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from pyrobot.brain import Brain
from pyrobot.brain.conx import *
from pyrobot.gui.plot.scatter import Scatter
import pyrobot.system.share as share
class NNPredict(Brain):
def setup(self):
""" Create the network. """
self.sensorCount = self.robot.range.count
self.net = Network()
self.net.addLayers(self.sensorCount + 2, 5, 2)
self.net.initialize()
self.net.setVerbosity(0)
self.net.setEpsilon(0.5)
self.net.setMomentum(.1)
self.net.setLearning(1)
self.trans = 0
self.rotate = 0
self.counter = 0
self.maxvalue = self.robot.range.getMaxvalue()
self.new = [self.scale(s.distance()) for s in self.robot.range["all"]]
self.plot = Scatter(app=share.gui, linecount=2, connectPoints=0,
xEnd=7.0, yEnd=1.2, legend=["Trained", "Test"],
title="NN Generalization", width=400)
self.min = 0.0
def destroy(self):
self.plot.destroy()
def scale(self, val):
return (val / self.maxvalue)
def avoid(self):
# set targets
target_trans = 1.0
target_rotate = 0.5
# left and right and front:
# This works for Pyro Version 4.3.1 or greater.
self.min = min([s.distance() for s in self.robot.range.span(45, -45)])
left = min([s.distance() for s in self.robot.range.span(45, -45)])
right = min([s.distance() for s in self.robot.range.span(45, -45)])
# Other versions, use: range["front"], range["front-left"], range["front-right"]
if left < 1.5 or right < 1.5:
target_trans = 0.5
elif left < 3.5 or right < 3.5:
target_trans = 0.75
return [target_trans, target_rotate]
def step(self):
target = self.avoid()
old = self.new + [self.trans, self.rotate] #trans and rotate
self.new = [self.scale(s.distance()) for s in self.robot.range["all"]]
# results
if self.net.learning:
e, c, t, p = self.net.step(input=old, output=target)
if self.counter % 10 == 0:
print "error = %.2f" % e
self.trans, self.rotate = target
else:
old = self.new + [self.trans, self.rotate]
self.net.step(input=old, output=target)
self.trans, self.rotate = self.net['output'].activation
if self.counter % 10 == 0:
print self.trans, self.rotate
self.plot.addPoint(self.min, self.trans, not self.net.learning)
self.robot.move((self.trans - .5)/2.0, (self.rotate - .5)/2.0)
self.counter += 1
def INIT(engine):
return NNPredict('NNPredict', engine) |
