StandardNormalVariate#

class chemotools.scatter.StandardNormalVariate[source]

Bases: TransformerMixin, OneToOneFeatureMixin, BaseEstimator

A transformer that calculates the standard normal variate of the input data.

Variables:

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

Raises:

UserWarning – If the standard deviation of a spectrum is zero (spectrum is flat), a warning is raised indicating that the result will contain NaNs.

References

[1] Åsmund Rinnan, Frans van den Berg, Søren Balling Engelsen,

“Review of the most common pre-processing techniques for near-infrared spectra,” TrAC Trends in Analytical Chemistry 28 (10) 1201-1222 (2009).

Examples

>>> from chemotools.datasets import load_fermentation_train
>>> from chemotools.scatter import StandardNormalVariate
>>> # Load sample data
>>> X, _ = load_fermentation_train()
>>> # Initialize StandardNormalVariate
>>> snv = StandardNormalVariate()
StandardNormalVariate()
>>> # Fit and transform the data
>>> X_scaled = snv.fit_transform(X)
fit(X: ndarray, y=None) → StandardNormalVariate[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:

StandardNormalVariate

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

Transform the input data by calculating the standard normal variate.

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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