From ae1ede4ed31973213b5945721b7b9fe8e4992a1c Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Mon, 24 Sep 2018 11:00:14 +0200
Subject: update doc+notebooks for convolutional

---
 .../plot_convolutional_barycenter.ipynb            | 90 ++++++++++++++++++++++
 1 file changed, 90 insertions(+)
 create mode 100644 docs/source/auto_examples/plot_convolutional_barycenter.ipynb

(limited to 'docs/source/auto_examples/plot_convolutional_barycenter.ipynb')

diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.ipynb b/docs/source/auto_examples/plot_convolutional_barycenter.ipynb
new file mode 100644
index 0000000..4981ba3
--- /dev/null
+++ b/docs/source/auto_examples/plot_convolutional_barycenter.ipynb
@@ -0,0 +1,90 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Convolutional Wasserstein Barycenter example\n\n\nThis example is designed to illustrate how the Convolutional Wasserstein Barycenter\nfunction of POT works.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Author: Nicolas Courty <ncourty@irisa.fr>\n#\n# License: MIT License\n\n\nimport numpy as np\nimport pylab as pl\nimport ot"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Data preparation\n----------------\n\nThe four distributions are constructed from 4 simple images\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]\nf2 = 1 - pl.imread('../data/duck.png')[:, :, 2]\nf3 = 1 - pl.imread('../data/heart.png')[:, :, 2]\nf4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]\n\nA = []\nf1 = f1 / np.sum(f1)\nf2 = f2 / np.sum(f2)\nf3 = f3 / np.sum(f3)\nf4 = f4 / np.sum(f4)\nA.append(f1)\nA.append(f2)\nA.append(f3)\nA.append(f4)\nA = np.array(A)\n\nnb_images = 5\n\n# those are the four corners coordinates that will be interpolated by bilinear\n# interpolation\nv1 = np.array((1, 0, 0, 0))\nv2 = np.array((0, 1, 0, 0))\nv3 = np.array((0, 0, 1, 0))\nv4 = np.array((0, 0, 0, 1))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Barycenter computation and visualization\n----------------------------------------\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pl.figure(figsize=(10, 10))\npl.title('Convolutional Wasserstein Barycenters in POT')\ncm = 'Blues'\n# regularization parameter\nreg = 0.004\nfor i in range(nb_images):\n    for j in range(nb_images):\n        pl.subplot(nb_images, nb_images, i * nb_images + j + 1)\n        tx = float(i) / (nb_images - 1)\n        ty = float(j) / (nb_images - 1)\n\n        # weights are constructed by bilinear interpolation\n        tmp1 = (1 - tx) * v1 + tx * v2\n        tmp2 = (1 - tx) * v3 + tx * v4\n        weights = (1 - ty) * tmp1 + ty * tmp2\n\n        if i == 0 and j == 0:\n            pl.imshow(f1, cmap=cm)\n            pl.axis('off')\n        elif i == 0 and j == (nb_images - 1):\n            pl.imshow(f3, cmap=cm)\n            pl.axis('off')\n        elif i == (nb_images - 1) and j == 0:\n            pl.imshow(f2, cmap=cm)\n            pl.axis('off')\n        elif i == (nb_images - 1) and j == (nb_images - 1):\n            pl.imshow(f4, cmap=cm)\n            pl.axis('off')\n        else:\n            # call to barycenter computation\n            pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm)\n            pl.axis('off')\npl.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
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