Negative Selection

The functions perform detector checks and utilize Numba decorators for Just-In-Time compilation

Function check_detector_bnsa_validity(...):

def check_detector_bnsa_validity(
    x_class: npt.NDArray,
    vector_x: npt.NDArray,
    aff_thresh: float
) -> bool:

Checks the validity of a candidate detector (vector_x) against samples from a class (x_class) using the Hamming distance. A detector is considered INVALID if its distance to any sample in x_class is less than or equal to aff_thresh.

Parameters:

• x_class (npt.NDArray): Array containing the class samples. Expected shape: (n_samples, n_features).
• vector_x (npt.NDArray): Array representing the detector. Expected shape: (n_features,).
• aff_thresh (float): Affinity threshold.

returns:

• True if the detector is valid, False otherwise.

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Function bnsa_class_prediction(...):

def bnsa_class_prediction(
    features: npt.NDArray,
    class_detectors: npt.NDArray,
    aff_thresh: float
) -> int:

Defines the class of a sample from the non-self detectors.

Parameters:

• features (npt.NDArray): binary sample to be classified (shape: [n_features]).
• class_detectors (npt.NDArray): Array containing the detectors of all classes (shape: [n_classes, n_detectors, n_features]).
• aff_thresh (float): Affinity threshold that determines whether a detector recognizes the sample as non-self.

returns:

• int: Index of the predicted class. Returns -1 if it is non-self for all classes.

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Function check_detector_rnsa_validity(...):

def check_detector_rnsa_validity(
    x_class: npt.NDArray,
    vector_x: npt.NDArray,
    threshold: float,
    metric: int,
    p: float
) -> bool:

Checks the validity of a candidate detector (vector_x) against samples from a class (x_class) using the Hamming distance. A detector is considered INVALID if its distance to any sample in x_class is less than or equal to aff_thresh.

Parameters:

• x_class (npt.NDArray): Array containing the class samples. Expected shape: (n_samples, n_features).
• vector_x (npt.NDArray): Array representing the detector. Expected shape: (n_features,).
• threshold (float): threshold.
• metric (int): Distance metric to be used. Available options: [0 (Euclidean), 1 (Manhattan), 2 (Minkowski)]
• p (float): Parameter for the Minkowski distance (used only if metric is "minkowski").

returns:

• int: Index of the predicted class. Returns -1 if it is non-self for all classes.

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