Min-Max Normalizer

Min/Max Normalizer

(Supports both "scale" and destructive "clip" modes)

neojax.data.normalizers.min_max_normalizer.MinMaxNormalizer

Normalizes data to be within a given min and max.

Minima and maxima can be passed the following ways:

1. During initialization if known or explicitly required (e.g. known physical bounds).
2. Learned from data by passing them to compute_stats

Data can be clipped or scaled to [min, max] range.

Parameters:

Name	Type	Description	Default
minima	float | int | Float[Array, ...] | None	Optional min for dynamical scaling. Default is None, i.e., use learned min from self.stats. If Array, should be broadcast-compatible with transformation data and maxima.	None
maxima	float | int | Float[Array, ...] | None	Optional max for dynamical scaling. Default is None, i.e., use learned max from self.stats. If Array, should be broadcast-compatible with transformation data and minima.	None

Internal Attributes

These fields store the internal state of the normalizer.

• stats (dict[str, Float[Array, ...] | None]): Dict of min and max if passed at initialization, else None.

Warning

Inverse transformation can only be applied to scaled transformations.

maxima property

Accessor for the max stored in stats.

minima property

Accessor for the min stored in stats.

compute_stats(data: Float[Array, 'c ...'], axis: int | tuple[int, ...] | None = None) -> MinMaxNormalizer

Computes min and max from given data.

To normalize per-channel for input (c, d1, ..., dN), pass axis=tuple(range(1, data.ndim)). To normalize across all axes, pass axis=None.

Parameters:

Name	Type	Description	Default
data	Float[Array, 'c ...']	The input array.	required
axis	int | tuple[int, ...] | None	Axis or axes along which the statistics are computed. The default (None) computes the global min/max.	None

Returns:

Type	Description
MinMaxNormalizer	A new MinMaxNormalizer instance with updated min and max.

transform(x: Float[Array, ...], mode: typing.Literal['clip', 'scale'] = 'scale') -> Float[Array, ...]

Normalizes input by clipping or rescaling it.

Uses learned minima or maxima from self.stats.

Parameters:

Name	Type	Description	Default
x	Float[Array, ...]	The input array to transform.	required
mode	Literal['clip', 'scale']	Whether to "clip" values to be in [minima, maxima] (inclusive) or "scale" values inside range with standard min/max scaling (x - min) / (max - min + eps). Default is "scale".	'scale'

Warning

"clip" is a destructive operation. Clipped data cannot be reconstructed:

    inverse_transform(transform(x, mode="clip", ...), ...) != x

Returns:

Type	Description
Float[Array, ...]	The min/max normalized array.

Raises:

Type	Description
ValueError	If the transformation mode is unsupported.

inverse_transform(x: Float[Array, ...]) -> Float[Array, ...]

Reverts scaled min/max normalization.

Can only reverse scaled normalization. Clipped normalizations are irreversible.

Parameters:

Name	Type	Description	Default
x	Float[Array, ...]	The standardized array to revert.	required

Returns:

Type	Description
Float[Array, ...]	The de-normalized array.

__call__(x: Float[Array, ...], mode: typing.Literal['clip', 'scale'] = 'scale') -> Float[Array, ...]

Standardizes input using learned min and max.

See transform for details.

Parameters:

Name	Type	Description	Default
x	Float[Array, ...]	Input array.	required
mode	Literal['clip', 'scale']	Whether to "clip" values to be in [minima, maxima] (inclusive) or "scale" values inside range with standard min/max scaling (x - min) / (max - min + eps). Default "scale".	'scale'

Returns:

Type	Description
Float[Array, ...]	Standardized input array.