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undetermined is a measurement-analysis library for estimating quantities from program runs—and it can refuse to give an estimate when its evidence does not support one. Rather than fitting arbitrary datasets, it is described as helping determine whether an observable, such as bytes per record or operations per element, has settled to a constant as input size changes.
What the library measures—and what it does not
The caller supplies an adapter that runs the target program at a sequence of input sizes and exposes named observables. In the article’s example, observables are functions of an input size, called “truth,” and a seed. The library analyzes those measurements; it does not need to know the program’s internal workings.
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That makes undetermined narrower than a general-purpose curve-fitting package. Its practical question is whether measurements across input sizes support a stable constant, not which curve best describes an arbitrary dataset.
When it returns a value—and when it refuses
The library is described as producing either an estimate with an uncertainty or an explicit UNDETERMINED result with a reason. That refusal matters because a number alone can look convincing even when the measurement is still changing or the observable does not provide enough evidence.
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Seed responsiveness
An observable that ignores its seed raises immediately. The method relies on comparing behavior across seeds, so an observable that does not respond to them cannot satisfy that check.
A three-rung plateau
A candidate constant must settle across three consecutive input-size rungs: the values must agree within two combined standard errors. If the observable continues moving instead, the result is reported as undetermined with a reason.
Enough variation to be informative
An observable must vary by at least three times its measurement error to count as informative. To select one candidate over another, the leading candidate must beat its runner-up by the same factor. An adapter with only one observable raises rather than treating a choice as demonstrated.
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What the demonstration shows
The article’s demonstration uses input sizes 8, 32, 128, and 512, with 2,500 trials. It reports heads as 1.9978 +/- 0.0032 for a fair-coin factor whose expected value in that example is 2. It reports flat as undetermined because three consecutive rungs do not agree and the value is still moving at the largest input size.
Those figures are illustrative software-demo outputs, not population statistics or independently replicated measurements. The useful contrast is between a measured value with an error bar and a refusal that identifies continued movement as the reason.
Why version 0.2.0 changed deterministic measurements
Version 0.2.0 is described as correcting a defect affecting deterministic observations. Previously, the error bar relied on Type A uncertainty from repeated-draw scatter, calculated as sd/sqrt(N). If every draw returned exactly the same value, the scatter and its standard error were zero. The ladder builder could then drop that rung and report that the constant could not be determined.
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u = sqrt(u_A² + u_B²)
The Type B component is described as granule/sqrt(12), where granule is the reporting resolution. Unlike the scatter-based term, this component is not divided by sqrt(N): taking more identical readings does not make the instrument’s reporting resolution finer.
The article says a test had asserted the earlier behavior and that the sibling package countfn exposed the issue through deterministic operation counts. The correction is intended to keep a deterministic reading from being treated as having zero uncertainty merely because repeated values have no observed scatter.
Python and JavaScript packages
The article describes the Python and JavaScript packages as the same tree at the same version, distributed through PyPI and npm. It says thresholds, explanatory strings, and number formatting are shared to keep output behavior aligned. That is a claim about implementation and intended consistency, not evidence of equal performance, independent benchmarks, or a maintenance guarantee.
| Package ecosystem | Language integration | Installation command shown in the article |
|---|---|---|
| PyPI | Python | pip install undetermined |
| npm | JavaScript | npm install undetermined |
The commands are the installation commands given in the article; they do not independently establish current registry availability.
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The article also says an external nondet dependency was considered but not used because its function-address model did not fit closure-based observables. The reproducibility check was instead implemented natively in both package halves.
Why refusing to answer can be the useful result
As original author Seth Wheeler puts it: “A tool that always produced a constant would be useless and would still pass every test that checks it produces one.” The library’s central idea is to make uncertainty and failure to establish a constant part of the result, rather than forcing every measurement into a confident-looking number.
For someone measuring program behavior, the key output is therefore not just an estimate. It is whether the observable responds as expected, whether it stabilizes across input sizes, and whether the evidence is strong enough to distinguish it from alternatives.
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