Add montecarloVisualisation script

src/test/resources/evaluationVisualisation.py

@@ -16,16 +16,16 @@ parser.add_argument('daimler', metavar='daimler',
 
 args = parser.parse_args()
 
-#'D:\\Software\\Praktikum\\EMAM2Middleware\\target\\evaluation\\autopilot\\emam\\clusteringResults.json'
+
 with open(args.autopilot) as data_file:
     autopilot_data = json.load(data_file)
-#'D:\\Software\\Praktikum\\EMAM2Middleware\\target\\evaluation\\pacman\\emam\\clusteringResults.json'
+
 with open(args.pacman) as data_file:
     pacman_data = json.load(data_file)
-#'D:\\Software\\Praktikum\\EMAM2Middleware\\target\\evaluation\\supermario\\emam\\clusteringResults.json'
+
 with open(args.supermario) as data_file:
     supermario_data = json.load(data_file)
-#'D:\\Software\\Praktikum\\clusteringResults.json'
+
 with open(args.daimler) as data_file:
     daimler_data = json.load(data_file)
 

src/test/resources/montecarlovisualisation.py

+import json
+import numpy as np
+import matplotlib.pyplot as plt
+import argparse
+import math
+
+parser = argparse.ArgumentParser(description='Get json files')
+parser.add_argument('autopilot', metavar='autopilot',
+                    help='montecarloresult json location of autopilot')
+parser.add_argument('pacman', metavar='pacman',
+                    help='montecarloresult json location of pacman')
+parser.add_argument('supermario', metavar='supermario',
+                    help='montecarloresult json location of supermario')
+parser.add_argument('daimler', metavar='daimler',
+                    help='montecarloresult json location of daimlermodel')
+
+args = parser.parse_args()
+
+with open(args.autopilot) as data_file:
+    autopilot_data = json.load(data_file)
+	
+with open(args.pacman) as data_file:
+    pacman_data = json.load(data_file)
+	
+with open(args.supermario) as data_file:
+    supermario_data = json.load(data_file)
+	
+with open(args.daimler) as data_file:
+    daimler_data = json.load(data_file)
+
+	
+autopilot_montecarlodata = []	
+pacman_montecarlodata = []
+supermario_montecarlodata = []		
+i = 1
+while i < 1001:
+    autopilot_montecarlodata.append(autopilot_data[0]["MCResult(" + str(i) + ")"])
+    pacman_montecarlodata.append(pacman_data[0]["MCResult(" + str(i) + ")"])
+    supermario_montecarlodata.append(supermario_data[0]["MCResult(" + str(i) + ")"])
+    i = i + 1
+	
+daimler_montecarlodata = []	
+i = 1
+while i < 101:
+    daimler_montecarlodata.append(daimler_data[0]["MCResult(" + str(i) + ")"])
+    i = i + 1
+	
+t = np.arange(1,1001,1)
+
+fig, ax = plt.subplots()
+ax.plot(t, autopilot_montecarlodata)
+
+ax.set(xlabel='Iterations', ylabel='Score',
+       title='Montecarlo Clustering of Autopilotmodel with 3 Clusters')
+ax.grid()
+plt.show()
+
+fig, ax = plt.subplots()
+ax.plot(t, pacman_montecarlodata)
+
+ax.set(xlabel='Iterations', ylabel='Score',
+       title='Montecarlo Clustering of Pacmanmodel with 3 Clusters')
+ax.grid()
+plt.show()
+
+fig, ax = plt.subplots()
+ax.plot(t, supermario_montecarlodata)
+
+ax.set(xlabel='Iterations', ylabel='Score',
+       title='Montecarlo Clustering of Supermariomodel with 3 Clusters')
+ax.grid()
+plt.show()
+
+t = np.arange(0,100,1)
+fig, ax = plt.subplots()
+ax.plot(t, daimler_montecarlodata)
+
+ax.set(xlabel='Iterations', ylabel='Score',
+       title='Montecarlo Clustering of Daimlermodel')
+ax.grid()
+plt.show()
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