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
def sample_n(self, n):
if self._is_gpu:
eps = cuda.cupy.random.standard_normal(
(n,)+self.loc.shape+(1,), dtype=self.loc.dtype)
else:
eps = numpy.random.standard_normal(
(n,)+self.loc.shape+(1,)).astype(numpy.float32)
return self.loc + squeeze.squeeze(
matmul.matmul(self.scale_tril, eps), axis=-1)
def sample_n(self, n):
if self._is_gpu:
eps = cuda.cupy.random.standard_normal(
(n,)+self.loc.shape+(1,), dtype=self.loc.dtype)
else:
eps = numpy.random.standard_normal(
(n,)+self.loc.shape+(1,)).astype(numpy.float32)
return self.loc + squeeze.squeeze(
matmul.matmul(self.scale_tril, eps), axis=-1)
def sample_n(self, n):
if self._is_gpu:
eps = cuda.cupy.random.standard_normal(
(n,)+self.loc.shape+(1,), dtype=self.loc.dtype)
else:
eps = numpy.random.standard_normal(
(n,)+self.loc.shape+(1,)).astype(numpy.float32)
noise = matmul.matmul(repeat.repeat(
expand_dims.expand_dims(self.scale_tril, axis=0), n, axis=0), eps)
noise = squeeze.squeeze(noise, axis=-1)
noise += repeat.repeat(expand_dims.expand_dims(
self.loc, axis=0), n, axis=0)
return noise
def sample_n(self, n):
if self._is_gpu:
eps = cuda.cupy.random.standard_normal(
(n,)+self.loc.shape+(1,), dtype=self.loc.dtype)
else:
eps = numpy.random.standard_normal(
(n,)+self.loc.shape+(1,)).astype(numpy.float32)
noise = matmul.matmul(repeat.repeat(
expand_dims.expand_dims(self.scale_tril, axis=0), n, axis=0), eps)
noise = squeeze.squeeze(noise, axis=-1)
noise += repeat.repeat(expand_dims.expand_dims(
self.loc, axis=0), n, axis=0)
return noise