summaryrefslogtreecommitdiff
path: root/docs/source/auto_examples/plot_free_support_barycenter.rst
blob: d1b3b80d415f0a4d34f558595c1895721777f35d (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
.. _sphx_glr_auto_examples_plot_free_support_barycenter.py:


====================================================
2D free support Wasserstein barycenters of distributions
====================================================

Illustration of 2D Wasserstein barycenters if discributions that are weighted
sum of diracs.




.. code-block:: python


    # 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



.. code-block:: python

    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
-------------



.. code-block:: python


    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
---------



.. code-block:: python


    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 * 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()



.. image:: /auto_examples/images/sphx_glr_plot_free_support_barycenter_001.png
    :align: center




**Total running time of the script:** ( 0 minutes  0.129 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_free_support_barycenter.py <plot_free_support_barycenter.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_free_support_barycenter.ipynb <plot_free_support_barycenter.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_