removed printing (again)

main.py

@@ -124,7 +124,6 @@ def get_step_size(self, algorithm):
         if self.counter < self.steps: # or algorithm.iteration == self.update_interval:
             if self.f_x is None:
                 self.f_x = algorithm.f(algorithm.solution) + algorithm.g(algorithm.solution)
-            print(f"Current function value: {self.f_x}")
             precond_grad = algorithm.preconditioner.apply(algorithm, algorithm.gradient_update)
             g_norm = algorithm.gradient_update.dot(precond_grad)
 
@@ -137,14 +136,11 @@ def get_step_size(self, algorithm):
                 x_new = algorithm.solution.copy().sapyb(1, precond_grad, -step_size)
                 algorithm.g.proximal(x_new, step_size, out=x_new)
                 f_x_new = algorithm.f(x_new) + algorithm.g(x_new)
-                print("f_x_new: ", f_x_new)
                 # Armijo condition check
                 if f_x_new <= self.f_x - self.tol * step_size * g_norm:
                     self.f_x = f_x_new
                     break
 
-                print(f"step_size: {step_size}")
-
                 # Reduce step size
                 step_size *= self.beta
 
@@ -154,13 +150,11 @@ def get_step_size(self, algorithm):
             if self.counter < self.steps:
                 self.counter += 1
 
-            print(f"Armijo step size: {step_size}")
             return step_size
 
         # Apply linear decay if Armijo steps are done
         if self.linear_decay is None: # or algorithm.iteration == self.update_interval:
             self.f_x = None
-            print(f"Linear decay with decay rate: {self.decay} and step size: {self.step_size}")
             self.linear_decay = LinearDecayStepSizeRule(self.step_size, self.decay)
 
         # Return decayed step size
@@ -231,7 +225,6 @@ def __init__(self, data: Dataset, update_objective_interval=10):
         decay_perc = 0.1
         decay = (1/(1-decay_perc) - 1)/update_interval
         beta = 0.5
-        print(self.num_subsets)
 
         data_subs, acq_models, obj_funs = partitioner.data_partition(data.acquired_data, data.additive_term,
                                                                     data.mult_factors, self.num_subsets, mode='staggered',
@@ -241,7 +234,7 @@ def __init__(self, data: Dataset, update_objective_interval=10):
         data.prior.set_penalisation_factor(data.prior.get_penalisation_factor() / len(obj_funs))
         data.prior.set_up(data.OSEM_image)
 
-        for f, d in zip(obj_funs, data_subs): # add prior to every objective function
+        for f in obj_funs: # add prior evenly to every objective function
             f.set_prior(data.prior)
 
         sampler = Sampler.random_without_replacement(len(obj_funs))