diff options
| author | Nathan Cassereau <84033440+ncassereau-idris@users.noreply.github.com> | 2022-12-03 22:44:41 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2022-12-03 22:44:41 +0100 |
| commit | ac830dd2b85cfd39f4fadd879a721b36ded033ea (patch) | |
| tree | 8a013c1bf3016bb950294d1707a0da4a54ba0564 | |
| parent | fa0d4f2afff73284f4b79bfebb085eed332c112f (diff) | |
[MRG] Wrong documentation in weak OT solver (#410)
* Docstrings of weak.py updated
* releases.md
| -rw-r--r-- | RELEASES.md | 1 | ||||
| -rw-r--r-- | ot/weak.py | 6 |
2 files changed, 5 insertions, 2 deletions
diff --git a/RELEASES.md b/RELEASES.md index 564fd4a..68487e8 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -26,6 +26,7 @@ roughly 2^31) (PR #381) - Added a work-around for scipy's bug, where you cannot compute the Hamming distance with a "None" weight attribute. (Issue #400, PR #402) - Fixed an issue where the doc could not be built due to some changes in matplotlib's API (Issue #403, PR #402) - Replaced Numpy C Compiler with Setuptools C Compiler due to deprecation issues (Issue #408, PR #409) +- Fixed weak optimal transport docstring (Issue #404, PR #410) ## 0.8.2 @@ -18,7 +18,7 @@ def weak_optimal_transport(Xa, Xb, a=None, b=None, verbose=False, log=False, G0= .. math:: - \gamma = \mathop{\arg \min}_\gamma \quad \|X_a-diag(1/a)\gammaX_b\|_F^2 + \gamma = \mathop{\arg \min}_\gamma \quad \sum_i \mathbf{a}_i \left(\mathbf{X^a}_i - \frac{1}{\mathbf{a}_i} \sum_j \gamma_{ij} \mathbf{X^b}_j \right)^2 s.t. \ \gamma \mathbf{1} = \mathbf{a} @@ -28,7 +28,7 @@ def weak_optimal_transport(Xa, Xb, a=None, b=None, verbose=False, log=False, G0= where : - - :math:`X_a` :math:`X_b` are the sample matrices. + - :math:`X^a` and :math:`X^b` are the sample matrices. - :math:`\mathbf{a}` and :math:`\mathbf{b}` are the sample weights @@ -49,6 +49,8 @@ def weak_optimal_transport(Xa, Xb, a=None, b=None, verbose=False, log=False, G0= Source histogram (uniform weight if empty list) b : (nt,) array-like, float Target histogram (uniform weight if empty list)) + G0 : (ns,nt) array-like, float + initial guess (default is indep joint density) numItermax : int, optional Max number of iterations numItermaxEmd : int, optional |