bijx.Shift

class bijx.Shift[source]

Bases: ScalarBijection

Shift transformation.

Applies element-wise shifting with a learnable shift parameter. This is a simpler version of AffineLinear without the scale term.

Type: \([-\infty, \infty] \to [-\infty, \infty]\)

Transform: \(x + b\) where \(b\) is a learnable shift parameter

Parameters:
  • shift (Union[Variable, Array, ndarray, Sequence[Union[int, Any]]]) – Shift parameter specification.

  • transform_shift (Callable | None) – Function to transform shift (default: None/identity).

  • rngs – Random number generators for parameter initialization.

Example

>>> bijection = Shift(rngs=rngs)
>>> x = jnp.array([-1.0, 0.0, 1.0])
>>> y, log_det = bijection.forward(x, jnp.zeros(3))
>>> all(y == x)  # Initially shift = 0, so y == x
True
__init__(shift=(), transform_shift=None, *, rngs=None)[source]
Parameters:
  • shift (Variable | Array | ndarray | Sequence[int | Any])

  • transform_shift (Callable | None)

Methods

forward(x, log_density, **kwargs)

Apply forward transformation with log-density update.

fwd(x, **kwargs)

Apply forward transformation.

invert()

Create an inverted version of this bijection.

log_jac(x, y, **kwargs)

Compute log absolute determinant of the Jacobian.

rev(y, **kwargs)

Apply reverse (inverse) transformation.

reverse(y, log_density, **kwargs)

Apply reverse transformation with log-density update.

log_jac(x, y, **kwargs)[source]

Compute log absolute determinant of the Jacobian.

Parameters:
  • x – Input values where Jacobian is computed.

  • y – Output values corresponding to x (i.e., y = fwd(x)).

  • **kwargs – Additional transformation-specific arguments.

Returns:

Log absolute Jacobian determinant \(\log \abs{f'(x)}\) with same shape as x.

fwd(x, **kwargs)[source]

Apply forward transformation.

Parameters:
  • x – Input values to transform.

  • **kwargs – Additional transformation-specific arguments.

Returns:

Transformed values \(y = f(x)\) with same shape as \(x\).

rev(y, **kwargs)[source]

Apply reverse (inverse) transformation.

Parameters:
  • y – Output values to inverse-transform.

  • **kwargs – Additional transformation-specific arguments.

Returns:

Inverse-transformed values \(x = f^{-1}(y)\) with same shape as \(y\).