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| author | Rémi Flamary <remi.flamary@gmail.com> | 2019-06-25 14:57:26 +0200 |
|---|---|---|
| committer | Rémi Flamary <remi.flamary@gmail.com> | 2019-06-25 14:57:26 +0200 |
| commit | 8ae85fd6b3649058da07b16c9ea139864c7f94a1 (patch) | |
| tree | 33a2f9c67272d66fca4f4b1a0f394614550db2c3 /ot | |
| parent | dea6d8aec8794a84bf46ee7196b0c9fe390e6afa (diff) | |
alpha for documentation
Diffstat (limited to 'ot')
| -rw-r--r-- | ot/gromov.py | 4 | ||||
| -rw-r--r-- | ot/unbalanced.py | 6 |
2 files changed, 5 insertions, 5 deletions
diff --git a/ot/gromov.py b/ot/gromov.py index cd961b0..3a7e24c 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -357,7 +357,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, Computes the FGW transport between two graphs see [24]
.. math::
- \gamma = arg\min_\gamma (1-\alpha)*<\gamma,M>_F + \alpha* \sum_{i,j,k,l}
+ \gamma = arg\min_\gamma (1-\\alpha)*<\gamma,M>_F + \\alpha* \sum_{i,j,k,l}
L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}
s.t. \gamma 1 = p
@@ -440,7 +440,7 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5 Computes the FGW distance between two graphs see [24]
.. math::
- \min_\gamma (1-\alpha)*<\gamma,M>_F + \alpha* \sum_{i,j,k,l}
+ \min_\gamma (1-\\alpha)*<\gamma,M>_F + \\alpha* \sum_{i,j,k,l}
L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}
diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 484ce95..bad12d6 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -19,7 +19,7 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \alpha KL(\gamma 1, a) + \alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) s.t. \gamma\geq 0 @@ -128,7 +128,7 @@ def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \alpha KL(\gamma 1, a) + \alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) s.t. \gamma\geq 0 @@ -239,7 +239,7 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \alpha KL(\gamma 1, a) + \alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) s.t. \gamma\geq 0 |