How to use the library

var ml = require('math-lib');
console.log(ml().numberHelper().checkPrime(7));

Function listing

checkPrime(input)

Checks if the input number is a prime number or not. Input number should be a positive integer.

var nh = require('math-lib');
console.log(ml().numberHelper().checkPrime(7)); // returns true
console.log(ml().numberHelper().checkPrime(25)); // returns true
console.log(ml().numberHelper().checkPrime(-25)); // returns Error
console.log(ml().numberHelper().checkPrime(25.7)); // returns Error

getFibonacciSeries(a0, a1, numberRequired)

Generates an array of Fibonacci Series that includes a0 and a1, which are passed as parameters.

var nh = require('math-lib');
console.log(ml().numberHelper().getFibonacciSeries(0, 1, 10)); // returns [ 0, 1, 1, 2, 3, 5, 8, 13, 21, 34 ];

getFibonacciSeriesUpto(a0, a1, upperLimit)

Generates an array of Fibonacci Series upto a user specified upper limit.

var nh = require('math-lib');
console.log(ml().numberHelper().getFibonacciSeriesUpto(0, 1, 10)); // returns [ 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89 ];

listPrimeNumbersUpto(input)

Generates a list all prime numbers up to a given input. Implements Sieve of Eratosthenes.

var nh = require('math-lib');
console.log(ml().numberHelper().listPrimeNumbersUpto(10)); // returns [ 2, 3, 5, 7 ]

getPrimeFactors(input)

Generates a list all prime factors of a given input.

var nh = require('math-lib');
console.log(ml().numberHelper().getPrimeFactors(600851475143)); // returns [ 71, 839, 1471, 6857 ]

computeSlope(foo, x)

Computes the first derivative (slope) of a function foo at x. Central difference method is used to compute the first derivative. Methodology used to compute the first derivative and the choice of step size is detailed here: https://en.wikipedia.org/wiki/Numerical_differentiation User has to ensure that the function foo is differentiable at x.

var nh = require('math-lib');
console.log(ml().numberHelper().computeSlope(function(x) {
  return x * x;
}, 2)); //returns 4.00000000

var foo2 = function(x) {
  return Math.sin(x);
};
console.log(ml().numberHelper().computeSlope(foo2, Math.PI)); // returns -0.99999999

computeSlope(foo, x)

Computes the second derivative of a function foo at x. Central difference method is used to compute the second derivative. Methodology used to compute the second derivative and the choice of step size is detailed here: https://en.wikipedia.org/wiki/Second_derivative User has to ensure that the function foo is differentiable twice at x.

console.log('foo1', ml().numberHelper().computeSecondDerivative(function(x) {
  return x * x;
}, 2)); // returns 1.999999

newtonRaphson(foo, x, numIterations)

Computes the nearest root of function foo using Newton Raphson method. The root finding algorithm begins the search at x and iterates for the root for the number of times as specified by the second parameter - numIterations.

var foo1 = function(x) {
  return x * x - 5 * x + 6;
};

function newtonRaphsonTest() {
  console.log(ml().numberHelper().newtonRaphson(foo1, -100, 100));
} // returns 2

function newtonRaphsonTest() {
  console.log(ml().numberHelper().newtonRaphson(foo1, -100, 100));
} // returns 2.999999

gaussianElimination(input)

Solves a series of simultaneous equtions using Gauss Jordan Elimination. Details of the method can be found here: https://en.wikipedia.org/wiki/Gaussian_elimination

@param {Number[Number[]]} input - A matrix is an array of arrays of numbers. Hence, the input is an array of arrays of numbers that describes the augmented matrix which is an input to the method. For example, for equations (2x + 3y = 5) and (7x + 10y = 17), the augmented matrix that is to be passed as an input is as follows: input = [[2, 3, 5], [7, 10, 17]];

@returns {[Number[]} - An array of array of numbers - an array output consisting of the rightmost column of the augmented matrix.

var set1 = [[2, 1, -1, 8],
             [-3, -1, 2, -11],
             [-2, 1, 2, -3]];

var set2  = [[2, 3, 5], [7, 10, 17]];

console.log(ml().matrixHelper().gaussianElimination(set1));
// returns [2, 3, -1]

console.log(ml().matrixHelper().gaussianElimination(set2));
// returns [1, 2]