def packParamBagForPost(pnu_K=None,
ptau_K=None,
w_KE=None,
P_KEE=None,
Post=None,
**kwargs):
''' Parse provided array args and pack into parameter bag
Returns
-------
Post : ParamBag, with K clusters
'''
pnu_K = as1D(pnu_K)
ptau_K = as1D(ptau_K)
w_KE = as2D(w_KE)
P_KEE = as3D(P_KEE)
K = pnu_K.size
E = w_KE.shape[1]
if Post is None:
Post = ParamBag(K=K, D=E - 1, E=E)
elif not hasattr(Post, 'E'):
Post.E = E
assert Post.K == K
assert Post.D == E - 1
assert Post.E == E
Post.setField('pnu_K', pnu_K, dims=('K'))
Post.setField('ptau_K', ptau_K, dims=('K'))
Post.setField('w_KE', w_KE, dims=('K', 'E'))
Post.setField('P_KEE', P_KEE, dims=('K', 'E', 'E'))
return Post
def createParamBagForPrior(
Data=None, D=0,
pnu=0, ptau=None, w_E=0,
P_EE=None, P_diag_E=None, P_diag_val=1.0,
Prior=None,
**kwargs):
''' Initialize Prior ParamBag attribute.
Returns
-------
Prior : ParamBag
with dimension attributes K, D, E
with parameter attributes pnu, ptau, w_E, P_EE
'''
if Data is None:
D = int(D)
else:
D = int(Data.dim)
E = D + 1
# Init parameters of 1D Wishart prior on delta
pnu = np.maximum(pnu, 1e-9)
ptau = np.maximum(ptau, 1e-9)
# Initialize precision matrix of the weight vector
if P_EE is not None:
P_EE = np.asarray(P_EE)
elif P_diag_E is not None:
P_EE = np.diag(np.asarray(P_diag_E))
else:
P_EE = np.diag(P_diag_val * np.ones(E))
assert P_EE.ndim == 2
assert P_EE.shape == (E,E)
# Initialize mean of the weight vector
w_E = as1D(np.asarray(w_E))
if w_E.size < E:
w_E = np.tile(w_E, E)[:E]
assert w_E.ndim == 1
assert w_E.size == E
if Prior is None:
Prior = ParamBag(K=0, D=D, E=E)
if not hasattr(Prior, 'E'):
Prior.E = E
assert Prior.D == D
assert Prior.E == E
Prior.setField('pnu', pnu, dims=None)
Prior.setField('ptau', ptau, dims=None)
Prior.setField('w_E', w_E, dims=('E'))
Prior.setField('P_EE', P_EE, dims=('E', 'E'))
Pw_E = np.dot(P_EE, w_E)
wPw_1 = np.dot(w_E, Pw_E)
Prior.setField('Pw_E', Pw_E, dims=('E'))
Prior.setField('wPw_1', wPw_1, dims=None)
return Prior
