- rearanged experiments.py into single runable files

- Reformated net.self_x functions (sa, st)
- corrected robustness_exp.py
- NO DEBUGGING DONE!!!!!

experiments/self_application_exp.py

@@ -0,0 +1,120 @@
+import os.path
+import pickle
+
+from tqdm import tqdm
+
+from experiments.helpers import check_folder, summary_fixpoint_experiment
+from functionalities_test import test_for_fixpoints
+from network import Net
+from visualization import bar_chart_fixpoints
+from visualization import plot_3d_self_application
+
+
+class SelfApplicationExperiment:
+    def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size,
+                 net_learning_rate, application_steps, train_nets, directory_name, training_steps
+                 ) -> None:
+        self.population_size = population_size
+        self.log_step_size = log_step_size
+        self.net_input_size = net_input_size
+        self.net_hidden_size = net_hidden_size
+        self.net_out_size = net_out_size
+
+        self.net_learning_rate = net_learning_rate
+        self.SA_steps = application_steps  #
+
+        self.train_nets = train_nets
+        self.ST_steps = training_steps
+
+        self.directory_name = directory_name
+        os.mkdir(self.directory_name)
+
+        """ Creating the nets & making the SA steps & (maybe) also training the networks. """
+        self.nets = []
+        # Create population:
+        self.populate_environment()
+
+        self.fixpoint_counters = {
+            "identity_func": 0,
+            "divergent": 0,
+            "fix_zero": 0,
+            "fix_weak": 0,
+            "fix_sec": 0,
+            "other_func": 0
+        }
+
+        self.weights_evolution_3d_experiment()
+        self.count_fixpoints()
+
+    def populate_environment(self):
+        loop_population_size = tqdm(range(self.population_size))
+        for i in loop_population_size:
+            loop_population_size.set_description("Populating SA experiment %s" % i)
+
+            net_name = f"SA_net_{str(i)}"
+
+            net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name
+                      )
+            for _ in range(self.SA_steps):
+                input_data = net.input_weight_matrix()
+                target_data = net.create_target_weights(input_data)
+
+                if self.train_nets == "before_SA":
+                    net.self_train(1, self.log_step_size, self.net_learning_rate)
+                    net.self_application(self.SA_steps, self.log_step_size)
+                elif self.train_nets == "after_SA":
+                    net.self_application(self.SA_steps, self.log_step_size)
+                    net.self_train(1, self.log_step_size, self.net_learning_rate)
+                else:
+                    net.self_application(self.SA_steps, self.log_step_size)
+
+            self.nets.append(net)
+
+    def weights_evolution_3d_experiment(self):
+        exp_name = f"SA_{str(len(self.nets))}_nets_3d_weights_PCA"
+        plot_3d_self_application(self.nets, exp_name, self.directory_name, self.log_step_size)
+
+    def count_fixpoints(self):
+        test_for_fixpoints(self.fixpoint_counters, self.nets)
+        exp_details = f"{self.SA_steps} SA steps"
+        bar_chart_fixpoints(self.fixpoint_counters, self.population_size, self.directory_name, self.net_learning_rate,
+                            exp_details)
+
+
+def run_SA_experiment(population_size, batch_size, net_input_size, net_hidden_size, net_out_size,
+                      net_learning_rate, runs, run_name, name_hash, application_steps, train_nets, training_steps):
+    experiments = {}
+
+    check_folder("self_application")
+
+    # Running the experiments
+    for i in range(runs):
+        directory_name = f"experiments/self_application/{run_name}_run_{i}_{str(population_size)}_nets_{application_steps}_SA_{str(name_hash)}"
+
+        SA_experiment = SelfApplicationExperiment(
+            population_size,
+            batch_size,
+            net_input_size,
+            net_hidden_size,
+            net_out_size,
+            net_learning_rate,
+            application_steps,
+            train_nets,
+            directory_name,
+            training_steps
+        )
+        pickle.dump(SA_experiment, open(f"{directory_name}/full_experiment_pickle.p", "wb"))
+        experiments[i] = SA_experiment
+
+    # Building a summary of all the runs
+    directory_name = f"experiments/self_application/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{application_steps}_SA_{str(name_hash)}"
+    os.mkdir(directory_name)
+
+    summary_pre_title = "SA"
+    summary_fixpoint_experiment(runs, population_size, application_steps, experiments, net_learning_rate,
+                                directory_name,
+                                summary_pre_title)
+
+
+if __name__ == '__main__':
+    raise NotImplementedError('Test this here!!!')