NoobJS

NoobJS contains Javascript implementations of many algorithms.

Algorithms Roadmap

Machine Learning

| Regression | Classifier | Cluster | | ---- | --- | --- | | ✔ Linear Regression | ✔ KNN Classifier | k-mean Clustering | | | KNN using kd-tree | | | | KNN using ball-tree | | | | Gaussian Naive Bayes Classifier | |

Math and Numbers

| Matrix | BigNumber | Prime | | ---- | --- | --- | | ✔ Matrix Multiplication (Brute Force) | ✔ Long Addition | ✔ Primity Test (Brute Force) | | ✔ Matrix Transpose | ✔ Long Subtraction | ✔ Sieve of Eranthoses | | ✔ Matrix Inverse (Gaussian Elimination) | ✔ Long Multiplication | Pollard's rho algorithm | | | ✔ Long Division ||

Data Structure

| Basic | Graph | | ---- | --- | | ✔ Queue | Depth-First Search | | ✔ Stack | Breadth-First search | | ✔ Priority Queue (Binary Heap) | Prim's algorithm | | ✔ Hash Priority Queue | |

Examples

Using Linear Regression

var { LinearRegression } = require('noobjs').ML;

var x = [ 
    [0.17], [2.47], [1.73], [4.43], [4.51], 
    [4.59], [1.24], [0.84], [0.09], [0.51], 
    [2.06], [2.89], [0.81], [4.39], [1.20] 
];

var y = [ 
    [100.0], [410.0], [250.0], [560.0], [440.0], 
    [510.0], [230.0], [170.0], [100.0], [140.0], 
    [250.0], [460.0], [160.0], [490.0], [210.0] 
];

var model = new LinearRegression();
model.fit(x, y);
console.log( model.predict( [ [4.0], [2.0], [1.5] ] ) );

Using Elementary Data Structures

var { PriorityQueue, Stack, Queue } = require('noobjs').Collections;

var p = new PriorityQueue();
p.push(7);
p.push(8);
p.push(6);

console.log(p.pop());  // 6
console.log(p.pop());  // 7
console.log(p.pop());  // 8

Using NoobJS to solve linear system of equations

var { LinearAlgebra } = noobjs.Numbers;

//  x + 2y -  z = 2
// 2x + 2y + 2z = 12
//  x -  y + 2z = 5
var a = [
  [ 1,  2,  -1],
  [ 2,  2,   2],
  [ 1, -1,   2]
];

var b = [ [2], [12], [5] ];

console.log(LinearAlgebra.mat_multiply(LinearAlgebra.mat_inverse(a), b));
// [ 1.00, 2.00, 3.00 ]