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| import math | |
| import warnings | |
| import operator as op | |
| import itertools as it | |
| import functools as ft | |
| import statistics as st | |
| from dataclasses import 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: | |
| 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') | |