def createParamBagForPrior(Data,
D=0,
nu=0,
beta=None,
m=None,
kappa=None,
MMat='zero',
ECovMat=None,
sF=1.0,
Prior=None,
**kwargs):
''' Initialize ParamBag of parameters which specify prior.
Returns
-------
Prior : ParamBag
'''
if Data is None:
D = int(D)
else:
D = int(Data.dim)
nu = np.maximum(nu, D + 2)
kappa = np.maximum(kappa, 1e-8)
if beta is None:
if ECovMat is None or isinstance(ECovMat, str):
ECovMat = createECovMatFromUserInput(D, Data, ECovMat, sF)
beta = np.diag(ECovMat) * (nu - 2)
else:
if beta.ndim == 0:
beta = np.asarray([beta], dtype=np.float)
if m is None:
if MMat == 'data':
m = np.sum(Data.X, axis=0)
else:
m = np.zeros(D)
elif m.ndim < 1:
m = np.asarray([m], dtype=np.float)
if Prior is None:
Prior = ParamBag(K=0, D=D)
assert Prior.D == D
Prior.setField('nu', nu, dims=None)
Prior.setField('kappa', kappa, dims=None)
Prior.setField('m', m, dims=('D'))
Prior.setField('beta', beta, dims=('D'))
return Prior
def createPrior(self, Data, nu=0, B=None, ECovMat=None, sF=1.0, **kwargs):
''' Initialize Prior ParamBag attribute.
Post Condition
------
Prior expected covariance matrix set to match provided value.
'''
D = self.D
nu = np.maximum(nu, D + 2)
if B is None:
if ECovMat is None or isinstance(ECovMat, str):
ECovMat = createECovMatFromUserInput(D, Data, ECovMat, sF)
B = ECovMat * (nu - D - 1)
else:
B = as2D(B)
self.Prior = ParamBag(K=0, D=D)
self.Prior.setField('nu', nu, dims=None)
self.Prior.setField('B', B, dims=('D', 'D'))
def createPrior(self,
Data,
D=None,
E=None,
nu=0,
B=None,
M=None,
V=None,
ECovMat=None,
sF=1.0,
VMat='eye',
sV=1.0,
MMat='zero',
sM=1.0,
**kwargs):
''' Initialize Prior ParamBag attribute.
Post Condition
------
Prior expected covariance matrix set to match provided value.
'''
if Data is None:
if D is None:
raise ValueError("Need to specify dimension D")
if E is None:
raise ValueError("Need to specify dimension E")
if Data is not None:
if D is None:
D = Data.X.shape[1]
else:
assert D == Data.X.shape[1]
if E is None:
E = Data.Xprev.shape[1]
else:
assert E == Data.Xprev.shape[1]
nu = np.maximum(nu, D + 2)
if B is None:
if ECovMat is None or isinstance(ECovMat, str):
ECovMat = createECovMatFromUserInput(D, Data, ECovMat, sF)
B = ECovMat * (nu - D - 1)
B = as2D(B)
if M is None:
if MMat == 'zero':
M = np.zeros((D, E))
elif MMat == 'eye':
assert D <= E
M = sM * np.eye(D)
M = np.hstack([M, np.zeros((D, E - D))])
assert M.shape == (D, E)
else:
raise ValueError('Unrecognized MMat: %s' % (MMat))
else:
M = as2D(M)
if V is None:
if VMat == 'eye':
V = sV * np.eye(E)
elif VMat == 'same':
assert D == E
V = sV * ECovMat
else:
raise ValueError('Unrecognized VMat: %s' % (VMat))
else:
V = as2D(V)
self.Prior = ParamBag(K=0, D=D, E=E)
self.Prior.setField('nu', nu, dims=None)
self.Prior.setField('B', B, dims=('D', 'D'))
self.Prior.setField('V', V, dims=('E', 'E'))
self.Prior.setField('M', M, dims=('D', 'E'))