class PredictionANN(PredictionBase):
def __init__(self, n=10):
super(PredictionANN, self).__init__(n)
self.mlp = MLP([1 + 3 * n, 3 * n, 2])
def get_ann_input(self, delta_time):
temp = [x.tolist() for x in self.pos_buffer]
for i in xrange(len(temp)):
temp[i].append(self.time_buffer[i] - self.time_buffer[0])
temp = [item for sublist in temp for item in sublist]
return [delta_time] + temp
def train(self, pos, delay):
if len(self.pos_buffer) == self.N:
self.mlp.train(
self.get_ann_input(
self.get_current_time(delay) - self.time_buffer[0]),
pos.tonp() / 100)
self.update(pos, delay)
def predict(self, delta_time):
if len(self.pos_buffer) != self.N:
return np.array([0, 0])
return self.mlp.proc(
self.get_ann_input(self.get_current_time() + delta_time -
self.time_buffer[0])) * 100
class PredictionANN(PredictionBase):
def __init__(self, n=10):
super(PredictionANN, self).__init__(n)
self.mlp = MLP([1+3*n, 3*n, 2])
def get_ann_input(self, delta_time):
temp = [x.tolist() for x in self.pos_buffer]
for i in xrange(len(temp)):
temp[i].append(self.time_buffer[i]-self.time_buffer[0])
temp = [item for sublist in temp for item in sublist]
return [delta_time] + temp
def train(self, pos, delay):
if len(self.pos_buffer) == self.N:
self.mlp.train(self.get_ann_input(self.get_current_time(delay)-self.time_buffer[0]), pos.tonp()/100)
self.update(pos, delay)
def predict(self, delta_time):
if len(self.pos_buffer) != self.N:
return np.array([0,0])
return self.mlp.proc(self.get_ann_input(self.get_current_time()+delta_time-self.time_buffer[0]))*100
def __init__(self, n=10):
super(PredictionANN, self).__init__(n)
self.mlp = MLP([1 + 3 * n, 3 * n, 2])
def __init__(self, n=10):
super(PredictionANN, self).__init__(n)
self.mlp = MLP([1+3*n, 3*n, 2])
