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scijs/ndarray for MATLAB users

Common scijs/ndarray operations for people familar with MATLAB (or at least not familiar with scijs)

Introduction

This document is a work in progress! Inspired by Numpy for Matlab users, it aspires to be NumPy for MATLAB users. The intent is both to communicate what is possible in JavaScript using scijs and to illuminate which parts still need work. I'll be mostly offline until the new year so I may not respond immediately, but comments, question and pull requests are more than welcome.

Memory Management

First things first, ndarrays are similar to but different to work with than MATLAB arrays. To get a sense for how managing ndarrays differs from managing MATLAB arrays, consider the diagonal of a 5×5 matrix:

var pool = require('ndarray-scratch')
var diag = require('ndarray-diagonal')

// A 5x5 matrix of ones:
var a = pool.ones([5,5])

// A view of the diagonal of a:
var x = diag(a)

Even though they have different dimensionality and shape, the internal data contained in ndarray x is identical by reference to that in a. The difference is that the strides and offsets of x are set to select only the diagonal elements of a. To see this directly, note the data of x still has 25 numbers even though it's shape is a vector of length 5:

console.log(x)
// => View1dfloat64 {
//   data: 
//    Float64Array {'0': 1, '1': 1, '2': 1, ..., '22': 1, '23': 1, '24': 1 },
//   shape: [ 5 ],
//   stride: [ 6 ],
//   offset: 0 }

Cloning simplifies the representation by allocating new storage and copying only the elements exposed by view x:

console.log(pool.clone(x))
// => View1dfloat64 {
//      data: Float64Array { '0': 1, '1': 1, '2': 1, '3': 1, '4': 1 },
//      shape: [ 5 ],
//      stride: [ 1 ],
//      offset: 0 }

As a result, a nice advantage of ndarrays the ability to manipulate representations without the need to iterate directly or allocate additional storage (beyond the lightweight ndarray wrapper). The example below uses in-place operations of ndarray-ops to assign the scalar 3 to the diagonal and double the first two columns of a:

var ops = require('ndarray-ops')
var show = require('ndarray-show')

// Set each element of the diagonal of a (5x5 matrix of ones) to 3:
ops.assigns(diag(a, 3))

// Double the first two columns:
ops.mulseq(a.hi(null,2))

console.log(show(a))
// => 6.000    2.000    1.000    1.000    1.000
//    2.000    6.000    1.000    1.000    1.000
//    2.000    2.000    3.000    1.000    1.000
//    2.000    2.000    1.000    3.000    1.000
//    2.000    2.000    1.000    1.000    3.000

Operations

The table below collects common matlab operations as well as their ndarray analogs. Not all operations have a counterpart, some because of features and shortcomings of the JavaScript language, some because of differences in memory management, and some because they're simply not yet implemented.

License

© 2015 Ricky Reusser. MIT License.