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Random Insertion Gap Allocation

Replay seed or one advancing injected source, uniform over the explicitly stated finite support, not cryptographically secure. Assign each labeled insertion independently to one of N+1 original sequence gaps uniformly; repeated gaps permitted, not uniform over count compositions.

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Description

Assign each labeled insertion independently to one of N+1 original sequence gaps uniformly; repeated gaps permitted, not uniform over count compositions.

A random construction is defined by its sampling support: positions, permutations, complete tilings, proposal sequences or another explicit set. Uniform choices at one stage do not necessarily make final visible outcomes uniform. A replay seed gives a deterministic sequence; equal values can hide different sampled positions. These tools are for reproducible synthetic examples rather than cryptographic entropy.

Assign each labeled insertion independently to one of N+1 original sequence gaps uniformly; repeated gaps permitted, not uniform over count compositions.

When to use this tool

Use this tool to assign each labeled insertion independently to one of N+1 original sequence gaps uniformly; repeated gaps permitted, not uniform over count compositions.

How to use the tool and how it works

  1. Enter length, insertions, seed using the definitions below.
  2. Run the operation. Resolve input-format errors before interpreting an output.
  3. Read result.seed, result.gaps, result.counts, using the stated boundary, ordering, sign and absence conventions.

For a source length 0–127, draw each insertion gap independently from 0…length. Return the gap for every insertion and a count for each of length+1 gaps.1

Input fieldMeaning and unitsAccepted input
lengthExplicit integer source/sequence length under the bounds stated above.Finite safe integer satisfying the stated bounds.
insertionsNumber of independent labeled insertions, integer 1–128.Finite safe integer satisfying the stated bounds.
seedNonblank replay seed, at most 256 UTF-16 code units; an injected source overrides seeded draws.Text, at most 8,192 UTF-16 code units; narrower grammar/range rules also apply.
Output fieldInterpretation
resultObject containing the fields below.
result.seedOriginal supplied seed string; injected-source replay additionally requires that source’s state.
result.gapsOne sampled zero-based insertion gap per labeled insertion.
result.countsNumber of insertions allocated to every original gap.
json
{
  "input": {
    "length": 3,
    "insertions": 2,
    "seed": "batch56"
  },
  "output": {
    "result": {
      "seed": "batch56",
      "gaps": [
        2,
        3
      ],
      "counts": [
        0,
        0,
        1,
        1
      ]
    }
  }
}

Limitations and assumptions

  • insertions is 1–128. Assignments of labeled insertions are uniform, whereas aggregate gap-count vectors are not uniformly distributed.
  • A nonblank seed of at most 256 UTF-16 code units is required. With ordinary use the seed initializes one deterministic advancing source; an application-injected random source takes precedence and the seed alone then cannot replay the result. Uniformity statements assume independent uniform source draws in [0,1). Source calls are bounded and degenerate injected sources can fail a rejection budget. This is not cryptographically secure.
  • Every declared outer field is required and unknown outer fields are rejected. Strings and JSON text have size limits, lists are bounded to 128 entries, and JSON nesting/node counts are bounded. Tighter operation-specific bounds stated above take precedence.
  • Ordinary numeric outputs must be finite and within safe-integer magnitude. Exact large lengths/counts use decimal strings. Original record indexes and code-unit positions are zero-based; span ends are exclusive unless stated otherwise.

Alternative or Complementary methods

Choose a generator whose support matches the intended experiment and retain source-position provenance. Use a separate statistically justified process model or cryptographic source where those properties matter.

References

  1. Tool behavior and example: Random Insertion Gap Allocation — tool.io

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