def __init__(self,
n,
m,
d=1,
A=None,
means=None,
covars=None,
w=None,
pi=None,
min_std=0.01,
init_type='uniform',
precision=numpy.double,
verbose=False):
GMHMM.__init__(self, n, m, d, A, means, covars, w, pi, min_std,
init_type, precision, verbose)
def __init__(self,
n,
m,
d=1,
A=None,
means=None,
covars=None,
w=None,
pi=None,
min_std=0.01,
init_type='uniform',
precision=numpy.double,
verbose=False):
print(
"Warning: weighted EMs may not converge to local optima, since the log-likelihood function may decrease."
)
print()
GMHMM.__init__(self, n, m, d, A, means, covars, w, pi, min_std,
init_type, precision, verbose) #@UndefinedVariable
Linear.__init__(self)
