diff --git a/code/network.py b/code/network.py
index 591f7be..e9d3a33 100644
--- a/code/network.py
+++ b/code/network.py
@@ -173,19 +173,36 @@ class AggregatingNeuralNetwork(NeuralNetwork):
     def deaggregate_identically(aggregate, amount):
         return [aggregate for _ in range(amount)]
     
+    @staticmethod
+    def shuffle_not(weights_list):
+        return weights_list
+    
+    @staticmethod
+    def shuffle_random(weights_list):
+        import random
+        random.shuffle(weights_list)
+        return weights_list
+    
     def __init__(self, aggregates, width, depth, **kwargs):
         super().__init__(**kwargs)
+        self.aggregates = aggregates
         self.width = width
         self.depth = depth
-        self.aggregates = aggregates
-        self.aggregator = self.params.get('aggregator', self.__class__.aggregate_average)
-        self.deaggregator = self.params.get('deaggregator', self.__class__.deaggregate_identically)
         self.model = Sequential()
         self.model.add(Dense(units=width, input_dim=self.aggregates, **self.keras_params))
         for _ in range(depth-1):
             self.model.add(Dense(units=width, **self.keras_params))
         self.model.add(Dense(units=self.aggregates, **self.keras_params))
     
+    def get_aggregator(self):
+        return self.params.get('aggregator', self.__class__.aggregate_average)
+        
+    def get_deaggregator(self):
+        return self.params.get('deaggregator', self.__class__.deaggregate_identically)
+        
+    def get_shuffler(self):
+        return self.params.get('shuffler', self.__class__.shuffle_not)
+    
     def get_amount_of_weights(self):
         total_weights = 0
         for layer_id,layer in enumerate(self.get_weights()):
@@ -215,15 +232,16 @@ class AggregatingNeuralNetwork(NeuralNetwork):
         collections[-1] += next_collection
         leftovers = len(next_collection)
         # call network
-        old_aggregations = [self.aggregator(collection) for collection in collections]
+        old_aggregations = [self.get_aggregator()(collection) for collection in collections]
         new_aggregations = self.apply(*old_aggregations)
         # generate list of new weights
         new_weights_list = []
         for aggregation_id,aggregation in enumerate(new_aggregations):
             if aggregation_id == self.aggregates - 1:
-                new_weights_list += self.deaggregator(aggregation, collection_size + leftovers)
+                new_weights_list += self.get_deaggregator()(aggregation, collection_size + leftovers)
             else:
-                new_weights_list += self.deaggregator(aggregation, collection_size)
+                new_weights_list += self.get_deaggregator()(aggregation, collection_size)
+        new_weights_list = self.get_shuffler()(new_weights_list)
         # write back new weights
         new_weights = copy.deepcopy(old_weights)
         current_weight_id = 0
@@ -286,8 +304,8 @@ if __name__ == '__main__':
         with FixpointExperiment() as exp:
             for run_id in tqdm(range(100)):
                 # net = WeightwiseNeuralNetwork(2, 2).with_keras_params(activation='linear')
-                # net = AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='linear').with_params(print_all_weight_updates=False)
-                net = RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params(print_all_weight_updates=True)
+                net = AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='linear').with_params(shuffler=AggregatingNeuralNetwork.shuffle_random, print_all_weight_updates=False)
+                # net = RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params(print_all_weight_updates=True)
                 # net.print_weights()
                 exp.run_net(net, 100)
             exp.log(exp.counters)