searchindex.js

Search.setIndex({"alltitles":{"(Fused) Gromov-Wasserstein Linear Dictionary Learning":[[50,null]],"0.1.10":[[137,"id45"]],"0.1.11":[[137,"id44"]],"0.1.3":[[137,"id48"]],"0.1.7":[[137,"id47"]],"0.1.9":[[137,"id46"]],"0.3":[[137,"id43"]],"0.3.1":[[137,"id42"]],"0.4":[[137,"id39"]],"0.5.0":[[137,"id36"]],"0.6.0":[[137,"id33"]],"0.7.0":[[137,"id31"]],"0.8.0":[[137,"id28"]],"0.8.1":[[137,"id25"]],"0.8.1.0":[[137,"id23"]],"0.8.2":[[137,"id20"]],"0.9.1":[[137,"id17"]],"0.9.2":[[137,"id14"]],"0.9.3":[[137,"id12"]],"0.9.4":[[137,"id9"]],"0.9.5":[[137,"id6"]],"0.9.6":[[137,"id3"]],"0.9.6.post1":[[137,"post1"]],"0.9.7":[[137,"id1"]],"1. Build a simple observed signal":[[73,"build-a-simple-observed-signal"]],"1D Unbalanced optimal transport":[[96,null]],"1D Wasserstein barycenter demo":[[15,null]],"1D Wasserstein barycenter demo for Unbalanced distributions":[[97,null]],"1D Wasserstein barycenter: exact LP vs entropic regularization":[[16,null]],"2. Interpret the signal as coming from a continuous linear dynamical system":[[73,"interpret-the-signal-as-coming-from-a-continuous-linear-dynamical-system"]],"2D data example":[[25,"d-data-example"],[84,"d-data-example"]],"2D examples of exact and entropic unbalanced optimal transport":[[104,null]],"2D free support Sinkhorn barycenters of distributions":[[21,null]],"2D free support Wasserstein barycenters of distributions":[[19,null]],"3. Sampling and preprocessing discrete trajectories of the dynamical system":[[73,"sampling-and-preprocessing-discrete-trajectories-of-the-dynamical-system"]],"4. Estimate the discrete-time operator":[[73,"estimate-the-discrete-time-operator"]],"6. Recover continuous-time spectral information from the discrete operator":[[73,"recover-continuous-time-spectral-information-from-the-discrete-operator"]],"A wider delay window for the SGOT experiments below":[[73,"a-wider-delay-window-for-the-sgot-experiments-below"]],"API and modules":[[0,null]],"Acknowledgements":[[136,"acknowledgements"]],"Acknowledgments":[[108,"acknowledgments"]],"Add node features":[[44,"add-node-features"],[53,"add-node-features"]],"Algorithm":[[76,"algorithm"]],"An example of convolutional barycenter (ot.bregman.convolutional_barycenter2d) computation":[[139,"an-example-of-convolutional-barycenter-ot-bregman-convolutional-barycenter2d-computation"]],"Anaconda installation with conda-forge":[[136,"anaconda-installation-with-conda-forge"]],"Animate trajectories of generated samples along iteration":[[8,"animate-trajectories-of-generated-samples-along-iteration"],[12,"animate-trajectories-of-generated-samples-along-iteration"]],"Animate trajectories of the barycenter along gradient descent":[[7,"animate-trajectories-of-the-barycenter-along-gradient-descent"]],"Animate trajectories of the gradient flow along iteration":[[7,"animate-trajectories-of-the-gradient-flow-along-iteration"]],"Animation of the regpath for UOT l2":[[103,"animation-of-the-regpath-for-uot-l2"]],"Animation of the regpath for semi-relaxed UOT l2":[[103,"animation-of-the-regpath-for-semi-relaxed-uot-l2"]],"Attribution":[[106,"attribution"]],"Barycenter computation":[[15,"barycenter-computation"],[43,"barycenter-computation"],[49,"barycenter-computation"],[97,"barycenter-computation"]],"Barycenter computation and plot":[[23,"barycenter-computation-and-plot"]],"Barycenter computation and visualization":[[17,"barycenter-computation-and-visualization"],[18,"barycenter-computation-and-visualization"]],"Barycenter of labeled graphs with FGW":[[43,null]],"Barycenters with fixed support":[[139,"barycenters-with-fixed-support"]],"Barycenters with free 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inverse-temperatures beta":[[90,"compare-expected-sliced-plans-with-different-inverse-temperatures-beta"]],"Compare OT plans":[[61,"compare-ot-plans"]],"Compare the Euclidean Wasserstein distance with the Wasserstein distance on the  circle":[[89,"compare-the-euclidean-wasserstein-distance-with-the-wasserstein-distance-on-the-circle"]],"Compare the results with the Sinkhorn algorithm":[[74,"compare-the-results-with-the-sinkhorn-algorithm"]],"Compare with Sinkhorn":[[61,"compare-with-sinkhorn"]],"Compare with original distributions":[[69,"compare-with-original-distributions"]],"Compare with the dedicated FUGW solver":[[4,"compare-with-the-dedicated-fugw-solver"]],"Comparing Computation Time":[[6,"comparing-computation-time"]],"Comparing all OT plans":[[84,"comparing-all-ot-plans"]],"Comparison across Grassmannian metrics for SGOT distance versus rotation angle":[[73,"comparison-across-grassmannian-metrics-for-sgot-distance-versus-rotation-angle"]],"Comparison of Fused Gromov-Wasserstein solvers":[[46,null]],"Comparison of the divergences":[[82,"comparison-of-the-divergences"]],"Computation times":[[13,null],[26,null],[37,null],[41,null],[55,null],[62,null],[75,null],[87,null],[94,null],[105,null],[138,null]],"Compute 2-Wasserstein Plan":[[90,"compute-2-wasserstein-plan"]],"Compute Bures-Wasserstein barycenters and plot them":[[22,"compute-bures-wasserstein-barycenters-and-plot-them"]],"Compute EMD":[[78,"compute-emd"]],"Compute EMD for the different losses":[[81,"compute-emd-for-the-different-losses"]],"Compute EWCA":[[65,"compute-ewca"]],"Compute Expected Sliced Plan":[[90,"compute-expected-sliced-plan"]],"Compute FGW/GW":[[45,"compute-fgw-gw"]],"Compute Factored OT and exact OT solutions":[[58,"compute-factored-ot-and-exact-ot-solutions"]],"Compute Fisher Discriminant Analysis":[[67,"compute-fisher-discriminant-analysis"]],"Compute GW with scalable stochastic methods with any loss function":[[48,"compute-gw-with-scalable-stochastic-methods-with-any-loss-function"]],"Compute Gromov-Wasserstein plans and distance":[[48,"compute-gromov-wasserstein-plans-and-distance"]],"Compute SUOT and USOT":[[98,"compute-suot-and-usot"]],"Compute Sinkhorn":[[78,"compute-sinkhorn"]],"Compute Sinkhorn divergence and visualize plans":[[82,"compute-sinkhorn-divergence-and-visualize-plans"]],"Compute Sinkhorn for the different losses":[[81,"compute-sinkhorn-for-the-different-losses"]],"Compute Wasserstein Discriminant Analysis":[[67,"compute-wasserstein-discriminant-analysis"]],"Compute Weak OT and exact OT solutions":[[68,"compute-weak-ot-and-exact-ot-solutions"]],"Compute distance kernels, normalize them and then display":[[48,"compute-distance-kernels-normalize-them-and-then-display"]],"Compute entropic kl-regularized UOT with Sinkhorn and Translation Invariant Sinkhorn":[[99,"compute-entropic-kl-regularized-uot-with-sinkhorn-and-translation-invariant-sinkhorn"]],"Compute entropic kl-regularized UOT, kl- and l2-regularized UOT":[[104,"compute-entropic-kl-regularized-uot-kl-and-l2-regularized-uot"]],"Compute free support Wasserstein barycenter":[[19,"compute-free-support-wasserstein-barycenter"]],"Compute min-sliced transport plan":[[90,"compute-min-sliced-transport-plan"]],"Compute partial Gromov-Wasserstein plans and distance":[[102,"compute-partial-gromov-wasserstein-plans-and-distance"]],"Compute partial Wasserstein plans and distance":[[102,"compute-partial-wasserstein-plans-and-distance"]],"Compute semi-relaxed and fully relaxed regularization paths":[[103,"compute-semi-relaxed-and-fully-relaxed-regularization-paths"]],"Compute the Nystr\u00f6m approximation of the Gaussian kernel":[[61,"compute-the-nystrom-approximation-of-the-gaussian-kernel"]],"Compute the Sliced Wasserstein Barycenter":[[7,"compute-the-sliced-wasserstein-barycenter"]],"Compute the Transportation Matrix for the Dual 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their semi-relaxed Fused Gromov-Wasserstein divergences":[[44,"compute-their-semi-relaxed-fused-gromov-wasserstein-divergences"],[53,"compute-their-semi-relaxed-fused-gromov-wasserstein-divergences"]],"Compute their semi-relaxed Gromov-Wasserstein divergences":[[53,"compute-their-semi-relaxed-gromov-wasserstein-divergences"]],"Computing 1-dimensional Barycenters via d-MMOT":[[69,null]],"Computing Wasserstein distance":[[139,"computing-wasserstein-distance"]],"Computing the Cost Matrices":[[6,"computing-the-cost-matrices"]],"Construct a 50x50 cost matrix":[[100,"construct-a-50x50-cost-matrix"]],"Contents":[[136,"contents"]],"Continuous OT plan estimation with Pytorch":[[9,null]],"Contributing to POT":[[107,null]],"Contributions and code of conduct":[[136,"contributions-and-code-of-conduct"]],"Contributors":[[108,null],[108,"id1"]],"Convert data to torch tensors":[[3,"convert-data-to-torch-tensors"],[9,"convert-data-to-torch-tensors"]],"Convolutional Wasserstein Barycenter example":[[17,null]],"Cost matrix":[[76,"cost-matrix"]],"Create structure matrices and across-feature distance matrix":[[45,"create-structure-matrices-and-across-feature-distance-matrix"]],"Creators and Maintainers":[[108,"creators-and-maintainers"]],"Data for logo":[[70,"data-for-logo"]],"Data generation":[[3,"data-generation"],[8,"data-generation"],[9,"data-generation"],[12,"data-generation"],[80,"data-generation"]],"Data preparation":[[17,"data-preparation"],[49,"data-preparation"]],"Dataset 1 : Plot OT Matrices":[[79,"dataset-1-plot-ot-matrices"]],"Dataset 1 : uniform sampling":[[79,"dataset-1-uniform-sampling"]],"Dataset 2 : Partial circle":[[79,"dataset-2-partial-circle"]],"Dataset 2 : Plot  OT Matrices":[[79,"dataset-2-plot-ot-matrices"]],"Debiased Minibatch OT":[[82,"debiased-minibatch-ot"]],"Debiased Sinkhorn barycenter demo":[[18,null]],"Debiased barycenter of 1D Gaussians":[[18,"debiased-barycenter-of-1d-gaussians"]],"Debiased barycenter of 2D images":[[18,"debiased-barycenter-of-2d-images"]],"Define Gaussian Covariances and distributions":[[22,"define-gaussian-covariances-and-distributions"]],"Define Group lasso regularization and gradient":[[85,"define-group-lasso-regularization-and-gradient"]],"Dependencies":[[136,"dependencies"]],"Dependency":[[111,null]],"Deprecation":[[137,"deprecation"]],"Detecting outliers by learning sample marginal distribution with CO-Optimal Transport and by using unbalanced Co-Optimal Transport":[[71,null]],"Different gradient computations for regularized optimal transport":[[2,null]],"Differentiable OT with PyTorch":[[1,null],[56,"differentiable-ot-with-pytorch"]],"Dirac Data":[[16,"dirac-data"]],"Discrete case":[[74,"discrete-case"]],"Documentation":[[107,"documentation"]],"Domain adaptation classes":[[139,"domain-adaptation-classes"]],"Domain adaptation for pixel distribution transfer":[[35,"domain-adaptation-for-pixel-distribution-transfer"]],"Domain adaptation with OT":[[27,null],[56,"domain-adaptation-with-ot"]],"Dual OT solvers for entropic and quadratic regularized OT with Pytorch":[[3,null]],"Empirical Sinkhorn":[[78,"empirical-sinkhorn"]],"Endow the dataset with node features":[[50,"endow-the-dataset-with-node-features"],[54,"endow-the-dataset-with-node-features"]],"Enforcement":[[106,"enforcement"]],"Entropic Gromov-Wasserstein":[[59,"entropic-gromov-wasserstein"]],"Entropic OT with Sinkhorn algorithm":[[84,"entropic-ot-with-sinkhorn-algorithm"]],"Entropic Wasserstein Component Analysis":[[65,null]],"Entropic regularized OT":[[139,"entropic-regularized-ot"]],"Entropic-regularized semi-relaxed (Fused) Gromov-Wasserstein example":[[44,null]],"Estimate a Fused Gromov-Wasserstein dictionary from the dataset of attributed graphs":[[50,"estimate-a-fused-gromov-wasserstein-dictionary-from-the-dataset-of-attributed-graphs"]],"Estimate linear mapping and transport":[[33,"estimate-linear-mapping-and-transport"]],"Estimate mapping and adapt":[[33,"estimate-mapping-and-adapt"]],"Estimate the Gromov-Wasserstein dictionary from the dataset":[[50,"estimate-the-gromov-wasserstein-dictionary-from-the-dataset"]],"Estimate the srFGW barycenter from the attributed graphs and visualize embeddings":[[54,"estimate-the-srfgw-barycenter-from-the-attributed-graphs-and-visualize-embeddings"]],"Estimate the srGW barycenter from the dataset and visualize embeddings":[[54,"estimate-the-srgw-barycenter-from-the-dataset-and-visualize-embeddings"]],"Estimated weights and convergence of the objective":[[10,"estimated-weights-and-convergence-of-the-objective"]],"Estimating deep dual variables for entropic OT":[[9,"estimating-deep-dual-variables-for-entropic-ot"]],"Estimating dual variables for entropic OT":[[3,"estimating-dual-variables-for-entropic-ot"]],"Estimating dual variables for quadratic OT":[[3,"estimating-dual-variables-for-quadratic-ot"]],"Example: concentric circles":[[86,"example-concentric-circles"]],"Example: rotating a linear dynamical system in 3D":[[73,"example-rotating-a-linear-dynamical-system-in-3d"]],"Examples":[[136,"examples"]],"Examples and Notebooks":[[136,"examples-and-notebooks"]],"Examples gallery":[[56,null]],"Examples of GW, regularized G and FGW barycenters":[[139,"examples-of-gw-regularized-g-and-fgw-barycenters"]],"Examples of Partial OT":[[139,"examples-of-partial-ot"]],"Examples of Unbalanced OT":[[139,"examples-of-unbalanced-ot"]],"Examples of Unbalanced OT barycenters":[[139,"examples-of-unbalanced-ot-barycenters"]],"Examples of Wasserstein and regularized Wasserstein barycenters":[[139,"examples-of-wasserstein-and-regularized-wasserstein-barycenters"]],"Examples of computation of GW, regularized G and FGW":[[139,"examples-of-computation-of-gw-regularized-g-and-fgw"]],"Examples of free support barycenter estimation":[[139,"examples-of-free-support-barycenter-estimation"]],"Examples of group Lasso regularization":[[139,"examples-of-group-lasso-regularization"]],"Examples of the generic solvers":[[139,"examples-of-the-generic-solvers"]],"Examples of the use of OTDA classes":[[139,"examples-of-the-use-of-otda-classes"]],"Examples of the use of WDA":[[139,"examples-of-the-use-of-wda"]],"Examples of use for Sinkhorn algorithm":[[139,"examples-of-use-for-sinkhorn-algorithm"]],"Examples of use for ot.emd":[[139,"examples-of-use-for-ot-emd"]],"Examples of use for ot.emd2":[[139,"examples-of-use-for-ot-emd2"]],"Examples of use of quadratic regularization":[[139,"examples-of-use-of-quadratic-regularization"]],"Examples using ot.coot.co_optimal_transport":[[113,"examples-using-ot-coot-co-optimal-transport"]],"Examples using ot.coot.co_optimal_transport2":[[113,"examples-using-ot-coot-co-optimal-transport2"]],"Examples using ot.da.BaseTransport":[[114,"examples-using-ot-da-basetransport"]],"Examples using ot.da.EMDLaplaceTransport":[[114,"examples-using-ot-da-emdlaplacetransport"]],"Examples using ot.da.EMDTransport":[[114,"examples-using-ot-da-emdtransport"]],"Examples using ot.da.JCPOTTransport":[[114,"examples-using-ot-da-jcpottransport"]],"Examples using ot.da.LinearGWTransport":[[114,"examples-using-ot-da-lineargwtransport"]],"Examples using ot.da.LinearTransport":[[114,"examples-using-ot-da-lineartransport"]],"Examples using ot.da.MappingTransport":[[114,"examples-using-ot-da-mappingtransport"]],"Examples using ot.da.SinkhornL1l2Transport":[[114,"examples-using-ot-da-sinkhornl1l2transport"]],"Examples using ot.da.SinkhornLpl1Transport":[[114,"examples-using-ot-da-sinkhornlpl1transport"]],"Examples using ot.da.SinkhornTransport":[[114,"examples-using-ot-da-sinkhorntransport"]],"Examples using ot.datasets.make_1D_gauss":[[115,"examples-using-ot-datasets-make-1d-gauss"]],"Examples using ot.datasets.make_2D_samples_gauss":[[115,"examples-using-ot-datasets-make-2d-samples-gauss"]],"Examples using ot.datasets.make_data_classif":[[115,"examples-using-ot-datasets-make-data-classif"]],"Examples using ot.dr.ewca":[[116,"examples-using-ot-dr-ewca"]],"Examples using ot.dr.fda":[[116,"examples-using-ot-dr-fda"]],"Examples using ot.dr.wda":[[116,"examples-using-ot-dr-wda"]],"Examples using ot.gaussian.bures_wasserstein_barycenter":[[118,"examples-using-ot-gaussian-bures-wasserstein-barycenter"]],"Examples using ot.gaussian.empirical_bures_wasserstein_distance":[[118,"examples-using-ot-gaussian-empirical-bures-wasserstein-distance"]],"Examples using ot.gaussian.empirical_bures_wasserstein_mapping":[[118,"examples-using-ot-gaussian-empirical-bures-wasserstein-mapping"]],"Examples using ot.gaussian.empirical_gaussian_gromov_wasserstein_mapping":[[118,"examples-using-ot-gaussian-empirical-gaussian-gromov-wasserstein-mapping"]],"Examples using ot.gmm.gmm_barycenter_fixed_point":[[119,"examples-using-ot-gmm-gmm-barycenter-fixed-point"]],"Examples using ot.gmm.gmm_ot_apply_map":[[119,"examples-using-ot-gmm-gmm-ot-apply-map"]],"Examples using ot.gmm.gmm_ot_loss":[[119,"examples-using-ot-gmm-gmm-ot-loss"]],"Examples using ot.gmm.gmm_ot_plan_density":[[119,"examples-using-ot-gmm-gmm-ot-plan-density"]],"Examples using ot.gmm.gmm_pdf":[[119,"examples-using-ot-gmm-gmm-pdf"]],"Examples using ot.lowrank.kernel_nystroem":[[122,"examples-using-ot-lowrank-kernel-nystroem"]],"Examples using ot.lowrank.sinkhorn_low_rank_kernel":[[122,"examples-using-ot-lowrank-sinkhorn-low-rank-kernel"]],"Examples using ot.mapping.nearest_brenier_potential_fit":[[124,"examples-using-ot-mapping-nearest-brenier-potential-fit"]],"Examples using ot.mapping.nearest_brenier_potential_predict_bounds":[[124,"examples-using-ot-mapping-nearest-brenier-potential-predict-bounds"]],"Examples using ot.optim.cg":[[125,"examples-using-ot-optim-cg"]],"Examples using ot.optim.gcg":[[125,"examples-using-ot-optim-gcg"]],"Examples using ot.plot.plot1D_mat":[[127,"examples-using-ot-plot-plot1d-mat"]],"Examples using ot.plot.plot2D_samples_mat":[[127,"examples-using-ot-plot-plot2d-samples-mat"]],"Examples using ot.plot.rescale_for_imshow_plot":[[127,"examples-using-ot-plot-rescale-for-imshow-plot"]],"Examples using ot.regpath.compute_transport_plan":[[128,"examples-using-ot-regpath-compute-transport-plan"]],"Examples using ot.regpath.regularization_path":[[128,"examples-using-ot-regpath-regularization-path"]],"Examples using ot.sgot.sgot_metric":[[129,"examples-using-ot-sgot-sgot-metric"]],"Examples using ot.smooth.smooth_ot_dual":[[131,"examples-using-ot-smooth-smooth-ot-dual"]],"Examples using ot.stochastic.loss_dual_entropic":[[132,"examples-using-ot-stochastic-loss-dual-entropic"]],"Examples using ot.stochastic.loss_dual_quadratic":[[132,"examples-using-ot-stochastic-loss-dual-quadratic"]],"Examples using ot.stochastic.plan_dual_entropic":[[132,"examples-using-ot-stochastic-plan-dual-entropic"]],"Examples using ot.stochastic.plan_dual_quadratic":[[132,"examples-using-ot-stochastic-plan-dual-quadratic"]],"Examples using ot.stochastic.solve_dual_entropic":[[132,"examples-using-ot-stochastic-solve-dual-entropic"]],"Examples using ot.stochastic.solve_semi_dual_entropic":[[132,"examples-using-ot-stochastic-solve-semi-dual-entropic"]],"Examples using ot.utils.BaryResult":[[134,"examples-using-ot-utils-baryresult"]],"Examples using ot.utils.BaseEstimator":[[134,"examples-using-ot-utils-baseestimator"]],"Examples using ot.utils.DataScaler":[[134,"examples-using-ot-utils-datascaler"]],"Examples using ot.utils.LazyTensor":[[134,"examples-using-ot-utils-lazytensor"]],"Examples using ot.utils.OTResult":[[134,"examples-using-ot-utils-otresult"]],"Examples using ot.utils.dist0":[[134,"examples-using-ot-utils-dist0"]],"Examples using ot.utils.proj_SDP":[[134,"examples-using-ot-utils-proj-sdp"]],"Examples using ot.utils.proj_simplex":[[134,"examples-using-ot-utils-proj-simplex"]],"Exceptions":[[134,"exceptions"]],"FAQ":[[139,"faq"]],"Factored an Low-Rank OT":[[56,"factored-an-low-rank-ot"],[57,null]],"Factored and Low rank OT":[[84,"factored-and-low-rank-ot"]],"Fast and accurate transport bijections using BSP-OT":[[80,null]],"Features":[[137,"features"],[137,"id34"],[137,"id37"],[137,"id40"]],"Fig 1 : plots source and target samples":[[28,"fig-1-plots-source-and-target-samples"],[31,"fig-1-plots-source-and-target-samples"],[32,"fig-1-plots-source-and-target-samples"]],"Fig 1 : plots source and target samples + matrix of pairwise distance":[[30,"fig-1-plots-source-and-target-samples-matrix-of-pairwise-distance"],[36,"fig-1-plots-source-and-target-samples-matrix-of-pairwise-distance"]],"Fig 2 : plot optimal couplings and transported samples":[[28,"fig-2-plot-optimal-couplings-and-transported-samples"],[31,"fig-2-plot-optimal-couplings-and-transported-samples"],[32,"fig-2-plot-optimal-couplings-and-transported-samples"]],"Fig 2 : plots optimal couplings for the different methods":[[30,"fig-2-plots-optimal-couplings-for-the-different-methods"],[36,"fig-2-plots-optimal-couplings-for-the-different-methods"]],"Fig 3 : plot transported samples":[[30,"fig-3-plot-transported-samples"],[36,"fig-3-plot-transported-samples"]],"Filing bugs":[[107,"filing-bugs"]],"Final figure":[[16,"final-figure"]],"First OT Problem":[[76,"first-ot-problem"]],"First 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