added ScaledFunction

2 files changed, 108 insertions, 0 deletions

diff --git a/Wrappers/Python/ccpi/optimisation/functions/Function.py b/Wrappers/Python/ccpi/optimisation/functions/Function.py
new file mode 100755
index 0000000..43ce900
--- /dev/null
+++ b/Wrappers/Python/ccpi/optimisation/functions/Function.py
@@ -0,0 +1,48 @@
+# -*- 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

+

+class Function(object):

+    '''Abstract class representing a function

+    

+    

+    '''

+    def __init__(self):

+        self.L = None

+    def __call__(self,x, out=None):

+        raise NotImplementedError

+    def call_adjoint(self, x, out=None):

+        raise NotImplementedError

+    def convex_conjugate(self, x, out=None):

+        raise NotImplementedError

+    def proximal_conjugate(self, x, tau, out = None):

+        raise NotImplementedError

+    def grad(self, x):

+        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):

+        warnings.warn('''This method will disappear in following 

+        versions of the CIL. Use proximal instead''', DeprecationWarning)

+        return self.proximal(x, out=None)

+    def gradient(self, x, out=None):

+        raise NotImplementedError

+    def proximal(self, x, tau, out=None):

+        raise NotImplementedError
\ No newline at end of file
diff --git a/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py b/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py
new file mode 100755
index 0000000..f2e39fb
--- /dev/null
+++ b/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py
@@ -0,0 +1,60 @@
+from numbers import Number

+import numpy

+

+class ScaledFunction(object):

+    '''ScaledFunction

+

+    A class to represent the scalar multiplication of an Operator with a scalar.

+    It holds an operator and a scalar. Basically it returns the multiplication

+    of the result of direct and adjoint of the operator with the scalar.

+    For the rest it behaves like the operator it holds.

+

+    Args:

+       operator (Operator): a Operator or LinearOperator

+       scalar (Number): a scalar multiplier

+    Example:

+       The scaled operator behaves like the following:

+       sop = ScaledOperator(operator, scalar)

+       sop.direct(x) = scalar * operator.direct(x)

+       sop.adjoint(x) = scalar * operator.adjoint(x)

+       sop.norm() = operator.norm()

+       sop.range_geometry() = operator.range_geometry()

+       sop.domain_geometry() = operator.domain_geometry()

+    '''

+    def __init__(self, function, scalar):

+        super(ScaledFunction, self).__init__()

+        self.L = None

+        if not isinstance (scalar, Number):

+            raise TypeError('expected scalar: got {}'.format(type(scalar)))

+        self.scalar = scalar

+        self.function = function

+

+    def __call__(self,x, out=None):

+        return self.scalar * self.function(x)

+

+    def call_adjoint(self, x, out=None):

+        return self.scalar * self.function.call_adjoint(x, out=out)

+

+    def convex_conjugate(self, x, out=None):

+        return self.scalar * self.function.convex_conjugate(x, out=out)

+

+    def proximal_conjugate(self, x, tau, out = None):

+        '''TODO check if this is mathematically correct'''

+        return self.function.proximal_conjugate(x, tau, out=out)

+

+    def grad(self, x):

+        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):

+        warnings.warn('''This method will disappear in following 

+        versions of the CIL. Use proximal instead''', DeprecationWarning)

+        return self.proximal(x, out=None)

+

+    def gradient(self, x, out=None):

+        return self.scalar * self.function.gradient(x, out=out)

+

+    def proximal(self, x, tau, out=None):

+        '''TODO check if this is mathematically correct'''

+        return self.function.proximal(x, tau, out=out)