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import math |
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import warnings |
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import operator as op |
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import itertools as it |
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import functools as ft |
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import statistics as st |
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from dataclasses import dataclass |
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@dataclass |
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class HDI: |
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lower: float |
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upper: float |
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def __iter__(self): |
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yield from (self.lower, self.upper) |
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def __contains__(self, item): |
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return self.lower <= item <= self.upper |
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def width(self): |
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return self.upper - self.lower |
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class HDInterval: |
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@ft.cached_property |
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def values(self): |
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view = sorted(filter(math.isfinite, self._values)) |
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if not view: |
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raise AttributeError('Empty data set') |
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return view |
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def __init__(self, values): |
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self._values = values |
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def __call__(self, ci=0.95): |
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if ci == 1: |
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args = (self.values[x] for x in (0, -1)) |
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else: |
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n = len(self.values) |
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exclude = n - math.floor(n * ci) |
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left = it.islice(self.values, exclude) |
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right = it.islice(self.values, n - exclude, None) |
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diffs = ((x, y, y - x) for (x, y) in zip(left, right)) |
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(*args, _) = min(diffs, key=op.itemgetter(-1)) |
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return HDI(*args) |
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def _at(self, target, tolerance, ci=1, jump=1): |
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if ci > 1: |
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raise OverflowError() |
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hdi = self(ci) |
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if any(x in tolerance for x in hdi): |
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return ci |
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adjust = op.sub if target in hdi else op.add |
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ci = adjust(ci, jump) |
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jump /= 2 |
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return self._at(target, tolerance, ci, jump) |
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def at(self, target, tolerance=1e-4): |
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assert tolerance > 0 |
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while tolerance < 1: |
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hdi = HDI(target, target + tolerance) |
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try: |
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return self._at(target, hdi) |
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except RecursionError: |
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tolerance *= 10 |
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warnings.warn(f'Tolerance reduced: {tolerance}') |
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raise FloatingPointError('Unable to converge') |
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