Random Mahjong Practice Draw Generator
Sample physical tile indices without replacement from a136tile four-copies-per-type mahjong teaching inventory. Imports the first34existing tile symbols; excludes flowers/seasons/jokers/backs, repeated glyphs are valid physical copies. Not a wall/deal simulator or gambling odds predictor.
Description
A teaching mahjong inventory can represent four physical copies of each selected tile type.
A teaching mahjong inventory can represent four physical copies of each selected tile type.1
Drawing distinct physical positions permits repeated tile glyphs without replacement.
When to use Random Mahjong Practice Draw Generator
Use this tool when you need to produce the reproducible draw or model experiment described below. Prepare the seed and the exact inventory or scenario inputs described below. Keep units, reference edition, and sampling convention explicit.
How to use Random Mahjong Practice Draw Generator and understand its results
- Prepare the seed and the exact inventory or scenario inputs described below. Keep units, reference edition, and sampling convention explicit.
- Enter seed, count, following the field table and calculation rules below, then run the tool.
- Read result. Inspect the nested fields in the output table below and keep any missing evidence or selected indices visible.
- Correct any input issues and run again. Before using a successful result, review its units, signs, evidence coverage, and stated model or reference assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| seed | Nonblank reproducibility seed; default seeded runs restart for each request | string | Required |
| count | Number of physical tiles | safe integer from 1 to 64 | Required |
Build 136 physical entries from the first 34 symbols of the imported mahjong generator, four copies each. Draw count in [1,64] without replacement using one advancing source. Return pieces, physical indices, inventory size, and undrawn count. Flowers, seasons, jokers, and backs are excluded; this is not a wall or deal simulator.2, 3, 4
Use a nonblank seed of at most 256 UTF-16 units and a safe-integer count from 1 to 64. By default the seed is hashed into a deterministic pseudorandom state; each run restarts that state, so identical full inputs and implementation reproduce the result. The SDK may provide context.random, which replaces the seeded source and advances across the request; the seed remains required. Every source value must be finite in [0,1), with a request limit of 100000 source calls. Draw an index as floor(U×remainingSize). With replacement draw from the full inventory each time; without replacement remove each chosen position from an ordered pool and reject counts exceeding the inventory size. Uniform-source draws are over positions, not necessarily distinct values. This is not secure randomness.
inventorySize=34×4=136; undrawn=136−count Read these symbols with the field meanings and branch or reference rules above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Object containing the fields below | object |
| result.pieces | Pieces | list |
| result.physicalPieceIndices | Zero-based physical tile positions, distinct even when glyphs match | list |
| result.inventorySize | Inventory Size | number |
| result.undrawnPieceCount | Undrawn Piece Count | number |
{
"input": {
"seed": "tool.io/batch42",
"count": 1
},
"output": {
"result": {
"pieces": [
"🀞"
],
"physicalPieceIndices": [
123
],
"inventorySize": 136,
"undrawnPieceCount": 135
}
}
}Limitations and assumptions
The default source is deterministic pseudorandomness, not an independently audited or secure draw. Replacement applies to inventory positions; distinct positions may render identically or yield equal answers. Review the supplied inventory or scenario domain independently before interpreting the sample.
Missing required fields, unknown fields, wrong types, and violated calculation rules return input issues. Numeric values must be finite; nonfinite numeric outputs are rejected. Extremely large composite outputs are rejected.
Alternative or Complementary methods
For a related task, use Random Urn Sampling Experiment Generator: An urn inventory represents physical items whose labels may repeat. Sampling distinct item positions preserves repeated colors or labels as valid outcomes. For a complementary step, use Random Xiangqi Piece Generator: Xiangqi pieces have role names and side-specific written labels. This generator samples a small annotated teaching inventory rather than constructing a legal game position.
References
-
Random Mahjong Practice Draw Generator — tool.io
-
Calculation or validation: random/number — tool.io
Similar or alternative tools
- Random Urn Sampling Experiment Generator
An urn inventory represents physical items whose labels may repeat. Sampling distinct item positions preserves repeated colors or labels as valid outcomes.
- Random Xiangqi Piece Generator
Xiangqi pieces have role names and side-specific written labels. This generator samples a small annotated teaching inventory rather than constructing a legal game position.