StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics https://doi.org/10.1109/TVCG.2020.3030352
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StackGenVis/frontend/node_modules/robust-linear-solve/README.md

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robust-linear-solve
===================
An exact linear solver for low dimensional systems.
# Example
```javascript
var linSolve = require("robust-linear-solve")
var A = [ [1, 2, 3],
[3, 2, 1],
[0, 0, 1] ]
var b = [1, 2, 3]
console.log(linSolve(A, b))
```
Output:
```javascript
[ [ -14 ], [ 23 ], [ -12 ], [ -4 ] ]
```
# Install
```
npm install robust-linear-solve
```
# API
#### `require("robust-linear-solve")(A, b)`
Finds the exact solution to a linear system, `Ax = b`, using Cramer's rule.
* `A` is a `n`-by-`n` square matrix, encoded as an array of arrays
* `b` is an `n` dimensional vector encoded as a length `n` array
**Returns** A projective `n+1` dimensional vector of non-overlapping increasing sequences representing the exact solution to the system. That is to say, if `x` is the returned solution then in psuedocode we have the following constraint:
`A [ x[0], x[1], ... , x[n-1] ] = b * x[n]`
Or in other words, the solution is given by the quotient:
`[ x[0] / x[n], x[1] / x[n], .... , x[n-1] / x[n] ]`
If the system is not solvable, then the last coefficient, `x[n]` will be `0`.
**Note** For up to `n=5`, you can avoid the extra method look up by calling `linSolve[n]` directly.
# Credits
(c) 2014 Mikola Lysenko. MIT License