def __init__(self,
D,
H,
Hprime,
gamma,
to_learn=['W', 'pi', 'sigma'],
comm=MPI.COMM_WORLD):
""" MCA-ET init method.
Takes data dimension *D*, number of hidden causes *H*,
and ET approximation parameters *Hprime* and *gamma*. Optional
list of parameters *to_learn* and MPI *comm* object.
"""
CAModel.__init__(self, D, H, Hprime, gamma, to_learn, comm)
#
self.rho_temp_bound = 1.05 # for rho: never use a T smaller than this
self.W_tol = 1e-4 # for W: ensure W[W<W_tol] = W_tol
# Noise Policy
W_tol = self.W_tol
self.noise_policy = {
'W': (W_tol, +np.inf, True),
'pi': (W_tol, 1 - W_tol, False),
'sigma': (W_tol, +np.inf, False)
}
def __init__(self,
D,
H,
Hprime,
gamma,
to_learn=['W', 'pi', 'sigma'],
comm=MPI.COMM_WORLD):
""" MMCA-ET init method.
Takes data dimension *D*, number of hidden causes *H*,
and ET approximation parameters *Hprime* and *gamma*. Optional
list of parameters *to_learn* and MPI *comm* object.
"""
CAModel.__init__(self, D, H, Hprime, gamma, to_learn, comm)
#
self.rho_T_bound = 1.20 # for rho: never use a T smaller than this
self.rho_lbound = 1 # for rho: never use a rho smaller than this
self.rho_ubound = 35 # for rho: never use a rho larger than this
self.tol = 1e-4 # for W: ensure W[W<tol] = tol
self.rev_corr = False
# Noise Policy
tol = self.tol
self.noise_policy = {
'W': (-np.inf, +np.inf, False),
'pi': (tol, 1 - tol, False),
'sigma': (tol, +np.inf, False)
}
def __init__(self,
D,
H,
Hprime,
gamma,
to_learn=['W', 'pi', 'sigma'],
comm=MPI.COMM_WORLD):
CAModel.__init__(self, D, H, Hprime, gamma, to_learn, comm)
def __init__(self, D, H, Hprime, gamma, to_learn=["W", "pi", "sigma"], comm=MPI.COMM_WORLD):
""" MCA-ET init method.
Takes data dimension *D*, number of hidden causes *H*,
and ET approximation parameters *Hprime* and *gamma*. Optional
list of parameters *to_learn* and MPI *comm* object.
"""
CAModel.__init__(self, D, H, Hprime, gamma, to_learn, comm)
#
self.rho_temp_bound = 1.05 # for rho: never use a T smaller than this
self.W_tol = 1e-4 # for W: ensure W[W<W_tol] = W_tol
# Noise Policy
W_tol = self.W_tol
self.noise_policy = {
"W": (W_tol, +np.inf, True),
"pi": (W_tol, 1 - W_tol, False),
"sigma": (W_tol, +np.inf, False),
}
def __init__(self, D, H, Hprime, gamma, to_learn=['W', 'pi', 'sigma'], comm=MPI.COMM_WORLD):
""" MMCA-ET init method.
Takes data dimension *D*, number of hidden causes *H*,
and ET approximation parameters *Hprime* and *gamma*. Optional
list of parameters *to_learn* and MPI *comm* object.
"""
CAModel.__init__(self, D, H, Hprime, gamma, to_learn, comm)
#
self.rho_T_bound = 1.20 # for rho: never use a T smaller than this
self.rho_lbound = 1 # for rho: never use a rho smaller than this
self.rho_ubound = 35 # for rho: never use a rho larger than this
self.tol = 1e-4 # for W: ensure W[W<tol] = tol
self.rev_corr = False
# Noise Policy
tol = self.tol
self.noise_policy = {
'W' : ( -np.inf, +np.inf, False ),
'pi' : ( tol, 1-tol, False ),
'sigma': ( tol, +np.inf, False )
}
def __init__(self, D, H, Hprime, gamma, to_learn=['W', 'pi', 'sigma'], comm=MPI.COMM_WORLD):
CAModel.__init__(self, D, H, Hprime, gamma, to_learn, comm)
