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API Documentation

opls

Category: Statistical Models

Classes

OPLS

Methods

init()

TODO:

  1. add arg for specifying the method for performing PLS
fit(x, y, n_comp, dot)

Fit PLS model.

Parameters
  • x: np.ndarray Variable matrix with size n samples by p variables.
  • y: np.ndarray Dependent matrix with size n samples by 1, or a vector
  • n_comp: int Number of components, default is None, which indicates that largest dimension which is smaller value between n and p will be used.
predict(X, n_component, return_scores)

Predict the new coming data matrx.

correct(x, n_component, return_scores, dot)

Correction of X

Parameters
  • x: np.ndarray Data matrix with size n by c, where n is number of samples, and c is number of variables
  • n_component: int | None Number of components. If is None, the number of components used in fitting the model is used. Default is None.
  • return_scores: bool Return orthogonal scores. Default is False.
Returns
  • xc: np.ndarray Corrected data, with same matrix size with input X.
  • t: np.ndarray Orthogonal score, n by n_component.
predictive_score(n_component)
Parameters
  • n_component: int The component number.
ortho_score(n_component)
Parameters
  • n_component: int The component number.
predictive_scores()

Orthogonal loadings.

predictive_loadings()

Predictive loadings.

weights_y()

y scores.

orthogonal_loadings()

Orthogonal loadings.

orthogonal_scores()

Orthogonal scores.