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files a/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_d2_thumb.png and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_d2_thumb.png differ diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png index a042411..959cc44 100644 Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png differ diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index c8925ac..26748c2 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -51,7 +51,7 @@ }, { "source": [ - "Plot distributions and loss matrix\n##################################\n\n" + "Plot distributions and loss matrix\n----------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Solve EMD\n#############################################################################\n\n" + "Solve EMD\n---------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Solve Sinkhorn\n#############################################################################\n\n" + "Solve Sinkhorn\n--------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py index 36f823f..719058f 100644 --- a/docs/source/auto_examples/plot_OT_1D.py +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -41,7 +41,7 @@ M /= M.max() ############################################################################## # Plot distributions and loss matrix -################################### +# ---------------------------------- #%% plot the distributions @@ -57,7 +57,8 @@ ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') ############################################################################## # Solve EMD -############################################################################## +# --------- + #%% EMD @@ -68,7 +69,8 @@ ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') ############################################################################## # Solve Sinkhorn -############################################################################## +# -------------- + #%% Sinkhorn diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index 32a88e7..b91916e 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -63,7 +63,7 @@ Generate data Plot distributions and loss matrix -################################## +---------------------------------- @@ -102,13 +102,14 @@ Plot distributions and loss matrix Solve EMD -############################################################################# +--------- .. code-block:: python + #%% EMD G0 = ot.emd(a, b, M) @@ -126,13 +127,14 @@ Solve EMD Solve Sinkhorn -############################################################################# +-------------- .. code-block:: python + #%% Sinkhorn lambd = 1e-3 @@ -169,7 +171,7 @@ Solve Sinkhorn 110|1.527180e-10| -**Total running time of the script:** ( 0 minutes 0.754 seconds) +**Total running time of the script:** ( 0 minutes 0.748 seconds) diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb index 0ed7367..41a37f3 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.ipynb +++ b/docs/source/auto_examples/plot_OT_2D_samples.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Plot data\n#############################################################################\n\n" + "Plot data\n---------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Compute EMD\n#############################################################################\n\n" + "Compute EMD\n-----------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Compute Sinkhorn\n#############################################################################\n\n" + "Compute Sinkhorn\n----------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py index f57d631..9818ec5 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.py +++ b/docs/source/auto_examples/plot_OT_2D_samples.py @@ -19,7 +19,7 @@ import ot ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters and data generation @@ -42,7 +42,7 @@ M /= M.max() ############################################################################## # Plot data -############################################################################## +# --------- #%% plot samples @@ -58,7 +58,7 @@ pl.title('Cost matrix M') ############################################################################## # Compute EMD -############################################################################## +# ----------- #%% EMD @@ -78,7 +78,7 @@ pl.title('OT matrix with samples') ############################################################################## # Compute Sinkhorn -############################################################################## +# ---------------- #%% sinkhorn diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst index f95ffaf..0ad9cf0 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ b/docs/source/auto_examples/plot_OT_2D_samples.rst @@ -31,7 +31,7 @@ sum of diracs. The OT matrix is plotted with the samples. Generate data -############################################################################# +------------- @@ -64,7 +64,7 @@ Generate data Plot data -############################################################################# +--------- @@ -103,7 +103,7 @@ Plot data Compute EMD -############################################################################# +----------- @@ -146,7 +146,7 @@ Compute EMD Compute Sinkhorn -############################################################################# +---------------- @@ -191,7 +191,7 @@ Compute Sinkhorn -**Total running time of the script:** ( 0 minutes 1.990 seconds) +**Total running time of the script:** ( 0 minutes 1.743 seconds) diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb index e738db7..2b9a364 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Dataset 1 : uniform sampling\n#############################################################################\n\n" + "Dataset 1 : uniform sampling\n----------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Dataset 1 : Plot OT Matrices\n#############################################################################\n\n" + "Dataset 1 : Plot OT Matrices\n----------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Dataset 2 : Partial circle\n#############################################################################\n\n" + "Dataset 2 : Partial circle\n--------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Dataset 2 : Plot OT Matrices\n#############################################################################\n\n" + "Dataset 2 : Plot OT Matrices\n-----------------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.py b/docs/source/auto_examples/plot_OT_L1_vs_L2.py index 49d37e1..090e809 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.py +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.py @@ -22,7 +22,7 @@ import ot ############################################################################## # Dataset 1 : uniform sampling -############################################################################## +# ---------------------------- n = 20 # nb samples xs = np.zeros((n, 2)) @@ -73,7 +73,7 @@ pl.tight_layout() ############################################################################## # Dataset 1 : Plot OT Matrices -############################################################################## +# ---------------------------- #%% EMD @@ -114,7 +114,7 @@ pl.show() ############################################################################## # Dataset 2 : Partial circle -############################################################################## +# -------------------------- n = 50 # nb samples xtot = np.zeros((n + 1, 2)) @@ -168,7 +168,7 @@ pl.tight_layout() ############################################################################## # Dataset 2 : Plot OT Matrices -############################################################################## +# ----------------------------- #%% EMD diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst index 8b5b133..f97b373 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst @@ -34,7 +34,7 @@ https://arxiv.org/pdf/1706.07650.pdf Dataset 1 : uniform sampling -############################################################################# +---------------------------- @@ -108,7 +108,7 @@ Dataset 1 : uniform sampling Dataset 1 : Plot OT Matrices -############################################################################# +---------------------------- @@ -162,7 +162,7 @@ Dataset 1 : Plot OT Matrices Dataset 2 : Partial circle -############################################################################# +-------------------------- @@ -239,7 +239,7 @@ Dataset 2 : Partial circle Dataset 2 : Plot OT Matrices -############################################################################# +----------------------------- @@ -290,7 +290,7 @@ Dataset 2 : Plot OT Matrices -**Total running time of the script:** ( 0 minutes 1.218 seconds) +**Total running time of the script:** ( 0 minutes 1.134 seconds) diff --git a/docs/source/auto_examples/plot_WDA.ipynb b/docs/source/auto_examples/plot_WDA.ipynb index 47a6eca..1661c53 100644 --- a/docs/source/auto_examples/plot_WDA.ipynb +++ b/docs/source/auto_examples/plot_WDA.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Plot data\n#############################################################################\n\n" + "Plot data\n---------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Compute Fisher Discriminant Analysis\n#############################################################################\n\n" + "Compute Fisher Discriminant Analysis\n------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Compute Wasserstein Discriminant Analysis\n#############################################################################\n\n" + "Compute Wasserstein Discriminant Analysis\n-----------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -105,7 +105,7 @@ }, { "source": [ - "Plot 2D projections\n#############################################################################\n\n" + "Plot 2D projections\n-------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_WDA.py b/docs/source/auto_examples/plot_WDA.py index 5928621..93cc237 100644 --- a/docs/source/auto_examples/plot_WDA.py +++ b/docs/source/auto_examples/plot_WDA.py @@ -24,7 +24,7 @@ from ot.dr import wda, fda ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters @@ -51,7 +51,7 @@ xt = np.hstack((xt, np.random.randn(n, nbnoise))) ############################################################################## # Plot data -############################################################################## +# --------- #%% plot samples pl.figure(1, figsize=(6.4, 3.5)) @@ -69,7 +69,7 @@ pl.tight_layout() ############################################################################## # Compute Fisher Discriminant Analysis -############################################################################## +# ------------------------------------ #%% Compute FDA p = 2 @@ -78,7 +78,7 @@ Pfda, projfda = fda(xs, ys, p) ############################################################################## # Compute Wasserstein Discriminant Analysis -############################################################################## +# ----------------------------------------- #%% Compute WDA p = 2 @@ -91,7 +91,7 @@ Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) ############################################################################## # Plot 2D projections -############################################################################## +# ------------------- #%% plot samples diff --git a/docs/source/auto_examples/plot_WDA.rst b/docs/source/auto_examples/plot_WDA.rst index a0c3389..64ddb47 100644 --- a/docs/source/auto_examples/plot_WDA.rst +++ b/docs/source/auto_examples/plot_WDA.rst @@ -36,7 +36,7 @@ Wasserstein Discriminant Analysis. Generate data -############################################################################# +------------- @@ -73,7 +73,7 @@ Generate data Plot data -############################################################################# +--------- @@ -104,7 +104,7 @@ Plot data Compute Fisher Discriminant Analysis -############################################################################# +------------------------------------ @@ -123,7 +123,7 @@ Compute Fisher Discriminant Analysis Compute Wasserstein Discriminant Analysis -############################################################################# +----------------------------------------- @@ -150,65 +150,25 @@ Compute Wasserstein Discriminant Analysis Compiling cost function... Computing gradient of cost function... iter cost val grad. norm - 1 +8.6305817354868675e-01 4.10110152e-01 - 2 +4.6939060757969131e-01 2.98553763e-01 - 3 +4.2106314200107775e-01 1.48552668e-01 - 4 +4.1376389458568069e-01 1.12319011e-01 - 5 +4.0984854988792835e-01 1.01126129e-01 - 6 +4.0415292614140025e-01 3.90875165e-02 - 7 +4.0297967887432584e-01 2.73716014e-02 - 8 +4.0252319029045258e-01 3.76498956e-02 - 9 +4.0158635935184972e-01 1.31986577e-02 - 10 +4.0118906894272482e-01 3.40307273e-02 - 11 +4.0052579694802176e-01 7.79567347e-03 - 12 +4.0049330810825384e-01 9.77921941e-03 - 13 +4.0042500151972926e-01 4.63602913e-03 - 14 +4.0031705300038767e-01 1.69742018e-02 - 15 +4.0013705338124350e-01 7.40310798e-03 - 16 +4.0006224569843946e-01 1.08829949e-02 - 17 +3.9998280287782945e-01 1.25733450e-02 - 18 +3.9986405111843215e-01 1.05626807e-02 - 19 +3.9974905002724365e-01 9.93566406e-03 - 20 +3.9971323753531823e-01 2.21199533e-02 - 21 +3.9958582328238779e-01 1.73335808e-02 - 22 +3.9937139582811110e-01 1.09182412e-02 - 23 +3.9923748818499571e-01 1.77304913e-02 - 24 +3.9900530515251881e-01 1.15381586e-02 - 25 +3.9883316307006128e-01 1.80225446e-02 - 26 +3.9860317631835845e-01 1.65011032e-02 - 27 +3.9852130309759393e-01 2.81245689e-02 - 28 +3.9824281033694675e-01 2.01114810e-02 - 29 +3.9799657608114836e-01 2.66040929e-02 - 30 +3.9746233677210713e-01 1.45779937e-02 - 31 +3.9671794378467928e-01 4.27487207e-02 - 32 +3.9573357685391913e-01 2.20071520e-02 - 33 +3.9536725156297214e-01 2.00817458e-02 - 34 +3.9515994339814914e-01 3.81472315e-02 - 35 +3.9448966390371887e-01 2.52129049e-02 - 36 +3.9351423238681266e-01 5.60677866e-02 - 37 +3.9082703288308568e-01 4.26859586e-02 - 38 +3.7139409489868136e-01 1.26067835e-01 - 39 +2.8085932518253526e-01 1.70133509e-01 - 40 +2.7330384726281814e-01 1.95523507e-01 - 41 +2.4806985554269162e-01 1.31192016e-01 - 42 +2.3748356968454920e-01 8.71616829e-02 - 43 +2.3501927152342389e-01 7.02789537e-02 - 44 +2.3183578112546338e-01 2.62025296e-02 - 45 +2.3154208568082749e-01 1.67845346e-02 - 46 +2.3139316710346300e-01 8.27285074e-03 - 47 +2.3136034106523354e-01 4.64818210e-03 - 48 +2.3134548827742521e-01 4.53144806e-04 - 49 +2.3134540503271503e-01 2.91010390e-04 - 50 +2.3134535764073319e-01 1.25662481e-04 - 51 +2.3134534692621381e-01 1.24751216e-05 - 52 +2.3134534685831357e-01 7.44008265e-06 - 53 +2.3134534684658337e-01 6.16933546e-06 - 54 +2.3134534682129679e-01 5.12152219e-07 - Terminated - min grad norm reached after 54 iterations, 24.53 seconds. + 1 +5.4993226050368416e-01 5.18285173e-01 + 2 +3.4883000507542844e-01 1.96795818e-01 + 3 +2.9841234004693890e-01 2.33029475e-01 + 4 +2.3976476757548179e-01 1.38593951e-01 + 5 +2.3614468346177828e-01 1.19615394e-01 + 6 +2.2586536502789240e-01 4.82430685e-02 + 7 +2.2451030967794622e-01 2.56564039e-02 + 8 +2.2421446331083625e-01 1.47932578e-02 + 9 +2.2407441444450052e-01 1.12040327e-03 + 10 +2.2407365923337522e-01 3.78899763e-04 + 11 +2.2407356874011675e-01 1.79740810e-05 + 12 +2.2407356862959993e-01 1.25643005e-05 + 13 +2.2407356853043561e-01 1.40415001e-06 + 14 +2.2407356852925220e-01 3.41183585e-07 + Terminated - min grad norm reached after 14 iterations, 6.78 seconds. Plot 2D projections -############################################################################# +------------------- @@ -256,7 +216,7 @@ Plot 2D projections -**Total running time of the script:** ( 0 minutes 25.326 seconds) +**Total running time of the script:** ( 0 minutes 7.637 seconds) diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb index 32cb2ec..a19e0fd 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.ipynb +++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Plot data\n#############################################################################\n\n" + "Plot data\n---------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Barycenter computation\n#############################################################################\n\n" + "Barycenter computation\n----------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Barycentric interpolation\n#############################################################################\n\n" + "Barycentric interpolation\n-------------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py index eef8536..620936b 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.py +++ b/docs/source/auto_examples/plot_barycenter_1D.py @@ -27,7 +27,7 @@ from matplotlib.collections import PolyCollection ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters @@ -50,7 +50,7 @@ M /= M.max() ############################################################################## # Plot data -############################################################################## +# --------- #%% plot the distributions @@ -62,7 +62,7 @@ pl.tight_layout() ############################################################################## # Barycenter computation -############################################################################## +# ---------------------- #%% barycenter computation @@ -92,7 +92,7 @@ pl.tight_layout() ############################################################################## # Barycentric interpolation -############################################################################## +# ------------------------- #%% barycenter interpolation diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst index b1794cd..413fae3 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.rst +++ b/docs/source/auto_examples/plot_barycenter_1D.rst @@ -39,7 +39,7 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. Generate data -############################################################################# +------------- @@ -72,7 +72,7 @@ Generate data Plot data -############################################################################# +--------- @@ -97,7 +97,7 @@ Plot data Barycenter computation -############################################################################# +---------------------- @@ -140,7 +140,7 @@ Barycenter computation Barycentric interpolation -############################################################################# +------------------------- @@ -230,7 +230,7 @@ Barycentric interpolation -**Total running time of the script:** ( 0 minutes 0.416 seconds) +**Total running time of the script:** ( 0 minutes 0.431 seconds) diff --git a/docs/source/auto_examples/plot_compute_emd.ipynb b/docs/source/auto_examples/plot_compute_emd.ipynb index a882bd1..b9b8bc5 100644 --- a/docs/source/auto_examples/plot_compute_emd.ipynb +++ b/docs/source/auto_examples/plot_compute_emd.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Plot data\n#############################################################################\n\n" + "Plot data\n---------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Compute EMD for the different losses\n#############################################################################\n\n" + "Compute EMD for the different losses\n------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Compute Sinkhorn for the different losses\n#############################################################################\n\n" + "Compute Sinkhorn for the different losses\n-----------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_compute_emd.py b/docs/source/auto_examples/plot_compute_emd.py index a84b249..73b42c3 100644 --- a/docs/source/auto_examples/plot_compute_emd.py +++ b/docs/source/auto_examples/plot_compute_emd.py @@ -22,7 +22,7 @@ from ot.datasets import get_1D_gauss as gauss ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters @@ -51,7 +51,7 @@ M2 /= M2.max() ############################################################################## # Plot data -############################################################################## +# --------- #%% plot the distributions @@ -67,7 +67,7 @@ pl.tight_layout() ############################################################################## # Compute EMD for the different losses -############################################################################## +# ------------------------------------ #%% Compute and plot distributions and loss matrix @@ -83,7 +83,7 @@ pl.legend() ############################################################################## # Compute Sinkhorn for the different losses -############################################################################## +# ----------------------------------------- #%% reg = 1e-2 diff --git a/docs/source/auto_examples/plot_compute_emd.rst b/docs/source/auto_examples/plot_compute_emd.rst index 58220a4..ce79e20 100644 --- a/docs/source/auto_examples/plot_compute_emd.rst +++ b/docs/source/auto_examples/plot_compute_emd.rst @@ -34,7 +34,7 @@ ground metrics and plot their values for diffeent distributions. Generate data -############################################################################# +------------- @@ -73,7 +73,7 @@ Generate data Plot data -############################################################################# +--------- @@ -102,7 +102,7 @@ Plot data Compute EMD for the different losses -############################################################################# +------------------------------------ @@ -131,7 +131,7 @@ Compute EMD for the different losses Compute Sinkhorn for the different losses -############################################################################# +----------------------------------------- @@ -162,7 +162,7 @@ Compute Sinkhorn for the different losses -**Total running time of the script:** ( 0 minutes 0.471 seconds) +**Total running time of the script:** ( 0 minutes 0.441 seconds) diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb index f9fec33..333331b 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Solve EMD\n#############################################################################\n\n" + "Solve EMD\n---------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Solve EMD with Frobenius norm regularization\n#############################################################################\n\n" + "Solve EMD with Frobenius norm regularization\n--------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Solve EMD with entropic regularization\n#############################################################################\n\n" + "Solve EMD with entropic regularization\n--------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -105,7 +105,7 @@ }, { "source": [ - "Solve EMD with Frobenius norm + entropic regularization\n#############################################################################\n\n" + "Solve EMD with Frobenius norm + entropic regularization\n-------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py index d753414..e1a737e 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.py +++ b/docs/source/auto_examples/plot_optim_OTreg.py @@ -32,7 +32,7 @@ import ot ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters @@ -51,7 +51,7 @@ M /= M.max() ############################################################################## # Solve EMD -############################################################################## +# --------- #%% EMD @@ -62,7 +62,7 @@ ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') ############################################################################## # Solve EMD with Frobenius norm regularization -############################################################################## +# -------------------------------------------- #%% Example with Frobenius norm regularization @@ -84,7 +84,7 @@ ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg') ############################################################################## # Solve EMD with entropic regularization -############################################################################## +# -------------------------------------- #%% Example with entropic regularization @@ -106,7 +106,7 @@ ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') ############################################################################## # Solve EMD with Frobenius norm + entropic regularization -############################################################################## +# ------------------------------------------------------- #%% Example with Frobenius norm + entropic regularization with gcg diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst index c3ec03b..f628024 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -44,7 +44,7 @@ arXiv preprint arXiv:1510.06567. Generate data -############################################################################# +------------- @@ -73,7 +73,7 @@ Generate data Solve EMD -############################################################################# +--------- @@ -97,7 +97,7 @@ Solve EMD Solve EMD with Frobenius norm regularization -############################################################################# +-------------------------------------------- @@ -359,7 +359,7 @@ Solve EMD with Frobenius norm regularization Solve EMD with entropic regularization -############################################################################# +-------------------------------------- @@ -621,7 +621,7 @@ Solve EMD with entropic regularization Solve EMD with Frobenius norm + entropic regularization -############################################################################# +------------------------------------------------------- @@ -667,7 +667,7 @@ Solve EMD with Frobenius norm + entropic regularization 4|1.609284e-01|-1.111407e-12 -**Total running time of the script:** ( 0 minutes 1.913 seconds) +**Total running time of the script:** ( 0 minutes 1.809 seconds) diff --git a/docs/source/auto_examples/plot_otda_classes.ipynb b/docs/source/auto_examples/plot_otda_classes.ipynb index 16634b1..6754fa5 100644 --- a/docs/source/auto_examples/plot_otda_classes.ipynb +++ b/docs/source/auto_examples/plot_otda_classes.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" + "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Fig 1 : plots source and target samples\n#############################################################################\n\n" + "Fig 1 : plots source and target samples\n---------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Fig 2 : plot optimal couplings and transported samples\n#############################################################################\n\n" + "Fig 2 : plot optimal couplings and transported samples\n------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_otda_classes.py b/docs/source/auto_examples/plot_otda_classes.py index ec57a37..b14c11a 100644 --- a/docs/source/auto_examples/plot_otda_classes.py +++ b/docs/source/auto_examples/plot_otda_classes.py @@ -19,8 +19,8 @@ import ot ############################################################################## -# generate data -############################################################################## +# Generate data +# ------------- n_source_samples = 150 n_target_samples = 150 @@ -31,7 +31,7 @@ Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) ############################################################################## # Instantiate the different transport algorithms and fit them -############################################################################## +# ----------------------------------------------------------- # EMD Transport ot_emd = ot.da.EMDTransport() @@ -59,7 +59,7 @@ transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) ############################################################################## # Fig 1 : plots source and target samples -############################################################################## +# --------------------------------------- pl.figure(1, figsize=(10, 5)) pl.subplot(1, 2, 1) @@ -80,7 +80,7 @@ pl.tight_layout() ############################################################################## # Fig 2 : plot optimal couplings and transported samples -############################################################################## +# ------------------------------------------------------ param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} diff --git a/docs/source/auto_examples/plot_otda_classes.rst b/docs/source/auto_examples/plot_otda_classes.rst index d1a13b1..f19a99f 100644 --- a/docs/source/auto_examples/plot_otda_classes.rst +++ b/docs/source/auto_examples/plot_otda_classes.rst @@ -31,8 +31,8 @@ approaches currently supported in POT. -generate data -############################################################################# +Generate data +------------- @@ -53,7 +53,7 @@ generate data Instantiate the different transport algorithms and fit them -############################################################################# +----------------------------------------------------------- @@ -94,33 +94,33 @@ Instantiate the different transport algorithms and fit them It. |Loss |Delta loss -------------------------------- - 0|9.984935e+00|0.000000e+00 - 1|2.126803e+00|-3.694808e+00 - 2|1.867272e+00|-1.389895e-01 - 3|1.803858e+00|-3.515488e-02 - 4|1.783036e+00|-1.167761e-02 - 5|1.774823e+00|-4.627422e-03 - 6|1.771947e+00|-1.623526e-03 - 7|1.767564e+00|-2.479535e-03 - 8|1.763484e+00|-2.313667e-03 - 9|1.761138e+00|-1.331780e-03 - 10|1.758879e+00|-1.284576e-03 - 11|1.758034e+00|-4.806014e-04 - 12|1.757595e+00|-2.497155e-04 - 13|1.756749e+00|-4.818562e-04 - 14|1.755316e+00|-8.161432e-04 - 15|1.754988e+00|-1.866236e-04 - 16|1.754964e+00|-1.382474e-05 - 17|1.754032e+00|-5.315971e-04 - 18|1.753595e+00|-2.492359e-04 - 19|1.752900e+00|-3.961403e-04 + 0|9.552437e+00|0.000000e+00 + 1|1.921833e+00|-3.970483e+00 + 2|1.671022e+00|-1.500942e-01 + 3|1.615147e+00|-3.459458e-02 + 4|1.594289e+00|-1.308252e-02 + 5|1.587287e+00|-4.411254e-03 + 6|1.581665e+00|-3.554702e-03 + 7|1.577022e+00|-2.943809e-03 + 8|1.573870e+00|-2.002870e-03 + 9|1.571645e+00|-1.415696e-03 + 10|1.569342e+00|-1.467590e-03 + 11|1.567863e+00|-9.432233e-04 + 12|1.566558e+00|-8.329769e-04 + 13|1.565414e+00|-7.311320e-04 + 14|1.564425e+00|-6.319985e-04 + 15|1.563955e+00|-3.007604e-04 + 16|1.563658e+00|-1.894627e-04 + 17|1.562886e+00|-4.941143e-04 + 18|1.562578e+00|-1.974031e-04 + 19|1.562445e+00|-8.468825e-05 It. |Loss |Delta loss -------------------------------- - 20|1.752850e+00|-2.869262e-05 + 20|1.562007e+00|-2.805136e-04 Fig 1 : plots source and target samples -############################################################################# +--------------------------------------- @@ -154,7 +154,7 @@ Fig 1 : plots source and target samples Fig 2 : plot optimal couplings and transported samples -############################################################################# +------------------------------------------------------ @@ -236,7 +236,7 @@ Fig 2 : plot optimal couplings and transported samples -**Total running time of the script:** ( 0 minutes 1.576 seconds) +**Total running time of the script:** ( 0 minutes 1.596 seconds) diff --git a/docs/source/auto_examples/plot_otda_color_images.ipynb b/docs/source/auto_examples/plot_otda_color_images.ipynb index 797b27d..2daf406 100644 --- a/docs/source/auto_examples/plot_otda_color_images.ipynb +++ b/docs/source/auto_examples/plot_otda_color_images.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Plot original image\n#############################################################################\n\n" + "Plot original image\n-------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Scatter plot of colors\n#############################################################################\n\n" + "Scatter plot of colors\n----------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" + "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -105,7 +105,7 @@ }, { "source": [ - "Plot new images\n#############################################################################\n\n" + "Plot new images\n---------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_otda_color_images.py b/docs/source/auto_examples/plot_otda_color_images.py index f1df9d9..e77aec0 100644 --- a/docs/source/auto_examples/plot_otda_color_images.py +++ b/docs/source/auto_examples/plot_otda_color_images.py @@ -42,7 +42,7 @@ def minmax(I): ############################################################################## # Generate data -############################################################################## +# ------------- # Loading images I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 @@ -62,7 +62,7 @@ Xt = X2[idx2, :] ############################################################################## # Plot original image -############################################################################## +# ------------------- pl.figure(1, figsize=(6.4, 3)) @@ -79,7 +79,7 @@ pl.title('Image 2') ############################################################################## # Scatter plot of colors -############################################################################## +# ---------------------- pl.figure(2, figsize=(6.4, 3)) @@ -101,7 +101,7 @@ pl.tight_layout() ############################################################################## # Instantiate the different transport algorithms and fit them -############################################################################## +# ----------------------------------------------------------- # EMDTransport ot_emd = ot.da.EMDTransport() @@ -127,7 +127,7 @@ I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) ############################################################################## # Plot new images -############################################################################## +# --------------- pl.figure(3, figsize=(8, 4)) diff --git a/docs/source/auto_examples/plot_otda_color_images.rst b/docs/source/auto_examples/plot_otda_color_images.rst index 88e93d2..4772bed 100644 --- a/docs/source/auto_examples/plot_otda_color_images.rst +++ b/docs/source/auto_examples/plot_otda_color_images.rst @@ -54,7 +54,7 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. Generate data -############################################################################# +------------- @@ -84,7 +84,7 @@ Generate data Plot original image -############################################################################# +------------------- @@ -114,7 +114,7 @@ Plot original image Scatter plot of colors -############################################################################# +---------------------- @@ -149,7 +149,7 @@ Scatter plot of colors Instantiate the different transport algorithms and fit them -############################################################################# +----------------------------------------------------------- @@ -185,7 +185,7 @@ Instantiate the different transport algorithms and fit them Plot new images -############################################################################# +--------------- @@ -235,7 +235,7 @@ Plot new images -**Total running time of the script:** ( 2 minutes 28.053 seconds) +**Total running time of the script:** ( 2 minutes 24.561 seconds) diff --git a/docs/source/auto_examples/plot_otda_d2.ipynb b/docs/source/auto_examples/plot_otda_d2.ipynb index 2331f8c..7bfcc9a 100644 --- a/docs/source/auto_examples/plot_otda_d2.ipynb +++ b/docs/source/auto_examples/plot_otda_d2.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# OT for empirical distributions\n\n\nThis example introduces a domain adaptation in a 2D setting. It explicits\nthe problem of domain adaptation and introduces some optimal transport\napproaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n" + "\n# OT for domain adaptation on empirical distributions\n\n\nThis example introduces a domain adaptation in a 2D setting. It explicits\nthe problem of domain adaptation and introduces some optimal transport\napproaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n" ], "cell_type": "markdown", "metadata": {} @@ -33,7 +33,7 @@ }, { "source": [ - "generate data\n#############################################################################\n\n" + "generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" + "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Fig 1 : plots source and target samples + matrix of pairwise distance\n#############################################################################\n\n" + "Fig 1 : plots source and target samples + matrix of pairwise distance\n---------------------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Fig 2 : plots optimal couplings for the different methods\n#############################################################################\n\n" + "Fig 2 : plots optimal couplings for the different methods\n---------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -105,7 +105,7 @@ }, { "source": [ - "Fig 3 : plot transported samples\n#############################################################################\n\n" + "Fig 3 : plot transported samples\n--------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_otda_d2.py b/docs/source/auto_examples/plot_otda_d2.py index 3daa0a6..e53d7d6 100644 --- a/docs/source/auto_examples/plot_otda_d2.py +++ b/docs/source/auto_examples/plot_otda_d2.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -============================== -OT for empirical distributions -============================== +=================================================== +OT for domain adaptation on empirical distributions +=================================================== This example introduces a domain adaptation in a 2D setting. It explicits the problem of domain adaptation and introduces some optimal transport @@ -24,7 +24,7 @@ import ot ############################################################################## # generate data -############################################################################## +# ------------- n_samples_source = 150 n_samples_target = 150 @@ -38,7 +38,7 @@ M = ot.dist(Xs, Xt, metric='sqeuclidean') ############################################################################## # Instantiate the different transport algorithms and fit them -############################################################################## +# ----------------------------------------------------------- # EMD Transport ot_emd = ot.da.EMDTransport() @@ -60,7 +60,7 @@ transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) ############################################################################## # Fig 1 : plots source and target samples + matrix of pairwise distance -############################################################################## +# --------------------------------------------------------------------- pl.figure(1, figsize=(10, 10)) pl.subplot(2, 2, 1) @@ -87,8 +87,7 @@ pl.tight_layout() ############################################################################## # Fig 2 : plots optimal couplings for the different methods -############################################################################## - +# --------------------------------------------------------- pl.figure(2, figsize=(10, 6)) pl.subplot(2, 3, 1) @@ -137,7 +136,7 @@ pl.tight_layout() ############################################################################## # Fig 3 : plot transported samples -############################################################################## +# -------------------------------- # display transported samples pl.figure(4, figsize=(10, 4)) diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst index 3aa1149..2b716e1 100644 --- a/docs/source/auto_examples/plot_otda_d2.rst +++ b/docs/source/auto_examples/plot_otda_d2.rst @@ -3,9 +3,9 @@ .. _sphx_glr_auto_examples_plot_otda_d2.py: -============================== -OT for empirical distributions -============================== +=================================================== +OT for domain adaptation on empirical distributions +=================================================== This example introduces a domain adaptation in a 2D setting. It explicits the problem of domain adaptation and introduces some optimal transport @@ -36,7 +36,7 @@ of what the transport methods are doing. generate data -############################################################################# +------------- @@ -60,7 +60,7 @@ generate data Instantiate the different transport algorithms and fit them -############################################################################# +----------------------------------------------------------- @@ -92,7 +92,7 @@ Instantiate the different transport algorithms and fit them Fig 1 : plots source and target samples + matrix of pairwise distance -############################################################################# +--------------------------------------------------------------------- @@ -132,13 +132,12 @@ Fig 1 : plots source and target samples + matrix of pairwise distance Fig 2 : plots optimal couplings for the different methods -############################################################################# +--------------------------------------------------------- .. code-block:: python - pl.figure(2, figsize=(10, 6)) pl.subplot(2, 3, 1) @@ -195,7 +194,7 @@ Fig 2 : plots optimal couplings for the different methods Fig 3 : plot transported samples -############################################################################# +-------------------------------- @@ -243,7 +242,7 @@ Fig 3 : plot transported samples -**Total running time of the script:** ( 0 minutes 32.275 seconds) +**Total running time of the script:** ( 0 minutes 32.084 seconds) diff --git a/docs/source/auto_examples/plot_otda_mapping.ipynb b/docs/source/auto_examples/plot_otda_mapping.ipynb index 5b3fd06..0374146 100644 --- a/docs/source/auto_examples/plot_otda_mapping.ipynb +++ b/docs/source/auto_examples/plot_otda_mapping.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Plot data\n#############################################################################\n\n" + "Plot data\n---------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" + "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Plot transported samples\n#############################################################################\n\n" + "Plot transported samples\n------------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_otda_mapping.py b/docs/source/auto_examples/plot_otda_mapping.py index e78fef4..167c3a1 100644 --- a/docs/source/auto_examples/plot_otda_mapping.py +++ b/docs/source/auto_examples/plot_otda_mapping.py @@ -25,7 +25,7 @@ import ot ############################################################################## # Generate data -############################################################################## +# ------------- n_source_samples = 100 n_target_samples = 100 @@ -45,7 +45,7 @@ Xt = Xt + 4 ############################################################################## # Plot data -############################################################################## +# --------- pl.figure(1, (10, 5)) pl.clf() @@ -57,7 +57,7 @@ pl.title('Source and target distributions') ############################################################################## # Instantiate the different transport algorithms and fit them -############################################################################## +# ----------------------------------------------------------- # MappingTransport with linear kernel ot_mapping_linear = ot.da.MappingTransport( @@ -88,7 +88,7 @@ transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) ############################################################################## # Plot transported samples -############################################################################## +# ------------------------ pl.figure(2) pl.clf() diff --git a/docs/source/auto_examples/plot_otda_mapping.rst b/docs/source/auto_examples/plot_otda_mapping.rst index ddc1ee9..6c1c780 100644 --- a/docs/source/auto_examples/plot_otda_mapping.rst +++ b/docs/source/auto_examples/plot_otda_mapping.rst @@ -37,7 +37,7 @@ a linear or a kernelized mapping as introduced in [8]. Generate data -############################################################################# +------------- @@ -67,7 +67,7 @@ Generate data Plot data -############################################################################# +--------- @@ -92,7 +92,7 @@ Plot data Instantiate the different transport algorithms and fit them -############################################################################# +----------------------------------------------------------- @@ -136,30 +136,28 @@ Instantiate the different transport algorithms and fit them It. |Loss |Delta loss -------------------------------- - 0|4.481482e+03|0.000000e+00 - 1|4.469389e+03|-2.698549e-03 - 2|4.468825e+03|-1.261217e-04 - 3|4.468580e+03|-5.486064e-05 - 4|4.468438e+03|-3.161220e-05 - 5|4.468352e+03|-1.930800e-05 - 6|4.468309e+03|-9.570658e-06 + 0|4.307233e+03|0.000000e+00 + 1|4.296694e+03|-2.446759e-03 + 2|4.296419e+03|-6.417421e-05 + 3|4.296328e+03|-2.110209e-05 + 4|4.296305e+03|-5.298603e-06 It. |Loss |Delta loss -------------------------------- - 0|4.504654e+02|0.000000e+00 - 1|4.461571e+02|-9.564116e-03 - 2|4.459105e+02|-5.528043e-04 - 3|4.457895e+02|-2.712398e-04 - 4|4.457041e+02|-1.914829e-04 - 5|4.456431e+02|-1.369704e-04 - 6|4.456032e+02|-8.944784e-05 - 7|4.455700e+02|-7.447824e-05 - 8|4.455447e+02|-5.688965e-05 - 9|4.455229e+02|-4.890051e-05 - 10|4.455084e+02|-3.262490e-05 + 0|4.325624e+02|0.000000e+00 + 1|4.281958e+02|-1.009489e-02 + 2|4.279370e+02|-6.042202e-04 + 3|4.278109e+02|-2.947651e-04 + 4|4.277212e+02|-2.096651e-04 + 5|4.276589e+02|-1.456221e-04 + 6|4.276141e+02|-1.048476e-04 + 7|4.275803e+02|-7.906213e-05 + 8|4.275531e+02|-6.360573e-05 + 9|4.275314e+02|-5.076642e-05 + 10|4.275129e+02|-4.325858e-05 Plot transported samples -############################################################################# +------------------------ @@ -208,7 +206,7 @@ Plot transported samples -**Total running time of the script:** ( 0 minutes 0.869 seconds) +**Total running time of the script:** ( 0 minutes 0.747 seconds) 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 3b3987a..56caa8a 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Domain adaptation for pixel distribution transfer\n#############################################################################\n\n" + "Domain adaptation for pixel distribution transfer\n-------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Plot original images\n#############################################################################\n\n" + "Plot original images\n--------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Plot pixel values distribution\n#############################################################################\n\n" + "Plot pixel values distribution\n------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -105,7 +105,7 @@ }, { "source": [ - "Plot transformed images\n#############################################################################\n\n" + "Plot transformed images\n-----------------------\n\n" ], "cell_type": "markdown", "metadata": {} 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 5590286..5f1e844 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.py +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.py @@ -45,7 +45,7 @@ def minmax(I): ############################################################################## # Generate data -############################################################################## +# ------------- # Loading images I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 @@ -66,7 +66,7 @@ Xt = X2[idx2, :] ############################################################################## # Domain adaptation for pixel distribution transfer -############################################################################## +# ------------------------------------------------- # EMDTransport ot_emd = ot.da.EMDTransport() @@ -97,7 +97,7 @@ Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) ############################################################################## # Plot original images -############################################################################## +# -------------------- pl.figure(1, figsize=(6.4, 3)) pl.subplot(1, 2, 1) @@ -114,7 +114,7 @@ pl.tight_layout() ############################################################################## # Plot pixel values distribution -############################################################################## +# ------------------------------ pl.figure(2, figsize=(6.4, 5)) @@ -136,7 +136,7 @@ pl.tight_layout() ############################################################################## # Plot transformed images -############################################################################## +# ----------------------- pl.figure(2, figsize=(10, 5)) 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 9995103..86b1312 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst @@ -57,7 +57,7 @@ estimation [8]. Generate data -############################################################################# +------------- @@ -88,7 +88,7 @@ Generate data Domain adaptation for pixel distribution transfer -############################################################################# +------------------------------------------------- @@ -168,7 +168,7 @@ Domain adaptation for pixel distribution transfer Plot original images -############################################################################# +-------------------- @@ -198,7 +198,7 @@ Plot original images Plot pixel values distribution -############################################################################# +------------------------------ @@ -233,7 +233,7 @@ Plot pixel values distribution Plot transformed images -############################################################################# +----------------------- @@ -283,7 +283,7 @@ Plot transformed images -**Total running time of the script:** ( 2 minutes 8.746 seconds) +**Total running time of the script:** ( 2 minutes 5.213 seconds) diff --git a/docs/source/conf.py b/docs/source/conf.py index 0a822e5..156b878 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -33,7 +33,7 @@ class Mock(MagicMock): return MagicMock() MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cudamat','autograd.numpy','pymanopt.manifolds','pymanopt.solvers'] # 'autograd.numpy','pymanopt.manifolds','pymanopt.solvers', -##sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +###sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! # If extensions (or modules to document with autodoc) are in another directory, -- cgit v1.2.3