diff options
Diffstat (limited to 'Wrappers/Python')
| -rw-r--r-- | Wrappers/Python/ccpi/optimisation/functions/FunctionComposition.py | 121 |
1 files changed, 0 insertions, 121 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/functions/FunctionComposition.py b/Wrappers/Python/ccpi/optimisation/functions/FunctionComposition.py deleted file mode 100644 index f24dc10..0000000 --- a/Wrappers/Python/ccpi/optimisation/functions/FunctionComposition.py +++ /dev/null @@ -1,121 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Wed Mar 6 19:45:06 2019 - -@author: evangelos -""" - -import numpy as np -#from ccpi.optimisation.funcs import Function -from ccpi.optimisation.functions import Function -from ccpi.framework import DataContainer, ImageData, ImageGeometry -from ccpi.framework import BlockDataContainer -from ccpi.optimisation.functions import BlockFunction - -class FunctionOperatorComposition(Function): - - def __init__(self, function, operator): - - self.function = functions - self.operator = operator - self.grad_Lipschitz_cnst = 2*self.function.alpha*operator.norm()**2 - super(FunctionOperatorComposition, self).__init__() - - def __call__(self, x): - - return self.function(operator.direct(x)) - - def call_adjoint(self, x): - - return self.function(operator.adjoint(x)) - - def convex_conjugate(self, x): - - return self.function.convex_conjugate(x) - - def proximal(self, x, tau): - - ''' proximal does not take into account the Operator''' - - return self.function.proximal(x, tau, out=None) - - def proximal_conjugate(self, x, tau): - - ''' proximal conjugate does not take into account the Operator''' - - return self.function.proximal_conjugate(x, tau, out=None) - - def gradient(self, x): - - ''' Gradient takes into account the Operator''' - - return self.adjoint(self.function.gradient(self.operator.direct(x))) - - - - - -class FunctionComposition_new(Function): - - def __init__(self, operator, *functions): - - self.functions = functions - self.operator = operator - self.length = len(self.functions) - - super(FunctionComposition_new, self).__init__() - - def __call__(self, x): - - if isinstance(x, ImageData): - x = CompositeDataContainer(x) - - t = 0 - for i in range(x.shape[0]): - t += self.functions[i](x.get_item(i)) - return t - - def convex_conjugate(self, x): - - if isinstance(x, ImageData): - x = CompositeDataContainer(x) - - t = 0 - for i in range(x.shape[0]): - t += self.functions[i].convex_conjugate(x.get_item(i)) - return t - - def proximal_conjugate(self, x, tau, out = None): - - if isinstance(x, ImageData): - x = CompositeDataContainer(x) - - out = [None]*self.length - for i in range(self.length): - out[i] = self.functions[i].proximal_conjugate(x.get_item(i), tau) - - if self.length==1: - return ImageData(*out) - else: - return CompositeDataContainer(*out) - - def proximal(self, x, tau, out = None): - - if isinstance(x, ImageData): - x = CompositeDataContainer(x) - - out = [None]*self.length - for i in range(self.length): - out[i] = self.functions[i].proximal(x.get_item(i), tau) - - if self.length==1: - return ImageData(*out) - else: - return CompositeDataContainer(*out) - - -if __name__ == '__main__': - - from operators import Operator - from IdentityOperator import Identity
\ No newline at end of file |