def __init__(self,
motorValues=range(-4, 4 + 1),
sparsity=0.02,
encoderResolution=0.5,
bmParams=None):
super(PositionBehaviorModel, self).__init__(motorValues=motorValues)
self.encoderResolution = encoderResolution
bmParams = bmParams or {}
numMotorColumns = len(self.motorValues)
bmParams["numMotorColumns"] = numMotorColumns
self.bm = BehaviorMemory(**bmParams)
self.sensorN = self.bm.numSensorColumns
self.sensorW = int(self.sensorN * sparsity) + 1
self.sensorEncoder = CoordinateEncoder(w=self.sensorW, n=self.sensorN)
class PositionBehaviorModel(Model):
def __init__(self,
motorValues=range(-4, 4 + 1),
sparsity=0.02,
encoderResolution=0.5,
bmParams=None):
super(PositionBehaviorModel, self).__init__(motorValues=motorValues)
self.encoderResolution = encoderResolution
bmParams = bmParams or {}
numMotorColumns = len(self.motorValues)
bmParams["numMotorColumns"] = numMotorColumns
self.bm = BehaviorMemory(**bmParams)
self.sensorN = self.bm.numSensorColumns
self.sensorW = int(self.sensorN * sparsity) + 1
self.sensorEncoder = CoordinateEncoder(w=self.sensorW, n=self.sensorN)
def update(self, sensorValue, motorValue, goalValue=None):
motorPattern = set([self.motorValues.index(motorValue)])
scale = 100
radius = int(self.encoderResolution * scale)
sensorInput = (numpy.array([int(sensorValue * scale)]), radius)
sensorPattern = set(
self.sensorEncoder.encode(sensorInput).nonzero()[0])
goalPattern = set()
if goalValue is not None:
goalInput = (numpy.array([int(goalValue * scale)]), radius)
goalPattern = set(
self.sensorEncoder.encode(goalInput).nonzero()[0])
self.bm.compute(motorPattern, sensorPattern, goalPattern)
if goalValue is not None:
return self.decodeMotor()
def decodeMotor(self):
idx = self.bm.motor.argmax()
return self.motorValues[idx]
class PositionBehaviorModel(Model):
def __init__(self, motorValues=range(-4, 4+1),
sparsity=0.02, encoderResolution=0.5, bmParams=None):
super(PositionBehaviorModel, self).__init__(motorValues=motorValues)
self.encoderResolution = encoderResolution
bmParams = bmParams or {}
numMotorColumns = len(self.motorValues)
bmParams["numMotorColumns"] = numMotorColumns
self.bm = BehaviorMemory(**bmParams)
self.sensorN = self.bm.numSensorColumns
self.sensorW = int(self.sensorN * sparsity) + 1
self.sensorEncoder = CoordinateEncoder(w=self.sensorW, n=self.sensorN)
def update(self, sensorValue, motorValue, goalValue=None):
motorPattern = set([self.motorValues.index(motorValue)])
scale = 100
radius = int(self.encoderResolution * scale)
sensorInput = (numpy.array([int(sensorValue * scale)]), radius)
sensorPattern = set(self.sensorEncoder.encode(sensorInput).nonzero()[0])
goalPattern = set()
if goalValue is not None:
goalInput = (numpy.array([int(goalValue * scale)]), radius)
goalPattern = set(self.sensorEncoder.encode(goalInput).nonzero()[0])
self.bm.compute(motorPattern, sensorPattern, goalPattern)
if goalValue is not None:
return self.decodeMotor()
def decodeMotor(self):
idx = self.bm.motor.argmax()
return self.motorValues[idx]
def __init__(self, motorValues=range(-4, 4+1),
sparsity=0.02, encoderResolution=0.5, bmParams=None):
super(PositionBehaviorModel, self).__init__(motorValues=motorValues)
self.encoderResolution = encoderResolution
bmParams = bmParams or {}
numMotorColumns = len(self.motorValues)
bmParams["numMotorColumns"] = numMotorColumns
self.bm = BehaviorMemory(**bmParams)
self.sensorN = self.bm.numSensorColumns
self.sensorW = int(self.sensorN * sparsity) + 1
self.sensorEncoder = CoordinateEncoder(w=self.sensorW, n=self.sensorN)
