pkg_pyknnclassifier.find_neighbors

Module Contents

Functions

find_neighbors(labeled_arraies, unlabeled_array, k)

Finds the indices of the 'k' nearest neighbors in a collection of labeled arrays

pkg_pyknnclassifier.find_neighbors.find_neighbors(labeled_arraies, unlabeled_array, k)

Finds the indices of the ‘k’ nearest neighbors in a collection of labeled arrays to a given unlabeled array.

This function computes the distance between each labeled array and the unlabeled array using the ‘calculate_distance’ function. It then selects the ‘k’ labeled arrays that are closest to the unlabeled array.

Parameters:

• labeled_arrays (array-like) – An iterable (list, NumPy array, etc.) of labeled arrays. Each array represents an individual data point in the feature space.

• unlabeled_array (array-like) – The array representing the unlabeled data point for which the neighbors are to be found. It should have the same length as each array in labeled_arrays.

• k (int) – The number of nearest neighbors to find. ‘k’ must be a positive integer, and it should not exceed the number of arrays in labeled_arrays.

Returns:

indices – An array of indices of the ‘k’ nearest neighbors from the labeled_arrays.

Return type:

numpy.ndarray

Notes

• The distance measurement used depends on the ‘calculate_distance’ function’s definition.

• This function assumes that all arrays (both labeled and unlabeled) are of the same dimensionality and are compatible for distance calculations.

Example

labeled_arrays = [

np.array([1, 2, 3]), np.array([4, 5, 6]), np.array([7, 8, 9])

] unlabeled_array = np.array([2, 3, 4]) k = 2

indices = find_neighbors(labeled_arrays, unlabeled_array, k) print(indices) # Output: array([0, 1])