diff options
| -rw-r--r-- | Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py | 64 |
1 files changed, 41 insertions, 23 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py b/Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py index 8895f3d..e788d5a 100644 --- a/Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py +++ b/Wrappers/Python/ccpi/optimisation/functions/FunctionOperatorComposition.py @@ -50,41 +50,59 @@ class FunctionOperatorComposition(Function): self.operator.adjoint(tmp, out=out) - - - #TODO do not know if we need it - #def call_adjoint(self, x): - # - # return self.function(self.operator.adjoint(x)) - - - #def convex_conjugate(self, x): - # - # ''' convex_conjugate does not take into account the Operator''' - # return self.function.convex_conjugate(x) - - - if __name__ == '__main__': - from ccpi.framework import ImageGeometry + from ccpi.framework import ImageGeometry, AcquisitionGeometry from ccpi.optimisation.operators import Gradient from ccpi.optimisation.functions import L2NormSquared + from ccpi.astra.ops import AstraProjectorSimple + import numpy as np - M, N, K = 2,3 + M, N= 50, 50 ig = ImageGeometry(voxel_num_x=M, voxel_num_y = N) - G = Gradient(ig) + detectors = N + angles_num = N + det_w = 1.0 + + angles = np.linspace(0, np.pi, angles_num, endpoint=False) + ag = AcquisitionGeometry('parallel', + '2D', + angles, + detectors,det_w) + + + Aop = AstraProjectorSimple(ig, ag, 'cpu') + + u = ig.allocate('random_int') + u1 = ig.allocate('random_int') + b = Aop.direct(u1) + + +# G = Gradient(ig) alpha = 0.5 - f = L2NormSquared() - f_comp = FunctionOperatorComposition(G, alpha * f) - x = ig.allocate('random_int') - print(f_comp.gradient(x).shape + f1 = alpha * L2NormSquared(b=b) + + f_comp = FunctionOperatorComposition(f1, Aop) + + print(f_comp(u)) + + + z1 = Aop.direct(u) + tmp = 0.5 * ((z1 - b)**2).sum() + + + print(tmp) + + + + + + - ) |