src.utils.ftransf.pca_transformer

Classes

PCATransformer(**kwargs)

class src.utils.ftransf.pca_transformer.PCATransformer(**kwargs)
Author:

Alberto M. Esmoris Pena

Class for transforming features by projecting them to a lower dimensionality space defined by the singular vectors of the centered matrix of features.

See FeatureTransformer.

Variables:

  • out_dim (int or float) – The number of features after the projection, i.e., the dimensionality of the output. It can be given as a float inside [0, 1] that represents how many variance must be preserved (1 preserves the 100%, 0 nothing).

  • whiten (False) – True to multiply the singular vectors by the square root of the number of points and divide by the corresponding singular value. False otherwise.

  • random_seed (int) – Optional attribute to specify a fixed random seed for the random computations of the model.

  • frenames (list) – The names for the output features (it must match the output dimensionality. If None, they will be determined automatically as PCA_{1}, …, PCA_{out_dim}.

  • pca – The internal PCA model.

static extract_ftransf_args(spec)

Extract the arguments to initialize/instantiate a PCATransformer.

Parameters:

spec – The key-word specification containing the arguments.

Returns:

The arguments to initialize/instantiate a PCATransformer.

__init__(**kwargs)

Initialize/instantiate a PCATransformer.

Parameters:

kwargs – The attributes for the PCATransformer.

transform(F, y=None, fnames=None, out_prefix=None)

The fundamental feature transform logic defining the PCA transformer.

See FeatureTransformer and feature_transformer.FeatureTransformer.transform().

get_names_of_transformed_features(**kwargs)

See FeatureTransformer and feature_transformer.FeatureTransformer.get_names_of_transformed_features()

build_new_las_header(pcloud)

See feature_transformer.FeatureTransformer.build_new_las_header().