Random Weighted Shuffle Generator

Description

Order supplied positions by sequential delegated weighted draws, with explicit equal-value removal and original-order zero-weight tails.

Sampling selects from a supplied population under rules for replacement, grouping, or eligibility. Draw probabilities, inclusion probabilities, and repeat counts describe different aspects of selection; a reproducible sample is not automatically representative of a real population.

Order supplied positions by sequential delegated weighted draws, with explicit equal-value removal and original-order zero-weight tails.

When to use this tool

Use this generator to create reproducible weighted shuffle cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply candidates, uniqueness, zeroWeightPolicy in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read order, omittedIndices. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Repeatedly draw among remaining positive-weight positions in proportion to their current weights. Remove either the chosen position or all equal values according to uniqueness. Once no positive weight remains, append permitted zero-weight survivors in original order or omit them, and report omitted original indexes.^[1]

Input field	Meaning and units	Accepted input
candidates	Candidates	Required. Array of Object; no unknown keys.; minimum items 0; maximum items 512.
candidates[].value	Value	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
candidates[].weight	Weight	Required. Finite number; minimum 0.
uniqueness	Value mode removes every equal-valued position after a draw; duplicate weights contribute separate draw mass.	Required. Text; one of "position", "value".
zeroWeightPolicy	Zero-weight remnants are appended in supplied order or omitted after every positive-weight draw.	Required. Text; one of "append", "omit".
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
order	Order	Required. Array of Object; no unknown keys.; minimum items 0; maximum items 512.
order[].sourceIndex	Source Index	Required. Safe integer; minimum 0.
order[].value	Value	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 128.
order[].weight	Weight	Required. Finite number; minimum 0.
order[].selection	Selection	Required. Object; no unknown keys.
order[].selection.probability	Conditional probability of the selected source POSITION, not the aggregate probability of its value. Equal-valued positions have separate mass; zero-tail entries have null probability.	Required. Finite number; minimum 0; maximum 1. or Null.
order[].selection.kind	Kind	Required. Text; one of "weighted", "zero-tail".
omittedIndices	Omitted Indices	Required. Array of Safe integer; minimum 0.; minimum items 0; maximum items 512.
randomness	Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.	Required. Object; no unknown keys.
randomness.seed	Optional numeric replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "candidates": [
      {
        "value": "red",
        "weight": 1
      },
      {
        "value": "blue",
        "weight": 3
      },
      {
        "value": "gray",
        "weight": 0
      }
    ],
    "uniqueness": "position",
    "zeroWeightPolicy": "append",
    "seed": 42
  },
  "output": {
    "order": [
      {
        "value": "blue",
        "weight": 3,
        "sourceIndex": 1,
        "selection": {
          "probability": 0.75,
          "kind": "weighted"
        }
      },
      {
        "value": "red",
        "weight": 1,
        "sourceIndex": 0,
        "selection": {
          "probability": 1,
          "kind": "weighted"
        }
      },
      {
        "value": "gray",
        "weight": 0,
        "sourceIndex": 2,
        "selection": {
          "probability": null,
          "kind": "zero-tail"
        }
      }
    ],
    "omittedIndices": [],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Weights are nonnegative with a finite sum. Equal-value removal changes the remaining support, and zero-tail entries have null weighted-selection probability. This is a sequential weighted order rather than a uniform permutation.
A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random Weighted Shuffle Generator — tool.io

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