diff options
| -rwxr-xr-x | Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py | 28 | ||||
| -rwxr-xr-x | Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py | 4 |
2 files changed, 23 insertions, 9 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py b/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py index 4266e51..ed1d5e5 100755 --- a/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py +++ b/Wrappers/Python/ccpi/optimisation/functions/MixedL21Norm.py @@ -87,17 +87,29 @@ class MixedL21Norm(Function): res = BlockDataContainer(*frac) return res - -# tmp2 = np.sqrt(x.as_array()[0]**2 + x.as_array()[1]**2 + 2*x.as_array()[2]**2)/self.alpha -# res = x.divide(ImageData(tmp2).maximum(1.0)) else: +# pass + - tmp = [ el*el for el in x] - res = (sum(tmp).sqrt()).maximum(1.0) - frac = [x[i]/res for i in range(x.shape[0])] - res = BlockDataContainer(*frac) +# # tmp2 = np.sqrt(x.as_array()[0]**2 + x.as_array()[1]**2 + 2*x.as_array()[2]**2)/self.alpha +# # res = x.divide(ImageData(tmp2).maximum(1.0)) +# if out is None: + + tmp = [ el*el for el in x] + res = (sum(tmp).sqrt()).maximum(1.0) + frac = [x[i]/res for i in range(x.shape[0])] + res = BlockDataContainer(*frac) - return res + return res + # else: + # tmp = [ el*el for el in x] + # res = (sum(tmp).sqrt()).maximum(1.0) + # #frac = [x[i]/res for i in range(x.shape[0])] + # for i in range(x.shape[0]): + # a = out.get_item(i) + # b = x.get_item(i) + # b /= res + # a.fill( b ) 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 046a4a6..9e2ba0c 100755 --- a/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py +++ b/Wrappers/Python/ccpi/optimisation/functions/ScaledFunction.py @@ -61,7 +61,9 @@ 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))
+ 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)'''
|