From c57314828e648fc9d206ff2fb0224fcf526f643d Mon Sep 17 00:00:00 2001
From: dkazanc <dkazanc@hotmail.com>
Date: Tue, 16 Oct 2018 17:56:44 +0100
Subject: Lipschitz has been replaced with tau

---
 Wrappers/Python/ccpi/plugins/regularisers.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

(limited to 'Wrappers/Python/ccpi')

diff --git a/Wrappers/Python/ccpi/plugins/regularisers.py b/Wrappers/Python/ccpi/plugins/regularisers.py
index d8ba997..5031f4d 100644
--- a/Wrappers/Python/ccpi/plugins/regularisers.py
+++ b/Wrappers/Python/ccpi/plugins/regularisers.py
@@ -36,10 +36,10 @@ class ROF_TV(Function):
         # evaluate objective function of TV gradient
         EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2)
         return 0.5*EnergyValTV[0]
-    def prox(self,x,Lipshitz):
+    def prox(self,x,tau):
         pars = {'algorithm' : ROF_TV, \
                'input' : np.asarray(x.as_array(), dtype=np.float32),\
-                'regularization_parameter':self.lambdaReg*Lipshitz, \
+                'regularization_parameter':self.lambdaReg*tau, \
                 'number_of_iterations' :self.iterationsTV ,\
                 'time_marching_parameter':self.time_marchstep}
         
@@ -63,10 +63,10 @@ class FGP_TV(Function):
         # evaluate objective function of TV gradient
         EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2)
         return 0.5*EnergyValTV[0]
-    def prox(self,x,Lipshitz):
+    def prox(self,x,tau):
         pars = {'algorithm' : FGP_TV, \
                'input' : np.asarray(x.as_array(), dtype=np.float32),\
-                'regularization_parameter':self.lambdaReg*Lipshitz, \
+                'regularization_parameter':self.lambdaReg*tau, \
                 'number_of_iterations' :self.iterationsTV ,\
                 'tolerance_constant':self.tolerance,\
                 'methodTV': self.methodTV ,\
@@ -96,10 +96,10 @@ class SB_TV(Function):
         # evaluate objective function of TV gradient
         EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2)
         return 0.5*EnergyValTV[0]
-    def prox(self,x,Lipshitz):
+    def prox(self,x,tau):
         pars = {'algorithm' : SB_TV, \
                'input' : np.asarray(x.as_array(), dtype=np.float32),\
-                'regularization_parameter':self.lambdaReg*Lipshitz, \
+                'regularization_parameter':self.lambdaReg*tau, \
                 'number_of_iterations' :self.iterationsTV ,\
                 'tolerance_constant':self.tolerance,\
                 'methodTV': self.methodTV ,\
-- 
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