def reset(self):
FA.reset(self)
# self.network = buildNetwork(self.indim, 2*(self.indim+self.outdim), self.outdim)
self.network = buildNetwork(self.indim, self.outdim, bias=True)
self.network._setParameters(random.normal(0, 0.1, self.network.params.shape))
self.pybdataset = SupervisedDataSet(self.indim, self.outdim)
def reset(self):
FA.reset(self)
# initialize the LWPR function
self.lwpr = LWPR(self.indim, self.outdim)
self.lwpr.init_D = 10.*np.eye(self.indim)
self.lwpr.init_alpha = 0.1*np.ones([self.indim, self.indim])
self.lwpr.meta = True
def reset(self):
FA.reset(self)
self.centers = [np.random.uniform(-1, 1, self.indim) for i in xrange(self.numCenters)]
self.W = np.random.random((self.numCenters, self.outdim))
# parameters for maximum map
self.alpha = 100.
self.SN = np.matrix(self.alpha*np.eye(self.numCenters))
self.mN = np.matrix(np.zeros((self.numCenters, 1), float))
def reset(self):
""" this initializes the function approximator to an initial state,
forgetting everything it has learned before. """
FA.reset(self)
self.matrix = np.random.uniform(-0.1, 0.1, (self.indim + 1, self.outdim))