hdinterval.py

import math
import warnings
import operator as op
import itertools as it
import functools as ft
import statistics as st
from dataclasses import dataclass

@dataclass
class HDI:
    lower: float
    upper: float

    def __iter__(self):
        yield from (self.lower, self.upper)

    def __contains__(self, item):
        return self.lower <= item <= self.upper

    def width(self):
        return self.upper - self.lower

class HDInterval:
    @ft.cached_property
    def values(self):
        view = sorted(filter(math.isfinite, self._values))
        if not view:
            raise AttributeError('Empty data set')

        return view

    def __init__(self, values):
        self._values = values

    #
    # See https://cran.r-project.org/package=HDInterval
    #
    def __call__(self, ci=0.95):
        if ci == 1:
            args = (self.values[x] for x in (0, -1))
        else:
            n = len(self.values)
            exclude = n - math.floor(n * ci)

            left = it.islice(self.values, exclude)
            right = it.islice(self.values, n - exclude, None)

            diffs = ((x, y, y - x) for (x, y) in zip(left, right))
            (*args, _) = min(diffs, key=op.itemgetter(-1))

        return HDI(*args)

    def _at(self, target, tolerance, ci=1, jump=1):
        if ci > 1:
            raise OverflowError()

        hdi = self(ci)
        if any(x in tolerance for x in hdi):
            return ci

        adjust = op.sub if target in hdi else op.add
        ci = adjust(ci, jump)
        jump /= 2

        return self._at(target, tolerance, ci, jump)

    def at(self, target, tolerance=1e-4):
        assert tolerance > 0

        while tolerance < 1:
            hdi = HDI(target, target + tolerance)
            try:
                return self._at(target, hdi)
            except RecursionError:
                tolerance *= 10
                warnings.warn(f'Tolerance reduced: {tolerance}')

        raise FloatingPointError('Unable to converge')