def __init__(self, dim, name=None, mix=5):
"""Initialize mixture density layer - mix gives the number of Gaussians
to mix, dim is the dimension of the target(!) vector."""
nUnits = mix * (dim + 2) # mean vec + stddev and mixing coeff
NeuronLayer.__init__(self, nUnits, name)
self.nGaussians = mix
self.nDims = dim
def __init__(self, dim, name = None, mix=5):
"""Initialize mixture density layer - mix gives the number of Gaussians
to mix, dim is the dimension of the target(!) vector."""
nUnits = mix * (dim + 2) # mean vec + stddev and mixing coeff
NeuronLayer.__init__(self, nUnits, name)
self.nGaussians = mix
self.nDims = dim
def __init__(self, dim, name=None):
NeuronLayer.__init__(self, dim, name)
# initialize sigmas to 0
ParameterContainer.__init__(self, dim, stdParams = 0)
# if autoalpha is set to True, alpha_sigma = alpha_mu = alpha*sigma^2
self.autoalpha = False
self.enabled = True
def __init__(self, dim, module, name=None, onesigma=True):
NeuronLayer.__init__(self, dim, name)
self.exploration = zeros(dim, float)
self.state = None
self.onesigma = onesigma
if self.onesigma:
# one single parameter: sigma
ParameterContainer.__init__(self, 1)
else:
# sigmas for all parameters in the exploration module
ParameterContainer.__init__(self, module.paramdim)
# a module for the exploration
assert module.outdim == dim, (
"Passed module does not have right dimension")
self.module = module
self.autoalpha = False
self.enabled = True
def __init__(self, dim, module, name=None, onesigma=True):
NeuronLayer.__init__(self, dim, name)
self.exploration = zeros(dim, float)
self.state = None
self.onesigma = onesigma
if self.onesigma:
# one single parameter: sigma
ParameterContainer.__init__(self, 1)
else:
# sigmas for all parameters in the exploration module
ParameterContainer.__init__(self, module.paramdim)
# a module for the exploration
assert module.outdim == dim, (
"Passed module does not have right dimension")
self.module = module
self.autoalpha = False
self.enabled = True
