Function similarity_search

machine_learning/similarity_search.py:37–140

:param dataset: Set containing the vectors. Should be ndarray. :param value_array: vector/vectors we want to know the nearest vector from dataset. :return: Result will be a list containing 1. the nearest vector 2. distance from the vector >>> dataset = np.ar

(
    dataset: np.ndarray, value_array: np.ndarray
)

def similarity_search(
    dataset: np.ndarray, value_array: np.ndarray
) -> list[list[list[float] | float]]:
    """
    :param dataset: Set containing the vectors. Should be ndarray.
    :param value_array: vector/vectors we want to know the nearest vector from dataset.
    :return: Result will be a list containing
            1. the nearest vector
            2. distance from the vector

    >>> dataset = np.array([[0], [1], [2]])
    >>> value_array = np.array([[0]])
    >>> similarity_search(dataset, value_array)
    [[[0], 0.0]]

    >>> dataset = np.array([[0, 0], [1, 1], [2, 2]])
    >>> value_array = np.array([[0, 1]])
    >>> similarity_search(dataset, value_array)
    [[[0, 0], 1.0]]

    >>> dataset = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]])
    >>> value_array = np.array([[0, 0, 1]])
    >>> similarity_search(dataset, value_array)
    [[[0, 0, 0], 1.0]]

    >>> dataset = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]])
    >>> value_array = np.array([[0, 0, 0], [0, 0, 1]])
    >>> similarity_search(dataset, value_array)
    [[[0, 0, 0], 0.0], [[0, 0, 0], 1.0]]

    These are the errors that might occur:

    1. If dimensions are different.
    For example, dataset has 2d array and value_array has 1d array:
    >>> dataset = np.array([[1]])
    >>> value_array = np.array([1])
    >>> similarity_search(dataset, value_array)
    Traceback (most recent call last):
        ...
    ValueError: Wrong input data's dimensions... dataset : 2, value_array : 1

    2. If data's shapes are different.
    For example, dataset has shape of (3, 2) and value_array has (2, 3).
    We are expecting same shapes of two arrays, so it is wrong.
    >>> dataset = np.array([[0, 0], [1, 1], [2, 2]])
    >>> value_array = np.array([[0, 0, 0], [0, 0, 1]])
    >>> similarity_search(dataset, value_array)
    Traceback (most recent call last):
        ...
    ValueError: Wrong input data's shape... dataset : 2, value_array : 3

    3. If data types are different.
    When trying to compare, we are expecting same types so they should be same.
    If not, it'll come up with errors.
    >>> dataset = np.array([[0, 0], [1, 1], [2, 2]], dtype=np.float32)
    >>> value_array = np.array([[0, 0], [0, 1]], dtype=np.int32)
    >>> similarity_search(dataset, value_array)  # doctest: +NORMALIZE_WHITESPACE
    Traceback (most recent call last):