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Vectors vs. matrices

Mike Anderson edited this page Jul 22, 2013 · 10 revisions

Introduction

core.matrix supports both vectors (1D arrays) and matrices (2D arrays). This page explains the differences between the two.

Matrix properties

  • Can be thought of as a 2D nested array, e.g. [[1 2 3] [4 5 6] [7 8 9]]
  • Always have dimensionality 2; equivalent to a 2D array
  • Are indexed by [row column]
  • Can be transposed, which reverses rows and columns
  • Support some matrix-only operations, e.g. det, trace
  • A slice of a Matrix is a 1D vector

Vector properties

  • Can be though of as a 1D array, e.g. [1 2 3]
  • Always have dimensionality 1; equivalent to a 1D array
  • Are indexed by [position]
  • Transposing a vector is the identity function: if you call transpose on a vector, you will get the same vector back unchanged
  • Supports some vector-only operations, e.g. distance, dot-product
  • A slice of a vector is a 0D scalar

Why have both?

Technically, you can always emulate vectors by using matrices. Use either a Nx1 column matrix or a 1xN row matrix. Indeed, some mathematics systems (like MATLAB) expect you to do just this.

However, there are some important reasons that we need to have both:

  • core.matrix is designed to be a general purpose N-Dimensional API, so it needs to support both the 1D and 2D cases in general.
  • Many underlying matrix implementations have both vectors and matrices. We need to support them.
  • It can catch some runtime type errors (e.g. passing a matrix where a vector is required).
  • In some cases, using vectors rather than matrices offers higher performance (due to a simpler data structure and less complex indexing).

Which should I use?

Usually this will be clear from what is needed for your problem. If you need a 2D array of numbers then use a matrix. If you need a 1D array of numbers then use a vector.

Further discussion / reading: