def set_global_params(self, hmodel=None,
U1=None, U0=None,
K=0, beta=None, topic_prior=None,
Ebeta=None, EbetaLeftover=None, theta=None, **kwargs):
if hmodel is not None:
self.K = hmodel.allocModel.K
self.U1 = hmodel.allocModel.U1
self.U0 = hmodel.allocModel.U0
self.set_helper_params()
return
if U1 is not None and U0 is not None:
self.U1 = U1
self.U0 = U0
self.K = U1.size
self.set_helper_params()
return
if Ebeta is not None and EbetaLeftover is not None:
Ebeta = np.squeeze(Ebeta)
EbetaLeftover = np.squeeze(EbetaLeftover)
beta = np.hstack( [Ebeta, EbetaLeftover])
self.K = beta.size - 1
elif beta is not None:
assert beta.size == K
beta = np.hstack([beta, np.min(beta)/100.])
beta = beta/np.sum(beta)
self.K = beta.size - 1
else:
raise ValueError('Bad parameters. Vector beta not specified.')
# Now, use the specified value of beta to find the best U1, U0
assert beta.size == self.K + 1
assert abs(np.sum(beta) - 1.0) < 0.001
vMean = OptimHDP.beta2v(beta)
# for each k=1,2...K
# find the multiplier vMass[k] such that both are true
# 1) vMass[k] * vMean[k] > 1.0
# 2) vMass[k] * (1-vMean[k]) > self.alpha0
vMass = np.maximum( 1./vMean , self.alpha0/(1.-vMean))
self.U1 = vMass * vMean
self.U0 = vMass * (1-vMean)
assert np.all( self.U1 >= 1.0 - 0.00001)
assert np.all( self.U0 >= self.alpha0 - 0.00001)
assert self.U1.size == self.K
assert self.U0.size == self.K
####################################### Set Global Params for Theta
if theta is not None and beta is not None:
self.theta = theta
else:
self.theta = np.ones( (self.nNodeTotal, self.K + 1 ) )
self.set_helper_params()
def insert_global_params(self, beta=None, **kwargs):
Knew = beta.size
beta = np.hstack([beta, np.min(beta)/100.])
beta = beta/np.sum(beta)
vMean = OptimHDP.beta2v(beta)
vMass = np.maximum( 1./vMean , self.alpha0/(1.-vMean))
self.K += Knew
self.U1 = np.append(self.U1, vMass * vMean )
self.U0 = np.append(self.U0, vMass * (1-vMean))
assert self.U1.size == self.K
assert self.U0.size == self.K
self.set_helper_params()
def _convert_beta2u(self, beta):
''' Given a vector beta (size K+1),
return educated guess for vectors u1, u0
Returns
--------
U1 : 1D array, size K
U0 : 1D array, size K
'''
assert abs(np.sum(beta) - 1.0) < 0.001
vMean = OptimHDP.beta2v(beta)
# for each k=1,2...K
# find the multiplier vMass[k] such that both are true
# 1) vMass[k] * vMean[k] > 1.0
# 2) vMass[k] * (1-vMean[k]) > self.alpha0
vMass = np.maximum( 1./vMean , self.alpha0/(1.-vMean))
U1 = vMass * vMean
U0 = vMass * (1-vMean)
return U1, U0
def set_global_params(self,
hmodel=None,
U1=None,
U0=None,
K=0,
beta=None,
topic_prior=None,
Ebeta=None,
EbetaLeftover=None,
theta=None,
**kwargs):
if hmodel is not None:
self.K = hmodel.allocModel.K
self.U1 = hmodel.allocModel.U1
self.U0 = hmodel.allocModel.U0
self.set_helper_params()
return
if U1 is not None and U0 is not None:
self.U1 = U1
self.U0 = U0
self.K = U1.size
self.set_helper_params()
return
if Ebeta is not None and EbetaLeftover is not None:
Ebeta = np.squeeze(Ebeta)
EbetaLeftover = np.squeeze(EbetaLeftover)
beta = np.hstack([Ebeta, EbetaLeftover])
self.K = beta.size - 1
elif beta is not None:
assert beta.size == K
beta = np.hstack([beta, np.min(beta) / 100.])
beta = beta / np.sum(beta)
self.K = beta.size - 1
else:
raise ValueError('Bad parameters. Vector beta not specified.')
# Now, use the specified value of beta to find the best U1, U0
assert beta.size == self.K + 1
assert abs(np.sum(beta) - 1.0) < 0.001
vMean = OptimHDP.beta2v(beta)
# for each k=1,2...K
# find the multiplier vMass[k] such that both are true
# 1) vMass[k] * vMean[k] > 1.0
# 2) vMass[k] * (1-vMean[k]) > self.alpha0
vMass = np.maximum(1. / vMean, self.alpha0 / (1. - vMean))
self.U1 = vMass * vMean
self.U0 = vMass * (1 - vMean)
assert np.all(self.U1 >= 1.0 - 0.00001)
assert np.all(self.U0 >= self.alpha0 - 0.00001)
assert self.U1.size == self.K
assert self.U0.size == self.K
####################################### Set Global Params for Theta
if theta is not None and beta is not None:
self.theta = theta
else:
self.theta = np.ones((self.nNodeTotal, self.K + 1))
self.set_helper_params()
