def main():
#neuralNet = NeuralNetwork()
pendulum = InvertedPendulum()
for n in np.arange(-0.5, 10, 0.5):
cart, theta = pendulum.applyforce(u=n, tmax=2.5, timeslice=0.01)
x, y = transform(theta)
showGraph(x, y, cart, 0.01, "Relative motion of cart and pendulum u={0}".format(n))
class Controller (object):
def __init__(self):
self.running_flag = False
self.pendulum = InvertedPendulum()
def isRunning(self):
return self.running_flag
def start(self, impulse=1, freq=0.01):
if self.running_flag: raise Exception('Already running')
self.frequency = freq
self.running_flag = True
self.pendulum.applyforce(u=impulse)
def stop(self):
self.running_flag = False
def __init__(self, genome, M=10, m=1, l=1, threshold=0.1):
self.threshold = threshold
#there are the physics constants for each individual
self.M = M
self.m = m
self.l = l
#this is the actual data we are evolving weights for
self.genome = genome
#these are the actual weights we are evolving
self.alleles = [uniform(-10, 10) for n in range(len(genome))]
self.pendulum = InvertedPendulum(M, m, l)
class Individual (object):
def __init__(self, genome, M=10, m=1, l=1, threshold=0.1):
self.threshold = threshold
#there are the physics constants for each individual
self.M = M
self.m = m
self.l = l
#this is the actual data we are evolving weights for
self.genome = genome
#these are the actual weights we are evolving
self.alleles = [uniform(-10, 10) for n in range(len(genome))]
self.pendulum = InvertedPendulum(M, m, l)
def fitness(self, func=None):
if func == None:
func = self.time_to_ground()
return func()
#in this instance we calculate fitness based on how long
#the pendulum stays up
#returns milliseconds
def time_to_ground(self):
#TODO perform calulatoin
time = self.pendulum.applyforce2(u=10)
return 0
#in this instance we calculate fitness based on how long
#the pendulum stays within plus or minus the threshold
#returns milliseconds
def time_to_threshold(self):
#TODO perform calulatoin
return 0
def __init__(self):
self.running_flag = False
self.pendulum = InvertedPendulum()
