"""Benchmark functions used in lab 5."""

from __future__ import annotations

import numpy as np
from numpy.typing import NDArray


Array = NDArray[np.float64]


def axis_parallel_hyperellipsoid(x: Array) -> float:
    """Axis-parallel hyper-ellipsoid benchmark function.

    Parameters
    ----------
    x:
        Point in :math:`\mathbb{R}^n`.

    Returns
    -------
    float
        The value of the hyper-ellipsoid function.
    """
    indices = np.arange(1, x.shape[0] + 1, dtype=np.float64)
    return float(np.sum(indices * np.square(x)))


def default_bounds(dimension: int, lower: float = -5.12, upper: float = 5.12) -> tuple[Array, Array]:
    """Construct symmetric bounds for each dimension."""
    x_min = np.full(dimension, lower, dtype=np.float64)
    x_max = np.full(dimension, upper, dtype=np.float64)
    return x_min, x_max