NorrisWilliams

class chemotools.derivative.NorrisWilliams(window_length: int = 5, gap_size: int = 3, deriv: int = 1, mode='nearest', window_size='deprecated', derivative_order='deprecated')

ベースクラス: TransformerMixin, OneToOneFeatureMixin, BaseEstimator

A transformer that calculates the Norris-Williams derivative of the input data.

パラメータ:

• window_length (int, optional, default=5) -- The size of the window to use for the derivative calculation. Must be odd. Default is 5.

• gap_size (int, optional, default=3) -- The size of the gap to use for the derivative calculation. Must be odd. Default is 3.

• deriv (int, optional, default=1) -- The order of the derivative to calculate. Can be 1 or 2. Default is 1.

• mode (str, optional, default="nearest") -- The mode to use for the derivative calculation. Can be "nearest", "constant", "reflect", "wrap", "mirror" or "interp". Default is "nearest".

• window_size (int, optional) -- Deprecated alias for window_length.

• derivative_order (int, optional) -- Deprecated alias for deriv.

変数:

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

参照

[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).

サンプル

>>> from chemotools.derivative import NorrisWilliams
>>> from chemotools.datasets import load_fermentation_train
>>> # Load sample data
>>> X, _ = load_fermentation_train()
>>> # Instantiate the transformer
>>> transformer = NorrisWilliams(window_size=5, gap_size=3)
NorrisWilliams()
>>> transformer.fit(X)
>>> # Calculate Norris-Williams derivative
>>> X_corrected = transformer.transform(X)

fit(X: ndarray, y=None) → NorrisWilliams

Fit the transformer to the input data.

パラメータ:

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

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

戻り値:

self -- The fitted transformer.

戻り値の型:

NorrisWilliams

transform(X: ndarray, y=None)

Transform the input data by calculating the Norris-Williams derivative.

パラメータ:

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

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

戻り値:

X_transformed -- The transformed data.

戻り値の型:

np.ndarray of shape (n_samples, n_features)