def calc_local_params(self, Data, LP, nnzPerRowLP=0, **kwargs):
''' Compute local parameters for each data item and component.
Parameters
-------
Data : bnpy.data.DataObj subclass
LP : dict
Local parameters as key-value string/array pairs
* E_log_soft_ev : 2D array, N x K
E_log_soft_ev[n,k] = log p(data obs n | comp k)
Returns
-------
LP : dict
Local parameters, with updated fields
* resp : 2D array, size N x K array
Posterior responsibility each comp has for each item
resp[n, k] = p(z[n] = k | x[n])
'''
lpr = LP['E_log_soft_ev']
K = lpr.shape[1]
if self.inferType.count('EM') > 0:
# Using point estimates, for EM algorithm
lpr += np.log(self.w + 1e-100)
if nnzPerRowLP and (nnzPerRowLP > 0 and nnzPerRowLP < K):
# SPARSE Assignments
LP['nnzPerRow'] = nnzPerRowLP
LP['spR'] = sparsifyLogResp(lpr, nnzPerRow=nnzPerRowLP)
assert np.all(np.isfinite(LP['spR'].data))
else:
lprPerItem = logsumexp(lpr, axis=1)
lpr -= lprPerItem[:, np.newaxis]
np.exp(lpr, out=lpr)
LP['resp'] = lpr
LP['evidence'] = lprPerItem.sum()
else:
# Full Bayesian approach, for VB or GS algorithms
lpr += self.Elogw
if nnzPerRowLP and (nnzPerRowLP > 0 and nnzPerRowLP < K):
# SPARSE Assignments
LP['nnzPerRow'] = nnzPerRowLP
LP['spR'] = sparsifyLogResp(lpr, nnzPerRow=nnzPerRowLP)
assert np.all(np.isfinite(LP['spR'].data))
else:
# DENSE Assignments
# Calculate exp in numerically safe way,
# in-place so no new allocations occur
NumericUtil.inplaceExpAndNormalizeRows(lpr)
LP['resp'] = lpr
assert np.allclose(lpr.sum(axis=1), 1)
return LP
def calcLocalParams(Data, LP, Elogbeta=None, nnzPerRowLP=None, **kwargs):
''' Compute local parameters for each data item.
Parameters
-------
Data : bnpy.data.DataObj subclass
LP : dict
Local parameters as key-value string/array pairs
* E_log_soft_ev : 2D array, N x K
E_log_soft_ev[n,k] = log p(data obs n | comp k)
Returns
-------
LP : dict
Local parameters, with updated fields
* resp : 2D array, size N x K array
Posterior responsibility each comp has for each item
resp[n, k] = p(z[n] = k | x[n])
'''
lpr = LP['E_log_soft_ev']
lpr += Elogbeta
K = LP['E_log_soft_ev'].shape[1]
if nnzPerRowLP and (nnzPerRowLP > 0 and nnzPerRowLP < K):
# SPARSE Assignments
LP['spR'] = sparsifyLogResp(lpr, nnzPerRow=nnzPerRowLP)
assert np.all(np.isfinite(LP['spR'].data))
LP['nnzPerRow'] = nnzPerRowLP
else:
# DENSE Assignments
# Calculate exp in numerically stable manner (first subtract the max)
# perform this in-place so no new allocations occur
NumericUtil.inplaceExpAndNormalizeRows(lpr)
LP['resp'] = lpr
return LP
def calcInitSparseResp(LP, alphaEbeta, nnzPerRowLP=0, **kwargs):
''' Compute initial sparse responsibilities
'''
assert 'ElogphiT' in LP
# Determine the top-L for each
logS = LP['ElogphiT'].copy()
logS += np.log(alphaEbeta)[np.newaxis, :]
init_spR = sparsifyLogResp(logS, nnzPerRowLP)
return init_spR