StandardNormalVariate

class chemotools.scatter.StandardNormalVariate

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.

Muestra:

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

Referencias

[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).

Ejemplos

>>> 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

Fit the transformer to the input data.

Parámetros:

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

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

Devuelve:

self – The fitted transformer.

Tipo del valor devuelto:

StandardNormalVariate

transform(X: ndarray, y=None) → ndarray

Transform the input data by calculating the standard normal variate.

Parámetros:

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

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

Devuelve:

X_transformed – The transformed data.

Tipo del valor devuelto:

np.ndarray of shape (n_samples, n_features)