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Unscaled median absolute deviation
MedianRove is a Windows AI coding client for examining median-based spread calculations. Ask a compatible model to review finite-number limits and outlier examples, then assess the proposed code in your own project.
● ● ●MedianRove●
A concrete starting point · Unscaled median absolute deviation ×
import math
def median_deviation(values):
if type(values) is not list or not 1 <= len(values) <= 4096:
raise ValueError("mad.input")
if any(type(x) not in (int, float) or not -10**100 <= x <= 10**100 or not math.isfinite(x) for x in values):
raise ValueError("mad.value")
def median(items):
ordered = sorted(items)
n = len(ordered)
return float(ordered[n//2]) if n % 2 else ordered[n//2-1]/2+ordered[n//2]/2
center = median(values)
return center, median([abs(x-center) for x in values])
✳Unscaled median absolute deviation
✓ ChangesReview before applying ↗
Illustrative workspace
Project
Test even lengths, equal values and an outlier, rejecting Booleans or nonfinite values while leaving the input order intact.
Review before applying
Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
A concrete starting point
import math
def median_deviation(values):
if type(values) is not list or not 1 <= len(values) <= 4096:
raise ValueError("mad.input")
if any(type(x) not in (int, float) or not -10**100 <= x <= 10**100 or not math.isfinite(x) for x in values):
raise ValueError("mad.value")
def median(items):
ordered = sorted(items)
n = len(ordered)
return float(ordered[n//2]) if n % 2 else ordered[n//2-1]/2+ordered[n//2]/2
center = median(values)
return center, median([abs(x-center) for x in values])
Make statistical helpers report the quantity you intended
Two functions can both claim to measure spread while returning quantities with different meanings. MedianRove is Windows AI coding software for reviewing those distinctions in your project. Bring the helper, the caller and a small reference dataset into the conversation so a proposed change preserves the statistic your application actually uses.
A practical task is inspecting an unscaled median absolute deviation calculation. Ask the connected model to examine the median convention for even-length inputs and the treatment of an outlier. State whether any scaling belongs elsewhere in the application, so the review does not silently introduce a different output under the same function name.
Follow the result into display, thresholds or stored records. A numeric change can affect decisions beyond the helper even when its return type stays the same. Review finite-value checks and the accepted size range, then run independently checked fixtures in your runtime. Include an input-order check if the caller expects its original collection to remain untouched.
MedianRove connects to supported OpenAI-compatible or Anthropic endpoints. An abliterated AI model can be selected when offered by your service, with behavior and tool support determined by the model and server. Its explanations support the review; your dataset expectations and project tests determine whether the patch is suitable.
Make statistical helpers report the quantity you intended — questions before editing
Why specify unscaled deviation in the request?
Scaling changes the quantity returned and can affect downstream thresholds. State which statistic the helper promises and inspect callers for additional transformations. A refactor should not quietly change interpretation while preserving the same name.
Which dataset makes an outlier discussion concrete?
Use a short set with a clear central cluster and one distant value, alongside an independently calculated median and deviation. Add an even-length case to expose the median convention. Avoid relying only on random data.
Should sorting be allowed to mutate the input?
Make that ownership rule explicit. If callers reuse the original order, an in-place sort can create an unrelated defect. Inspect the implementation and check the input after execution as well as the returned statistic.
What invalid values should the review consider?
Use the helper’s stated type, size and finite-number contract. Ask the model to preserve or deliberately revise each boundary, then test rejection and caller handling. Do not assume a plausible statistic makes malformed input acceptable.
MedianRove · Updated 2026-10-25