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author | RĂ©mi Flamary <remi.flamary@gmail.com> | 2018-06-11 11:24:57 +0200 |
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committer | GitHub <noreply@github.com> | 2018-06-11 11:24:57 +0200 |
commit | 47730fc727c0f54e8459964d9208ad824e3f91da (patch) | |
tree | 48952166e88e602f1843bd15c0187c7d5ffb6cac /docs/source/auto_examples/plot_OT_1D.ipynb | |
parent | 4641ec5f2ddbff1a468afaf65741aecae44738cc (diff) | |
parent | 530dc93a60e9b81fb8d1b44680deea77dacf660b (diff) |
Merge branch 'master' into remove_otda_v05
Diffstat (limited to 'docs/source/auto_examples/plot_OT_1D.ipynb')
-rw-r--r-- | docs/source/auto_examples/plot_OT_1D.ipynb | 68 |
1 files changed, 34 insertions, 34 deletions
diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index 649efa6..bd0439e 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -8,35 +9,35 @@ "outputs": [], "source": [ "%matplotlib inline" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n# 1D optimal transport\n\n\nThis example illustrates the computation of EMD and Sinkhorn transport plans\nand their visualization.\n\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "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\nimport ot.plot\nfrom ot.datasets import get_1D_gauss as gauss" - ], - "cell_type": "code" + "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -44,17 +45,17 @@ "outputs": [], "source": [ "#%% parameters\n\nn = 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": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Plot distributions and loss matrix\n----------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -62,17 +63,17 @@ "outputs": [], "source": [ "#%% plot the distributions\n\npl.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": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Solve EMD\n---------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -80,17 +81,17 @@ "outputs": [], "source": [ "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Solve Sinkhorn\n--------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -98,29 +99,28 @@ "outputs": [], "source": [ "#%% Sinkhorn\n\nlambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" - ], - "cell_type": "code" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python", "codemirror_mode": { "name": "ipython", "version": 3 }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", "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" + "version": "3.6.5" } }, - "nbformat_minor": 0, - "nbformat": 4 + "nbformat": 4, + "nbformat_minor": 0 }
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