`culebra.trainer.aco.ACO_FSConvergenceDetector` class¶

class ACO_FSConvergenceDetector(convergence_check_freq: int | None = None)¶

Create a convergence detector.

Parameters:: convergence_check_freq (int, optional) – Convergence assessment frequency. If set to None, DEFAULT_CONVERGENCE_CHECK_FREQ will be used. Defaults to None

Methods¶

ACO_FSConvergenceDetector.has_converged(trainer) → None¶

Detect if the trainer has converged.

Parameters:: trainer (An ACO_FS instance) – The trainer
Returns:: True if the trainer has converged
Return type:: bool

This work is supported by projects New Computing Paradigms and Heterogeneous Parallel Architectures for High-Performance and Energy Efficiency of Classification and Optimization Tasks on Biomedical Engineering Applications (HPEE-COBE), with reference PGC2018-098813-B-C31, and Advanced Methods of Biomedical Data Analysis and Brain Modeling Optimized for High-Performance and Energy-Efficient Computing (HPEEC-BIOBRAIN), with reference PID2022-137461NB-C31, both funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, and by the European Regional Development Fund (ERDF).