fix methods

1 files changed, 88 insertions, 31 deletions

diff --git a/Wrappers/Python/ccpi/optimisation/functions/IndicatorBox.py b/Wrappers/Python/ccpi/optimisation/functions/IndicatorBox.py
index df8dc89..dd2162c 100755
--- a/Wrappers/Python/ccpi/optimisation/functions/IndicatorBox.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/IndicatorBox.py
@@ -1,21 +1,25 @@
-# -*- 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 2019 Science Technology Facilities Council
+# Copyright 2019 University of Manchester
+#
+# This work is part of the Core Imaging Library developed by Science Technology
+# Facilities Council and University of Manchester
+#
+# 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.txt
+#
+# 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.
+#
+#=========================================================================
 
-#   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.
 from ccpi.optimisation.functions import Function
 import numpy
 
@@ -43,23 +47,76 @@ class IndicatorBox(Function):
             val = numpy.inf
         return val
     
-    def prox(self,x,tau=None):
-        return  (x.maximum(self.lower)).minimum(self.upper)
+    def gradient(self,x):
+        return ValueError('Not Differentiable') 
+    
+    def convex_conjugate(self,x):
+        # support function sup <x^*, x>
+        return 0 
     
     def proximal(self, x, tau, out=None):
+        
         if out is None:
-            return self.prox(x, tau)
+            return (x.maximum(self.lower)).minimum(self.upper)        
+        else:                   
+            x.maximum(self.lower, out=out)
+            out.minimum(self.upper, out=out) 
+            
+    def proximal_conjugate(self, x, tau, out=None):
+
+        if out is None:
+            
+            return x - tau * self.proximal(x/tau, tau)
+        
         else:
-            if not x.shape == out.shape:
-                raise ValueError('Norm1 Incompatible size:',
-                                 x.shape, out.shape)
-            #(x.abs() - tau*self.gamma).maximum(0) * x.sign()
-            x.abs(out = out)
-            out.__isub__(tau*self.gamma)
-            out.maximum(0, out=out)
-            if self.sign_x is None or not x.shape == self.sign_x.shape:
-                self.sign_x = x.sign()
-            else:
-                x.sign(out=self.sign_x)
+            
+            self.proximal(x/tau, tau, out=out)
+            out *= -1*tau
+            out += x
+
+            
+            
+if __name__ == '__main__':  
+
+    from ccpi.framework import ImageGeometry
+
+    N, M = 2,3
+    ig = ImageGeometry(voxel_num_x = N, voxel_num_y = M)            
+    
+    u = ig.allocate('random_int')
+    tau = 10
+    
+    f = IndicatorBox(2, 3)
+    
+    lower = 10
+    upper = 30
+    
+    x = u
+    
+    z1 = (x.maximum(lower)).minimum(upper)
+    
+    z2 = x - tau * ((x/tau).maximum(lower)).minimum(upper)
+    
+    z = z1 + z2/tau
+    
+    print(z.array, x.array)
+    
+    
+#    prox = f.proximal(u, tau)
+#    prox_conj = f.proximal_conjugate(u/tau, tau) 
+#    
+#    
+#    z = prox + tau * prox_conj
+#    print(z.as_array(), u.array)
+    
+    
+#    x - tau * ((x/tau).maximum(self.lower)).minimum(self.upper) + 
+            
+            
+            
+
+
                 
-            out.__imul__( self.sign_x )
+            
+            
+