Machine learning in Javascript

Install

``
npm i mult --save
`

Usage

$3

#### ES6
`ecma script level 6
import { Matrix, LinearRegression, append, dot, determinant, inverse, LUPDecompose, prepend, solve, transpose } from 'jonatanai'
`
#### CommonJS
`javascript
const { Matrix, LinearRegression, append, dot, determinant, inverse, LUPDecompose, prepend, solve, transpose } = require('jonatanai')

`

$3

`
let a = [[2, 2], [2, 2]]
new Matrix([[2, 2], [2, 2]])
Matrix.from([[2, 2], [2, 2]])
`

$3

`
let a = [[2, 2], [2, 2]]
new Matrix(a)
Matrix.from(a)
a[0][0] = 3 // Will change the matrices. If this is not desired do new Matrix(a, true)
`

$3

`
Matrix.empty(2) // = [[undefined, undefined], [undefined, undefined]]
Matrix.empty(2, 3) // = [[undefined, undefined], [undefined, undefined], [undefined, undefined]]
`

$3

`
Matrix.ones(2) // = [[1, 1], [1, 1]]
Matrix.ones(2, 3) // = [[0, 0], [0, 0], [0, 0]]
`




$3

`
Matrix.zeros(2) // = [[0, 0], [0, 0]]
Matrix.zeros(2, 3) // = [[0, 0], [0, 0], [0, 0]]
`

$3

`
Matrix.identity(3) // = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]
`

$3


`
let a = [[2, 2], [2, 2]]
let b = [[2, 2], [2, 2]]
matmul(a, b) // = [[8, 8], [8, 8]]

const m1 = new Matrix(a)
const m2 = new Matrix(b)

m1.matmul(m2) // = [[8, 8], [8, 8]]

`


$3

`
const a = [[1,2],[3,4],[5,6]]

transpose(a) // [[1,3,5],[2,4,6]]
Matrix.from(a).transpose() // [[1,3,5],[2,4,6]]
Matrix.from(a).T // [[1,3,5],[2,4,6]]
`

$3

`
determinant([[1, 2], [3, 4]]) // -2
determinant([[1, 3, 5, 9], [1, 3, 1, 7], [4, 3, 9, 7], [5, 2, 0, 9]]) // -376
`

$3

`
inverse([[1, 2], [3, 4]]) // [[-2, 1], [3 / 2, -1 / 2]]

inverse([[1, 3, 5, 9], [1, 3, 1, 7], [4, 3, 9, 7], [5, 2, 0, 9]])
// = [
// [-13 / 47, 2 / 47, 7 / 47, 6 / 47],
// [-5 / 8, 7 / 8, 1 / 4, -1 / 4],
// [39 / 376, -56 / 376, 13 / 188, -9 / 188],
// [55 / 188, -41 / 188, -13 / 94, 9 / 94]
//]
`

$3

`
const v = [0,0,0]
const a = [
[1,2,3],
[4,5,6],
[7,8,9]
]
prepend(a,v,0)
// const a = [
// [0,0,0],
// [1,2,3],
// [4,5,6],
// [7,8,9]
// ]
prepend(a,v,1)
// const a = [
// [0,1,2,3],
// [0,4,5,6],
// [0,7,8,9]
// ]
`

$3

`
const v = [0,0,0]
const a = [
[1,2,3],
[4,5,6],
[7,8,9]
]
append(a,v,0)
// const a = [
// [1,2,3],
// [4,5,6],
// [7,8,9],
// [0,0,0]
// ]
append(a,v,1)
// const a = [
// [1,2,3,0],
// [4,5,6,0],
// [7,8,9,0]
// ]
`

$3


`
const m1 = Matrix.from([[1, 1], [1, 1]])
const m2 = Matrix.from([[1, 1], [1, 1]])
m1.add(m2) // [[2, 2], [2, 2]]
`

$3

`
const m1 = Matrix.from([[2, 2], [2, 2]])
const m2 = Matrix.from([[1, 1], [1, 1]])
m1.subtract(m2) // [[1, 1], [1, 1]]
`

$3

`
let m = Matrix.from([[1, 1], [1, 1]])
m.norm() // 2

m = Matrix.from([[2, 2], [2, 2]])
m.norm() // 4

m = Matrix.from([[3, 3], [3, 3]])
m.norm() // 6
`




$3

`
let A = [[2, 3, -2], [1, -1, -3], [1, 5, 2]]
let B = [7, 5, 10]
solve(A, B) // [99, -35, 43]
`

$3

`
const clf = new LinearRegression()
const X = [
[0.18, 0.89],
[1.0, 0.26],
[0.92, 0.11],
[0.07, 0.37],
[0.85, 0.16],
[0.99, 0.41],
[0.87, 0.47],
]
const y = [
[109.85],
[155.72],
[137.66],
[76.17],
[139.75],
[162.6],
[151.77],
]
const X_test = [
[0.49, 0.18],
[0.57, 0.83],
[0.56, 0.64],
[0.76, 0.18],
]
const y_test = [
[105.21455835],
[142.67095131],
[132.93605469],
[129.70175405],
]
clf.train(lr_X, lr_y)
clf.predict(lr_X_test) // === y_test
`

$3

Return a new copy of the matrix shorthand for new Matrix(m.toArry())

`
const a = [[1,2],[3,4],[5,6]]

Matrix.from(a).copy() // [[1,2],[3,4],[5,6]]
`

$3

`
m = Matrix.ones(4, 3)
m.size() // = [4,3]
`

$3

`
m = Matrix.fill(3, 3, 4)
m.toString()


[4,4,4]

[4,3,3]

[4,4,4]

``

License

CC BY-NC-ND 4.0