SNN2.src.model.RLPerfEvaluation.correlation module

SNN2.src.model.RLPerfEvaluation.correlation.convert(val: Any, input_type: Type, Transformer: Callable) → Any

SNN2.src.model.RLPerfEvaluation.correlation.correlation(margin: ~tensorflow.python.framework.tensor.Tensor, probabilities: ~tensorflow.python.framework.tensor.Tensor, *args, current_episode: int | None = None, p0_correlation_threshold: str | float = -90.0, p1_correlation_threshold: str | float = 90.0, margin_chainging_point_threshold: str | float = 0.0, exp_exploration_threshold: str | int = 6, logger=None, write_msg=<function f_logger.<locals>.__dummy_log>, **kwargs) → bool

SNN2.src.model.RLPerfEvaluation.correlation.default(*args, logger=None, write_msg=<function f_logger.<locals>.__dummy_log>, **kwargs) → bool

SNN2.src.model.RLPerfEvaluation.correlation.get_correlation(a: ndarray, b: ndarray) → float

SNN2.src.model.RLPerfEvaluation.correlation.get_margin_chainging_point(m: ~numpy.ndarray, p: ~numpy.ndarray, *, logger=None, write_msg=<function f_logger.<locals>.__dummy_log>, **kwargs) → float | None