numnjs

Lightweight numerical analysis C++ add-on for nodejs

Description

numnjs is a lightweight numerical analysis C++ add-on for nodejs.
Although, numnjs has been tested, it’s still in a development phase, and its API can change in future versions.
At the moment, numnjs doesn’t implement any major speed optimizations to archive maximum speed.
It’s biggest speed gain derives from the fact that it’s implemented in C++ as opposed to JavaScript.
However, future versions might be significantly faster by adding of speed optimizations.
Many numnjs functions handle NaN and Inf gracefully by bypassing their calculations. Although, this behavior might change in order to speed up the calculations.

Use numnjs when:

You don’t need a full-blown numerical analysis package.
You want a functionality similar to numpy.
Your data contains NaN and Inf.
Your data can be Arrays, TypedArray (Float32Array and Float64Array), or matrices (Array of Array).

Installation

[sudo] npm install -g numnjs

Requirements

numnjs uses node-gyp to compile the C++ addon to binary format.
So read the node-gyp requirements section to determined what is the required software for your platform.

numnjs is implemented in C++11, therefore the C++ compiler must support C++11 compilation.

Data Types

a: Array, TypedArray(Float32Array or Float64Array).
m: Matrix(Array of Array).
v: Real Number.
k: Scalar (Real Number).
d: Dimensions (Positive number).
T: Output Data Type:
- numnjs.AUTO = 0;
- numnjs.NUMBER = 1;
- numnjs.ARRAY = 10;
- numnjs.MATRIX = 11;
- numnjs.FLOATARRAY32 = 20;
- numnjs.FLOATARRAY64 = 21;

Functions Categories

numnjs is organized into files to resemble the numpy method’s organization, as defined in these documents:

But internally, numnjs functions are organized according to their data inputs and outputs:

Category	Description
Aggregator	Input: Array/TypedArray or Matrix. Output: Real Number.
Transform	Input: Array/TypedArray or Matrix. Output: Same type as input.
Binary	Input: two Array/TypedArray or Matrices of same dimension and length. Output: Same type as the first input, except if 1st input is a scalar.
nInputs	Input: n Array/TypedArray or Matrices Output: Real Number, Array/TypedArray or Matrix.
Generator	Input: zero Data Output: Array/TypedArray or Matrix.

Note: For TypedArray different from Float32Array or Float64Array, it returns Float64Array.

Invalid Data

Inf and NaN are ignored in the calculations.
When there is more than one data input, if at least one is invalid, it returns the value of the first input if this invalid other the 2nd input.

Example:
avr([5.5,NaN,6.5,Inf])==avr([5.5,6.5]).
add([4,Inf,6,NaN],[6,NaN,Inf,Inf])=[10,Inf,Inf,Nan].

Functions

Information

Function	Description
s = ver()	Returns the numnjs version in semver format

Trigonometric

Function	Description
am = sin(am)	Returns the sine of each element of the input
am = cos(am)	Returns the cosine of each element of the input
am = tan(am)	Returns the tangent of each element of the input
am = arcsin(am)	Returns the inverse sine of each element of the input
am = arccos(am)	Returns the inverse cosine of each element of the input
am = arctan(am)	Returns the inverse tangent of each element of the input
am = degrees(am)	Returns the `xi * (180 / PI)` of each element of the input
am = radians(am)	Returns the `xi / (180 / PI)` of each element of the input

Hyperbolic

Function	Description
am = sinh(am)	Returns the hyperbolic sine of each element of the input
am = cosh(am)	Returns the hyperbolic cosine of each element of the input
am = tanh(am)	Returns the hyperbolic tangent of each element of the input
am = arcsinh(am)	Returns the inverse hyperbolic sine of each element of the input
am = arccosh(am)	Returns the inverse hyperbolic cosine of each element of the input
am = arctanh(am)	Returns the inverse hyperbolic tangent of each element of the input

Rounding

Function	Description
am = round(am)	Returns the the input rounded to the nearest number
am = floor(am)	Returns the the input rounded to the lowerest number
am = ceil(am)	Returns the the input rounded to the highest number

Sums, products, differences

Function	Description
v = sum(am)	Returns the sum of all the values

Exponents and logarithms

Function	Description
am = exp(am)	Returns the exponential of each element of the input
am = expm1(am)	Returns the `exp(xi) -1` of each element of the input
am = exp2(am)	Returns the natural logarithm of each element of the input
am = log(am)	Returns the natural logarithm of each element of the input
am = log10(am)	Returns the base 10 logarithm of each element of the input
am = log2(am)	Returns the base 2 logarithm of each element of the input

Arithmetic

Function	Description
am = add(am1, am2)	Returns the sum between the two inputs for each element
am = reciprocal(am)	Returns the `1/xi` of each element of the input
am = negative(am)	Returns the negative value of each element of the input
am = multiply(kam1, am2)	Returns the multiplication between the two inputs for each element
am = divide(am1, kam2)	Returns the division between the two inputs for each element
am = power(am1, am2)	Returns the power of the first raised by the second for each element
am = subtract(am1, am2)	Returns the subtraction between the two inputs for each element
am = mod(am1, am2)	Returns the modulus between the two inputs for each element It uses C++ `fmod`, and for negative values, It has same behavior as JavaScript `x%y` but different from numpy `np.mod`

Miscellaneous

Function	Description
am = sqrt(am)	Returns the sqrt of each element of the input
am = cbrt(am)	Returns the cubic root of each element of the input
am = square(am)	Returns the square of each element of the input
am = sign(am)	Returns the sign of each element of the input
am = abs(am)	Returns the absolute value of each element of the input
am = maximum(am1, am2)	Returns the maximum value between the two inputs of each element of the input
am = minimum(am1, am2)	Returns the minimum value between the two inputs of each element of the input
am = max(am)	Returns the maximum value
am = min(am)	Returns the minimum value

Matrix

Function	Description
v = det(m)	Returns the determinant of matrix m
a = diagonal(m{, T})	Returns the diagonal of matrix m. The return value is of type T. if T is missing it returns an array
m = eye(d)	Returns a matrix (dxd) with zero and diagonal with ones
m = ones(d1,d2)	Returns the matrix (d1xd2) with ones
m = rand(d1,d2)	Returns the matrix (d1xd2) with random values [0,1]
m = zeros(d1,d2)	Returns the matrix (d1xd2) with zeros

Statistics

Function	Description
v = avg(a)	Returns the average value
v = var(a)	Returns the variance
v = std(a)	Returns the standard deviation
v = skew(a)	Returns the skewness
v = kurtosis(a {, fisher})	Returns the kurtosis (default is Pearson’s definition) If the 2nd parameter is True, uses Fisher’s definition The default it’s the opposite of numpy
v = cov(a1, a2)	Returns the covariance measure Uses an unbiased estimate (N-1 denominator) `numpy.cov(x,y)=[[numnjs.cov(x,x),numnjs.cov(x,y)],[numnjs.cov(y,x),numnjs.cov(y,y)]]`

Example

  const numnjs = require('numnjs');
  const input1 = [4.5, 6.5, -5.6, NaN];
  const output1 = numnjs.min(input1);
  console.log(output1); // -5.6

  const input2 = new Float64Array([4.5, 6.5, Inf, -6]);
  const output2 = numnjs.negate(input2);
  console.log(output2); // Float64Array([-4.5, -6.5, Inf, 6])

  const input3a = [[4.5, 6.5],[89, 9.7],[76, Inf]];
  const input3b = [[14.5, -16.5],[-189, 9.7],[-76, Inf]];
  const output3 = numnjs.multiply(input3a, input3b);
  console.log(output3);

License

MIT License+uuid License