SavitzkyGolayFilter

class chemotools.smooth.SavitzkyGolayFilter(window_length: int = 3, polyorder: int = 1, mode: Literal['mirror', 'constant', 'nearest', 'wrap', 'interp'] = 'nearest', axis: int = 1, window_size='deprecated', polynomial_order='deprecated')

ベースクラス: _BaseFIRFilter

A transformer that calculates the Savitzky-Golay filter of the input data.

パラメータ:

• window_length (int, optional) -- The size of the window to use for the Savitzky-Golay filter. Must be odd. Default is 3.

• polyorder (int, optional) -- The order of the polynomial to use for the Savitzky-Golay filter. Must be less than window_length. Default is 1.

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

• polynomial_order (int, optional) -- Deprecated alias for polyorder.

• mode (str, optional) -- The mode to use for the Savitzky-Golay filter. Can be "nearest", "constant", "reflect", "wrap", "mirror" or "interp". Default is "nearest".

変数:

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

サンプル

>>> from chemotools.datasets import load_fermentation_train
>>> from chemotools.smooth import SavitzkyGolayFilter
>>> # Load sample data
>>> X, _ = load_fermentation_train()
>>> # Initialize SavitzkyGolayFilter
>>> sgf = SavitzkyGolayFilter()
SavitzkyGolayFilter()
>>> # Fit and transform the data
>>> X_smoothed = sgf.fit_transform(X)

fit(X: ndarray, y: ndarray | None = None) → SavitzkyGolayFilter

Fit the Savitzky-Golay filter to the data.

パラメータ:

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

• y (Ignored) -- Not used, present for API consistency by convention.

戻り値:

self -- The fitted transformer.

戻り値の型:

SavitzkyGolayFilter

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

Transform the input data by applying the Savitzky-Golay filter.

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)