def logpdf(self, rowid, targets, constraints=None, inputs=None):
DistributionGpm.logpdf(self, rowid, targets, constraints, inputs)
x = targets[self.outputs[0]]
if x not in [0, 1]:
return -float('inf')
return Bernoulli.calc_predictive_logp(x, self.N, self.x_sum,
self.alpha, self.beta)
def logpdf(self, rowid, targets, constraints=None, inputs=None):
DistributionGpm.logpdf(self, rowid, targets, constraints, inputs)
x = targets[self.outputs[0]]
if x < 0:
return -float('inf')
return Exponential.calc_predictive_logp(x, self.N, self.sum_x, self.a,
self.b)
def logpdf(self, rowid, targets, constraints=None, inputs=None):
DistributionGpm.logpdf(self, rowid, targets, constraints, inputs)
x = targets[self.outputs[0]]
if not (x % 1 == 0 and x >= 0):
return -float('inf')
return Poisson.calc_predictive_logp(x, self.N, self.sum_x, self.a,
self.b)
def logpdf(self, rowid, targets, constraints=None, inputs=None):
DistributionGpm.logpdf(self, rowid, targets, constraints, inputs)
x = targets[self.outputs[0]]
if not (x % 1 == 0 and 0 <= x < self.k):
return -float('inf')
return Categorical.calc_predictive_logp(int(x), self.N, self.counts,
self.alpha)
def logpdf(self, rowid, targets, constraints=None, inputs=None):
DistributionGpm.logpdf(self, rowid, targets, constraints, inputs)
x = targets[self.outputs[0]]
if not (0 <= x <= 2 * pi):
return -float('inf')
return Vonmises.calc_predictive_logp(x, self.N, self.sum_sin_x,
self.sum_cos_x, self.a, self.b,
self.k)
def logpdf(self, rowid, targets, constraints=None, inputs=None):
DistributionGpm.logpdf(self, rowid, targets, constraints, inputs)
x = targets[self.outputs[0]]
if x <= 0:
return -float('inf')
return - log(x) + \
Normal.calc_predictive_logp(
log(x), self.N, self.sum_log_x, self.sum_log_x_sq, self.m,
self.r, self.s, self.nu)
def logpdf(self, rowid, targets, constraints=None, inputs=None):
DistributionGpm.logpdf(self, rowid, targets, constraints, inputs)
x = targets[self.outputs[0]]
if not (self.l <= x <= self.h):
return -float('inf')
logpdf_unorm = NormalTrunc.calc_predictive_logp(
x, self.mu, self.sigma, self.l, self.h)
logcdf_norm = NormalTrunc.calc_log_normalizer(self.mu, self.sigma,
self.l, self.h)
return logpdf_unorm - logcdf_norm
def logpdf(self, rowid, targets, constraints=None, inputs=None):
DistributionGpm.logpdf(self, rowid, targets, constraints, inputs)
x = targets[self.outputs[0]]
if not 0 < x < 1:
return -float('inf')
return Beta.calc_predictive_logp(x, self.strength, self.balance)
def logpdf(self, rowid, targets, constraints=None, inputs=None):
DistributionGpm.logpdf(self, rowid, targets, constraints, inputs)
x = targets[self.outputs[0]]
return Normal.calc_predictive_logp(x, self.N, self.sum_x,
self.sum_x_sq, self.m, self.r,
self.s, self.nu)