +@pytest.mark.skipif(nogpu, reason="No GPU available") +def test_gpu_sinkhorn_lpl1(): + + rng = np.random.RandomState(0) for n_samples in [50, 100, 500]: print(n_samples) a = rng.rand(n_samples // 4, 100) labels_a = np.random.randint(10, size=(n_samples // 4)) b = rng.rand(n_samples, 100) - time1 = time.time() - transport = ot.da.OTDA_lpl1() - transport.fit(a, labels_a, b) - G1 = transport.G - time2 = time.time() - transport = ot.gpu.da.OTDA_lpl1() - transport.fit(a, labels_a, b) - G2 = transport.G - time3 = time.time() - print("Normal sinkhorn lpl1, time: {:6.2f} sec ".format( - time2 - time1)) - describe_res(G1) - print(" GPU sinkhorn lpl1, time: {:6.2f} sec ".format( - time3 - time2)) - describe_res(G2) - - np.testing.assert_allclose(G1, G2, rtol=1e-3, atol=1e-3) + + wa = ot.unif(n_samples // 4) + wb = ot.unif(n_samples) + + M = ot.dist(a.copy(), b.copy()) + M2 = ot.gpu.dist(a.copy(), b.copy(), to_numpy=False) + + reg = 1 + + G = ot.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M, reg) + G1 = ot.gpu.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M, reg) + + np.testing.assert_allclose(G1, G, rtol=1e-10) + + ot.gpu.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M2, reg, to_numpy=False, log=True) diff --git a/test/test_gromov.py b/test/test_gromov.py index 07cd874..43b63e1 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -28,7 +28,7 @@ def test_gromov(): C1 /= C1.max()
C2 /= C2.max()
- G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss')
+ G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss', verbose=True)
# check constratints
np.testing.assert_allclose(
@@ -69,7 +69,7 @@ def test_entropic_gromov(): C2 /= C2.max()
G = ot.gromov.entropic_gromov_wasserstein(
- C1, C2, p, q, 'square_loss', epsilon=5e-4)
+ C1, C2, p, q, 'square_loss', epsilon=5e-4, verbose=True)
# check constratints
np.testing.assert_allclose(
@@ -107,7 +107,8 @@ def test_gromov_barycenter(): [ot.unif(ns), ot.unif(nt)
], ot.unif(n_samples), [.5, .5],
'square_loss', # 5e-4,
- max_iter=100, tol=1e-3)
+ max_iter=100, tol=1e-3,
+ verbose=True)
np.testing.assert_allclose(Cb.shape, (n_samples, n_samples))
Cb2 = ot.gromov.gromov_barycenters(n_samples, [C1, C2],
@@ -134,7 +135,8 @@ def test_gromov_entropic_barycenter(): [ot.unif(ns), ot.unif(nt)
], ot.unif(n_samples), [.5, .5],
'square_loss', 2e-3,
- max_iter=100, tol=1e-3)
+ max_iter=100, tol=1e-3,
+ verbose=True)
np.testing.assert_allclose(Cb.shape, (n_samples, n_samples))
Cb2 = ot.gromov.entropic_gromov_barycenters(n_samples, [C1, C2],
diff --git a/test/test_ot.py b/test/test_ot.py index 399e549..7652394 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -70,7 +70,7 @@ def test_emd_empty(): def test_emd2_multi(): - n = 1000 # nb bins + n = 500 # nb bins # bin positions x = np.arange(n, dtype=np.float64) @@ -78,7 +78,7 @@ def test_emd2_multi(): # Gaussian distributions a = gauss(n, m=20, s=5) # m= mean, s= std - ls = np.arange(20, 1000, 20) + ls = np.arange(20, 500, 20) nb = len(ls) b = np.zeros((n, nb)) for i in range(nb): @@ -135,6 +135,21 @@ def test_lp_barycenter(): np.testing.assert_allclose(bary.sum(), 1) +def test_free_support_barycenter(): + + measures_locations = [np.array([-1.]).reshape((1, 1)), np.array([1.]).reshape((1, 1))] + measures_weights = [np.array([1.]), np.array([1.])] + + X_init = np.array([-12.]).reshape((1, 1)) + + # obvious barycenter location between two diracs + bar_locations = np.array([0.]).reshape((1, 1)) + + X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init) + + np.testing.assert_allclose(X, bar_locations, rtol=1e-5, atol=1e-7) + + @pytest.mark.skipif(not ot.lp.cvx.cvxopt, reason="No cvxopt available") def test_lp_barycenter_cvxopt(): @@ -192,11 +207,11 @@ def test_warnings(): def test_dual_variables(): - n = 5000 # nb bins - m = 6000 # nb bins + n = 500 # nb bins + m = 600 # nb bins - mean1 = 1000 - mean2 = 1100 + mean1 = 300 + mean2 = 400 # bin positions x = np.arange(n, dtype=np.float64) diff --git a/test/test_plot.py b/test/test_plot.py index f77d879..caf84de 100644 --- a/test/test_plot.py +++ b/test/test_plot.py @@ -5,10 +5,18 @@ # License: MIT License import numpy as np -import matplotlib -matplotlib.use('Agg') +import pytest +try: # test if matplotlib is installed + import matplotlib + matplotlib.use('Agg') + nogo = False +except ImportError: + nogo = True + + +@pytest.mark.skipif(nogo, reason="Matplotlib not installed") def test_plot1D_mat(): import ot @@ -30,6 +38,7 @@ def test_plot1D_mat(): ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') +@pytest.mark.skipif(nogo, reason="Matplotlib not installed") def test_plot2D_samples_mat(): import ot diff --git a/test/test_stochastic.py b/test/test_stochastic.py new file mode 100644 index 0000000..f0f3fc8 --- /dev/null +++ b/test/test_stochastic.py @@ -0,0 +1,215 @@ +""" +========================== +Stochastic test +========================== + +This example is designed to test the stochatic optimization algorithms module +for descrete and semicontinous measures from the POT library. + +""" + +# Author: Kilian Fatras <kilian.fatras@gmail.com> +# +# License: MIT License + +import numpy as np +import ot + + +############################################################################# +# COMPUTE TEST FOR SEMI-DUAL PROBLEM +############################################################################# + +############################################################################# +# +# TEST SAG algorithm +# --------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a learning rate and a number of iteration + + +def test_stochastic_sag(): + # test sag + n = 15 + reg = 1 + numItermax = 30000 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "sag", + numItermax=numItermax) + + # check constratints + np.testing.assert_allclose( + u, G.sum(1), atol=1e-04) # cf convergence sag + np.testing.assert_allclose( + u, G.sum(0), atol=1e-04) # cf convergence sag + + +############################################################################# +# +# TEST ASGD algorithm +# --------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a learning rate and a number of iteration + + +def test_stochastic_asgd(): + # test asgd + n = 15 + reg = 1 + numItermax = 100000 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd", + numItermax=numItermax) + + # check constratints + np.testing.assert_allclose( + u, G.sum(1), atol=1e-03) # cf convergence asgd + np.testing.assert_allclose( + u, G.sum(0), atol=1e-03) # cf convergence asgd + + +############################################################################# +# +# TEST Convergence SAG and ASGD toward Sinkhorn's solution +# -------------------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a learning rate and a number of iteration + + +def test_sag_asgd_sinkhorn(): + # test all algorithms + n = 15 + reg = 1 + nb_iter = 100000 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + M = ot.dist(x, x) + + G_asgd = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd", + numItermax=nb_iter) + G_sag = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "sag", + numItermax=nb_iter) + G_sinkhorn = ot.sinkhorn(u, u, M, reg) + + # check constratints + np.testing.assert_allclose( + G_sag.sum(1), G_sinkhorn.sum(1), atol=1e-03) + np.testing.assert_allclose( + G_sag.sum(0), G_sinkhorn.sum(0), atol=1e-03) + np.testing.assert_allclose( + G_asgd.sum(1), G_sinkhorn.sum(1), atol=1e-03) + np.testing.assert_allclose( + G_asgd.sum(0), G_sinkhorn.sum(0), atol=1e-03) + np.testing.assert_allclose( + G_sag, G_sinkhorn, atol=1e-03) # cf convergence sag + np.testing.assert_allclose( + G_asgd, G_sinkhorn, atol=1e-03) # cf convergence asgd + + +############################################################################# +# COMPUTE TEST FOR DUAL PROBLEM +############################################################################# + +############################################################################# +# +# TEST SGD algorithm +# --------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a batch_size and a number of iteration + + +def test_stochastic_dual_sgd(): + # test sgd + n = 10 + reg = 1 + numItermax = 15000 + batch_size = 10 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size, + numItermax=numItermax) + + # check constratints + np.testing.assert_allclose( + u, G.sum(1), atol=1e-03) # cf convergence sgd + np.testing.assert_allclose( + u, G.sum(0), atol=1e-03) # cf convergence sgd + + +############################################################################# +# +# TEST Convergence SGD toward Sinkhorn's solution +# -------------------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a batch_size and a number of iteration + + +def test_dual_sgd_sinkhorn(): + # test all dual algorithms + n = 10 + reg = 1 + nb_iter = 15000 + batch_size = 10 + rng = np.random.RandomState(0) + +# Test uniform + x = rng.randn(n, 2) + u = ot.utils.unif(n) + M = ot.dist(x, x) + + G_sgd = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size, + numItermax=nb_iter) + + G_sinkhorn = ot.sinkhorn(u, u, M, reg) + + # check constratints + np.testing.assert_allclose( + G_sgd.sum(1), G_sinkhorn.sum(1), atol=1e-03) + np.testing.assert_allclose( + G_sgd.sum(0), G_sinkhorn.sum(0), atol=1e-03) + np.testing.assert_allclose( + G_sgd, G_sinkhorn, atol=1e-03) # cf convergence sgd + +# Test gaussian + n = 30 + reg = 1 + batch_size = 30 + + a = ot.datasets.make_1D_gauss(n, 15, 5) # m= mean, s= std + b = ot.datasets.make_1D_gauss(n, 15, 5) + X_source = np.arange(n, dtype=np.float64) + Y_target = np.arange(n, dtype=np.float64) + M = ot.dist(X_source.reshape((n, 1)), Y_target.reshape((n, 1))) + M /= M.max() + + G_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, + numItermax=nb_iter) + + G_sinkhorn = ot.sinkhorn(a, b, M, reg) + + # check constratints + np.testing.assert_allclose( + G_sgd.sum(1), G_sinkhorn.sum(1), atol=1e-03) + np.testing.assert_allclose( + G_sgd.sum(0), G_sinkhorn.sum(0), atol=1e-03) + np.testing.assert_allclose( + G_sgd, G_sinkhorn, atol=1e-03) # cf convergence sgd diff --git a/test/test_utils.py b/test/test_utils.py index b524ef6..640598d 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -12,7 +12,7 @@ import sys def test_parmap(): - n = 100 + n = 10 def f(i): return 1.0 * i * i |
