change to ZeroFunction

1 files changed, 61 insertions, 0 deletions

diff --git a/Wrappers/Python/ccpi/optimisation/functions/ZeroFunction.py b/Wrappers/Python/ccpi/optimisation/functions/ZeroFunction.py
new file mode 100644
index 0000000..cce519a
--- /dev/null
+++ b/Wrappers/Python/ccpi/optimisation/functions/ZeroFunction.py
@@ -0,0 +1,61 @@
+# -*- 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 Evangelos Papoutsellis 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.
+
+from ccpi.optimisation.functions import Function
+from ccpi.framework import BlockDataContainer 
+
+class ZeroFunction(Function):
+    
+    ''' ZeroFunction: f(x) = 0
+    
+    
+    '''
+    
+    def __init__(self):
+        super(ZeroFunction, self).__init__()
+              
+    def __call__(self,x):
+        return 0
+    
+    def convex_conjugate(self, x):
+        
+        ''' This is the support function sup <x, x^{*}>  which in fact is the 
+        indicator function for the set = {0}
+        So 0 if x=0, or inf if x neq 0                
+        '''
+        
+        if x.shape[0]==1:
+            return x.maximum(0).sum()
+        else:
+            if isinstance(x, BlockDataContainer):
+                return x.get_item(0).maximum(0).sum() + x.get_item(1).maximum(0).sum()
+            else:
+                return x.maximum(0).sum() + x.maximum(0).sum()
+    
+    def proximal(self, x, tau, out=None):
+        if out is None:
+            return x.copy()
+        else:
+            out.fill(x)
+        
+    def proximal_conjugate(self, x, tau, out = None):
+        if out is None:
+            return 0
+        else:
+            return 0