diff options
Diffstat (limited to 'ot/da.py')
-rw-r--r-- | ot/da.py | 12 |
1 files changed, 6 insertions, 6 deletions
@@ -26,7 +26,7 @@ from .optim import gcg 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 group lasso regularization @@ -137,7 +137,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, def sinkhorn_l1l2_gl(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 group lasso regularization @@ -245,7 +245,7 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, verbose2=False, numItermax=100, numInnerItermax=10, stopInnerThr=1e-6, stopThr=1e-5, log=False, **kwargs): - """Joint OT and linear mapping estimation as proposed in [8] + r"""Joint OT and linear mapping estimation as proposed in [8] The function solves the following optimization problem: @@ -434,7 +434,7 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', numItermax=100, numInnerItermax=10, stopInnerThr=1e-6, stopThr=1e-5, log=False, **kwargs): - """Joint OT and nonlinear mapping estimation with kernels as proposed in [8] + r"""Joint OT and nonlinear mapping estimation with kernels as proposed in [8] The function solves the following optimization problem: @@ -645,7 +645,7 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, wt=None, bias=True, log=False): - """ return OT linear operator between samples + r""" return OT linear operator between samples The function estimates the optimal linear operator that aligns the two empirical distributions. This is equivalent to estimating the closed @@ -1228,7 +1228,7 @@ class BaseTransport(BaseEstimator): class LinearTransport(BaseTransport): - """ OT linear operator between empirical distributions + r""" OT linear operator between empirical distributions The function estimates the optimal linear operator that aligns the two empirical distributions. This is equivalent to estimating the closed |