def _log_pdf(self, x):
assert x.shape[-1] == self.mu.size
if x.ndim == 1:
squeeze = True
x = broadcast_to(x, (1, self.mu.size))
else:
squeeze = False
assert x.ndim == 2
d = x - self.mu
d = d.transpose()
logp = (
-0.5 * (solve(self.cov, d) * d).sum(axis=0)
- (self.mu.size * 0.5) * log(2 * np.pi)
- 0.5 * logdet(self.cov)
)
if squeeze:
logp = logp.sum()
return logp
def _log_pdf(self, x):
try:
return -SigmoidCrossEntropy().forward(self.logit, x)
except AttributeError:
return x * log(self.mu) + (1 - x) * log(1 - self.mu)
def _log_pdf(self, x):
try:
return -SoftmaxCrossEntropy(axis=self.axis).forward(self.logit, x)
except (KeyError, AttributeError):
return (x * log(self.mu).sum(axis=self.axis))
def _log_pdf(self, x):
return (log(gamma(self.alpha.sum(axis=self.axis))) + sum(
(self.alpha - 1) * log(x) - log(gamma(self.alpha)),
axis=self.axis))
def _log_pdf(self, x):
return -self.rate * x + log(self.rate)
def _log_pdf(self, x):
return log(self.pdf(x))
def _log_pdf(self, x):
return (self.shape * log(self.rate) + (self.shape - 1) * log(x) -
self.rate * x - log(gamma(self.shape)))
def _log_pdf(self, x):
return (-np.log(np.pi) - log(self.scale) -
log(1 + square((x - self.loc) / self.scale)))
def _log_pdf(self, x):
return np.log(0.5) - abs(x - self.loc) / self.scale - log(self.scale)