update notebooks

1 files changed, 36 insertions, 108 deletions

diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb
index a19e0fd..01e759a 100644
--- a/docs/source/auto_examples/plot_barycenter_1D.ipynb
+++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb
@@ -1,126 +1,54 @@
 {
-  "nbformat_minor": 0, 
-  "nbformat": 4, 
   "cells": [
     {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "%matplotlib inline"
-      ], 
-      "outputs": [], 
-      "metadata": {
-        "collapsed": false
-      }
-    }, 
-    {
-      "source": [
-        "\n# 1D Wasserstein barycenter demo\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [3].\n\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
-    {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D  # noqa\nfrom matplotlib.collections import PolyCollection"
-      ], 
-      "outputs": [], 
-      "metadata": {
-        "collapsed": false
-      }
-    }, 
-    {
-      "source": [
-        "Generate data\n-------------\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
-    {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "#%% parameters\n\nn = 100  # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.get_1D_gauss(n, m=20, s=5)  # m= mean, s= std\na2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()"
-      ], 
-      "outputs": [], 
+      "execution_count": null,
       "metadata": {
         "collapsed": false
-      }
-    }, 
-    {
+      },
+      "outputs": [],
       "source": [
-        "Plot data\n---------\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
-    {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n    pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()"
-      ], 
-      "outputs": [], 
-      "metadata": {
-        "collapsed": false
-      }
-    }, 
+        "%matplotlib inline"
+      ],
+      "cell_type": "code"
+    },
     {
+      "metadata": {},
       "source": [
-        "Barycenter computation\n----------------------\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
+        "\n# 1D Wasserstein barycenter demo\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [3].\n\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n"
+      ],
+      "cell_type": "markdown"
+    },
     {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "#%% barycenter computation\n\nalpha = 0.2  # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n    pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()"
-      ], 
-      "outputs": [], 
+      "execution_count": null,
       "metadata": {
         "collapsed": false
-      }
-    }, 
-    {
+      },
+      "outputs": [],
       "source": [
-        "Barycentric interpolation\n-------------------------\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
-    {
-      "execution_count": null, 
-      "cell_type": "code", 
-      "source": [
-        "#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n    alpha = alpha_list[i]\n    weights = np.array([1 - alpha, alpha])\n    B_l2[:, i] = A.dot(weights)\n    B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n    ys = B_l2[:, i]\n    verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n    ys = B_wass[:, i]\n    verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()"
-      ], 
-      "outputs": [], 
-      "metadata": {
-        "collapsed": false
-      }
+        "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D  # noqa\nfrom matplotlib.collections import PolyCollection\n\n#\n# Generate data\n# -------------\n\n#%% parameters\n\nn = 100  # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.get_1D_gauss(n, m=20, s=5)  # m= mean, s= std\na2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n#\n# Plot data\n# ---------\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n    pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.2  # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n    pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#\n# Barycentric interpolation\n# -------------------------\n\n#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n    alpha = alpha_list[i]\n    weights = np.array([1 - alpha, alpha])\n    B_l2[:, i] = A.dot(weights)\n    B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n    ys = B_l2[:, i]\n    verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n    ys = B_wass[:, i]\n    verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()"
+      ],
+      "cell_type": "code"
     }
-  ], 
+  ],
   "metadata": {
-    "kernelspec": {
-      "display_name": "Python 2", 
-      "name": "python2", 
-      "language": "python"
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     "language_info": {
-      "mimetype": "text/x-python", 
-      "nbconvert_exporter": "python", 
-      "name": "python", 
-      "file_extension": ".py", 
-      "version": "2.7.12", 
-      "pygments_lexer": "ipython2", 
+      "name": "python",
       "codemirror_mode": {
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-        "name": "ipython"
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+        "name": "ipython",
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+      "nbconvert_exporter": "python",
+      "version": "3.5.2",
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+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3",
+      "language": "python"
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+  },
+  "nbformat_minor": 0,
+  "nbformat": 4
 }
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