def fillSharedMemDictForLocalStep(self, ShMem=None):
""" Get dict of shared mem arrays needed for parallel local step.
Returns
-------
ShMem : dict of RawArray objects
"""
# No shared memory required here.
if not isinstance(ShMem, dict):
ShMem = dict()
K = self.K
if 'startTheta' in ShMem:
shared_startTheta = sharedMemToNumpyArray(ShMem['startTheta'])
assert shared_startTheta.size >= K + 1
shared_startTheta[:K + 1] = self.startTheta
shared_transTheta = sharedMemToNumpyArray(ShMem['transTheta'])
assert shared_transTheta.shape[0] >= K
assert shared_transTheta.shape[1] >= K + 1
shared_transTheta[:K, :K + 1] = self.transTheta
else:
ShMem['startTheta'] = numpyToSharedMemArray(self.startTheta)
ShMem['transTheta'] = numpyToSharedMemArray(self.transTheta)
return ShMem
def fillSharedMemDictForLocalStep(self, ShMem=None):
""" Get dict of shared mem arrays needed for parallel local step.
Returns
-------
ShMem : dict of RawArray objects
"""
if ShMem is None:
ShMem = dict()
if 'nu' in ShMem:
fillSharedMemArray(ShMem['nu'], self.Post.nu)
fillSharedMemArray(ShMem['kappa'], self.Post.kappa)
fillSharedMemArray(ShMem['m'], self.Post.m)
fillSharedMemArray(ShMem['beta'], self.Post.beta)
fillSharedMemArray(ShMem['E_logL'], self._E_logL('all'))
else:
ShMem['nu'] = numpyToSharedMemArray(self.Post.nu)
ShMem['kappa'] = numpyToSharedMemArray(self.Post.kappa)
# Post.m is strided, so we need to copy it to do shared mem.
ShMem['m'] = numpyToSharedMemArray(self.Post.m.copy())
ShMem['beta'] = numpyToSharedMemArray(self.Post.beta.copy())
ShMem['E_logL'] = numpyToSharedMemArray(self._E_logL('all'))
return ShMem
def getRawDataAsSharedMemDict(self):
''' Create dict with copies of raw data as shared memory arrays
'''
dataShMemDict = dict()
dataShMemDict['doc_range'] = numpyToSharedMemArray(self.doc_range)
dataShMemDict['word_id'] = numpyToSharedMemArray(self.word_id)
dataShMemDict['word_count'] = numpyToSharedMemArray(self.word_count)
dataShMemDict['vocab_size'] = self.vocab_size
return dataShMemDict
def getRawDataAsSharedMemDict(self):
''' Create dict with copies of raw data as shared memory arrays
'''
dataShMemDict = dict()
dataShMemDict['X'] = numpyToSharedMemArray(self.X)
dataShMemDict['doc_range'] = numpyToSharedMemArray(self.doc_range)
dataShMemDict['nDocTotal'] = self.nDocTotal
if hasattr(self, 'Xprev'):
dataShMemDict['Xprev'] = numpyToSharedMemArray(self.Xprev)
return dataShMemDict
def fillSharedMemDictForLocalStep(self, ShMem=None):
""" Get dict of shared mem arrays needed for parallel local step.
Returns
-------
ShMem : dict of RawArray objects
"""
if ShMem is None:
ShMem = dict()
if 'nu' in ShMem:
fillSharedMemArray(ShMem['nu'], self.Post.nu)
fillSharedMemArray(ShMem['cholB'], self._cholB('all'))
fillSharedMemArray(ShMem['E_logdetL'], self._E_logdetL('all'))
else:
ShMem['nu'] = numpyToSharedMemArray(self.Post.nu)
ShMem['cholB'] = numpyToSharedMemArray(self._cholB('all'))
ShMem['E_logdetL'] = numpyToSharedMemArray(self._E_logdetL('all'))
return ShMem
def fillSharedMemDictForLocalStep(self, ShMem=None):
""" Get dict of shared mem arrays needed for parallel local step.
Returns
-------
ShMem : dict of RawArray objects
"""
ElogphiT, Elog1mphiT = self.GetCached('E_logphiT_log1mphiT', 'all')
K = self.K
if ShMem is None:
ShMem = dict()
if 'ElogphiT' not in ShMem:
ShMem['ElogphiT'] = numpyToSharedMemArray(ElogphiT)
ShMem['Elog1mphiT'] = numpyToSharedMemArray(Elog1mphiT)
else:
ElogphiT_shView = sharedMemToNumpyArray(ShMem['ElogphiT'])
assert ElogphiT_shView.shape >= K
ElogphiT_shView[:, :K] = ElogphiT
Elog1mphiT_shView = sharedMemToNumpyArray(ShMem['Elog1mphiT'])
assert Elog1mphiT_shView.shape >= K
Elog1mphiT_shView[:, :K] = Elog1mphiT
return ShMem
def fillSharedMemDictForLocalStep(self, ShMem=None):
""" Get dict of shared mem arrays needed for parallel local step.
Returns
-------
ShMem : dict of RawArray objects
"""
# No shared memory required here.
if not isinstance(ShMem, dict):
ShMem = dict()
alphaEbeta = self.alpha_E_beta()
if 'alphaEbeta' in ShMem:
shared_alphaEbeta = sharedMemToNumpyArray(ShMem['alphaEbeta'])
assert shared_alphaEbeta.size >= self.K
shared_alphaEbeta[:alphaEbeta.size] = alphaEbeta
else:
ShMem['alphaEbeta'] = numpyToSharedMemArray(alphaEbeta.copy())
return ShMem
