summaryrefslogtreecommitdiffstats
diff options
context:
space:
mode:
authorDaniil Kazantsev <dkazanc3@googlemail.com>2018-10-18 13:45:36 +0100
committerGitHub <noreply@github.com>2018-10-18 13:45:36 +0100
commit0cb81711927adee9f2d1973a8af2b7799dd28ab6 (patch)
treecfe147d9c927ecdc30bee46e0f739a5f430939a4
parent5b0077ea9531f5212d82868eeb63a9a574479594 (diff)
parentc57314828e648fc9d206ff2fb0224fcf526f643d (diff)
downloadframework-plugins-0cb81711927adee9f2d1973a8af2b7799dd28ab6.tar.gz
framework-plugins-0cb81711927adee9f2d1973a8af2b7799dd28ab6.tar.bz2
framework-plugins-0cb81711927adee9f2d1973a8af2b7799dd28ab6.tar.xz
framework-plugins-0cb81711927adee9f2d1973a8af2b7799dd28ab6.zip
Merge pull request #17 from vais-ral/lipschitz_fix
Lipschitz has been replaced with tau
-rw-r--r--Wrappers/Python/ccpi/plugins/regularisers.py12
1 files changed, 6 insertions, 6 deletions
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 ,\