From 6ac8d405f16832e671c432d7b03ce3da38f8fedc Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Mon, 20 Apr 2020 16:01:15 +0200
Subject: add all pages in documentation

---
 .../source/auto_examples/plot_screenkhorn_1D.ipynb | 108 +++++++++++++++++++++
 1 file changed, 108 insertions(+)
 create mode 100644 docs/source/auto_examples/plot_screenkhorn_1D.ipynb

(limited to 'docs/source/auto_examples/plot_screenkhorn_1D.ipynb')

diff --git a/docs/source/auto_examples/plot_screenkhorn_1D.ipynb b/docs/source/auto_examples/plot_screenkhorn_1D.ipynb
new file mode 100644
index 0000000..1c27d3b
--- /dev/null
+++ b/docs/source/auto_examples/plot_screenkhorn_1D.ipynb
@@ -0,0 +1,108 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# 1D Screened optimal transport\n\n\nThis example illustrates the computation of Screenkhorn:\nScreening Sinkhorn Algorithm for Optimal transport.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Author: Mokhtar Z. Alaya <mokhtarzahdi.alaya@gmail.com>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss\nfrom ot.bregman import screenkhorn"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Generate data\n-------------\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "n = 100  # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5)  # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot distributions and loss matrix\n----------------------------------\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n# plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Solve Screenkhorn\n-----------------------\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Screenkhorn\nlambd = 2e-03  # entropy parameter\nns_budget = 30  # budget number of points to be keeped in the source distribution\nnt_budget = 30  # budget number of points to be keeped in the target distribution\n\nG_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True)\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn')\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": "3.6.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
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