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Largest-remainder allocation
QuotaMosaic provides a Windows workspace for AI-assisted review of allocation logic. Open your project folder, discuss the remainder and tie rules with a compatible model and test the proposed changes against the required total.
● ● ●QuotaMosaic●
A concrete starting point · Largest-remainder allocation ×
def allocate(weights, seats):
if type(weights) not in (list, tuple) or len(weights) > 1000:
raise ValueError("weights.container")
weights = tuple(weights)
if type(seats) is not int or not 0 <= seats <= 10000:
raise ValueError("seats.range")
if len(weights) > 1000 or any(type(w) is not int or w < 0 for w in weights):
raise ValueError("weights.invalid")
total = sum(weights)
if total == 0:
if seats:
raise ValueError("weight.zero")
return [0] * len(weights)
result = [w * seats // total for w in weights]
remaining = seats - sum(result)
order = sorted(range(len(weights)), key=lambda i: (-(weights[i] * seats % total), i))
for i in order[:remaining]:
result[i] += 1
return result✳Allocate up to 10000 integer seats across at most 1000 nonnegative integer weights using largest remainders.
✓ ChangesReview before applying ↗
Illustrative workspace
Project
Check zero weights and tied remainders with input-order tie-breaking, rejecting a positive allocation with zero total weight before division.
Review before applying
Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
A concrete starting point
def allocate(weights, seats):
if type(weights) not in (list, tuple) or len(weights) > 1000:
raise ValueError("weights.container")
weights = tuple(weights)
if type(seats) is not int or not 0 <= seats <= 10000:
raise ValueError("seats.range")
if len(weights) > 1000 or any(type(w) is not int or w < 0 for w in weights):
raise ValueError("weights.invalid")
total = sum(weights)
if total == 0:
if seats:
raise ValueError("weight.zero")
return [0] * len(weights)
result = [w * seats // total for w in weights]
remaining = seats - sum(result)
order = sorted(range(len(weights)), key=lambda i: (-(weights[i] * seats % total), i))
for i in order[:remaining]:
result[i] += 1
return resultMake allocation changes explain their totals and ties
An allocation routine can satisfy the total while distributing individual shares differently from the rule users expect. QuotaMosaic is Windows AI coding software for examining that distinction in your own project. Bring the implementation, the caller and a few expected allocations into a conversation before asking for a source change.
One review task is a largest-remainder allocation where whole units must be assigned from nonnegative weights. Ask the connected model to inspect how the base allocation and remaining units fit together. Include a tied-remainder example and state the tie rule explicitly, so a change in ordering does not quietly alter the outcome.
Review the interface as carefully as the arithmetic. Define what an empty input or an all-zero total should mean when units are requested, then inspect how failures reach the caller. Run the project checks against small independently verified examples and assert both the final sum and individual assignments. Preserving one invariant does not prove the other.
QuotaMosaic connects to supported OpenAI-compatible or Anthropic endpoints. An abliterated AI model can be selected where available, with its capabilities and limits set by the model and server. Use the model to examine the rules and prepare a reviewable patch, then verify the resulting behavior in your application.
Make allocation changes explain their totals and ties — questions before editing
What should I state about ties before requesting a patch?
Specify how equal remainders are ordered, including whether original input order matters. Provide a tied example with the expected allocation. Otherwise a seemingly harmless sort change can alter which recipient receives the final unit.
Is checking the sum enough to verify the change?
No. Several different allocations can have the same total. Check the assigned amount for each recipient and the tie outcome as well. Use independent expected examples rather than only repeating the implementation’s calculation in a test.
How should zero total weight be treated?
Define separate expectations for requesting no units and requesting a positive number. Review the existing failure contract with the caller. The model should not invent recipients or divide by an undefined total to make the function return something.
Does the example define every allocation policy?
It defines a particular review context with whole units and nonnegative weights. If your application has caps, minimums or other priorities, include them explicitly. Inspect whether the proposed change still implements the policy your users require.
QuotaMosaic · Updated 2026-10-25