diff options
Diffstat (limited to 'ot/gromov/_semirelaxed.py')
| -rw-r--r-- | ot/gromov/_semirelaxed.py | 15 |
1 files changed, 9 insertions, 6 deletions
diff --git a/ot/gromov/_semirelaxed.py b/ot/gromov/_semirelaxed.py index 638bb1c..cb2bf28 100644 --- a/ot/gromov/_semirelaxed.py +++ b/ot/gromov/_semirelaxed.py @@ -21,7 +21,7 @@ from ._utils import init_matrix_semirelaxed, gwloss, gwggrad def semirelaxed_gromov_wasserstein(C1, C2, p, loss_fun='square_loss', symmetric=None, log=False, G0=None, max_iter=1e4, tol_rel=1e-9, tol_abs=1e-9, **kwargs): r""" - Returns the semi-relaxed gromov-wasserstein divergence transport from :math:`(\mathbf{C_1}, \mathbf{p})` to :math:`\mathbf{C_2}` + Returns the semi-relaxed Gromov-Wasserstein divergence transport from :math:`(\mathbf{C_1}, \mathbf{p})` to :math:`\mathbf{C_2}` The function solves the following optimization problem: @@ -32,6 +32,7 @@ def semirelaxed_gromov_wasserstein(C1, C2, p, loss_fun='square_loss', symmetric= s.t. \ \mathbf{\gamma} \mathbf{1} &= \mathbf{p} \mathbf{\gamma} &\geq 0 + Where : - :math:`\mathbf{C_1}`: Metric cost matrix in the source space @@ -58,7 +59,7 @@ def semirelaxed_gromov_wasserstein(C1, C2, p, loss_fun='square_loss', symmetric= symmetric : bool, optional Either C1 and C2 are to be assumed symmetric or not. If let to its default None value, a symmetry test will be conducted. - Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymetric). + Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric). verbose : bool, optional Print information along iterations log : bool, optional @@ -156,6 +157,7 @@ def semirelaxed_gromov_wasserstein2(C1, C2, p, loss_fun='square_loss', symmetric s.t. \ \mathbf{\gamma} \mathbf{1} &= \mathbf{p} \mathbf{\gamma} &\geq 0 + Where : - :math:`\mathbf{C_1}`: Metric cost matrix in the source space @@ -166,6 +168,7 @@ def semirelaxed_gromov_wasserstein2(C1, C2, p, loss_fun='square_loss', symmetric Note that when using backends, this loss function is differentiable wrt the matrices (C1, C2) but not yet for the weights p. + .. note:: This function is backend-compatible and will work on arrays from all compatible backends. However all the steps in the conditional gradient are not differentiable. @@ -184,7 +187,7 @@ def semirelaxed_gromov_wasserstein2(C1, C2, p, loss_fun='square_loss', symmetric symmetric : bool, optional Either C1 and C2 are to be assumed symmetric or not. If let to its default None value, a symmetry test will be conducted. - Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymetric). + Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric). verbose : bool, optional Print information along iterations log : bool, optional @@ -278,7 +281,7 @@ def semirelaxed_fused_gromov_wasserstein(M, C1, C2, p, loss_fun='square_loss', s symmetric : bool, optional Either C1 and C2 are to be assumed symmetric or not. If let to its default None value, a symmetry test will be conducted. - Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymetric). + Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric). alpha : float, optional Trade-off parameter (0 < alpha < 1) G0: array-like, shape (ns,nt), optional @@ -415,7 +418,7 @@ def semirelaxed_fused_gromov_wasserstein2(M, C1, C2, p, loss_fun='square_loss', symmetric : bool, optional Either C1 and C2 are to be assumed symmetric or not. If let to its default None value, a symmetry test will be conducted. - Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymetric). + Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric). alpha : float, optional Trade-off parameter (0 < alpha < 1) G0: array-like, shape (ns,nt), optional @@ -435,7 +438,7 @@ def semirelaxed_fused_gromov_wasserstein2(M, C1, C2, p, loss_fun='square_loss', Returns ------- srfgw-divergence : float - Semi-relaxed Fused gromov wasserstein divergence for the given parameters. + Semi-relaxed Fused Gromov-Wasserstein divergence for the given parameters. log : dict Log dictionary return only if log==True in parameters. |