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authorRĂ©mi Flamary <remi.flamary@gmail.com>2020-04-21 17:48:37 +0200
committerGitHub <noreply@github.com>2020-04-21 17:48:37 +0200
commita303cc6b483d3cd958c399621e22e40574bcbbc8 (patch)
treedea049cb692020462da8f00d9e117f93b839bb55 /docs/source/auto_examples/plot_OT_1D.py
parent0b2d808aaebb1cab60a272ea7901d5f77df43a9f (diff)
[MRG] Actually run sphinx-gallery (#146)
* generate gallery * remove mock * add sklearn to requirermnt?txt for example * remove latex from fgw example * add networks for graph example * remove all * add requirement.txt rtd * rtd debug * update readme * eradthedoc with redirection * add conf rtd
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-# -*- coding: utf-8 -*-
-"""
-====================
-1D optimal transport
-====================
-
-This example illustrates the computation of EMD and Sinkhorn transport plans
-and their visualization.
-
-"""
-
-# Author: Remi Flamary <remi.flamary@unice.fr>
-#
-# License: MIT License
-
-import numpy as np
-import matplotlib.pylab as pl
-import ot
-import ot.plot
-from ot.datasets import make_1D_gauss as gauss
-
-##############################################################################
-# Generate data
-# -------------
-
-
-#%% parameters
-
-n = 100 # nb bins
-
-# bin positions
-x = np.arange(n, dtype=np.float64)
-
-# Gaussian distributions
-a = gauss(n, m=20, s=5) # m= mean, s= std
-b = gauss(n, m=60, s=10)
-
-# loss matrix
-M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
-M /= M.max()
-
-
-##############################################################################
-# Plot distributions and loss matrix
-# ----------------------------------
-
-#%% plot the distributions
-
-pl.figure(1, figsize=(6.4, 3))
-pl.plot(x, a, 'b', label='Source distribution')
-pl.plot(x, b, 'r', label='Target distribution')
-pl.legend()
-
-#%% plot distributions and loss matrix
-
-pl.figure(2, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')
-
-##############################################################################
-# Solve EMD
-# ---------
-
-
-#%% EMD
-
-G0 = ot.emd(a, b, M)
-
-pl.figure(3, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')
-
-##############################################################################
-# Solve Sinkhorn
-# --------------
-
-
-#%% Sinkhorn
-
-lambd = 1e-3
-Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)
-
-pl.figure(4, figsize=(5, 5))
-ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')
-
-pl.show()