diff options
Diffstat (limited to 'Wrappers')
| -rwxr-xr-x | Wrappers/Python/ccpi/optimisation/algs.py | 12 | ||||
| -rwxr-xr-x | Wrappers/Python/ccpi/optimisation/funcs.py | 10 |
2 files changed, 11 insertions, 11 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/algs.py b/Wrappers/Python/ccpi/optimisation/algs.py index 6cae65a..a45100c 100755 --- a/Wrappers/Python/ccpi/optimisation/algs.py +++ b/Wrappers/Python/ccpi/optimisation/algs.py @@ -20,7 +20,7 @@ import numpy import time -from ccpi.optimisation.funcs import BaseFunction +from ccpi.optimisation.funcs import Function def FISTA(x_init, f=None, g=None, opt=None): '''Fast Iterative Shrinkage-Thresholding Algorithm @@ -37,8 +37,8 @@ def FISTA(x_init, f=None, g=None, opt=None): opt: additional algorithm ''' # default inputs - if f is None: f = BaseFunction() - if g is None: g = BaseFunction() + if f is None: f = Function() + if g is None: g = Function() # algorithmic parameters if opt is None: @@ -108,9 +108,9 @@ def FBPD(x_init, f=None, g=None, h=None, opt=None): opt: additional algorithm ''' # default inputs - if f is None: f = BaseFunction() - if g is None: g = BaseFunction() - if h is None: h = BaseFunction() + if f is None: f = Function() + if g is None: g = Function() + if h is None: h = Function() # algorithmic parameters if opt is None: diff --git a/Wrappers/Python/ccpi/optimisation/funcs.py b/Wrappers/Python/ccpi/optimisation/funcs.py index da6a3de..d11d6c3 100755 --- a/Wrappers/Python/ccpi/optimisation/funcs.py +++ b/Wrappers/Python/ccpi/optimisation/funcs.py @@ -21,14 +21,14 @@ from ccpi.optimisation.ops import Identity, FiniteDiff2D import numpy -class BaseFunction(object): +class Function(object): def __init__(self): self.op = Identity() def __call__(self,x): return 0 def grad(self,x): return 0 def prox(self,x,tau): return x -class Norm2(BaseFunction): +class Norm2(Function): def __init__(self, gamma=1.0, @@ -63,7 +63,7 @@ class TV2D(Norm2): # Define a class for squared 2-norm -class Norm2sq(BaseFunction): +class Norm2sq(Function): ''' f(x) = c*||A*x-b||_2^2 @@ -98,7 +98,7 @@ class Norm2sq(BaseFunction): return self.c*( ( (self.A.direct(x)-self.b)**2).sum() ) -class ZeroFun(BaseFunction): +class ZeroFun(Function): def __init__(self,gamma=0,L=1): self.gamma = gamma @@ -113,7 +113,7 @@ class ZeroFun(BaseFunction): # A more interesting example, least squares plus 1-norm minimization. # Define class to represent 1-norm including prox function -class Norm1(BaseFunction): +class Norm1(Function): def __init__(self,gamma): # Do nothing |