plotting

code/soup.py

@@ -1,9 +1,5 @@
 import random
-import copy
 
-from tqdm import tqdm
-
-from experiment import *
 from network import *
 
 
@@ -21,7 +17,19 @@ class Soup:
         self.params = dict(attacking_rate=0.1, train_other_rate=0.1, train=0)
         self.params.update(kwargs)
         self.time = 0
-    
+
+    def __copy__(self):
+        copy_ = Soup(self.size, self.generator, **self.params)
+        copy_.__dict__ = {attr: self.__dict__[attr] for attr in self.__dict__ if
+                          attr not in ['particles', 'historical_particles']}
+        return copy_
+
+    def without_particles(self):
+        self_copy = copy.copy(self)
+        # self_copy.particles = [particle.states for particle in self.particles]
+        self_copy.historical_particles = {key: val.states for key, val in self.historical_particles.items()}
+        return self_copy
+
     def with_params(self, **kwargs):
         self.params.update(kwargs)
         return self
@@ -94,6 +102,7 @@ class Soup:
             particle.print_weights()
             print(particle.is_fixpoint())
 
+
 class ParticleDecorator:
     
     next_uid = 0
@@ -131,7 +140,6 @@ class ParticleDecorator:
         return self.states
         
 
-
 if __name__ == '__main__':
     if False:
         with SoupExperiment() as exp:
@@ -155,12 +163,11 @@ if __name__ == '__main__':
                 # 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(10, net_generator).with_params(remove_divergent=True, remove_zero=True, train=200)
+                soup = Soup(10, net_generator).with_params(remove_divergent=True, remove_zero=True, train=10)
                 soup.seed()
-                for _ in tqdm(range(10)):
+                for _ in tqdm(range(100)):
                     soup.evolve()
                     soup.print_all()
                 exp.log(soup.count())
-                exp.save(soup=soup) # you can access soup.historical_particles[particle_uid].states[time_step]['loss']
+                exp.save(soup=soup.without_particles())  # you can access soup.historical_particles[particle_uid].states[time_step]['loss']
                                      #             or soup.historical_particles[particle_uid].states[time_step]['weights'] from soup.dill
-