approximate non central chi squared

construct_inverse_non_central_chi_squared_interpolated_polynomial_approximation(degrees_of_freedom, polynomial_order=1, n_intervals=16, n_interpolating_functions=16)

Computes a polynomial approximation to the inverse cumulative distribution function for the non-central $\chi^2$ distribution for a fixed number of degrees of freedom $\nu$. The approximation is parametrised by a non-centrality parameter $\lambda$.

Parameters:

Name	Type	Description	Default
degrees_of_freedom	float	The degrees of freedom $\nu$.	required
polynomial_order	int	The polynomial order.	1
n_intervals	int	The number of intervals.	16
n_interpolating_functions	int	The number of interpolating functions for interpolating the non-centrality parameter $\lambda$.	16

Returns:

Type	Description
Callable	The approximation.

Source code in src/pyarv/non_central_chi_squared/_approximate_non_central_chi_squared.py

def construct_inverse_non_central_chi_squared_interpolated_polynomial_approximation(degrees_of_freedom: float,
                                                                                    polynomial_order: int =1,
                                                                                    n_intervals: int =16,
                                                                                    n_interpolating_functions:int =16) -> Callable:
    r"""
    Computes a polynomial approximation to the inverse cumulative distribution function for the non-central
    \( \chi^2 \) distribution for a fixed number of degrees of freedom \( \nu \). The approximation is parametrised
    by a non-centrality parameter \( \lambda \).

    Parameters
    ----------
    degrees_of_freedom
        The degrees of freedom \( \nu \).
    polynomial_order
        The polynomial order. 
    n_intervals
        The number of intervals. 
    n_interpolating_functions
        The number of interpolating functions for interpolating the non-centrality parameter \( \lambda \). 

    Returns
    -------
    Callable
        The approximation. 
    """
    interpolation_function = lambda f: f ** 0.5
    interpolation_function_deriv_first = lambda f: 0.5 * f ** -0.5
    interpolation_function_deriv_second = lambda f: -0.25 * f ** -1.5

    interpolation_function_contour_spacing = 1.0 / (n_interpolating_functions - 1)
    interpolation_values = ([interpolation_function(1.0) - n * interpolation_function_contour_spacing for n in range(n_interpolating_functions - 1)] + [interpolation_function(0)])[::-1]  # interpolation key values
    interpolation_points = [0.0] + [root_scalar(lambda a: interpolation_function(a) - y, x0=0.5, bracket=[0.0, 1.0], fprime=interpolation_function_deriv_first, fprime2=interpolation_function_deriv_second).root for y in interpolation_values[1:-1]] + [1.0]  # non-centrality for interpolating functions
    # We approximate the function P
    functions_exact = [None] * n_interpolating_functions  # The exact functions
    functions_exact[0] = norm.ppf  # Limiting case as y -> 0
    # The following odd syntax with y=... ensures y is evaluated at declaration and not taken by reference:
    functions_exact[1:-1] = [lambda u, y=y_interpolation_points: np.sqrt(degrees_of_freedom / (4.0 * y)) * (y / degrees_of_freedom * ncx2.ppf(u, df=degrees_of_freedom, nc=(1.0 - y) * degrees_of_freedom / y) - 1.0) for y_interpolation_points in interpolation_points[1:-1]]
    functions_exact[-1] = lambda u: np.sqrt(degrees_of_freedom / 4.0) * (chi2.ppf(u, df=degrees_of_freedom) / degrees_of_freedom - 1.0)
    functions_approx = [dyadic_function_approximation_constructor(f, n_intervals, polynomial_order) for f in progressbar(functions_exact)]  # By piecewise dyadic construction

    def construct_linear_interpolation(functions: tuple[Callable, Callable], 
                                       weightings: tuple[float, float]):
        """
        Builds a linear interpolation between two functions.
        """
        f1, f2 = functions
        w1, w2 = weightings
        return lambda u: f1(u) * w1 + f2(u) * w2

    def get_interpolation_functions_and_weightings(non_centrality: float):
        """
        Determines the interpolation functions to use and their weights.
        """
        interpolation_value = interpolation_function(non_centrality)
        insertion_index = bisect(interpolation_values, interpolation_value, lo=0)
        lower_index, upper_index = insertion_index - 1, insertion_index
        assert lower_index >= 0
        assert upper_index <= len(interpolation_values)
        if upper_index == len(interpolation_values):
            return [[functions_approx[lower_index]] * 2, [1.0, 0.0]]
        functions = [functions_approx[i] for i in [lower_index, upper_index]]
        interpolation_lower, interpolation_upper = [interpolation_values[i] for i in [lower_index, upper_index]]
        w_lower = (interpolation_upper - interpolation_value) / (interpolation_upper - interpolation_lower)
        w_upper = 1.0 - w_lower
        weights = [w_lower, w_upper]
        return [functions, weights]

    def inverse_non_central_chi_squared_interpolated_polynomial_approximation(u: Array, non_centrality: float):
        """
        Polynomial approximation to the inverse cumulative distribution function for the non-central
        \( \chi^2 \) distribution
        """
        functions, weightings = get_interpolation_functions_and_weightings(degrees_of_freedom / (non_centrality + degrees_of_freedom))
        interpolated_function = construct_linear_interpolation(functions, weightings)
        return non_centrality + degrees_of_freedom + 2.0 * np.sqrt(non_centrality + degrees_of_freedom) * interpolated_function(u)

    return inverse_non_central_chi_squared_interpolated_polynomial_approximation

dyadic_function_approximation_constructor(f, n_intervals, polynomial_order)

Constructs a piecewise polynomial approximation to a function $f$ which is piecewise $L^2$ optimal on dyadic intervals on each of $[0, \tfrac{1}{2} )$ and $[\tfrac{1}{2}, 1]$.

Parameters:

Name	Type	Description	Default
f	Callable	$f$.	required
n_intervals	int	The number of intervals.	required
polynomial_order	int	The polynomial order.	required

Returns:

Type	Description
Callable	The approximations.

Source code in src/pyarv/non_central_chi_squared/_approximate_non_central_chi_squared.py

def dyadic_function_approximation_constructor(f: Callable, n_intervals: int, polynomial_order:int) -> Callable:
    r"""
    Constructs a piecewise polynomial approximation to a function \( f \)  which is piecewise
    \( L^2 \) optimal on dyadic intervals on each of \( [0, \tfrac{1}{2} ) \) and \( [\tfrac{1}{2}, 1] \).

    Parameters
    ----------
    f
        \( f \).
    n_intervals
        The number of intervals.
    polynomial_order
        The polynomial order. 

    Returns
    -------
    Callable
        The approximations.
    """
    f_lower = f
    f_upper = lambda u, *args, **kwargs: f(1.0 - u, *args, **kwargs)
    coeffs_lower = piecewise_polynomial_coefficients_in_half_interval(f_lower, n_intervals, polynomial_order)
    coeffs_upper = piecewise_polynomial_coefficients_in_half_interval(f_upper, n_intervals, polynomial_order)
    index_of_dyadic_interval = construct_index_of_dyadic_interval(n_intervals)

    def inverse_cumulative_distribution_function_approximation(u):
        """
        Polynomial approximation of the inverse cumulative distribution function.
        """
        in_lower = (u < 0.5)
        u = u * in_lower + np.logical_not(in_lower) * (1.0 - u)
        interval = index_of_dyadic_interval(u)
        y_lower, y_upper = [sum([coeffs[i][interval] * u ** i for i in range(polynomial_order + 1)]) for coeffs in [coeffs_lower, coeffs_upper]]
        y = y_lower * in_lower + y_upper * np.logical_not(in_lower)
        return y

    return inverse_cumulative_distribution_function_approximation