src.model.deeplearn.dlrun.hierarchical_fps_pre_processorpp

Classes

HierarchicalFPSPreProcessorPP(**kwargs)

class src.model.deeplearn.dlrun.hierarchical_fps_pre_processorpp.HierarchicalFPSPreProcessorPP(**kwargs)
Author:

Alberto M. Esmoris Pena

C++ version of the HierarchicalFPSPreProcessor.

__init__(**kwargs)

C++ version of FurthestPointSubsamplingPreProcessor.__init__().

__call__(inputs)

C++ version of FurthestPointSubsamplingPreProcessor.__call__().

static optimize_indexing_memory(I)

Optimize the encoding of the received array of indices (I) to use as few bytes as possible. This method assists HierarchicalFPSPreProcessorPP.optimize_indices().

Parameters:

I (np.ndarray of int) – The array of integer indices whose memory encoding must be optimized.

static optimize_indices(depth, NDs, NUs, Ns)

Optimize the memory required to encode the given hierarchical neighborhoods. This method is assisted by HierarchicalFPSPreProcessorPP.optimize_indexing_memory().

Parameters:

  • depth (int) – The depth of the hierarchy.

  • NDs (list) – List whose elements are the downsampling neighborhoods at each depth, i.e. NDs[d] gives the downsampling neighborhoods at depth d.

  • NUs (list) – List whose elements are the upsampling neighborhoods at each depth, i.e. NUs[d] gives the upsampling neighborhoods at depth d.

  • Ns (list) – List whose elements are the neighborhoods at a given depth, i.e. Ns[d] gives the upsampling neighborhoods at depth d.

Returns:

Nothing, but the hierarchical neighborhods (NDs, NUs, Ns) are updated in place.

reduce_labels(X_rf, y, I=None)

C++ version of FurthestPointSubsamplingPreProcessor.reduce_labels().

reduce_labels_python(X_rf, y, I=None)

Method that mimics a call to HierarchicalFPSPreProcessor.reduce_labels() to provide a Python alternative to label reduction.

NOTE that this method should only be used for testing and debugging purposes.