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author | RĂ©mi Flamary <remi.flamary@gmail.com> | 2020-04-21 17:48:37 +0200 |
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committer | GitHub <noreply@github.com> | 2020-04-21 17:48:37 +0200 |
commit | a303cc6b483d3cd958c399621e22e40574bcbbc8 (patch) | |
tree | dea049cb692020462da8f00d9e117f93b839bb55 /docs/source/auto_examples/plot_free_support_barycenter.py | |
parent | 0b2d808aaebb1cab60a272ea7901d5f77df43a9f (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
Diffstat (limited to 'docs/source/auto_examples/plot_free_support_barycenter.py')
-rw-r--r-- | docs/source/auto_examples/plot_free_support_barycenter.py | 69 |
1 files changed, 0 insertions, 69 deletions
diff --git a/docs/source/auto_examples/plot_free_support_barycenter.py b/docs/source/auto_examples/plot_free_support_barycenter.py deleted file mode 100644 index 64b89e4..0000000 --- a/docs/source/auto_examples/plot_free_support_barycenter.py +++ /dev/null @@ -1,69 +0,0 @@ -# -*- coding: utf-8 -*- -""" -==================================================== -2D free support Wasserstein barycenters of distributions -==================================================== - -Illustration of 2D Wasserstein barycenters if discributions that are weighted -sum of diracs. - -""" - -# Author: Vivien Seguy <vivien.seguy@iip.ist.i.kyoto-u.ac.jp> -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot - - -############################################################################## -# Generate data -# ------------- -#%% parameters and data generation -N = 3 -d = 2 -measures_locations = [] -measures_weights = [] - -for i in range(N): - - n_i = np.random.randint(low=1, high=20) # nb samples - - mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean - - A_i = np.random.rand(d, d) - cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix - - x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations - b_i = np.random.uniform(0., 1., (n_i,)) - b_i = b_i / np.sum(b_i) # Dirac weights - - measures_locations.append(x_i) - measures_weights.append(b_i) - - -############################################################################## -# Compute free support barycenter -# ------------- - -k = 10 # number of Diracs of the barycenter -X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations -b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized) - -X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b) - - -############################################################################## -# Plot data -# --------- - -pl.figure(1) -for (x_i, b_i) in zip(measures_locations, measures_weights): - color = np.random.randint(low=1, high=10 * N) - pl.scatter(x_i[:, 0], x_i[:, 1], s=b_i * 1000, label='input measure') -pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') -pl.title('Data measures and their barycenter') -pl.legend(loc=0) -pl.show() |