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