# Pseudorandom Numbers, Seeds, and Reproducibility

`D00-F12-A06` · Financial Mathematics, Statistics, and Data Foundations → Statistical Computing and Reproducibility · archetype `record-transform` · difficulty 1/5 · verification **verified**

Full page: https://docs.thefintechbuilder.com/foundations/statistical-computing-and-reproducibility/pseudorandom-numbers-seeds-and-reproducibility/
Agent skill: `npx skills add IslamBaraka90/Fintech-Algorithms-Library` — https://docs.thefintechbuilder.com/guides/agent-skill/

## Install and import

```bash
npm install fintech-algorithms
```

```ts
import { pseudorandomNumbersSeedsAndReproducibility } from "fintech-algorithms/foundations/statistical-computing-and-reproducibility/pseudorandom-numbers-seeds-and-reproducibility";
```

## Signature

```ts
pseudorandomNumbersSeedsAndReproducibility(input)
```

Draws a run of unit-interval numbers from a seeded linear congruential generator, so the same seed always replays the same stream.

## Parameters

| Name | Type | Required | Notes |
| --- | --- | --- | --- |
| `input` | `D00Input` | yes | Reads `seed`, the 32-bit starting state, and `sampleCount`, how many draws to take. A non-empty `values` list of finite numbers must also be present; it is validated but not used here. |

## Returns

`D00Output`

`samples` holds the draws, each in the half-open interval from zero to one, `seed` is echoed back, `finalState` is the generator state left after the last draw, and `reproducible` is always true.

## Errors

- When `values` is absent, empty, or holds a non-finite number — throws RangeError
- When `sampleCount` is below one — throws RangeError

## Complexity

Time `O(n)`, space `O(n)`.

## Worked example

This is the worked example published in the article, replayed by the test suite on every run. The output cannot drift.

### Input

`input`:

```json
{
  "values": [1, 2, 3, 4, 5],
  "floatingValue": 0.1,
  "maxSafeMagnitude": 1.7976931348623157e+308,
  "window": 3,
  "rawValues": [1, null, 2, "bad", 3],
  "invalidPolicy": "drop-and-report",
  "seed": 42,
  "sampleCount": 5,
  "leftVector": [
    {
      "index": "A",
      "value": 1
    },
    {
      "index": "B",
      "value": 2
    }
  ],
  "rightVector": [
    {
      "index": "B",
      "value": 20
    },
    {
      "index": "C",
      "value": 30
    }
  ],
  "train": [10, 12, 14, 16],
  "test": [18, 20],
  "actual": [1, 2.0000001, 3],
  "expected": [1, 2, 3]
}
```

Showing 14 of 18 fields.

### Call

```ts
pseudorandomNumbersSeedsAndReproducibility(input)
```

### Returns

object with 2 fields: samples, seed

```json
{
  "samples": [
    0.2523451747838408,
    0.08812504541128874,
    0.5772811982315034,
    0.22255426598712802,
    0.37566019711084664
  ],
  "seed": 42
}
```

## Verification and provenance

Tier: **verified** (via input-expected).

The worked example below is the figure published in this algorithm's article, replayed and asserted by the test suite on every build. The arithmetic cannot drift without the build failing.

Both tiers guarantee the signature. Full explanation: https://docs.thefintechbuilder.com/guides/verification/

Generated from the docs.json payload shipped inside fintech-algorithms@0.13.0.
The signature and parameter list are checked against the compiled implementation at build time,
so a description that contradicts the code fails the build rather than reaching this file.

## Links

- Article (how it works, step by step): https://thefintechbuilder.com/foundations/statistical-computing-and-reproducibility/pseudorandom-numbers-seeds-and-reproducibility/
- Implementation source: https://github.com/IslamBaraka90/Fintech-Algorithms-Library/blob/main/src/foundations/statistical-computing-and-reproducibility/pseudorandom-numbers-seeds-and-reproducibility/impl.ts
- Package on npm: https://www.npmjs.com/package/fintech-algorithms
- Domain index for agents: https://docs.thefintechbuilder.com/foundations/llms.txt