Description
Generate bounded minterm indices with delegated binary assignments and balanced map dimensions, without Gray-code placement or minimization.
A generated practice exercise contains a prompt and an answer key under a specific rule set. A planted answer establishes one possible solution; only an explicit enumeration or uniqueness check establishes whether alternatives exist.
Generate bounded minterm indices with delegated binary assignments and balanced map dimensions, without Gray-code placement or minimization.
When to use this tool
Use this generator to create reproducible karnaugh minterm practice corpus 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 minimumVariables, maximumVariables, 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 cases. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.
Draw a variable count and minterm index, name variables x1 onward, and delegate binary minterm decoding and Karnaugh grid dimensions. Return bits, minterm text, and grid evidence for every case.1
Delegated operations used for this result: minterm index binary decoder; kmap cell count grid resolver. Their calculation or parsing conventions apply to the corresponding result fields.2, 3
| Input field | Meaning and units | Accepted input |
|---|---|---|
| minimumVariables | Minimum Variables | Required. Safe integer; minimum 0; maximum 12. |
| maximumVariables | Maximum Variables | Required. Safe integer; minimum 0; maximum 12. |
| 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 |
|---|---|---|
| cases | Cases | Required. Array of Object; no unknown keys.; minimum items 1; maximum items 64. |
| cases[].variables | Variables | Required. Array of Text.; minimum items 0; maximum items 12. |
| cases[].index | Index | Required. Safe integer. |
| cases[].bits | Bits | Required. Text. |
| cases[].minterm | Minterm | Required. Text. |
| cases[].grid | Grid | Required. Object; no unknown keys. |
| cases[].grid.cellCount | Cell Count | Required. Safe integer. |
| cases[].grid.rowCount | Row Count | Required. Safe integer. |
| cases[].grid.columnCount | Column Count | Required. Safe integer. |
| cases[].grid.rowVariables | Row Variables | Required. Safe integer. |
| cases[].grid.columnVariables | Column Variables | Required. Safe integer. |
| 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": {
"minimumVariables": 2,
"maximumVariables": 4,
"count": 2,
"seed": 42
},
"output": {
"cases": [
{
"variables": [
"x1",
"x2",
"x3"
],
"index": 3,
"bits": "011",
"minterm": "!x1 && x2 && x3",
"grid": {
"cellCount": 8,
"rowCount": 2,
"columnCount": 4,
"rowVariables": 1,
"columnVariables": 2
}
},
{
"variables": [
"x1",
"x2",
"x3",
"x4"
],
"index": 10,
"bits": "1010",
"minterm": "x1 && !x2 && x3 && !x4",
"grid": {
"cellCount": 16,
"rowCount": 4,
"columnCount": 4,
"rowVariables": 2,
"columnVariables": 2
}
}
],
"randomness": {
"seed": 42,
"source": "seed"
}
}
}Limitations and assumptions
- The index range is zero through two-to-the-variable-count minus one. A minterm and grid dimensions do not produce a filled Gray-code map or a Boolean-minimization solution.
- 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 Linear Congruence Practice Set Generator
Generate bounded linear congruences with exact delegated residue/period keys and enumerate every solution class modulo the supplied generated modulus.
- Random Modular Inverse Practice Set Generator
Generate bounded integer/modulus practice pairs and delegate exact modular inverses or retain documented noninvertibility answers.