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kan_scaler

chronax.kan_scaler

Per-window scalers for KAN: identity (NF default) and robust (median/MAD).

Scaler

chronax.kan_scaler.Scaler

A protocol defining the interface for per-window scalers.

stats(self, x, axis=1)

Calculates the shift and scale parameters necessary for normalization.

Parameter Type Default Description
x jnp.ndarray - The input array to calculate statistics from.
axis int 1 The axis over which to calculate statistics.

Returns: tuple[jnp.ndarray, jnp.ndarray] (shift, scale).

transform(self, x, shift, scale)

Applies the normalization transformation.

Parameter Type Default Description
x jnp.ndarray - The input array to transform.
shift jnp.ndarray - The calculated shift parameter.
scale jnp.ndarray - The calculated scale parameter.

Returns: jnp.ndarray (The normalized array).

inverse(self, z, shift, scale)

Applies the inverse normalization transformation.

Parameter Type Default Description
z jnp.ndarray - The normalized input array.
shift jnp.ndarray - The calculated shift parameter.
scale jnp.ndarray - The calculated scale parameter.

Returns: jnp.ndarray (The denormalized array).

IdentityScaler

chronax.kan_scaler.IdentityScaler

No-op scaler (shift=0, scale=1). Matches neuralforecast's scaler_type='identity'.

stats(self, x, axis=1)

Calculates shift (zeros) and scale (ones).

Parameter Type Default Description
x jnp.ndarray - (undocumented)
axis int 1 (undocumented)

Returns: tuple[jnp.ndarray, jnp.ndarray]

transform(self, x, shift, scale)

Returns x unchanged.

Parameter Type Default Description
x jnp.ndarray - (undocumented)
shift jnp.ndarray - (undocumented)
scale jnp.ndarray - (undocumented)

Returns: jnp.ndarray

inverse(self, z, shift, scale)

Returns z unchanged.

Parameter Type Default Description
z jnp.ndarray - (undocumented)
shift jnp.ndarray - (undocumented)
scale jnp.ndarray - (undocumented)

Returns: jnp.ndarray

RobustScaler

chronax.kan_scaler.RobustScaler

Median + MAD scaler with 0.6745*std fallback when MAD=0.

stats(self, x, axis=1)

Calculates median (shift) and MAD (scale), using a standard deviation fallback when MAD is zero.

Parameter Type Default Description
x jnp.ndarray - (undocumented)
axis int 1 (undocumented)

Returns: tuple[jnp.ndarray, jnp.ndarray]

transform(self, x, shift, scale)

Applies the transformation: (x - shift) / scale.

Parameter Type Default Description
x jnp.ndarray - (undocumented)
shift jnp.ndarray - (undocumented)
scale jnp.ndarray - (undocumented)

Returns: jnp.ndarray

inverse(self, z, shift, scale)

Applies the inverse transformation: z * scale + shift.

Parameter Type Default Description
z jnp.ndarray - (undocumented)
shift jnp.ndarray - (undocumented)
scale jnp.ndarray - (undocumented)

Returns: jnp.ndarray

resolve_scaler

chronax.kan_scaler.resolve_scaler

Resolve a scaler from a name ('identity'/'robust') or a Scaler instance.

Parameter Type Default Description
scaler str \| Scaler - The name of the scaler ('identity' or 'robust') or an existing Scaler instance.

Returns: Scaler Raises: ValueError (If an unknown string name is provided).