def __init__(self, indim, outdim, nhidden=20):
FA.__init__(self, indim, outdim)
# learning rate
self.alpha = 0.1
# number of neurons in each layer
self.indim = indim
self.nhidden = nhidden
self.outdim = outdim
# change output activation if task is classification
self.classification = False
# online training or batch, if batch, then train for that many epochs
self.online = False
self.epochs = 100
# initialize weights randomly (+1 for bias)
self.hWeights = 0.01 * np.random.random((self.nhidden, self.indim+1))
self.oWeights = 0.01 * np.random.random((self.outdim, self.nhidden+1))
# activations of neurons (sum of inputs)
self.hActivation = np.zeros((self.nhidden, 1), dtype=float)
self.oActivation = np.zeros((self.outdim, 1), dtype=float)
# outputs of neurons (after sigmoid function)
self.iOutput = np.zeros((self.indim+1, 1), dtype=float) # +1 for bias
self.hOutput = np.zeros((self.nhidden+1, 1), dtype=float) # +1 for bias
self.oOutput = np.zeros((self.outdim, 1), dtype=float)
# deltas for hidden and output layer
self.hDelta = np.zeros((self.nhidden), dtype=float)
self.oDelta = np.zeros((self.outdim), dtype=float)
def __init__(self, indim, outdim, bayes=True, rbf=False):
""" initialize function approximator with input and output dimension. """
self.rbf = rbf
self.bayes = bayes
if self.rbf:
self.numCenters = 20
else:
self.numCenters = indim
FA.__init__(self, indim, outdim)
def __init__(self, indim, outdim):
FA.__init__(self, indim, outdim)
self.filename = None
