From af99d3ff66062811a81454ea03e6b831a1292ae4 Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Thu, 31 May 2018 11:26:32 +0200
Subject: add smooth.py + first tests

---
 ot/smooth.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

(limited to 'ot')

diff --git a/ot/smooth.py b/ot/smooth.py
index b8c0a9a..f8bb20a 100644
--- a/ot/smooth.py
+++ b/ot/smooth.py
@@ -85,10 +85,12 @@ class Regularization(object):
         """
         Compute delta_Omega(X[:, j]) for each X[:, j].
         delta_Omega(x) = sup_{y >= 0} y^T x - Omega(y).
+        
         Parameters
         ----------
         X: array, shape = len(a) x len(b)
             Input array.
+            
         Returns
         -------
         v: array, len(b)
@@ -107,6 +109,7 @@ class Regularization(object):
         ----------
         X: array, shape = len(a) x len(b)
             Input array.
+            
         Returns
         -------
         v: array, len(b)
@@ -124,6 +127,7 @@ class Regularization(object):
         ----------
         T: array, shape = len(a) x len(b)
             Input array.
+            
         Returns
         -------
         value: float
@@ -232,6 +236,7 @@ def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500):
         Tolerance parameter.
     max_iter: int
         Maximum number of iterations.
+        
     Returns
     -------
     alpha: array, shape = len(a)
@@ -270,6 +275,7 @@ def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500):
 def semi_dual_obj_grad(alpha, a, b, C, regul):
     """
     Compute objective value and gradient of semi-dual objective.
+    
     Parameters
     ----------
     alpha: array, shape = len(a)
@@ -281,6 +287,7 @@ def semi_dual_obj_grad(alpha, a, b, C, regul):
         Ground cost matrix.
     regul: Regularization object
         Should implement a max_Omega(X) method.
+        
     Returns
     -------
     obj: float
@@ -307,6 +314,7 @@ def semi_dual_obj_grad(alpha, a, b, C, regul):
 def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500):
     """
     Solve the "smoothed" semi-dual objective.
+    
     Parameters
     ----------
     a: array, shape = len(a)
@@ -322,6 +330,7 @@ def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500):
         Tolerance parameter.
     max_iter: int
         Maximum number of iterations.
+        
     Returns
     -------
     alpha: array, shape = len(a)
@@ -344,6 +353,7 @@ def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500):
 def get_plan_from_dual(alpha, beta, C, regul):
     """
     Retrieve optimal transportation plan from optimal dual potentials.
+    
     Parameters
     ----------
     alpha: array, shape = len(a)
@@ -353,6 +363,7 @@ def get_plan_from_dual(alpha, beta, C, regul):
         Ground cost matrix.
     regul: Regularization object
         Should implement a delta_Omega(X) method.
+        
     Returns
     -------
     T: array, shape = len(a) x len(b)
@@ -365,6 +376,7 @@ def get_plan_from_dual(alpha, beta, C, regul):
 def get_plan_from_semi_dual(alpha, b, C, regul):
     """
     Retrieve optimal transportation plan from optimal semi-dual potentials.
+    
     Parameters
     ----------
     alpha: array, shape = len(a)
@@ -375,6 +387,7 @@ def get_plan_from_semi_dual(alpha, b, C, regul):
         Ground cost matrix.
     regul: Regularization object
         Should implement a delta_Omega(X) method.
+        
     Returns
     -------
     T: array, shape = len(a) x len(b)
-- 
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