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Stream reservoir sampling
SampleThicket is a Windows AI coding client for examining sampling logic over finite streams. Review a compatible model’s suggested reservoir updates, keep the seed explicit and test short-stream rejection before using the code.
● ● ●SampleThicket●
A concrete starting point · Stream reservoir sampling ×
from random import Random
def reservoir_sample(items, size, seed):
if type(size) is not int or not 0 <= size <= 32:
raise ValueError("size.range")
if type(seed) is not int or not 0 <= seed < 2**32:
raise ValueError("seed.range")
rng, sample, count = Random(seed), [], 0
for index, item in enumerate(items):
count = index + 1
if count > 1000:
raise ValueError("stream.limit")
if index < size:
sample.append(item)
elif size:
position = rng.randrange(count)
if position < size:
sample[position] = item
if size > count:
raise ValueError("stream.short")
return sample✳Select a reservoir of at most 32 items from a finite stream of at most 1000 items using a local seeded generator.
✓ ChangesReview before applying ↗
Illustrative workspace
Project
Repeat the seed to check reproducibility and reject a stream shorter than the sample, leaving input lists unchanged.
Review before applying
Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
A concrete starting point
from random import Random
def reservoir_sample(items, size, seed):
if type(size) is not int or not 0 <= size <= 32:
raise ValueError("size.range")
if type(seed) is not int or not 0 <= seed < 2**32:
raise ValueError("seed.range")
rng, sample, count = Random(seed), [], 0
for index, item in enumerate(items):
count = index + 1
if count > 1000:
raise ValueError("stream.limit")
if index < size:
sample.append(item)
elif size:
position = rng.randrange(count)
if position < size:
sample[position] = item
if size > count:
raise ValueError("stream.short")
return sampleMake sampling changes reproducible enough to review
Randomized code needs a review trail even when individual outputs vary. SampleThicket gives sampling-related project work a Windows AI coding workspace, where the source and its reproducibility assumptions can be discussed together. Open the relevant folder and describe the input stream, requested sample and expected handling of invalid requests.
For a finite reservoir-sampling task, ask the connected model to inspect how the seed is supplied and how the sample changes as items arrive. Keep a short known stream available for tracing. Separate a repeatable example from any broader claim about statistical quality; a fixed seed helps reproduce a path but does not prove unbiased behavior.
Review the request boundary before accepting implementation changes. A sample larger than the stream and a stream beyond the supported limit need explicit outcomes. Check how callers receive those failures, then run deterministic fixtures and any appropriate statistical evaluation using your own tools. Preserve the seed and relevant configuration when recording a result.
SampleThicket supports OpenAI-compatible or Anthropic endpoint connections. An available uncensored AI model may assist with inspection, subject to its model behavior and server limits. The workspace helps you prepare and examine a patch; the project’s reproducible checks provide evidence for the behavior you intend to keep.
Make sampling changes reproducible enough to review — questions before editing
- What should a reproducible sampling report contain?
Include the input sequence or a reproducible source, requested size, seed and relevant runtime details. Keep the example small enough to inspect when possible. This lets you distinguish a code change from a changed random path.
- Does matching a fixed-seed output prove unbiased sampling?
No. It establishes repeatability for that setup and case. Statistical properties require separate reasoning and suitable evaluation. Ask the model to distinguish those claims rather than treating one deterministic fixture as evidence of distribution quality.
- How should an oversized sample request behave?
State whether it must fail or follow another deliberate policy. For the finite-stream example, review short-stream rejection and its caller. Avoid silently returning fewer items if the consuming code assumes the requested count is guaranteed.
- Why retain limits in a sampling refactor?
Limits can define the accepted workload and failure contract. Ask the model to identify them before changing the loop or input handling. Test boundary cases so a refactor does not accidentally remove a deliberate guard.
SampleThicket · Updated 2026-10-25