fix memopt and docstrings

5 files changed, 95 insertions, 83 deletions

diff --git a/Wrappers/Python/ccpi/framework/BlockDataContainer.py b/Wrappers/Python/ccpi/framework/BlockDataContainer.py
index 529a1ce..75ee4b2 100755
--- a/Wrappers/Python/ccpi/framework/BlockDataContainer.py
+++ b/Wrappers/Python/ccpi/framework/BlockDataContainer.py
@@ -97,7 +97,7 @@ class BlockDataContainer(object):
                     a = el.is_compatible(other)
                 else:
                     a = el.shape == other.shape
-                print ("current element" , el.shape, "other ", other.shape, "same shape" , a)
+#                print ("current element" , el.shape, "other ", other.shape, "same shape" , a)
                 ret = ret and a
             return ret
             #return self.get_item(0).shape == other.shape
@@ -468,3 +468,7 @@ class BlockDataContainer(object):
         '''Inline truedivision'''
         return self.__idiv__(other)
 
+
+
+
+
diff --git a/Wrappers/Python/ccpi/optimisation/functions/L1Norm.py b/Wrappers/Python/ccpi/optimisation/functions/L1Norm.py
index 163eefa..4e53f2c 100644
--- a/Wrappers/Python/ccpi/optimisation/functions/L1Norm.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/L1Norm.py
@@ -16,11 +16,82 @@
 #   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.
-"""
-Created on Wed Mar  6 19:42:34 2019
 
-@author: evangelos
-"""
+
+from ccpi.optimisation.functions import Function
+from ccpi.optimisation.functions.ScaledFunction import ScaledFunction        
+from ccpi.optimisation.operators import ShrinkageOperator 
+ 
+
+class L1Norm(Function):
+    
+    ''' 
+    
+    Class: L1Norm
+        
+    Cases:  a) f(x) = ||x||_{1}
+    
+            b) f(x) = ||x - b||_{1}
+                             
+    '''      
+    
+    def __init__(self, **kwargs):
+        
+        super(L1Norm, self).__init__()
+        self.b = kwargs.get('b',None) 
+        
+    def __call__(self, x):
+        
+        ''' Evaluate L1Norm at x: f(x) '''
+        
+        y = x
+        if self.b is not None: 
+            y = x - self.b
+        return y.abs().sum()  
+    
+    def gradient(self,x):
+        #TODO implement subgradient???
+        return ValueError('Not Differentiable')   
+    
+    def convex_conjugate(self,x):
+        #TODO implement Indicator infty???
+
+        y = 0        
+        if self.b is not None:
+            y =  0 + (self.b * x).sum()
+        return y  
+    
+    def proximal(self, x, tau, out=None):
+        
+        # TODO implement shrinkage operator, we will need it later e.g SplitBregman
+        
+        if out is None:
+            if self.b is not None:
+                return self.b + ShrinkageOperator.__call__(self, x - self.b, tau)
+            else:
+                return ShrinkageOperator.__call__(self, x, tau)             
+        else:
+            if self.b is not None:
+                out.fill(self.b + ShrinkageOperator.__call__(self, x - self.b, tau))
+            else:
+                out.fill(ShrinkageOperator.__call__(self, x, tau))
+                                    
+    def proximal_conjugate(self, x, tau, out=None):
+        
+        if out is None:
+            if self.b is not None:
+                return (x - tau*self.b).divide((x - tau*self.b).abs().maximum(1.0))
+            else:
+                return x.divide(x.abs().maximum(1.0))
+        else:
+            if self.b is not None:
+                out.fill((x - tau*self.b).divide((x - tau*self.b).abs().maximum(1.0)))
+            else:
+                out.fill(x.divide(x.abs().maximum(1.0)) )                
+            
+    def __rmul__(self, scalar):
+        return ScaledFunction(self, scalar)
+
 
 #import numpy as np
 ##from ccpi.optimisation.funcs import Function
@@ -92,67 +163,7 @@ Created on Wed Mar  6 19:42:34 2019
 #        
 
 ###############################################################################                
-from ccpi.optimisation.functions import Function
-from ccpi.optimisation.functions.ScaledFunction import ScaledFunction        
-from ccpi.optimisation.operators import ShrinkageOperator 
- 
-
-class L1Norm(Function):
-    
-    def __init__(self, **kwargs):
-        
-        super(L1Norm, self).__init__()
-        self.b = kwargs.get('b',None) 
-        
-    def __call__(self, x):
-        
-        y = x
-        if self.b is not None: 
-            y = x - self.b
-        return y.abs().sum()  
-    
-    def gradient(self,x):
-        #TODO implement subgradient???
-        return ValueError('Not Differentiable')   
-    
-    def convex_conjugate(self,x):
-        #TODO implement Indicator infty???
-
-        y = 0        
-        if self.b is not None:
-            y =  0 + (self.b * x).sum()
-        return y  
-    
-    def proximal(self, x, tau, out=None):
-        
-        # TODO implement shrinkage operator, we will need it later e.g SplitBregman
-        
-        if out is None:
-            if self.b is not None:
-                return self.b + ShrinkageOperator.__call__(self, x - self.b, tau)
-            else:
-                return ShrinkageOperator.__call__(self, x, tau)             
-        else:
-            if self.b is not None:
-                out.fill(self.b + ShrinkageOperator.__call__(self, x - self.b, tau))
-            else:
-                out.fill(ShrinkageOperator.__call__(self, x, tau))
-                                    
-    def proximal_conjugate(self, x, tau, out=None):
-        
-        if out is None:
-            if self.b is not None:
-                return (x - tau*self.b).divide((x - tau*self.b).abs().maximum(1.0))
-            else:
-                return x.divide(x.abs().maximum(1.0))
-        else:
-            if self.b is not None:
-                out.fill((x - tau*self.b).divide((x - tau*self.b).abs().maximum(1.0)))
-            else:
-                out.fill(x.divide(x.abs().maximum(1.0)) )                
-            
-    def __rmul__(self, scalar):
-        return ScaledFunction(self, scalar)             
+             
                 
 
 
diff --git a/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py b/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py
index 903dafa..7397cfb 100644
--- a/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py
@@ -17,19 +17,16 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
-import numpy
 from ccpi.optimisation.functions import Function
 from ccpi.optimisation.functions.ScaledFunction import ScaledFunction
 
 class L2NormSquared(Function):
     
-    ''' 
-    
-    Class: L2NormSquared 
-        
-    Cases:  a) f(x) = ||x||^{2}
+    '''
+            
+    Cases:  a) f(x) = \|x\|^{2}_{2}
     
-            b) f(x) = ||x - b||^{2}, b
+            b) f(x) = ||x - b||^{2}_{2}
                              
     '''    
     
@@ -50,9 +47,7 @@ class L2NormSquared(Function):
         except AttributeError as ae:
             # added for compatibility with SIRF 
             return (y.norm()**2)
-        
-            
-        
+                
     def gradient(self, x, out=None):        
         
         ''' Evaluate gradient of L2NormSquared at x: f'(x) '''
@@ -127,11 +122,10 @@ class L2NormSquared(Function):
                                         
     def __rmul__(self, scalar):
         
-        ''' Allows multiplication of L2NormSquared with a scalar
+        ''' Multiplication of L2NormSquared with a scalar
         
         Returns: ScaledFunction
-        
-                
+                        
         '''
         
         return ScaledFunction(self, scalar)        
@@ -139,7 +133,8 @@ class L2NormSquared(Function):
 
 if __name__ == '__main__':
     
-    
+    from ccpi.framework import ImageGeometry
+    import numpy
     # TESTS for L2 and scalar * L2
     
     M, N, K = 2,3,5
diff --git a/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py b/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py
index 24c47f4..c0e8a6a 100755
--- a/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py
@@ -99,11 +99,12 @@ class MixedL21Norm(Function):
                 res = BlockDataContainer(*frac)    
                 return res
             else:
+                
+                
                 res1 = functools.reduce(lambda a,b: a + b*b, x.containers, x.get_item(0) * 0 )
                 res = res1.sqrt().maximum(1.0)
                 x.divide(res, out=out)
-                #for i,el in enumerate(x.containers):
-                #    el.divide(res, out=out.get_item(i))
+                              
 
     def __rmul__(self, scalar):
         return ScaledFunction(self, scalar) 
diff --git a/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py b/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py
index 464b944..3fbb858 100755
--- a/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py
@@ -61,7 +61,8 @@ class ScaledFunction(object):
         if out is None:

             return self.scalar * self.function.proximal_conjugate(x/self.scalar, tau/self.scalar)

         else:

-            out.fill(self.scalar*self.function.proximal_conjugate(x/self.scalar, tau/self.scalar))

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

+            out *= self.scalar

 

     def grad(self, x):

         '''Alias of gradient(x,None)'''