.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_unbalanced-partial_plot_UOT_barycenter_1D.py: =========================================================== 1D Wasserstein barycenter demo for Unbalanced distributions =========================================================== This example illustrates the computation of regularized Wasserstein Barycenter as proposed in [10] for Unbalanced inputs. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. .. GENERATED FROM PYTHON SOURCE LINES 14-28 .. code-block:: Python # Author: Hicham Janati # # License: MIT License # sphinx_gallery_thumbnail_number = 2 import numpy as np import matplotlib.pylab as pl import ot # necessary for 3d plot even if not used from mpl_toolkits.mplot3d import Axes3D # noqa from matplotlib.collections import PolyCollection .. GENERATED FROM PYTHON SOURCE LINES 29-31 Generate data ------------- .. GENERATED FROM PYTHON SOURCE LINES 31-54 .. code-block:: Python # parameters n = 100 # nb bins # bin positions x = np.arange(n, dtype=np.float64) # Gaussian distributions a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) # make unbalanced dists a2 *= 3. # creating matrix A containing all distributions A = np.vstack((a1, a2)).T n_distributions = A.shape[1] # loss matrix + normalization M = ot.utils.dist0(n) M /= M.max() .. GENERATED FROM PYTHON SOURCE LINES 55-57 Plot data --------- .. GENERATED FROM PYTHON SOURCE LINES 57-66 .. code-block:: Python # plot the distributions pl.figure(1, figsize=(6.4, 3)) for i in range(n_distributions): pl.plot(x, A[:, i]) pl.title('Distributions') pl.tight_layout() .. image-sg:: /auto_examples/unbalanced-partial/images/sphx_glr_plot_UOT_barycenter_1D_001.png :alt: Distributions :srcset: /auto_examples/unbalanced-partial/images/sphx_glr_plot_UOT_barycenter_1D_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 67-69 Barycenter computation ---------------------- .. GENERATED FROM PYTHON SOURCE LINES 69-98 .. code-block:: Python # non weighted barycenter computation weight = 0.5 # 0<=weight<=1 weights = np.array([1 - weight, weight]) # l2bary bary_l2 = A.dot(weights) # wasserstein reg = 1e-3 alpha = 1. bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) pl.figure(2) pl.clf() pl.subplot(2, 1, 1) for i in range(n_distributions): pl.plot(x, A[:, i]) pl.title('Distributions') pl.subplot(2, 1, 2) pl.plot(x, bary_l2, 'r', label='l2') pl.plot(x, bary_wass, 'g', label='Wasserstein') pl.legend() pl.title('Barycenters') pl.tight_layout() .. image-sg:: /auto_examples/unbalanced-partial/images/sphx_glr_plot_UOT_barycenter_1D_002.png :alt: Distributions, Barycenters :srcset: /auto_examples/unbalanced-partial/images/sphx_glr_plot_UOT_barycenter_1D_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 99-101 Barycentric interpolation ------------------------- .. GENERATED FROM PYTHON SOURCE LINES 101-167 .. code-block:: Python # barycenter interpolation n_weight = 11 weight_list = np.linspace(0, 1, n_weight) B_l2 = np.zeros((n, n_weight)) B_wass = np.copy(B_l2) for i in range(0, n_weight): weight = weight_list[i] weights = np.array([1 - weight, weight]) B_l2[:, i] = A.dot(weights) B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) # plot interpolation pl.figure(3) cmap = pl.cm.get_cmap('viridis') verts = [] zs = weight_list for i, z in enumerate(zs): ys = B_l2[:, i] verts.append(list(zip(x, ys))) ax = pl.gcf().add_subplot(projection='3d') poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) poly.set_alpha(0.7) ax.add_collection3d(poly, zs=zs, zdir='y') ax.set_xlabel('x') ax.set_xlim3d(0, n) ax.set_ylabel(r'$\alpha$') ax.set_ylim3d(0, 1) ax.set_zlabel('') ax.set_zlim3d(0, B_l2.max() * 1.01) pl.title('Barycenter interpolation with l2') pl.tight_layout() pl.figure(4) cmap = pl.cm.get_cmap('viridis') verts = [] zs = weight_list for i, z in enumerate(zs): ys = B_wass[:, i] verts.append(list(zip(x, ys))) ax = pl.gcf().add_subplot(projection='3d') poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) poly.set_alpha(0.7) ax.add_collection3d(poly, zs=zs, zdir='y') ax.set_xlabel('x') ax.set_xlim3d(0, n) ax.set_ylabel(r'$\alpha$') ax.set_ylim3d(0, 1) ax.set_zlabel('') ax.set_zlim3d(0, B_l2.max() * 1.01) pl.title('Barycenter interpolation with Wasserstein') pl.tight_layout() pl.show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/unbalanced-partial/images/sphx_glr_plot_UOT_barycenter_1D_003.png :alt: Barycenter interpolation with l2 :srcset: /auto_examples/unbalanced-partial/images/sphx_glr_plot_UOT_barycenter_1D_003.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/unbalanced-partial/images/sphx_glr_plot_UOT_barycenter_1D_004.png :alt: Barycenter interpolation with Wasserstein :srcset: /auto_examples/unbalanced-partial/images/sphx_glr_plot_UOT_barycenter_1D_004.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/circleci/project/ot/unbalanced.py:1022: RuntimeWarning: overflow encountered in divide u = (A / Kv) ** fi /home/circleci/project/ot/unbalanced.py:1027: RuntimeWarning: invalid value encountered in divide v = (Q / Ktu) ** fi /home/circleci/project/ot/unbalanced.py:1034: UserWarning: Numerical errors at iteration 595 warnings.warn('Numerical errors at iteration %s' % i) /home/circleci/project/ot/unbalanced.py:1027: RuntimeWarning: overflow encountered in divide v = (Q / Ktu) ** fi /home/circleci/project/ot/unbalanced.py:1034: UserWarning: Numerical errors at iteration 974 warnings.warn('Numerical errors at iteration %s' % i) /home/circleci/project/ot/unbalanced.py:1034: UserWarning: Numerical errors at iteration 615 warnings.warn('Numerical errors at iteration %s' % i) /home/circleci/project/ot/unbalanced.py:1034: UserWarning: Numerical errors at iteration 455 warnings.warn('Numerical errors at iteration %s' % i) /home/circleci/project/ot/unbalanced.py:1034: UserWarning: Numerical errors at iteration 361 warnings.warn('Numerical errors at iteration %s' % i) /home/circleci/project/examples/unbalanced-partial/plot_UOT_barycenter_1D.py:123: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead. cmap = pl.cm.get_cmap('viridis') /home/circleci/project/examples/unbalanced-partial/plot_UOT_barycenter_1D.py:145: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead. cmap = pl.cm.get_cmap('viridis') .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.641 seconds) .. _sphx_glr_download_auto_examples_unbalanced-partial_plot_UOT_barycenter_1D.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_UOT_barycenter_1D.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_UOT_barycenter_1D.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_