added defaults for emd_laplace

1 files changed, 7 insertions, 8 deletions

diff --git a/ot/da.py b/ot/da.py
index 300af30..e615993 100644
--- a/ot/da.py
+++ b/ot/da.py
@@ -748,9 +748,9 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None,
         return A, b
 
 
-def emd_laplace(a, b, xs, xt, M, sim, sim_param, reg, eta, alpha,
-                numItermax, stopThr, numInnerItermax,
-                stopInnerThr, log=False, verbose=False, **kwargs):
+def emd_laplace(a, b, xs, xt, M, sim='knn', sim_param=None, reg='pos', eta=1, alpha=.5,
+                numItermax=100, stopThr=1e-9, numInnerItermax=100000,
+                stopInnerThr=1e-9, log=False, verbose=False):
     r"""Solve the optimal transport problem (OT) with Laplacian regularization
 
     .. math::
@@ -1765,15 +1765,14 @@ class EMDLaplaceTransport(BaseTransport):
          in NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014.
     """
 
-    def __init__(self, reg_type='pos', reg_lap=1., reg_src=1., alpha=0.5,
-                 metric="sqeuclidean", norm=None, similarity="knn", similarity_param=None, max_iter=100, tol=1e-9,
+    def __init__(self, reg_type='pos', reg_lap=1., reg_src=1., metric="sqeuclidean",
+                 norm=None, similarity="knn", similarity_param=None, max_iter=100, tol=1e-9,
                  max_inner_iter=100000, inner_tol=1e-9, log=False, verbose=False,
                  distribution_estimation=distribution_estimation_uniform,
                  out_of_sample_map='ferradans'):
         self.reg = reg_type
         self.reg_lap = reg_lap
         self.reg_src = reg_src
-        self.alpha = alpha
         self.metric = metric
         self.norm = norm
         self.similarity = similarity
@@ -1815,8 +1814,8 @@ class EMDLaplaceTransport(BaseTransport):
         super(EMDLaplaceTransport, self).fit(Xs, ys, Xt, yt)
 
         returned_ = emd_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_,
-                                xt=self.xt_, M=self.cost_, sim=self.similarity, sim_param=self.sim_param, reg=self.reg, eta=self.reg_lap, alpha=self.reg_src,
-                                numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter,
+                                xt=self.xt_, M=self.cost_, sim=self.similarity, sim_param=self.sim_param, reg=self.reg, eta=self.reg_lap,
+                                alpha=self.reg_src, numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter,
                                 stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose)
 
         # coupling estimation