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+{
+  "nbformat_minor": 0, 
+  "nbformat": 4, 
+  "cells": [
+    {
+      "execution_count": null, 
+      "cell_type": "code", 
+      "source": [
+        "%matplotlib inline"
+      ], 
+      "outputs": [], 
+      "metadata": {
+        "collapsed": false
+      }
+    }, 
+    {
+      "source": [
+        "\n# Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Gromov-Wassertsein distance\ncomputation in POT.\n\n"
+      ], 
+      "cell_type": "markdown", 
+      "metadata": {}
+    }, 
+    {
+      "execution_count": null, 
+      "cell_type": "code", 
+      "source": [
+        "# Author: Erwan Vautier <erwan.vautier@gmail.com>\r\n#         Nicolas Courty <ncourty@irisa.fr>\r\n#\r\n# License: MIT License\r\n\r\nimport scipy as sp\r\nimport numpy as np\r\nimport matplotlib.pylab as pl\r\nfrom mpl_toolkits.mplot3d import Axes3D  # noqa\r\nimport ot"
+      ], 
+      "outputs": [], 
+      "metadata": {
+        "collapsed": false
+      }
+    }, 
+    {
+      "source": [
+        "Sample two Gaussian distributions (2D and 3D)\r\n ---------------------------------------------\r\n\r\n The Gromov-Wasserstein distance allows to compute distances with samples that\r\n do not belong to the same metric space. For demonstration purpose, we sample\r\n two Gaussian distributions in 2- and 3-dimensional spaces.\r\n\n"
+      ], 
+      "cell_type": "markdown", 
+      "metadata": {}
+    }, 
+    {
+      "execution_count": null, 
+      "cell_type": "code", 
+      "source": [
+        "n_samples = 30  # nb samples\r\n\r\nmu_s = np.array([0, 0])\r\ncov_s = np.array([[1, 0], [0, 1]])\r\n\r\nmu_t = np.array([4, 4, 4])\r\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\r\n\r\n\r\nxs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\r\nP = sp.linalg.sqrtm(cov_t)\r\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t"
+      ], 
+      "outputs": [], 
+      "metadata": {
+        "collapsed": false
+      }
+    }, 
+    {
+      "source": [
+        "Plotting the distributions\r\n--------------------------\r\n\n"
+      ], 
+      "cell_type": "markdown", 
+      "metadata": {}
+    }, 
+    {
+      "execution_count": null, 
+      "cell_type": "code", 
+      "source": [
+        "fig = pl.figure()\r\nax1 = fig.add_subplot(121)\r\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\r\nax2 = fig.add_subplot(122, projection='3d')\r\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\r\npl.show()"
+      ], 
+      "outputs": [], 
+      "metadata": {
+        "collapsed": false
+      }
+    }, 
+    {
+      "source": [
+        "Compute distance kernels, normalize them and then display\r\n---------------------------------------------------------\r\n\n"
+      ], 
+      "cell_type": "markdown", 
+      "metadata": {}
+    }, 
+    {
+      "execution_count": null, 
+      "cell_type": "code", 
+      "source": [
+        "C1 = sp.spatial.distance.cdist(xs, xs)\r\nC2 = sp.spatial.distance.cdist(xt, xt)\r\n\r\nC1 /= C1.max()\r\nC2 /= C2.max()\r\n\r\npl.figure()\r\npl.subplot(121)\r\npl.imshow(C1)\r\npl.subplot(122)\r\npl.imshow(C2)\r\npl.show()"
+      ], 
+      "outputs": [], 
+      "metadata": {
+        "collapsed": false
+      }
+    }, 
+    {
+      "source": [
+        "Compute Gromov-Wasserstein plans and distance\r\n---------------------------------------------\r\n\n"
+      ], 
+      "cell_type": "markdown", 
+      "metadata": {}
+    }, 
+    {
+      "execution_count": null, 
+      "cell_type": "code", 
+      "source": [
+        "p = ot.unif(n_samples)\r\nq = ot.unif(n_samples)\r\n\r\ngw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\ngw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\n\r\nprint('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist))\r\n\r\npl.figure()\r\npl.imshow(gw, cmap='jet')\r\npl.colorbar()\r\npl.show()"
+      ], 
+      "outputs": [], 
+      "metadata": {
+        "collapsed": false
+      }
+    }
+  ], 
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 2", 
+      "name": "python2", 
+      "language": "python"
+    }, 
+    "language_info": {
+      "mimetype": "text/x-python", 
+      "nbconvert_exporter": "python", 
+      "name": "python", 
+      "file_extension": ".py", 
+      "version": "2.7.12", 
+      "pygments_lexer": "ipython2", 
+      "codemirror_mode": {
+        "version": 2, 
+        "name": "ipython"
+      }
+    }
+  }
+}
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