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| author | Daniil Kazantsev <dkazanc3@googlemail.com> | 2018-10-18 13:45:36 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2018-10-18 13:45:36 +0100 |
| commit | 0cb81711927adee9f2d1973a8af2b7799dd28ab6 (patch) | |
| tree | cfe147d9c927ecdc30bee46e0f739a5f430939a4 | |
| parent | 5b0077ea9531f5212d82868eeb63a9a574479594 (diff) | |
| parent | c57314828e648fc9d206ff2fb0224fcf526f643d (diff) | |
| download | framework-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.py | 12 |
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 ,\ |