def var(self):
try:
return self._var
except AttributeError:
return square(self.std)
def _pdf(self, x):
return (1 / (np.pi * self.scale * (1 + square(
(x - self.loc) / self.scale))))
def tau(self):
try:
return self._tau
except AttributeError:
return 1 / square(self.std)
def _pdf(self, x):
return (
exp(-0.5 * square((x - self.mu) / self.std))
/ sqrt(2 * np.pi) / self.std
)
def forward(self):
eps = normal(0, 1, (self.ndim, ) + self.std.shape)
return sqrt(square(self.std * eps).sum(axis=0))
def _log_pdf(self, x):
return ((self.ndim - 1) * log(x) - 0.5 * square(x / self.std) -
self.ndim * log(self.std) - np.log(sp.gamma(0.5 * self.ndim)))
def _pdf(self, x):
gauss = (exp(-0.5 * square(
(x - self.mu) / self.std)) / sqrt(2 * np.pi) / self.std)
return (self.coef * gauss).sum(axis=self.axis)
def _log_pdf(self, x):
return (
-np.log(np.pi)
- log(self.scale)
- log(1 + square((x - self.loc) / self.scale))
)
def _pdf(self, x):
return (exp(-0.5 * square(
(x - self.mu) / self.std)) / sqrt(2 * np.pi) / self.std)
def var(self):
try:
return self._var
except AttributeError:
return square(self.std)
def tau(self):
try:
return self._tau
except AttributeError:
return 1 / square(self.std)
def var(self):
return square(self.parameter["std"])
def _pdf(self, x):
gauss = (
exp(-0.5 * square((x - self.mu) / self.std))
/ sqrt(2 * np.pi) / self.std
)
return (self.coef * gauss).sum(axis=self.axis)
def _log_pdf(self, x):
return (-np.log(np.pi) - log(self.scale) -
log(1 + square((x - self.loc) / self.scale)))
def var(self):
return square(self.parameter["std"])
def kl_gaussian(q, p):
qvar = square(q.std)
pvar = square(p.std)
return log(p.std) - log(q.std) + 0.5 * (qvar + square(p.mean - q.mean)) / pvar - 0.5
def _pdf(self, x):
return (
1 / (np.pi * self.scale * (1 + square((x - self.loc) / self.scale)))
)