def run_experiments(self):
for exp in range(conf.max_experiments):
random.seed(exp)
self.actual_time = 0.0
self.pop = XCSClassifierSet(self.env, self.actual_time)
self.init()
for iteration in range(conf.max_iterations):
self.run_explor()
self.run_exploit(iteration)
print "now" + str(exp)
self.file_writer(exp)
self.performance_writer(exp)
self.make_graph()
['40%', '50%', '60%', '70%', '80%', '90%', '100%', ''])
ax.grid()
filenamepng = "performance.png"
plt.savefig(filenamepng, dpi=150)
filenameeps = "performance.eps"
plt.savefig(filenameeps)
plt.show()
if __name__ == '__main__':
"""
print("main start")
xcs = XCSProgram()
print("initialized XCSProgram")
xcs.run_experiments()
"""
xcs = XCSProgram()
xcs.pop = XCSClassifierSet(xcs.env, 0.0)
xcs.init()
xcs.env.set_state()
xcs.match_set = XCSMatchSet(pop, xcs.env, 0.0)
xcs.generate_prediction_array()
xcs.match_set = XCSMatchSet(xcs.pop, xcs.env, 0.0)
xcs.generate_prediction_array()
xcs.select_action()
xcs.action_set = XCSActionSet(xcs.match_set, xcs.action, xcs.env, 0.0)
actset = xcs.action_set.get_cls()
actset = xcs.action_set.get_cls()
actset[0].get_cond()
