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undetermined is a measurement-analysis library for estimating quantities from program runs—and it can refuse to return a number when the measurements do not support one. Instead of treating every fitted value as trustworthy, it reports an estimate with uncertainty when its checks pass, or an explicit undetermined result with a reason when they do not.
What undetermined measures
This is not a general-purpose curve fitter for arbitrary datasets. The caller supplies an adapter that runs a target program at a series of input sizes and exposes named observables. Each observable is evaluated as a function of the input size, called “truth” in the example, and a seed. The library analyzes those measurements without needing to know what the target program does.
That makes the approach relevant when a quantity is believed to be constant but is not directly available: examples include bytes per record or operations per element. The program is run at a ladder of sizes, and the library checks whether an observable behaves like a stable constant rather than simply emitting a value.
When it returns an estimate—and when it refuses
The article describes three safeguards that determine whether an estimate is credible enough to report:
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- Seed responsiveness: an observable that ignores its seed raises immediately. The method relies on comparing behavior across seeds, so a seed-insensitive observable does not meet its basic premise.
- A stable plateau: a candidate constant must settle across three consecutive input-size rungs within two combined standard errors. If it continues moving, the result is undetermined and includes a reason.
- Enough evidence to distinguish candidates: an observable must vary by at least three times its measurement error to count as informative. To select one candidate over another, it must also beat its runner-up by that same factor. An adapter with only one observable raises rather than implying that a choice has been demonstrated.
These are rules described by the package’s author, not independently tested guarantees. Their practical value is that a refusal is tied to a visible failure condition: the observable may not respond to the seed, may not have settled, or may not stand out clearly enough from alternatives.
What the demo shows
The article’s example 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 expected to be 2 in that example. The result illustrates an estimate accompanied by an error bar; it is a software-demo output, not a general population statistic or an independently replicated measurement.
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The same demo reports flat as UNDETERMINED. Its values do not agree across three consecutive rungs and are still moving at the largest rung. The refusal makes the distinction between “the program produced a number” and “the measurements support a stable constant” explicit.
Why version 0.2.0 changed deterministic measurements
Version 0.2.0 is described as correcting a defect that affected deterministic observations. Previously, uncertainty came from Type A scatter across repeated draws: sd/sqrt(N). If every repetition returned exactly the same value, the observed scatter—and therefore its standard error—was zero. The ladder builder could then drop that rung and conclude that the constant could not be determined.
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The correction combines Type A uncertainty with a Type B component based on the measurement’s reporting resolution:
u = sqrt(u_A² + u_B²)
The article defines the Type B component as granule/sqrt(12), where the granule is the reporting resolution. Unlike the repeated-draw component, this resolution term is not divided by sqrt(N). Repeating an identical measurement cannot make the instrument or reporting granularity more precise. The article says a test had previously asserted the defective behavior, while the sibling package countfn exposed the problem through deterministic operation counts.
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Python and JavaScript packages
The article describes the Python and JavaScript packages as the same code tree at the same version, distributed through PyPI and npm. It says the implementations share thresholds, explanatory strings, and number-formatting behavior to keep results aligned. That is a claim about intended cross-language behavior, not evidence of independent benchmarks or a maintenance guarantee.
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →| Integration | Install command given in the article | What the article says |
|---|---|---|
| Python | pip install undetermined |
Python distribution through PyPI; described as sharing the tree and version with JavaScript. |
| JavaScript | npm install undetermined |
JavaScript distribution through npm; described as sharing thresholds and output behavior with Python. |
The commands are those given in the article; they do not independently verify current registry availability.
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The author also says an external nondet dependency was considered but not used: its function-address model did not fit the library’s closure-based observables. The reproducibility check was instead implemented natively in both language packages.
How to interpret the refusal
An undetermined result is not a claim that the quantity can never be measured. It means the measurements supplied to this run did not satisfy the stated criteria. A reader or developer should use the accompanying reason to identify the failure mode, then consider whether the observable, seed behavior, or chosen size ladder needs attention. A value that clears the checks is still an estimate with uncertainty, not an exact constant stripped of measurement limits.
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