def setup(self):
""" Create the network. """
self.sensorCount = self.robot.range.count
self.net = Network()
self.net.addThreeLayers(self.sensorCount, 16, 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=10.0,
yEnd=1.0,
legend=["Trained", "Test"],
title="NN Generalization",
width=400,
xLabel="Distance to wall",
yLabel="Speed")
self.plot.addLine(0, .5, 10, .5, color="green")
self.min = 0.0
self.robot.range._noise = [0.0] * self.robot.range.count
def setup(self):
""" Create the network. """
self.sensorCount = self.robot.range.count
self.net = Network()
self.net.addThreeLayers(self.sensorCount, 16, 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=10.0, yEnd=1.0, legend=["Trained", "Test"],
title="NN Generalization", width=400,
xLabel = "Distance to wall",
yLabel = "Speed")
self.plot.addLine(0, .5, 10, .5, color = "green")
self.min = 0.0
self.robot.range._noise = [0.0] * self.robot.range.count
class NNPredict(Brain):
def setup(self):
""" Create the network. """
self.sensorCount = self.robot.range.count
self.net = Network()
self.net.addThreeLayers(self.sensorCount, 16, 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=10.0,
yEnd=1.0,
legend=["Trained", "Test"],
title="NN Generalization",
width=400,
xLabel="Distance to wall",
yLabel="Speed")
self.plot.addLine(0, .5, 10, .5, color="green")
self.min = 0.0
self.robot.range._noise = [0.0] * self.robot.range.count
#self.fp = open("train.dat", "w")
def destroy(self):
self.plot.destroy()
def scale(self, val):
return (val / self.maxvalue)
def teacher(self):
# set targets
target_trans = 1.0
target_rotate = 0.5
self.min = min([s.distance() for s in self.robot.range.span(20, -20)])
if self.min < 3:
target_trans = 0.5
elif self.min < 4:
target_trans = 0.65
elif self.min < 5:
target_trans = 0.8
elif self.min < 6:
target_trans = 0.95
return [target_trans, target_rotate]
def step(self):
target = self.teacher()
old = self.new
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
#self.fp.write(" ".join(map(lambda f: str(f), old)) +
# (" %f %f\n" % (target[0], target[1])))
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
colors = ["red", "blue"]
self.plot.addPoint(self.min,
self.trans,
int(not self.net.learning),
color=colors[int(not self.net.learning)])
self.robot.move((self.trans - .5) / 2.0, (self.rotate - .5) / 2.0)
self.counter += 1
class NNPredict(Brain):
def setup(self):
""" Create the network. """
self.sensorCount = self.robot.range.count
self.net = Network()
self.net.addThreeLayers(self.sensorCount, 16, 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=10.0, yEnd=1.0, legend=["Trained", "Test"],
title="NN Generalization", width=400,
xLabel = "Distance to wall",
yLabel = "Speed")
self.plot.addLine(0, .5, 10, .5, color = "green")
self.min = 0.0
self.robot.range._noise = [0.0] * self.robot.range.count
#self.fp = open("train.dat", "w")
def destroy(self):
self.plot.destroy()
def scale(self, val):
return (val / self.maxvalue)
def teacher(self):
# set targets
target_trans = 1.0
target_rotate = 0.5
self.min = min([s.distance() for s in self.robot.range.span(20, -20)])
if self.min < 3:
target_trans = 0.5
elif self.min < 4:
target_trans = 0.65
elif self.min < 5:
target_trans = 0.8
elif self.min < 6:
target_trans = 0.95
return [target_trans, target_rotate]
def step(self):
target = self.teacher()
old = self.new
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
#self.fp.write(" ".join(map(lambda f: str(f), old)) +
# (" %f %f\n" % (target[0], target[1])))
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
colors= ["red", "blue"]
self.plot.addPoint(self.min, self.trans, int(not self.net.learning),
color=colors[int(not self.net.learning)])
self.robot.move((self.trans - .5)/2.0, (self.rotate - .5)/2.0)
self.counter += 1
return self.INFTY
for i in range(self.vectorSize):
d += (v1[i] - v2[i])*(v1[i] - v2[i])
return sqrt(d)
def __str__(self):
result = ""
for i in range(len(self.nodes)):
result += str(self.nodes[i])+"\n"
result += "Net Error: "+str(self.error)
return result
if __name__ == '__main__':
from pyrobot.gui.plot.scatter import Scatter
sp = Scatter(connectPoints = 0, linecount=3)
length = 2
gng = GNG(length, .05) # make second number smaller to match better
print(gng)
print("\n------------------------------------------------\n")
for i in range(100):
#vector = [(1 + sin((i+n) * 2 * pi * 3.0/70.0))/2 for n in range(2)]
vector = [random() for x in range(length)]
sp.addPoint(vector[0], vector[1], line = 1)
gng.newInput(vector)
if (i%10 == 0):
print(gng)
print("\n------------------------------------------------\n")
sp.clear(2)
for node in gng.nodes:
sp.addPoint(node.modelVector[0], node.modelVector[1],
return self.INFTY
for i in range(self.vectorSize):
d += (v1[i] - v2[i])*(v1[i] - v2[i])
return sqrt(d)
def __str__(self):
result = ""
for i in range(len(self.nodes)):
result += str(self.nodes[i])+"\n"
result += "Net Error: "+str(self.error)
return result
if __name__ == '__main__':
from pyrobot.gui.plot.scatter import Scatter
sp = Scatter(connectPoints = 0, linecount=3)
length = 2
gng = GNG(length, .05) # make second number smaller to match better
print gng
print "\n------------------------------------------------\n"
for i in range(100):
#vector = [(1 + sin((i+n) * 2 * pi * 3.0/70.0))/2 for n in range(2)]
vector = [random() for x in range(length)]
sp.addPoint(vector[0], vector[1], line = 1)
gng.newInput(vector)
if (i%10 == 0):
print gng
print "\n------------------------------------------------\n"
sp.clear(2)
for node in gng.nodes:
sp.addPoint(node.modelVector[0], node.modelVector[1],
