pca module¶

The Principal Component Analysis (PCA) to reduce the dimension of a variable.

The PCA class wraps the PCA from Scikit-learn.

Dependence¶

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

class gemseo.mlearning.transform.dimension_reduction.pca.PCA(name='PCA', n_components=None, **parameters)[source]¶

Principal component dimension reduction algorithm.

Parameters:

name (str) –
A name for this transformer.

By default it is set to “PCA”.
n_components (int | None) – The number of components of the latent space. If None, use the maximum number allowed by the technique, typically min(n_samples, n_features).
**parameters (str) – The optional parameters for sklearn PCA constructor.

compute_jacobian(data)[source]¶

Compute Jacobian of transformer.transform().

Parameters:: data (ndarray) – The data where the Jacobian is to be computed.
Returns:: The Jacobian matrix.
Return type:: ndarray

compute_jacobian_inverse(data)[source]¶

Compute Jacobian of the transformer.inverse_transform().

Parameters:: data (ndarray) – The data where the Jacobian is to be computed.
Returns:: The Jacobian matrix.
Return type:: ndarray

duplicate()¶

Duplicate the current object.

fit(data, *args)¶

Fit the transformer to the data.

Parameters:

Return type:

NoReturn

fit_transform(data, *args)¶

Fit the transformer to the data and transform the data.

Parameters:

Returns:

The transformed data.

Return type:

ndarray

inverse_transform(data)[source]¶

Perform an inverse transform on the data.

transform(data)[source]¶

Transform the data.