Stable Summation and Mean Calculation
Install and import#
npm install fintech-algorithmsimport { stableSummationAndMeanCalculation } from "fintech-algorithms/financial-mathematics-statistics-and-data-foundations/statistical-computing-and-reproducibility/stable-summation-and-mean-calculation";Signature#
stableSummationAndMeanCalculation(input)Worked example#
verified This is the worked example published in the article, replayed by the test suite on every run. The output cannot drift.
Input#
{
"values": [10000000000000000, 1, -10000000000000000, 3],
"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#
stableSummationAndMeanCalculation(input)Returns#
object with 2 fields: naiveSum, stableSum
{
"naiveSum": 3,
"stableSum": 4
}Diagrams#
How it works#
This page states the contract — how to call it correctly. The article explains the concept: why it works, and where it breaks.
References#
- Floating-Point Arithmetic — IEEE 754-2019
- math.fsum — Python Documentation
- Random Sampling — NumPy Documentation
- Common Pitfalls and Recommended Practices — scikit-learn
- Historical-example decision