NSA._base Class
The _Base class contains utility functions with the protected modifier that can be inherited by various classes for ease of use. It includes functions for distance calculation, data separation to improve training and prediction efficiency, accuracy measurement and other functions.
def score(...)
def score(self, X: npt.NDArray, y: list) -> float
Score function calculates forecast accuracy.
This function performs the prediction of X and checks how many elements are equal between vector y and y_predicted. This function was added for compatibility with some scikit-learn functions.
Parameters:
- X:
np.ndarrayFeature set with shape (n_samples, n_features). - y:
np.ndarrayTrue values with shape (n_samples,).
Returns:
- accuracy:
floatThe accuracy of the model.
Protected Functions:
def _distance(...):
def _distance(self, u: npt.NDArray, v: npt.NDArray)
Function to calculate the distance between two points by the chosen metric.
Parameters:
- u (
npt.NDArray): Coordinates of the first point. - v (
npt.NDArray): Coordinates of the second point.
returns:
- Distance (
double) between the two points.
def _check_and_raise_exceptions_fit(...):
def _check_and_raise_exceptions_fit(self, X: npt.NDArray = None, y: npt.NDArray = None, _class_: Literal['RNSA', 'BNSA'] = 'RNSA')
Function responsible for verifying fit function parameters and throwing exceptions if the verification is not successful.
Parameters:
- X (
npt.NDArray): Training array, containing the samples and their characteristics, [N samples(rows)][``N features`` (columns)]. - y (
npt.NDArray): Array of target classes ofXwith [N samples(lines)]. - class (Literal[RNSA, BNSA], optional): Current class. Defaults to 'RNSA'.
Abstract methods
def fit(...)
def fit(self, X: npt.NDArray, y: npt.NDArray, verbose: bool = True)
Fit the model to the training data.
Implementation:
def predict(...)
def predict(self, X) -> Optional[npt.NDArray]:
Performs label prediction for the given data.
Implementation: