diff options
Diffstat (limited to 'ot/da.py')
| -rw-r--r-- | ot/da.py | 10 |
1 files changed, 5 insertions, 5 deletions
@@ -28,7 +28,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, verbose=False, log=False): r""" - Solve the entropic regularization optimal transport problem with nonconvex + Solve the entropic regularization optimal transport problem with non-convex group lasso regularization The function solves the following optimization problem: @@ -172,13 +172,13 @@ def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, - :math:`\mathbf{M}` is the (`ns`, `nt`) metric cost matrix - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - :math:`\Omega_g` is the group lasso regulaization term + - :math:`\Omega_g` is the group lasso regularization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^2` where :math:`\mathcal{I}_c` are the index of samples from class `c` in the source domain. - :math:`\mathbf{a}` and :math:`\mathbf{b}` are source and target weights (sum to 1) - The algorithm used for solving the problem is the generalised conditional + The algorithm used for solving the problem is the generalized conditional gradient as proposed in :ref:`[5, 7] <references-sinkhorn-l1l2-gl>`. @@ -296,7 +296,7 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, material of :ref:`[8] <references-joint-OT-mapping-linear>`) using the bias optional argument. The algorithm used for solving the problem is the block coordinate - descent that alternates between updates of :math:`\mathbf{G}` (using conditionnal gradient) + descent that alternates between updates of :math:`\mathbf{G}` (using conditional gradient) and the update of :math:`\mathbf{L}` using a classical least square solver. @@ -494,7 +494,7 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', material of :ref:`[8] <references-joint-OT-mapping-kernel>`) using the bias optional argument. The algorithm used for solving the problem is the block coordinate - descent that alternates between updates of :math:`\mathbf{G}` (using conditionnal gradient) + descent that alternates between updates of :math:`\mathbf{G}` (using conditional gradient) and the update of :math:`\mathbf{L}` using a classical kernel least square solver. |