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):
'''
Construct a new Continuous HMM.
In order to initialize the model with custom parameters,
pass values for (A,means,covars,w,pi), and set the init_type to 'user'.
Normal initialization uses a uniform distribution for all probablities,
and is not recommended.
'''
_BaseHMM.__init__(self, n, m, precision, verbose) #@UndefinedVariable
self.d = d
self.A = A
self.pi = pi
self.means = means
self.covars = covars
self.w = w
self.min_std = min_std
self.reset(init_type=init_type)
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):
'''
Construct a new Continuous HMM.
In order to initialize the model with custom parameters,
pass values for (A,means,covars,w,pi), and set the init_type to 'user'.
Normal initialization uses a uniform distribution for all probablities,
and is not recommended.
'''
_BaseHMM.__init__(self,n,m,precision,verbose) #@UndefinedVariable
self.d = d
self.w = w
self.A = A
# print 'start A: '
# print A
# print 'start w: '
# print w
self.pi = pi
self.means = means
self.covars = covars
self.min_std = min_std
# path = os.path.join(os.getcwd(), 'res')
# self.path = path
# if not os.path.exists(path):
# os.makedirs(path)
# self._saveModel('_init')
self.reset(init_type=init_type)
def __init__(self,n,m,A=None,B=None,pi=None,init_type='uniform',precision=numpy.double,verbose=False):
'''
Construct a new Discrete HMM.
In order to initialize the model with custom parameters,
pass values for (A,B,pi), and set the init_type to 'user'.
Normal initialization uses a uniform distribution for all probablities,
and is not recommended.
'''
_BaseHMM.__init__(self,n,m,precision,verbose) #@UndefinedVariable
self.A = A
self.pi = pi
self.B = B
self.reset(init_type=init_type)
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):
'''
Construct a new Continuous HMM.
In order to initialize the model with custom parameters,
pass values for (A,means,covars,w,pi), and set the init_type to 'user'.
Normal initialization uses a uniform distribution for all probablities,
and is not recommended.
'''
_BaseHMM.__init__(self,n,m,precision,verbose) #@UndefinedVariable
self.d = d
self.A = A
self.pi = pi
self.means = means
self.covars = covars
self.w = w
self.min_std = min_std
self.reset(init_type=init_type)
def __init__(self,
n,
m,
A=None,
B=None,
pi=None,
init_type='uniform',
precision=numpy.double,
verbose=False):
'''
Construct a new Discrete HMM.
In order to initialize the model with custom parameters,
pass values for (A,B,pi), and set the init_type to 'user'.
Normal initialization uses a uniform distribution for all probablities,
and is not recommended.
'''
_BaseHMM.__init__(self, n, m, precision, verbose) #@UndefinedVariable
self.A = A
self.pi = pi
self.B = B
self.reset(init_type=init_type)
