Description
Sample a supplied normal mixture using one advancing checked source and retain component truth; density estimation and bandwidth selection are separate.
A chart dataset contains values plus the labels, grouping, totals, or missing-value conventions needed to interpret them. Separating the numeric construction from plotting makes conservation rules and deliberate edge cases visible before a chart is rendered.
Sample a supplied normal mixture using one advancing checked source and retain component truth; density estimation and bandwidth selection are separate.
When to use this tool
Use this generator to create reproducible violin density shape dataset 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 components, count 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 components, observations. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.
Normalize positive mixture weights by their largest value, draw a component index and a normal variate from one advancing source, and return mean+standardDeviation×z with its component truth label.1
Delegated operations used for this result: normal box muller sampler. Their calculation or parsing conventions apply to the corresponding result fields.2
| Input field | Meaning and units | Accepted input |
|---|---|---|
| components | Component loading vectors in component order, each aligned with the original feature columns. | Required. Array of Object; no unknown keys.; minimum items 1; maximum items 8. |
| components[].mean | Mean | Required. Finite number. |
| components[].standardDeviation | Standard Deviation | Required. Finite number; minimum 0. |
| components[].weight | Weight | Required. Finite number; minimum 0. |
| count | Requested count; the mechanics explain whether one draw expands into several returned records. | Required. Safe integer; minimum 1; maximum 64. |
| 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 |
|---|---|---|
| components | Component loading vectors in component order, each aligned with the original feature columns. | Required. Array of Object; no unknown keys.; minimum items 1; maximum items 8. |
| components[].mean | Mean | Required. Finite number. |
| components[].standardDeviation | Standard Deviation | Required. Finite number. |
| components[].weight | Weight | Required. Finite number. |
| observations | Observations | Required. Array of Object; no unknown keys.; minimum items 1; maximum items 64. |
| observations[].componentIndex | Component Index | Required. Safe integer. |
| observations[].value | Value | Required. Finite number. |
| 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.
{
"input": {
"components": [
{
"mean": 0,
"standardDeviation": 1,
"weight": 1
},
{
"mean": 10,
"standardDeviation": 2,
"weight": 1
}
],
"count": 1,
"seed": 42
},
"output": {
"components": [
{
"mean": 0,
"standardDeviation": 1,
"weight": 1
},
{
"mean": 10,
"standardDeviation": 2,
"weight": 1
}
],
"observations": [
{
"componentIndex": 1,
"value": 11.520715809172257
}
],
"randomness": {
"seed": 42,
"source": "seed"
}
}
}Limitations and assumptions
- At least one mixture weight must be positive. The observations are synthetic mixture draws; the tool does not calculate a violin density curve, choose a bandwidth or guarantee a particular sampled shape.
- 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
Similar or alternative tools
- Random Heatmap Block Structure Dataset Generator
Construct labeled heatmap blocks with bounded integer noise and optional mirrored symmetry, retaining the planted block mask.
- Random Histogram Bin Edge Challenge Generator
Generate observations exactly on or one supplied epsilon around explicit bin edges, retaining endpoint-policy membership and outside labels.