workbench_algorithms.experimental.utils.numeric_utils

Assorted numerical utils.

l1_normalize

l1_normalize(input_list: Iterable[float]) -> list[float]

Normalize an input list with respect to the L1-orm of the elements.

l2_normalize

l2_normalize(
    input_list: Iterable[float],
    allow_complex: bool = False,
    eps: float = 1e-15,
) -> np.ndarray

Normalize a 1D array so that the sum of squares equals 1 (L2 norm).

Parameters:

Name	Type	Description	Default
input_list	Iterable[float]	Input array	required
allow_complex	bool	Whether to allow complex numbers	False
eps	float	Tolerance for zero norm	1e-15

Returns:

Type	Description
ndarray	L2-normalized array

l2_norm_diff

l2_norm_diff(arr1: np.ndarray, arr2: np.ndarray) -> float

Computes the L2-norm (Euclidean distance) between two real or complex arrays.

Parameters:

Name	Type	Description	Default
arr1	ndarray	First input array (real or complex).	required
arr2	ndarray	Second input array (real or complex).	required

Returns:

Type	Description
float	L2-norm distance between arr1 and arr2.

Raises:

Type	Description
ValueError	If array shapes don't match or types don't match.

generate_complex_array

generate_complex_array(
    n: int,
    real_range: tuple[int, int] = (-1, 1),
    imag_range: tuple[int, int] = (-1, 1),
    random_number_generator: np.random.Generator
    | None = None,
) -> np.ndarray

Generate an array of n complex numbers.

The array is generated with real and imaginary parts sampled from uniform distributions within specified ranges.

Parameters:

Name	Type	Description	Default
n	int	Number of complex numbers to generate.	required
real_range	tuple[int, int]	Range (min, max) for real parts.	(-1, 1)
imag_range	tuple[int, int]	Range (min, max) for imaginary parts.	(-1, 1)
random_number_generator	Generator | None	Generator for the random numbers.	None

Returns:

Type	Description
ndarray	Array of complex numbers.