From cfe16a4d31f4c6d1748edadcfc0706bd6f9ee7cf Mon Sep 17 00:00:00 2001
From: epapoutsellis <epapoutsellis@gmail.com>
Date: Sat, 20 Apr 2019 18:48:39 +0100
Subject: changes for SymmetrizedGradient

---
 .../ccpi/optimisation/functions/MixedL21Norm.py    | 60 ++++++++--------------
 1 file changed, 22 insertions(+), 38 deletions(-)

(limited to 'Wrappers/Python')

diff --git a/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py b/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py
index 2004e5f..e8f6da4 100755
--- a/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py
@@ -43,19 +43,9 @@ class MixedL21Norm(Function):
         '''
         if not isinstance(x, BlockDataContainer):
             raise ValueError('__call__ expected BlockDataContainer, got {}'.format(type(x))) 
-                     
-        if self.SymTensor:
-            
-            #TODO fix this case
-            param = [1]*x.shape[0]
-            param[-1] = 2
-            tmp = [param[i]*(x[i] ** 2) for i in range(x.shape[0])]
-            res = sum(tmp).sqrt().sum()   
-            
-        else:
-                        
-            tmp = [ el**2 for el in x.containers ]
-            res = sum(tmp).sqrt().sum()
+                                         
+        tmp = [ el**2 for el in x.containers ]
+        res = sum(tmp).sqrt().sum()
 
         return res
                             
@@ -67,7 +57,12 @@ class MixedL21Norm(Function):
         ''' This is the Indicator function of ||\cdot||_{2, \infty}
             which is either 0 if ||x||_{2, \infty} or \infty        
         '''
+        
         return 0.0
+        
+        #tmp = [ el**2 for el in x.containers ]
+        #print(sum(tmp).sqrt().as_array().max())
+        #return sum(tmp).sqrt().as_array().max()
     
     def proximal(self, x, tau, out=None):
         
@@ -80,35 +75,24 @@ class MixedL21Norm(Function):
     
     def proximal_conjugate(self, x, tau, out=None): 
 
-        if self.SymTensor:
-            
-            param = [1]*x.shape[0]
-            param[-1] = 2
-            tmp = [param[i]*(x[i] ** 2) for i in range(x.shape[0])]
-            frac = [x[i]/(sum(tmp).sqrt()).maximum(1.0) for i in range(x.shape[0])]
-            res = BlockDataContainer(*frac) 
-            
-            return res
-        
-        else:
 
-            if out is None:                                        
-                tmp = [ el*el for el in x.containers]
-                res = sum(tmp).sqrt().maximum(1.0) 
-                frac = [el/res for el in x.containers]
-                return  BlockDataContainer(*frac)   
+        if out is None:                                        
+            tmp = [ el*el for el in x.containers]
+            res = sum(tmp).sqrt().maximum(1.0) 
+            frac = [el/res for el in x.containers]
+            return  BlockDataContainer(*frac)   
+        
             
-                
-            #TODO this is slow, why???
+        #TODO this is slow, why???
 #                return x.divide(x.pnorm().maximum(1.0))
-            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)
-                
+        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)
+            
 #                x.divide(sum([el*el for el in x.containers]).sqrt().maximum(1.0), out=out)
-                #TODO this is slow, why ???
+            #TODO this is slow, why ???
 #                 x.divide(x.pnorm().maximum(1.0), out=out)
                               
 
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