def calc_local_params(self, Data, LP, **kwargs):
""" Calculate local parameters for each data item and each component.
This is part of the E-step.
Args
-------
Data : bnpy data object with Data.nObs observations
LP : local param dict with fields
E_log_soft_ev : Data.nObs x K array
E_log_soft_ev[n,k] = log p(data obs n | comp k)
Returns
-------
LP : local param dict with fields
resp : Data.nObs x K array whose rows sum to one
resp[n,k] = posterior responsibility that comp. k has for data n
"""
lpr = LP["E_log_soft_ev"]
if self.inferType.count("VB") > 0:
lpr += self.Elogw
# Calculate exp in numerically stable manner (first subtract the max)
# perform this in-place so no new allocations occur
lpr -= np.max(lpr, axis=1)[:, np.newaxis]
np.exp(lpr, out=lpr)
# Normalize, so rows sum to one
lpr /= lpr.sum(axis=1)[:, np.newaxis]
elif self.inferType == "EM" > 0:
lpr += np.log(self.w)
lprPerItem = logsumexp(lpr, axis=1)
np.exp(lpr - lprPerItem[:, np.newaxis], out=lpr)
LP["evidence"] = lprPerItem.sum()
LP["resp"] = lpr
assert np.allclose(lpr.sum(axis=1), 1)
return LP
def calc_local_params(self, Data, LP, **kwargs):
''' Calculate local parameters for each data item and each component.
This is part of the E-step.
Args
-------
Data : bnpy data object with Data.nObs observations
LP : local param dict with fields
E_log_soft_ev : Data.nObs x K array
E_log_soft_ev[n,k] = log p(data obs n | comp k)
Returns
-------
LP : local param dict with fields
resp : Data.nObs x K array whose rows sum to one
resp[n,k] = posterior responsibility that comp. k has for data n
'''
lpr = LP['E_log_soft_ev']
if self.inferType.count('VB') > 0:
lpr += self.Elogw
# Calculate exp in numerically stable manner (first subtract the max)
# perform this in-place so no new allocations occur
lpr -= np.max(lpr, axis=1)[:,np.newaxis]
np.exp(lpr, out=lpr)
# Normalize, so rows sum to one
lpr /= lpr.sum(axis=1)[:,np.newaxis]
elif self.inferType == 'EM' > 0:
lpr += np.log(self.w)
lprPerItem = logsumexp(lpr, axis=1)
np.exp(lpr-lprPerItem[:,np.newaxis], out=lpr)
LP['evidence'] = lprPerItem.sum()
LP['resp'] = lpr
assert np.allclose(lpr.sum(axis=1), 1)
return LP
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