from individual import INDIVIDUAL
import copy, pickle
# ----------------------
parent = INDIVIDUAL()
parent.Evaluate(True)
print parent.fitness
for g in range(0, 200):
child = copy.deepcopy(parent)
child.Mutate()
child.Evaluate(True)
print '[g:', g, ']',
print '[pw:', parent.genome, ']',
print '[p:', parent.fitness, ']',
print '[c:', child.fitness, ']'
if (child.fitness > parent.fitness):
parent = child
from robot import ROBOT
from individual import INDIVIDUAL
import pyrosim
import matplotlib.pyplot as plt
import random
for i in range(0, 10):
individual = INDIVIDUAL()
individual.Evaluate()
print(individual.fitness)
'''
Sensor data from red cylinder
sensorData = sim.get_sensor_data(sensor_id = P2)
print(sensorData)
Plot of sensor data
f = plt.figure()
panel = f.add_subplot(111)
plt.plot(sensorData)
panel.set_ylim(-1, +2)
plt.show()
'''
import pyrosim
import matplotlib.pyplot as plt
from robot import ROBOT
from individual import INDIVIDUAL
import random
import copy
import pickle
parent = INDIVIDUAL()
parent.Evaluate(True)
print(parent.fitness)
for i in range(100):
child = copy.deepcopy(parent)
child.Mutate()
child.Evaluate(True)
print('[g:', i,'] [pw:', parent.genome, '] [p:', parent.fitness, '] [c:', child.fitness, ']')
if (child.fitness > parent.fitness):
parent = child
child.Evaluate(True)
# f = open('robot.p', 'wb')
# pickle.dump(parent, f)
# f.close()
from pyrosim import PYROSIM
from robot import ROBOT
from individual import INDIVIDUAL
import copy as cp
import pickle
import random
parent = INDIVIDUAL()
for i in range(0,100):
child = cp.deepcopy(parent)
child.Mutate()
parent.Evaluate(hideSim=True)
child.Evaluate(hideSim=True)
print("[g: ", i, "] [PW: ", parent.genome," ][P: ", parent.fitness, "] [C:", child.fitness, "]")
if (child.fitness > parent.fitness):
parent = child
f = open("robot.p", "wb")
pickle.dump(parent, f)
f.close()
parent.Evaluate(hideSim=False)
########################################################################################################################
# Data Visualization
########################################################################################################################
import pyrosim
from individual import INDIVIDUAL
import matplotlib.pyplot as plt
from robot import ROBOT
import random
import copy
import pickle
parent = INDIVIDUAL()
parent.Evaluate(False)
print(parent.fitness)
for i in range(0, 100):
child = copy.deepcopy(parent)
child.Mutate()
child.Evaluate(True)
print("[g:", i, "]", "[pw:", parent.genome, "]", "[p:", parent.fitness,
"]", "[C:", child.fitness, "]")
if (child.fitness > parent.fitness):
child.Evaluate(False)
parent = child
f = open('robot.p', 'wb') #ojo, colocar wb en vez de solo w
pickle.dump(parent, f)
f.close()
#for i in range(0,10):
# individual=INDIVIDUAL()
# individual.Evaluate()
# print(individual.fitness)
# sim=pyrosim.Simulator(eval_time=100)
# robot=ROBOT(sim,random.random()*2-1)
import constants as c from individual import INDIVIDUAL def Random_X(): return random.random()*(c.endX-c.startX) + c.startX def Random_Y(): return random.random()*(c.endY-c.startY) + c.startY def Random_Theta(): return random.random()*2.0*math.pi # --------------- Start of the program --------------- ind = INDIVIDUAL() initialX = Random_X() initialY = Random_Y() initialTheta = Random_Theta() ind.Evaluate(initialX,initialY,initialTheta,pp=False,pb=False) print 'total light collected = ' + str(ind.fitness)
