readme updated

README.md

@@ -1,6 +1,11 @@
 # self-rep NN paper - ALIFE journal edition
 
-- [x] Plateau / Pillar sizeWhat does happen to the fixpoints after noise introduction and retraining?Options beeing: Same Fixpoint, Similar Fixpoint (Basin), Different Fixpoint? Do they do the clustering thingy?
+- [x] Plateau / Pillar sizeWhat does happen to the fixpoints after noise introduction and retraining?Options beeing: Same Fixpoint, Similar Fixpoint (Basin), 
+    - Different Fixpoint?
+      Yes, we did not found same (10-5)
+    - Do they do the clustering thingy?
+      Kind of: Small movement towards (MIM-Distance getting smaller) parent fixpoint.
+      Small movement for everyone? -> Distribution
 
     - see `journal_basins.py` for the "train -> spawn with noise -> train again and see where they end up" functionality. Apply noise follows the `vary` function that was used in the paper robustness test with `+- prng() * eps`. Change if desired.
 
@@ -9,6 +14,9 @@
 
 - [ ] Same Thing with Soup interactionWe would expect the same behaviour...Influence of interaction with near and far away particles.
 
+- [ ] How are basins / "attractor areas" shaped?
+      - Weired.... tbc...
+
 - [x] Robustness test with a trained NetworkTraining for high quality fixpoints, compare with the "perfect" fixpoint. Average Loss per application step
     
     - see `journal_robustness.py` for robustness test modeled after cristians robustness-exp (with the exeption that we put noise on the weights). Has `synthetic` bool to switch to hand-modeled perfect fixpoint instead of naturally trained ones. 
@@ -19,7 +27,7 @@
 
 - [ ] Adjust Self Training so that it favors second order fixpoints-> Second order test implementation (?)
 
-- [ ] Barplot over clones -> how many become a fixpoint cs how many diverge per noise level
+- [x] Barplot over clones -> how many become a fixpoint cs how many diverge per noise level
 
 - [ ] Box-Plot of Avg. Distance of clones from parent
 

experiments/self_train_exp.py

@@ -55,8 +55,6 @@ class SelfTrainExperiment:
             net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
 
             for _ in range(self.epochs):
-              input_data = net.input_weight_matrix()
-              target_data = net.create_target_weights(input_data)
               net.self_train(1, self.log_step_size, self.net_learning_rate)
 
             print(f"\nLast weight matrix (epoch: {self.epochs}):\n{net.input_weight_matrix()}\nLossHistory: {net.loss_history[-10:]}")
@@ -113,5 +111,6 @@ def run_ST_experiment(population_size, batch_size, net_input_size, net_hidden_si
     summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, summary_directory_name,
                                 summary_pre_title)
 
+
 if __name__ == '__main__':
     raise NotImplementedError('Test this here!!!')

journal_robustness.py

@@ -195,7 +195,6 @@ class RobustnessComparisonExperiment:
 
             print(f"\nTime as fixpoint: ")
             # print(tabulate(time_as_fixpoint, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
-
         return time_as_fixpoint, time_to_vergence
 
     def count_fixpoints(self):