kpca module¶
The Kernel Principal Component Analysis (KPCA) to reduce the dimension of a variable.
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=None, fit_inverse_transform=True, kernel='linear', **parameters)[source]¶
Bases:
DimensionReduction
Kernel principal component dimension reduction algorithm.
- Parameters:
name (str) –
A name for this transformer.
By default it is set to “KPCA”.
n_components (int | None) – The number of components of the latent space. If
None
, use the maximum number allowed by the technique, typicallymin(n_samples, n_features)
.fit_inverse_transform (bool) –
If True, learn the inverse transform for non-precomputed kernels.
By default it is set to True.
kernel (str) –
The name of the kernel, either ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘cosine’ or ‘precomputed’.
By default it is set to “linear”.
**parameters (str) – The optional parameters for sklearn KPCA constructor.
- compute_jacobian(data)¶
Compute Jacobian of transformer.transform().
- compute_jacobian_inverse(data)¶
Compute Jacobian of the transformer.inverse_transform().
- duplicate()¶
Duplicate the current object.
- Returns:
A deepcopy of the current instance.
- Return type:
- fit(data, *args)¶
Fit the transformer to the data.
- fit_transform(data, *args)¶
Fit the transformer to the data and transform the data.
- CROSSED = False¶