tried out some stuff

code/network.py

@@ -177,6 +177,13 @@ class AggregatingNeuralNetwork(NeuralNetwork):
             count += 1
         return total / float(count)
     
+    @staticmethod
+    def aggregate_max(weights):
+        max_found = weights[0]
+        for weight in weights:
+            max_found = weight > max_found and weight or max_found
+        return max_found
+    
     @staticmethod
     def deaggregate_identically(aggregate, amount):
         return [aggregate for _ in range(amount)]
@@ -307,11 +314,12 @@ class RecurrentNeuralNetwork(NeuralNetwork):
         return new_weights
                     
 
+
 if __name__ == '__main__':
     with FixpointExperiment() as exp:
         for run_id in tqdm(range(100)):
             # net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')
-            net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation='linear').with_params(shuffler=AggregatingNeuralNetwork.shuffle_random, print_all_weight_updates=False)
+            net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation='linear').with_params(shuffler=AggregatingNeuralNetwork.shuffle_random, print_all_weight_updates=False, use_bias=True)
             # net = RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear').with_params(print_all_weight_updates=True)
             # net.print_weights()
             exp.run_net(net, 100)

code/soup.py

@@ -33,6 +33,11 @@ class Soup:
                     other_particle_id = int(prng() * len(self.particles))
                     other_particle = self.particles[other_particle_id]
                     particle.attack(other_particle)
+                if self.params.get('remove_divergent') and particle.is_diverged():
+                    self.particles[particle_id] = self.generator()
+                if self.params.get('remove_zero') and particle.is_zero():
+                    self.particles[particle_id] = self.generator()
+
     
     def count(self):
         counters = dict(divergent=0, fix_zero=0, fix_other=0, fix_sec=0, other=0)
@@ -53,9 +58,12 @@ class Soup:
 
 if __name__ == '__main__':
     with SoupExperiment() as exp:
-        for run_id in tqdm(range(1)):
-            net_generator = lambda: AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='linear').with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
-            soup = Soup(100, net_generator)
+        for run_id in range(1):
+            net_generator = lambda: WeightwiseNeuralNetwork(2, 2).with_keras_params(activation='sigmoid').with_params()
+            # net_generator = lambda: AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='sigmoid').with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
+            # net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
+            soup = Soup(100, net_generator).with_params(remove_divergent=True, remove_zero=True)
             soup.seed()
-            soup.evolve(100)
+            for _ in tqdm(range(100)):
+                soup.evolve()
             exp.log(soup.count())

code/test.py

@@ -0,0 +1,32 @@
+from experiment import *
+from network import *
+from soup import *
+import numpy as np
+
+def vary(e=0.0, f=0.0):
+    return [
+        np.array([[1.0+e, 0.0+f], [0.0+f, 0.0+f], [0.0+f, 0.0+f], [0.0+f, 0.0+f]], dtype=np.float32),
+        np.array([[1.0+e, 0.0+f], [0.0+f, 0.0+f]], dtype=np.float32),
+        np.array([[1.0+e], [0.0+f]], dtype=np.float32)
+    ]
+
+net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='sigmoid')
+if False:
+    net.set_weights([
+        np.array([[1.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]], dtype=np.float32),
+        np.array([[1.0, 0.0], [0.0, 0.0]], dtype=np.float32),
+        np.array([[1.0], [0.0]], dtype=np.float32)
+    ])
+    print(net.get_weights())
+    net.self_attack(100)
+    print(net.get_weights())
+    print(net.is_fixpoint())
+if True:
+    net.set_weights(vary(0.01, 0.0))
+    print(net.get_weights())
+    for _ in range(5):
+        net.self_attack()
+        print(net.get_weights())
+    print(net.is_fixpoint())
+    
+