Random Streaming Microcluster Benchmark Generator

Description

Generate one labeled Gaussian point sequence and recompute the existing order-dependent maximum-radius BIRCH-style microclusters at supplied batch-prefix boundaries; thresholds and reported centroids use one explicit uniform coordinate scale, preserve inherited ten-significant-digit centroids, and do not imply an incremental-state API or full BIRCH tree.

Synthetic statistical data separates a specified generating model from the values observed in a finite draw. A model parameter, a sample summary, and the result of a fitted analysis answer different questions; retaining them separately makes numerical and statistical checks easier to interpret.

When to use this tool

Use this generator to create reproducible streaming microcluster benchmark 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 clusters, batchSizes, shuffle, normalizedRadiusThreshold 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 points, sourceLabels, distanceScale, snapshots. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Generate labeled Gaussian cluster points, optionally shuffle them, normalize coordinates by a shared distance scale, and partition into exact batches. At every batch prefix, recompute the delegated radius-limited microclusters and return the resulting summaries with source labels.^[1]

Delegated operations used for this result: birch microclusters. Their calculation or parsing conventions apply to the corresponding result fields.^[2]

Input field	Meaning and units	Accepted input
clusters	Clusters	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 8.
clusters[].center	Center	Required. Array of Finite number.; minimum items 1; maximum items 8.
clusters[].standardDeviation	Standard Deviation	Required. Finite number; minimum 0.
clusters[].pointCount	Point Count	Required. Safe integer; minimum 1; maximum 128.
batchSizes	Batch Sizes	Required. Array of Safe integer; minimum 1; maximum 128.; minimum items 1; maximum items 16.
shuffle	Shuffle	Required. Boolean.
normalizedRadiusThreshold	Normalized Radius Threshold	Required. Finite number; minimum 0.
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
points	Points	Required. Array of Array of Finite number.; minimum items 1; maximum items 8.; minimum items 1; maximum items 128.
sourceLabels	Source Labels	Required. Array of Safe integer; minimum -1; maximum 7.; minimum items 1; maximum items 128.
distanceScale	Distance Scale	Required. Finite number; strictly above 0.
snapshots	Snapshots	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 16.
snapshots[].throughIndex	Through Index	Required. Safe integer; minimum 1; maximum 128.
snapshots[].pointCount	Point Count	Required. Safe integer; minimum 1; maximum 128.
snapshots[].clusters	Clusters	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 128.
snapshots[].clusters[].centroid	Centroid	Required. Array of Finite number.; minimum items 1; maximum items 8.
snapshots[].clusters[].count	Requested count; the mechanics explain whether one draw expands into several returned records.	Required. Safe integer; minimum 1; maximum 128.
snapshots[].clusterCount	Cluster Count	Required. Safe integer; minimum 1; maximum 128.
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": {
    "clusters": [
      {
        "center": [
          0
        ],
        "standardDeviation": 0,
        "pointCount": 2
      },
      {
        "center": [
          10
        ],
        "standardDeviation": 0,
        "pointCount": 2
      }
    ],
    "batchSizes": [
      2,
      2
    ],
    "shuffle": true,
    "normalizedRadiusThreshold": 0,
    "seed": 42
  },
  "output": {
    "points": [
      [
        10
      ],
      [
        0
      ],
      [
        0
      ],
      [
        10
      ]
    ],
    "sourceLabels": [
      1,
      0,
      0,
      1
    ],
    "distanceScale": 10,
    "snapshots": [
      {
        "throughIndex": 2,
        "pointCount": 2,
        "clusters": [
          {
            "centroid": [
              1
            ],
            "count": 1
          },
          {
            "centroid": [
              0
            ],
            "count": 1
          }
        ],
        "clusterCount": 2
      },
      {
        "throughIndex": 4,
        "pointCount": 4,
        "clusters": [
          {
            "centroid": [
              1
            ],
            "count": 2
          },
          {
            "centroid": [
              0
            ],
            "count": 2
          }
        ],
        "clusterCount": 2
      }
    ],
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

This is a synthetic benchmark of order-dependent clustering, not a persistent streaming API or a complete BIRCH tree. Prefix recomputation has a bounded work budget; returned centroids use the declared rounding convention.
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 Streaming Microcluster Benchmark Generator — tool.io
Calculation or validation: clustering/birch-microclusters — tool.io

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