gemseo / mlearning / transform / dimension_reduction

kpca module

Kernel Principal Component Analysis

The KPCA class implements the KCPA wraps the KPCA from Scikit-learn.

Dependence

This dimension reduction algorithm relies on the PCA class of the scikit-learn library.

class gemseo.mlearning.transform.dimension_reduction.kpca.KPCA(name='KPCA', n_components=5, fit_inverse_transform=True, kernel='linear', **parameters)[source]

Bases: gemseo.mlearning.transform.dimension_reduction.dimension_reduction.DimensionReduction

Kernel principal component dimension reduction algorithm.

Constructor.

Parameters

  • name (str) – transformer name. Default: ‘KPCA’.

  • n_components (int) – number of components. Default: 5.

  • fit_inverse_transform (bool) – Learn the inverse transform for non-precomputed kernels. Default: True.

  • kernel (str) – kernel name (‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘cosine’ or ‘precomputed’). Default: ‘linear’.

  • parameters – Optional parameters for sklearn KPCA constructor.

fit(data)[source]

Fit transformer to data.

Parameters

data (ndarray) – data to be fitted.

inverse_transform(data)[source]

Perform an inverse transform on the data.

Parameters

data (ndarray) – data to be inverse transformed.

Returns

inverse transformed data.

Return type

ndarray

transform(data)[source]

Transform data.

Parameters

data (ndarray) – data to be transformed.

Returns

transformed data.

Return type

ndarray