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Merged
rflamary merged 18 commits into from
Apr 5, 2022
Merged

[MRG] Update examples in the doc #359

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cab43f4
add transparent color logo
rflamary Mar 30, 2022
426464d
add transparent color logo
rflamary Mar 30, 2022
5b7aaec
move screenkhorn
rflamary Mar 30, 2022
e41a0f4
move stochastic and install ffmpeg on circleci
rflamary Mar 31, 2022
3dede19
try something
rflamary Mar 31, 2022
3639ce4
add sudo
rflamary Mar 31, 2022
714a207
install ffmpeg before python
rflamary Apr 1, 2022
447e6f8
Merge branch 'master' into doc_8.2
rflamary Apr 4, 2022
a65ed52
cleanup examples
rflamary Apr 4, 2022
0412ef6
test svg scrapper
rflamary Apr 4, 2022
ccb55cd
add animation for reg path
rflamary Apr 4, 2022
8a46f08
better example OT sivergence
rflamary Apr 4, 2022
3a7d1dd
update ttles and add plots
rflamary Apr 4, 2022
e8d88e0
update free support
rflamary Apr 4, 2022
294bea9
proper figure indexes
rflamary Apr 4, 2022
f50e7dd
have less frame sin animation
rflamary Apr 4, 2022
31b91b3
update readme and release file
rflamary Apr 5, 2022
ed052c5
add tests for python 3.10
rflamary Apr 5, 2022
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cleanup examples
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@rflamary
rflamary committed Apr 4, 2022
commit a65ed521e172ed7a1f0bc903dd3f1f7390e5696d
8 changes: 4 additions & 4 deletions examples/backends/plot_wass1d_torch.py
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@@ -1,9 +1,9 @@
r"""
=================================
Wasserstein 1D with PyTorch
=================================
=================================================
Wasserstein 1D (flow and barycenter) with PyTorch
=================================================

In this small example, we consider the following minization problem:
In this small example, we consider the following minimization problem:

.. math::
\mu^* = \min_\mu W(\mu,\nu)
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2 changes: 1 addition & 1 deletion examples/plot_OT_2D_samples.py
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@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
"""
====================================================
2D Optimal transport between empirical distributions
Optimal Transport between 2D empirical distributions
====================================================

Illustration of 2D optimal transport between discributions that are weighted
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24 changes: 12 additions & 12 deletions examples/plot_OT_L1_vs_L2.py
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Expand Up @@ -4,7 +4,7 @@
2D Optimal transport for different metrics
==========================================

2D OT on empirical distributio with different gound metric.
2D OT on empirical distributio with different ground metric.

Stole the figure idea from Fig. 1 and 2 in
https://arxiv.org/pdf/1706.07650.pdf
Expand All @@ -23,7 +23,7 @@
import ot
import ot.plot

##############################################################################
# %%
# Dataset 1 : uniform sampling
# ----------------------------

Expand All @@ -46,7 +46,7 @@
M2 /= M2.max()

# loss matrix
Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))
Mp = ot.dist(xs, xt, metric='cityblock')
Mp /= Mp.max()

# Data
Expand All @@ -71,7 +71,7 @@

pl.subplot(1, 3, 3)
pl.imshow(Mp, interpolation='nearest')
pl.title('Sqrt Euclidean cost')
pl.title('L1 (cityblock cost')
pl.tight_layout()

##############################################################################
Expand Down Expand Up @@ -109,22 +109,22 @@
pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
pl.axis('equal')
# pl.legend(loc=0)
pl.title('OT sqrt Euclidean')
pl.title('OT L1 (cityblock)')
pl.tight_layout()

pl.show()


##############################################################################
# %%
# Dataset 2 : Partial circle
# --------------------------

n = 50 # nb samples
n = 20 # nb samples
xtot = np.zeros((n + 1, 2))
xtot[:, 0] = np.cos(
(np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)
(np.arange(n + 1) + 1.0) * 0.8 / (n + 2) * 2 * np.pi)
xtot[:, 1] = np.sin(
(np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)
(np.arange(n + 1) + 1.0) * 0.8 / (n + 2) * 2 * np.pi)

xs = xtot[:n, :]
xt = xtot[1:, :]
Expand All @@ -140,7 +140,7 @@
M2 /= M2.max()

# loss matrix
Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))
Mp = ot.dist(xs, xt, metric='cityblock')
Mp /= Mp.max()


Expand All @@ -166,7 +166,7 @@

pl.subplot(1, 3, 3)
pl.imshow(Mp, interpolation='nearest')
pl.title('Sqrt Euclidean cost')
pl.title('L1 (cityblock) cost')
pl.tight_layout()

##############################################################################
Expand Down Expand Up @@ -204,7 +204,7 @@
pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')
pl.axis('equal')
# pl.legend(loc=0)
pl.title('OT sqrt Euclidean')
pl.title('OT L1 (cityblock)')
pl.tight_layout()

pl.show()