def log_prob(self, x):
scale_tril_inv = \
_batch_triangular_inv(self.scale_tril.reshape(-1, self.d, self.d))
scale_tril_inv = scale_tril_inv.reshape(self.batch_shape +
(self.d, self.d))
bsti = broadcast.broadcast_to(scale_tril_inv, x.shape + (self.d, ))
bl = broadcast.broadcast_to(self.loc, x.shape)
m = matmul.matmul(bsti, expand_dims.expand_dims(x - bl, axis=-1))
m = matmul.matmul(swapaxes.swapaxes(m, -1, -2), m)
m = squeeze.squeeze(m, axis=-1)
m = squeeze.squeeze(m, axis=-1)
logz = LOGPROBC * self.d - self._logdet(self.scale_tril)
return broadcast.broadcast_to(logz, m.shape) - 0.5 * m
def log_prob(self, x):
scale_tril_inv = \
_batch_triangular_inv(self.scale_tril.reshape(-1, self.d, self.d))
scale_tril_inv = scale_tril_inv.reshape(
self.batch_shape+(self.d, self.d))
bsti = broadcast.broadcast_to(scale_tril_inv, x.shape + (self.d,))
bl = broadcast.broadcast_to(self.loc, x.shape)
m = matmul.matmul(
bsti,
expand_dims.expand_dims(x - bl, axis=-1))
m = matmul.matmul(swapaxes.swapaxes(m, -1, -2), m)
m = squeeze.squeeze(m, axis=-1)
m = squeeze.squeeze(m, axis=-1)
logz = LOGPROBC * self.d - self._logdet(self.scale_tril)
return broadcast.broadcast_to(logz, m.shape) - 0.5 * m
