https://github.com/mikolalysenko/csr-matrix

Compressed sparse row matrix for node.js
https://github.com/mikolalysenko/csr-matrix

Last synced: about 2 months ago
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Compressed sparse row matrix for node.js

• Host: GitHub
• URL: https://github.com/mikolalysenko/csr-matrix
• Owner: mikolalysenko
• Created: 2013-03-13T18:29:55.000Z (almost 12 years ago)
• Default Branch: master
• Last Pushed: 2016-05-13T23:58:07.000Z (over 8 years ago)
• Last Synced: 2024-10-20T14:27:40.047Z (2 months ago)
• Language: JavaScript
• Size: 9.77 KB
• Stars: 19
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
csr-matrix

===========

A very minimal [incremental compressed sparse row](http://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_.28CSR_or_CRS.29) matrix library for JavaScript.

# Install

```

npm i csr-matrix

```

# Example

```javascript

var dict = {}

dict[[1,2]] = 1

dict[[0,0]] = 2.5

dict[[3,4]] = 5

var CSRMatrix = require("csr-matrix")

var M = CSRMatrix.fromDictionary(dict, 4, 5)

console.log(M.apply([1,2,3,4,5], []))

```

# API

CSRMatrices are optimized for exactly one thing:  fast matrix-vector multiplies.  The way you do this is by calling the following method:

## Constructors

There are several ways to create csr-matrices.  The most direct way to do this is to just call the constructor yourself:

#### `CSRMatrix.fromList(items[, nrows, ncols])`

Turns an array of entries of the form `[row, column, value]` into a sparse matrix.  Note that if there are some zero rows or columns at the end of the matrix, you need to specify the number of rows/columns in the optional nrows/ncols arguments.

#### `CSRMatrix.fromDictionary(dict[, nrows, ncols])`

Converts a JavaScript object with entries for the form `"row,column"` into a sparse matrix.

#### `CSRMatrix.fromDense(mat)`

Turns an array-of-arrays into a csr matrix

#### `CSRMatrix.fromNDArray(ndarr)`

Turns a 2D dimensional [ndarray](https://github.com/scijs/ndarray) into a csr matrix

## Method

#### `matrix.apply(vector[, result])`

This computes the normal matrix-vector product, but is often much faster than a dense multiply since the matrix is stored in a compressed sparse format.

* `vector` is the vector to be multiplied

* `result` is an optional array which gets the result.  If not specified, a new vector gets allocated

Returns the resulting product

#### `matrix.transpose()`

Returns the transpose of the matrix

#### `matrix.rowCount`

Returns the number of rows

#### `matrix.columnCount`

Returns the number of columns

#### `matrix.toList()`

Converts matrix into a list format

#### `matrix.toDictionary()`

Converts matrix into hash table

#### `matrix.toDense()`

Converts matrix into array of arrays

#### `matrix.toNDArray(out)`

Converts matrix into ndarray

* `out` is the output ndarray

# Credits

(c) 2013-2015 Mikola Lysenko. BSD