Commit 508805e2 authored by Alexander David Hellwig's avatar Alexander David Hellwig

Merge branch 'ML_clustering' of...

Merge branch 'ML_clustering' of https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMAM2Middleware into ML_clustering
parents 27ad974a 9c12e3a1
Pipeline #111339 failed with stages
...@@ -45,37 +45,63 @@ while i < 101: ...@@ -45,37 +45,63 @@ while i < 101:
daimler_montecarlodata.append(daimler_data[0]["MCResult(" + str(i) + ")"]) daimler_montecarlodata.append(daimler_data[0]["MCResult(" + str(i) + ")"])
i = i + 1 i = i + 1
autopilot_minmax = (autopilot_data[0]["MaxValueMC"],autopilot_data[0]["MinValueMC"])
pacman_minmax = (pacman_data[0]["MaxValueMC"],pacman_data[0]["MinValueMC"])
supermario_minmax = (supermario_data[0]["MaxValueMC"],supermario_data[0]["MinValueMC"])
daimler_minmax = (daimler_data[0]["MaxValueMC"],daimler_data[0]["MinValueMC"])
t = np.arange(1,1001,1) t = np.arange(1,1001,1)
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.plot(t, autopilot_montecarlodata) ax.plot(t, autopilot_montecarlodata)
ax.set(xlabel='Iterations', ylabel='Score', ax.set(xlabel='Iterations', ylabel='Score',
title='Montecarlo Clustering of Autopilotmodel with 3 Clusters') title='Montecarlo Clustering of Autopilotmodel with 3 Clusters')
ax.grid() ax.grid()
textstr = '\n'.join((
"MaxValueMC = " + str(autopilot_minmax[1]),
"MinValueMC = " + str(autopilot_minmax[0])))
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
ax.text(0.95, 0.05, textstr, transform=ax.transAxes, fontsize=14,
verticalalignment='bottom', ha='right', bbox=props)
plt.show() plt.show()
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.plot(t, pacman_montecarlodata) ax.plot(t, pacman_montecarlodata)
ax.set(xlabel='Iterations', ylabel='Score', ax.set(xlabel='Iterations', ylabel='Score',
title='Montecarlo Clustering of Pacmanmodel with 3 Clusters') title='Montecarlo Clustering of Pacmanmodel with 3 Clusters')
ax.grid() ax.grid()
textstr = '\n'.join((
"MaxValueMC = " + str(pacman_minmax[1]),
"MinValueMC = " + str(pacman_minmax[0])))
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
ax.text(0.95, 0.05, textstr, transform=ax.transAxes, fontsize=14,
verticalalignment='bottom', ha='right', bbox=props)
plt.show() plt.show()
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.plot(t, supermario_montecarlodata) ax.plot(t, supermario_montecarlodata)
ax.set(xlabel='Iterations', ylabel='Score', ax.set(xlabel='Iterations', ylabel='Score',
title='Montecarlo Clustering of Supermariomodel with 3 Clusters') title='Montecarlo Clustering of Supermariomodel with 3 Clusters')
ax.grid() ax.grid()
textstr = '\n'.join((
"MaxValueMC = " + str(supermario_minmax[1]),
"MinValueMC = " + str(supermario_minmax[0])))
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
ax.text(0.95, 0.05, textstr, transform=ax.transAxes, fontsize=14,
verticalalignment='bottom', ha='right', bbox=props)
plt.show() plt.show()
t = np.arange(0,100,1) t = np.arange(0,100,1)
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.plot(t, daimler_montecarlodata) ax.plot(t, daimler_montecarlodata)
ax.set(xlabel='Iterations', ylabel='Score', ax.set(xlabel='Iterations', ylabel='Score',
title='Montecarlo Clustering of Daimlermodel') title='Montecarlo Clustering of Daimlermodel')
ax.grid() ax.grid()
textstr = '\n'.join((
"MaxValueMC = " + str(daimler_minmax[1]),
"MinValueMC = " + str(daimler_minmax[0])))
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
ax.text(0.95, 0.05, textstr, transform=ax.transAxes, fontsize=14,
verticalalignment='bottom', ha='right', bbox=props)
plt.show() plt.show()
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