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# -*- coding: utf-8 -*-
# This work is part of the Core Imaging Library developed by
# Visual Analytics and Imaging System Group of the Science Technology
# Facilities Council, STFC
# Copyright 2018-2019 Jakob Jorgensen, Daniil Kazantsev and Edoardo Pasca
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from ccpi.optimisation.functions.ScaledFunction import ScaledFunction
class Function(object):
'''Abstract class representing a function
Members:
L is the Lipschitz constant of the gradient of the Function
'''
def __init__(self):
self.L = None
def __call__(self,x, out=None):
'''Evaluates the function at x '''
raise NotImplementedError
def gradient(self, x, out=None):
'''Returns the gradient of the function at x, if the function is differentiable'''
raise NotImplementedError
def proximal(self, x, tau, out=None):
'''This returns the proximal operator for the function at x, tau'''
raise NotImplementedError
def convex_conjugate(self, x, out=None):
'''This evaluates the convex conjugate of the function at x'''
raise NotImplementedError
def proximal_conjugate(self, x, tau, out = None):
'''This returns the proximal operator for the convex conjugate of the function at x, tau'''
raise NotImplementedError
def grad(self, x):
'''Alias of gradient(x,None)'''
warnings.warn('''This method will disappear in following
versions of the CIL. Use gradient instead''', DeprecationWarning)
return self.gradient(x, out=None)
def prox(self, x, tau):
'''Alias of proximal(x, tau, None)'''
warnings.warn('''This method will disappear in following
versions of the CIL. Use proximal instead''', DeprecationWarning)
return self.proximal(x, tau, out=None)
def __rmul__(self, scalar):
'''Defines the multiplication by a scalar on the left
returns a ScaledFunction'''
return ScaledFunction(self, scalar)
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