From e65606ae498bd611f6a994868c2a66dfbea403cd Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 20 Apr 2020 15:19:09 +0200 Subject: big update examples --- docs/source/auto_examples/plot_stochastic.rst | 234 +++++++++++++++++--------- 1 file changed, 153 insertions(+), 81 deletions(-) (limited to 'docs/source/auto_examples/plot_stochastic.rst') diff --git a/docs/source/auto_examples/plot_stochastic.rst b/docs/source/auto_examples/plot_stochastic.rst index d531045..63fc74f 100644 --- a/docs/source/auto_examples/plot_stochastic.rst +++ b/docs/source/auto_examples/plot_stochastic.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_stochastic.py: + .. _sphx_glr_auto_examples_plot_stochastic.py: ========================== @@ -12,8 +18,7 @@ algorithms for descrete and semicontinous measures from the POT library. - -.. code-block:: python +.. code-block:: default # Author: Kilian Fatras @@ -32,6 +37,7 @@ algorithms for descrete and semicontinous measures from the POT library. + COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM ############################################################################ ############################################################################ @@ -44,8 +50,7 @@ COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM and the target measures and finally the cost matrix c. - -.. code-block:: python +.. code-block:: default n_source = 7 @@ -67,6 +72,7 @@ COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM + Call the "SAG" method to find the transportation matrix in the discrete case --------------------------------------------- @@ -74,8 +80,7 @@ Define the method "SAG", call ot.solve_semi_dual_entropic and plot the results. - -.. code-block:: python +.. code-block:: default method = "SAG" @@ -89,7 +94,9 @@ results. .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none [[2.55553509e-02 9.96395660e-02 1.76579142e-02 4.31178196e-06] [1.21640234e-01 1.25357448e-02 1.30225078e-03 7.37891338e-03] @@ -100,6 +107,8 @@ results. [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 @@ -110,8 +119,7 @@ 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 +.. code-block:: default n_source = 7 @@ -134,6 +142,7 @@ are defined the source and the target measures and finally the cost matrix c. + Call the "ASGD" method to find the transportation matrix in the semicontinous case --------------------------------------------- @@ -142,8 +151,7 @@ Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the results. - -.. code-block:: python +.. code-block:: default method = "ASGD" @@ -158,17 +166,21 @@ results. .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none + + [3.89943264 7.64823414 3.9284189 2.67501041 1.42825446 3.26039819 + 2.79237712] [-2.50786905 -2.42684838 -0.93647774 5.87119517] + [[2.50229922e-02 1.00367920e-01 1.74615056e-02 4.72486104e-06] + [1.20583329e-01 1.27839737e-02 1.30373565e-03 8.18610462e-03] + [3.49243139e-03 7.68200813e-02 6.25444833e-02 1.46879008e-07] + [2.58205995e-02 3.39501207e-02 8.26360982e-02 4.50324517e-04] + [8.94164918e-03 7.02183713e-04 9.92028326e-03 1.23293027e-01] + [1.97360234e-02 8.46022708e-04 1.72001583e-03 1.20555081e-01] + [4.10386980e-02 2.70289873e-02 7.21425804e-02 2.64687723e-03]] + - [3.98220325 7.76235856 3.97645524 2.72051681 1.23219313 3.07696856 - 2.84476972] [-2.65544161 -2.50838395 -0.9397765 6.10360206] - [[2.34528761e-02 1.00491956e-01 1.89058354e-02 6.47543413e-06] - [1.16616747e-01 1.32074516e-02 1.45653361e-03 1.15764107e-02] - [3.16154850e-03 7.42892944e-02 6.54061055e-02 1.94426150e-07] - [2.33152216e-02 3.27486992e-02 8.61986263e-02 5.94595747e-04] - [6.34131496e-03 5.31975896e-04 8.12724003e-03 1.27856612e-01] - [1.41744829e-02 6.49096245e-04 1.42704389e-03 1.26606520e-01] - [3.73127657e-02 2.62526499e-02 7.57727161e-02 3.51901117e-03]] Compare the results with the Sinkhorn algorithm @@ -177,8 +189,7 @@ Compare the results with the Sinkhorn algorithm Call the Sinkhorn algorithm from POT - -.. code-block:: python +.. code-block:: default sinkhorn_pi = ot.sinkhorn(a, b, M, reg) @@ -191,27 +202,29 @@ Call the Sinkhorn algorithm from POT .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none + + [[2.55553508e-02 9.96395661e-02 1.76579142e-02 4.31178193e-06] + [1.21640234e-01 1.25357448e-02 1.30225079e-03 7.37891333e-03] + [3.56123974e-03 7.61451746e-02 6.31505947e-02 1.33831455e-07] + [2.61515201e-02 3.34246014e-02 8.28734709e-02 4.07550425e-04] + [9.85500876e-03 7.52288523e-04 1.08262629e-02 1.21423583e-01] + [2.16904255e-02 9.03825804e-04 1.87178504e-03 1.18391107e-01] + [4.15462212e-02 2.65987989e-02 7.23177217e-02 2.39440105e-03]] + - [[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 +.. code-block:: default pl.figure(4, figsize=(5, 5)) @@ -222,8 +235,18 @@ Plot SAG results -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_004.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_001.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:119: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() @@ -232,8 +255,7 @@ Plot ASGD results ----------------- - -.. code-block:: python +.. code-block:: default pl.figure(4, figsize=(5, 5)) @@ -244,8 +266,18 @@ Plot ASGD results -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_005.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_002.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:128: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() @@ -254,8 +286,7 @@ Plot Sinkhorn results --------------------- - -.. code-block:: python +.. code-block:: default pl.figure(4, figsize=(5, 5)) @@ -266,8 +297,18 @@ Plot Sinkhorn results -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_006.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_003.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:137: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() @@ -285,8 +326,7 @@ COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM are defined the source and the target measures and finally the cost matrix c. - -.. code-block:: python +.. code-block:: default n_source = 7 @@ -311,6 +351,7 @@ COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM + Call the "SGD" dual method to find the transportation matrix in the semicontinous case --------------------------------------------- @@ -318,8 +359,7 @@ semicontinous case Call ot.solve_dual_entropic and plot the results. - -.. code-block:: python +.. code-block:: default sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, @@ -334,17 +374,21 @@ Call ot.solve_dual_entropic and plot the results. .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none + + [0.91421006 2.78075506 1.06828701 0.01979397 0.60914807 1.81887037 + 0.1152939 ] [0.33964624 0.47604281 1.57223631 4.93843308] + [[2.18038772e-02 9.24355133e-02 1.08426805e-02 9.39355366e-08] + [1.59966167e-02 1.79248770e-03 1.23251128e-04 2.47779034e-05] + [3.44864558e-03 8.01760930e-02 4.40119061e-02 3.30922887e-09] + [3.12954103e-02 4.34915712e-02 7.13747533e-02 1.24533534e-05] + [6.79742497e-02 5.64192090e-03 5.37416946e-02 2.13851205e-02] + [8.05141568e-02 3.64790957e-03 5.00040902e-03 1.12213345e-02] + [4.86643900e-02 3.38763749e-02 6.09634969e-02 7.16139950e-05]] + - [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 @@ -353,8 +397,7 @@ Compare the results with the Sinkhorn algorithm Call the Sinkhorn algorithm from POT - -.. code-block:: python +.. code-block:: default sinkhorn_pi = ot.sinkhorn(a, b, M, reg) @@ -366,23 +409,26 @@ Call the Sinkhorn algorithm from POT .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none + + [[2.55553508e-02 9.96395661e-02 1.76579142e-02 4.31178193e-06] + [1.21640234e-01 1.25357448e-02 1.30225079e-03 7.37891333e-03] + [3.56123974e-03 7.61451746e-02 6.31505947e-02 1.33831455e-07] + [2.61515201e-02 3.34246014e-02 8.28734709e-02 4.07550425e-04] + [9.85500876e-03 7.52288523e-04 1.08262629e-02 1.21423583e-01] + [2.16904255e-02 9.03825804e-04 1.87178504e-03 1.18391107e-01] + [4.15462212e-02 2.65987989e-02 7.23177217e-02 2.39440105e-03]] + - [[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 +.. code-block:: default pl.figure(4, figsize=(5, 5)) @@ -393,8 +439,18 @@ Plot SGD results -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_007.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_004.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:199: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() @@ -403,8 +459,7 @@ Plot Sinkhorn results --------------------- - -.. code-block:: python +.. code-block:: default pl.figure(4, figsize=(5, 5)) @@ -413,28 +468,45 @@ Plot Sinkhorn results -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_008.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_005.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:208: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() -**Total running time of the script:** ( 0 minutes 20.889 seconds) +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 8.885 seconds) + + +.. _sphx_glr_download_auto_examples_plot_stochastic.py: .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_stochastic.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_stochastic.ipynb ` @@ -443,4 +515,4 @@ Plot Sinkhorn results .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ -- cgit v1.2.3