update notebooks

1 files changed, 36 insertions, 108 deletions

diff --git a/docs/source/auto_examples/plot_gromov.ipynb b/docs/source/auto_examples/plot_gromov.ipynb
index 865848e..6d6b522 100644
--- a/docs/source/auto_examples/plot_gromov.ipynb
+++ b/docs/source/auto_examples/plot_gromov.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# 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": [], 
+      "execution_count": null,
       "metadata": {
         "collapsed": false
-      }
-    }, 
-    {
+      },
+      "outputs": [],
       "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
-      }
-    }, 
+        "%matplotlib inline"
+      ],
+      "cell_type": "code"
+    },
     {
+      "metadata": {},
       "source": [
-        "Compute distance kernels, normalize them and then display\r\n---------------------------------------------------------\r\n\n"
-      ], 
-      "cell_type": "markdown", 
-      "metadata": {}
-    }, 
+        "\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"
+    },
     {
-      "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": [], 
+      "execution_count": null,
       "metadata": {
         "collapsed": false
-      }
-    }, 
-    {
+      },
+      "outputs": [],
       "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
-      }
+        "# Author: Erwan Vautier <erwan.vautier@gmail.com>\n#         Nicolas Courty <ncourty@irisa.fr>\n#\n# License: MIT License\n\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nfrom mpl_toolkits.mplot3d import Axes3D  # noqa\nimport ot\n\n\n#\n# Sample two Gaussian distributions (2D and 3D)\n# ---------------------------------------------\n#\n# The Gromov-Wasserstein distance allows to compute distances with samples that\n# do not belong to the same metric space. For demonstration purpose, we sample\n# two Gaussian distributions in 2- and 3-dimensional spaces.\n\n\nn_samples = 30  # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4, 4])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t\n\n\n#\n# Plotting the distributions\n# --------------------------\n\n\nfig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()\n\n\n#\n# Compute distance kernels, normalize them and then display\n# ---------------------------------------------------------\n\n\nC1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nC1 /= C1.max()\nC2 /= C2.max()\n\npl.figure()\npl.subplot(121)\npl.imshow(C1)\npl.subplot(122)\npl.imshow(C2)\npl.show()\n\n#\n# Compute Gromov-Wasserstein plans and distance\n# ---------------------------------------------\n\np = ot.unif(n_samples)\nq = ot.unif(n_samples)\n\ngw0, log0 = ot.gromov.gromov_wasserstein(\n    C1, C2, p, q, 'square_loss', verbose=True, log=True)\n\ngw, log = ot.gromov.entropic_gromov_wasserstein(\n    C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n\n\nprint('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\nprint('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(gw0, cmap='jet')\npl.title('Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(gw, cmap='jet')\npl.title('Entropic Gromov Wasserstein')\n\npl.show()"
+      ],
+      "cell_type": "code"
     }
-  ], 
+  ],
   "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", 
+      "name": "python",
       "codemirror_mode": {
-        "version": 2, 
-        "name": "ipython"
-      }
+        "name": "ipython",
+        "version": 3
+      },
+      "nbconvert_exporter": "python",
+      "version": "3.5.2",
+      "pygments_lexer": "ipython3",
+      "file_extension": ".py",
+      "mimetype": "text/x-python"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3",
+      "language": "python"
     }
-  }
+  },
+  "nbformat_minor": 0,
+  "nbformat": 4
 }
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