diff options
| -rw-r--r-- | README.md | 2 | ||||
| -rw-r--r-- | ot/da.py | 2 | ||||
| -rw-r--r-- | test/test_da.py | 2 |
3 files changed, 3 insertions, 3 deletions
@@ -29,7 +29,7 @@ It provides the following solvers: * Non regularized free support Wasserstein barycenters [20]. * Unbalanced OT with KL relaxation distance and barycenter [10, 25]. * Screening Sinkhorn Algorithm for OT [26]. -* JCPOT algorithm for multi-source target shift [27]. +* JCPOT algorithm for multi-source domain adaptation with target shift [27]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -16,7 +16,7 @@ import scipy.linalg as linalg from .bregman import sinkhorn, jcpot_barycenter from .lp import emd -from .utils import unif, dist, kernel, cost_normalization, laplacian +from .utils import unif, dist, kernel, cost_normalization from .utils import check_params, BaseEstimator from .unbalanced import sinkhorn_unbalanced from .optim import cg diff --git a/test/test_da.py b/test/test_da.py index 1517cec..b58cf51 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -565,7 +565,7 @@ def test_jcpot_transport_class(): Xs = [Xs1, Xs2] ys = [ys1, ys2] - otda = ot.da.JCPOTTransport(reg_e=0.01, max_iter=1000, tol=1e-9, verbose=True, log=True) + otda = ot.da.JCPOTTransport(reg_e=1, max_iter=10000, tol=1e-9, verbose=True, log=True) # test its computed otda.fit(Xs=Xs, ys=ys, Xt=Xt) |