src.utils.ptransf.min_dist_decimator_decorator

Classes

MinDistDecimatorDecorator(**kwargs)

class src.utils.ptransf.min_dist_decimator_decorator.MinDistDecimatorDecorator(**kwargs)
Author:

Alberto M. Esmoris Pena

Class representing a minimum distance decimator that can be used to decorate different components of the framework.

A point cloud \(\pmb{P} = [\pmb{X} | \pmb{F} | \pmb{y}]\) can be reduced to a minimum distance decimated representation \(\pmb{P'} = [\pmb{X'} | \pmb{F'} | \pmb{y'}]\). More concretely, an input point cloud with dimensionalities \(\pmb{X} \in \mathbb{R}^{m \times n_x}\), \(\pmb{F} \in \mathbb{R}^{m \times n_f}\), \(\pmb{y} \in \mathbb{Z}^{m}\) will be transformed to a representation with dimensionalities \(\pmb{X'} \in \mathbb{R}^{R \times n_x}\), \(\pmb{F'} \in \mathbb{R}^{R \times n_f}\), \(\pmb{y'} \in \mathbb{Z}^{R}\) where \(R \leq m\).

Variables:

min_distance (float) – The minimum distance such that any pair of points in the decimated representation must be separated by at least this distance.

__init__(**kwargs)

Initialize/instantiate a minimum distance decimator

Parameters:

kwargs – The key-word specification to instantiate the MinDistDecimatorDecorator.

Keyword Arguments:
transform(X, F=None, y=None, out_prefix=None)

See PointCloudSamplerDecorator.transform().

static apply_min_distance_decimation(X, min_distance)

Apply the minimum distance decimation algorithm to the given structure space.

Parameters:
  • X (np.ndarray) – The structure space to be decimated.

  • min_distance (float) – The minimum distance between points governing the decimation.

Returns:

The decimated representation of the input structure space.

Return type:

np.ndarray