bijx.Sigmoid

class bijx.Sigmoid

Bases: ScalarBijection

Sigmoid normalization transform.

Maps the real line to the unit interval using the logistic sigmoid function.

Type: \([-\infty, \infty] \to [0, 1]\)

Transform: \(\sigma(x) = \frac{1}{1 + e^{-x}}\)

Example

>>> bijection = Sigmoid()
>>> x = jnp.array([-2.0, 0.0, 2.0])
>>> y, log_det = bijection.forward(x, jnp.zeros(3))

__init__(*args, **kwargs)

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)	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)

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)

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)

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 \(y\).