plot the figure after the simualtion

.gitignore

@@ -4,8 +4,10 @@
 .*.js
 .*/*.js
 .*/*/*.js
-bike/__pycache__/*
+src/__pycache__/*
 debug.log
 data/*
-
+src/__pycache__/gen.cpython-38.pyc
+src/__pycache__/genetic_algo.cpython-38.pyc
+src/__pycache__/simu_bike.cpython-38.pyc
 images/popu*

src/bike.py

-import src.gen as g
+import gen as g
 import numpy as np
 import sys
 import matplotlib.pyplot as plt

src/genetic_algo.py

@@ -324,6 +324,7 @@ random.randint(start_point[1], start_point[1]+side_length)),
         # REPEAT  
         last_ten_average = [0]*10
         file_counter=0
+        score_list=[]
         while abs(average_fitness_score- np.mean(last_ten_average[-10:])) > 0.1:
             last_ten_average.append(self.p.average_fitness_score())
             self.p.save_population_to_file("data/population_"+str(file_counter)+".json")
@@ -341,7 +342,7 @@ random.randint(start_point[1], start_point[1]+side_length)),
             # UNTIL population has converged
             # STOP
             score_list.append(average_fitness_score)
-            if len(score_list)>50:
+            if len(score_list)>100:
                 break
         return score_list
         #return the fittest child among the last siblings

src/scorePlot.py

@@ -2,4 +2,6 @@ import matplotlib.pyplot as plt
 
 def plot(scores):
     plt.plot(range( len(scores)),scores,'--b')
+    plt.xlabel('Generation')
+    plt.ylabel('Ave. score')
     plt.show()