NormScaler#

class chemotools.scale.NormScaler(l_norm: int = 2)[source]

Bases: TransformerMixin, OneToOneFeatureMixin, BaseEstimator

A transformer that scales the input data by the L-norm of the spectrum.

Parameters:

l_norm (int, optional, default=2) – The L-norm to use. Default is 2.

Variables:

n_features_in (int) – The number of features in the input data.

Examples

>>> from chemotools.datasets import load_fermentation_train
>>> from chemotools.scale import NormScaler
>>> # Load sample data
>>> X, _ = load_fermentation_train()
>>> # Initialize NormScaler
>>> scaler = NormScaler(l_norm=2)
NormScaler()
>>> # Fit and transform the data
>>> X_scaled = scaler.fit_transform(X)
fit(X: ndarray, y=None) → NormScaler[source]

Fit the transformer to the input data.

Parameters:

  • X (np.ndarray of shape (n_samples, n_features)) – The input data to fit the transformer to.

  • y (None) – Ignored to align with API.

Returns:

self – The fitted transformer.

Return type:

NormScaler

transform(X: ndarray, y=None) → ndarray[source]

Transform the input data by scaling by the L-norm.

Parameters:

  • X (np.ndarray of shape (n_samples, n_features)) – The input data to transform.

  • y (None) – Ignored to align with API.

Returns:

X_transformed – The transformed data.

Return type:

np.ndarray of shape (n_samples, n_features)

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