def remove_value(self, shared, value):
count_sum = 0
for i, xi in value:
count_sum += xi
self._counts[i] -= xi
self._ratio += gammaln(xi + 1)
self._ratio -= gammaln(count_sum + 1)
def score_data(self, shared):
a_sum = sum(shared._alphas)
n_sum = sum(self._counts)
score = self._ratio
for ai, ni in zip(self._alphas, self._counts):
score += gammaln(ni + ai) - gammaln(ai)
score += gammaln(a_sum) - gammaln(a_sum + n_sum)
return score
def score_student_t(x, nu, mu, sigmasq):
"""
\cite{murphy2007conjugate}, Eq. 304
"""
score = gammaln(.5 * (nu + 1.)) - gammaln(.5 * nu)
score -= .5 * log(nu * pi * sigmasq)
xt = (x - mu)
s = xt * xt / sigmasq
score += -(.5 * (nu + 1.)) * log(1. + s / nu)
return score
def score_data(self, shared):
"""
\cite{murphy2007conjugate}, Eq. 171
"""
post = shared.plus_group(self)
return gammaln(post.nu / 2.) - gammaln(shared.nu / 2.) \
+ 0.5 * log(shared.kappa / post.kappa) \
+ (0.5 * shared.nu) * log(shared.nu * shared.sigmasq) \
- (0.5 * post.nu) * log(post.nu * post.sigmasq) \
- self.count / 2. * 1.1447298858493991
def score_student_t(x, nu, mu, sigmasq):
"""
\cite{murphy2007conjugate}, Eq. 304
"""
score = gammaln(.5 * (nu + 1.)) - gammaln(.5 * nu)
score -= .5 * log(nu * pi * sigmasq)
xt = (x - mu)
s = xt * xt / sigmasq
score += -(.5 * (nu + 1.)) * log(1. + s / nu)
return score
def score_group(self, group):
"""
\cite{murphy2007conjugate}, Eq. 171
"""
post = self.plus_group(group)
return gammaln(post.nu / 2.) - gammaln(self.nu / 2.) \
+ 0.5 * log(self.kappa / post.kappa) \
+ (0.5 * self.nu) * log(self.nu * self.sigmasq) \
- (0.5 * post.nu) * log(post.nu * post.sigmasq) \
- group.count / 2. * 1.1447298858493991
def score_data(self, shared):
"""
\cite{murphy2007conjugate}, Eq. 171
"""
post = shared.plus_group(self)
return gammaln(post.nu / 2.) - gammaln(shared.nu / 2.) \
+ 0.5 * log(shared.kappa / post.kappa) \
+ (0.5 * shared.nu) * log(shared.nu * shared.sigmasq) \
- (0.5 * post.nu) * log(post.nu * post.sigmasq) \
- self.count / 2. * 1.1447298858493991
def score_data(self, shared):
assert len(shared.betas), 'betas is empty'
"""
See doc/dpd.pdf Equation (3)
"""
score = 0.
for i, count in self.counts.iteritems():
prior_i = shared.betas[i] * shared.alpha
score += gammaln(prior_i + count) - gammaln(prior_i)
score += gammaln(shared.alpha) - gammaln(shared.alpha + self.total)
return score
def score_group(self, group):
assert len(self.betas), 'betas is empty'
"""
See doc/dpd.pdf Equation (3)
"""
score = 0.
for i, count in group.counts.iteritems():
prior_i = self.betas[i] * self.alpha
score += gammaln(prior_i + count) - gammaln(prior_i)
score += gammaln(self.alpha) - gammaln(self.alpha + group.total)
return score
def score_data(self, shared):
assert len(shared.betas), 'betas is empty'
"""
See doc/dpd.pdf Equation (3)
"""
score = 0.
for i, count in self.counts.iteritems():
assert count >= 0, "cannot score while in debt"
prior_i = shared.betas[i] * shared.alpha
score += gammaln(prior_i + count) - gammaln(prior_i)
score += gammaln(shared.alpha) - gammaln(shared.alpha + self.total)
return score
def score_data(self, shared):
"""
\cite{jordan2001more} Eqn 22.
Michael Jordan's CS281B/Stat241B
Advanced Topics in Learning and Decision Making course,
'More on Marginal Likelihood'
"""
dim = shared.dim
a = shared.alphas
m = self.counts
score = sum(gammaln(a[k] + m[k]) - gammaln(a[k]) for k in xrange(dim))
score += gammaln(a.sum())
score -= gammaln(a.sum() + m.sum())
return score
def score_data(self, shared):
"""
\cite{jordan2001more} Eqn 22.
Michael Jordan's CS281B/Stat241B
Advanced Topics in Learning and Decision Making course,
'More on Marginal Likelihood'
"""
dim = shared.dim
a = shared.alphas
m = self.counts
score = sum(gammaln(a[k] + m[k]) - gammaln(a[k]) for k in xrange(dim))
score += gammaln(a.sum())
score -= gammaln(a.sum() + m.sum())
return score
def score_data(self, shared):
post = shared.plus_group(self)
score = gammaln(shared.alpha + shared.beta)
score -= gammaln(post.alpha + post.beta)
score += gammaln(post.alpha) - gammaln(shared.alpha)
score += gammaln(post.beta) - gammaln(shared.beta)
return score
def score_data(self, shared):
post = shared.plus_group(self)
score = gammaln(shared.alpha + shared.beta)
score -= gammaln(post.alpha + post.beta)
score += gammaln(post.alpha) - gammaln(shared.alpha)
score += gammaln(post.beta) - gammaln(shared.beta)
return score
def score_value(self, shared, value):
post = shared.plus_group(self)
alpha = post.alpha + shared.r
beta = post.beta + value
score = gammaln(post.alpha + post.beta)
score -= gammaln(alpha + beta)
score += gammaln(alpha) - gammaln(post.alpha)
score += gammaln(beta) - gammaln(post.beta)
return score
def score_value(self, shared, value):
post = shared.plus_group(self)
alpha = post.alpha + shared.r
beta = post.beta + value
score = gammaln(post.alpha + post.beta)
score -= gammaln(alpha + beta)
score += gammaln(alpha) - gammaln(post.alpha)
score += gammaln(beta) - gammaln(post.beta)
return score
def score_data(self, shared):
"""
\cite{jordan2001more} Eqn 22.
Michael Jordan's CS281B/Stat241B
Advanced Topics in Learning and Decision Making course,
'More on Marginal Likelihood'
"""
alpha = shared.alpha + self.heads
beta = shared.beta + self.tails
score = gammaln(shared.alpha + shared.beta) - gammaln(alpha + beta)
score += gammaln(alpha) - gammaln(shared.alpha)
score += gammaln(beta) - gammaln(shared.beta)
return score
def score_data(self, shared):
"""
\cite{jordan2001more} Eqn 22.
Michael Jordan's CS281B/Stat241B
Advanced Topics in Learning and Decision Making course,
'More on Marginal Likelihood'
"""
alpha = shared.alpha + self.heads
beta = shared.beta + self.tails
score = gammaln(shared.alpha + shared.beta) - gammaln(alpha + beta)
score += gammaln(alpha) - gammaln(shared.alpha)
score += gammaln(beta) - gammaln(shared.beta)
return score
def score_data(self, shared):
post = shared.plus_group(self)
return gammaln(post.alpha) - gammaln(shared.alpha) \
- post.alpha * log(post.inv_beta) \
+ shared.alpha * log(shared.inv_beta) \
- self.log_prod
def score_value(self, shared, value):
post = shared.plus_group(self)
return gammaln(post.alpha + value) - gammaln(post.alpha) \
+ post.alpha * log(post.inv_beta) \
- (post.alpha + value) * log(1. + post.inv_beta) \
- log(factorial(value))
def score_value(self, shared, value):
post = shared.plus_group(self)
return gammaln(post.alpha + value) - gammaln(post.alpha) \
+ post.alpha * log(post.inv_beta) \
- (post.alpha + value) * log(1. + post.inv_beta) \
- log(factorial(value))
def score_data(self, shared):
post = shared.plus_group(self)
return gammaln(post.alpha) - gammaln(shared.alpha) \
- post.alpha * log(post.inv_beta) \
+ shared.alpha * log(shared.inv_beta) \
- self.log_prod
