https://github.com/mljs/linear-sum-assignment
Package to perform a linear sum assignment even if the cost matrix is rectangular.
https://github.com/mljs/linear-sum-assignment
Last synced: 5 months ago
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Package to perform a linear sum assignment even if the cost matrix is rectangular.
- Host: GitHub
- URL: https://github.com/mljs/linear-sum-assignment
- Owner: mljs
- License: mit
- Created: 2022-05-03T18:22:46.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-03-16T10:29:18.000Z (over 1 year ago)
- Last Synced: 2025-02-24T04:42:17.959Z (10 months ago)
- Language: TypeScript
- Homepage:
- Size: 454 KB
- Stars: 0
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# linear sum assignment
Package to perform a linear sum assignment even if the cost matrix is rectangular.
This package is the implementation of Jonker-Volgenant shortest
augmenting path algorithm based on the publication [On implementing 2D rectangular assignment algorithms](https://doi.org/10.1109/TAES.2016.140952)
If the number of rows is <= the number of columns, then every row is assigned to one column; otherwise every column is assigned to one row. The assignment minimizes the sum of the assigned elements.
## Instalation
`$ npm i linear-sum-assignment`
## Usage
```js
import linearSumAssignment from 'linear-sum-assignment';
import { xCostMatrix } from 'ml-spectra-processing';
/**
* there is one more value in the experimental values, so one of
* them will be not assigned.
**/
const experimental = [1, 2, 3, 4, 5, 7];
const predicted = [3.1, 1.1, 1.9, 3.99, 5.2];
/**
* We will compute a cost matrix where experimental are
* rows and predicted in columns.
* In this case we will look for the closest peak for each experimental peak value.
**/
const diff = xCostMatrix(experimental, predicted, {
fct: (a, b) => Math.abs(a - b),
});
const result = linearSumAssignment(diff, { maximaze: false });
console.log(result);
/**
{
rowAssignments: Float64Array(6) [ 1, 2, 0, 3, 4, -1 ],
columnAssignments: Float64Array(5) [ 2, 0, 1, 3, 4 ],
gain: 0.5100000000000002,
dualVariableForColumns: Float64Array(5) [
0.0900000000000003,
0.0900000000000003,
0.0900000000000003,
0,
0.1900000000000004
],
dualVariableForRows: Float64Array(6) [ 0, 0, 0, 0, 0, 0 ]
}
*/
```
`rowAssignments` contains the index of the column assigned to each element in the rows (experimental).
`columnAssignments` contains the index of the row assigned to each element in the columns. So the first element in predicted is assigned to third element in experimental.
`dualVariableForColumns` and `dualVariableForRows` are the Lagrange multipliers or dual variables.
`gain` the sum of the elements in the cost matrix.
## License
[MIT](./LICENSE)
[npm-image]: https://img.shields.io/npm/v/linearSumAssignment.svg
[npm-url]: https://www.npmjs.com/package/linearSumAssignment
[ci-image]: https://github.com/mljs/linear-sum-assignment/workflows/Node.js%20CI/badge.svg?branch=main
[ci-url]: https://github.com/mljs/linear-sum-assignment/actions?query=workflow%3A%22Node.js+CI%22
[codecov-image]: https://img.shields.io/codecov/c/github/mljs/linear-sum-assignment.svg
[codecov-url]: https://codecov.io/gh/mljs/linear-sum-assignment
[download-image]: https://img.shields.io/npm/dm/linearSumAssignment.svg
[download-url]: https://www.npmjs.com/package/linearSumAssignment