alpha for documentation

Author: rflamary

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}
 
 

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