bar plots

code/setups/mixed-self-fixpoints.py

@@ -1,6 +1,7 @@
 import sys
+import os
 
-sys.path += ['../', './']
+from typing import Tuple
 
 from util import *
 from experiment import *
@@ -9,10 +10,32 @@ from network import *
 import keras.backend
 
 
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
+
+
 def generate_counters():
+    """
+    Initial build of the counter dict, to store counts.
+
+    :rtype: dict
+    :return: dictionary holding counter for: 'divergent', 'fix_zero', 'fix_sec', 'other'
+    """
     return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
 
+
 def count(counters, net, notable_nets=[]):
+    """
+    Count the occurences ot the types of weight trajectories.
+
+    :param counters:      A counter dictionary.
+    :param net:           A Neural Network
+    :param notable_nets:  A list to store and save intersting candidates
+
+    :rtype      Tuple[dict, list]
+    :return:    Both the counter dictionary and the list of interessting nets.
+    """
+
     if net.is_diverged():
         counters['divergent'] += 1
     elif net.is_fixpoint():
@@ -28,6 +51,7 @@ def count(counters, net, notable_nets=[]):
         counters['other'] += 1
     return counters, notable_nets
 
+
 with Experiment('training_fixpoint') as exp:
     exp.trials = 20
     exp.selfattacks = 4
@@ -69,4 +93,4 @@ with Experiment('training_fixpoint') as exp:
     for exp_id, name in enumerate(all_names):
         exp.log(all_names[exp_id])
         exp.log(all_data[exp_id])
-        exp.log('\n')
+        exp.log('\n')