gitter-badger/node-linalg

node-linalg

This module integrates the efficient libraries created by really smart folks into the rich javascript environment provided by nodejs.

At present this is in pre-alpha ( == void? ). It's tested in real world ML apps and holds up very well.

I need to make the install easier - sorry. Check the Docker file for linux prerequistes.

Of note:

• underlying data type is float (can we template this?)
• matrices are stored in column major order (for cuda compatibility)
• limited validation of inputs is present in this version (to be be improved)

Help

• need anyone who can build on windows
• (polite) suggestions for matrix operations

Coming Soon

• NVIDIA CUDA support - beta testing in progress (20kx20k mul() in 500ms!!!)
• caffe integration - let's get the caffe chaps' good work into a real environment (running for cover)
• human brain integration - (stretch goal)

Installation

npm install lalg

BUT there are several prerequesites (see below) Promise to make this better, but I am still learning about software.

Prerequisites

• nodejs of course ( tested on 7.2.0 )
• gyp - npm install -g node-gyp
• make
• python ( oh boy! - can't avoid python - even integrating C++ to javascript !!!!! )
• C++ compiler
• openblas
• lapacke

If you would like to try this out (at present no cuda reqd.).

Example Dockerfile ( again 16.04 is for the cuda compatibility )

FROM ubuntu:16.04

RUN apt update && \
	apt -y install curl && \
	apt -y install xz-utils && \
       	curl -L -s https://nodejs.org/dist/v7.2.0/node-v7.2.0-linux-x64.tar.xz -o node-v7.2.0-linux-x64.tar.xz  && \
	tar -xJf node-v7.2.0-linux-x64.tar.xz  && \
	rm node-v7.2.0-linux-x64.tar.xz  && \
	chown -R root:root /node-v7.2.0-linux-x64/bin/node && \
	ln -s /node-v7.2.0-linux-x64/bin/node /usr/bin && \
	ln -s /node-v7.2.0-linux-x64/bin/npm /usr/bin && \
       	apt -y install python && \
       	apt -y install libopenblas-dev && \
       	apt -y install liblapacke-dev && \
       	apt -y install make && \
       	apt -y install g++ && \
	npm install lalg

#
# This line starts up a node instance directly
#
CMD [ "/usr/bin/node" ]

run the docker as interactive & try it out. Example rcorbish/lalg is shown, whoch opens into a nodejs environment

Try this as a test...

	docker run -i -t rcorbish/lalg 
        >
	var lalg = require ( "lalg/build/Release/lalg" ) ;
        lalg.rand( 5 ) ;

It should output something like this (not exactly - it's a random matrix)

5 x 5 
  4.00   0.00  -1.00  -2.00   0.00 
  2.00   4.00   0.00   4.00   3.00 
 -2.00   2.00  -5.00   0.00   3.00 
  0.00   0.00   2.00   0.00  -2.00 
 -2.00  -3.00   0.00  -5.00  -1.00 

API

This is the C++ docs, which shows all the nodejs functions and a few examples. I'll keep this up to date as the unerlying code changes.

documentation

Examples

linear regression

OK we'll start with a complex example, why not? It showcases the non-blocking features of the library.

Who wants to wait? A skilled nodejs developer ;)

This implements the linear regression simple cals: theta = inv(X' X) X' y where X' is a transpose operation ( thanks MATLAB )

To run this you need fast-csv npm install fast-csv

var linalg = require('lalg/build/Release/lalg');
var fs = require('fs');
var csv = require("fast-csv");

const rr = fs.createReadStream('wine.csv');
var csvStream = csv() ;
rr.pipe(csvStream);

linalg.read( csvStream ) 
.then( function(X) { 
  	var y = X.removeColumn() ;
	var COV = X.transpose().mulp(X);
	return Promise.all( [ COV, X, y ] )  ;
})
.then( function(X) { 
	var Z = X[0].inv().mulp( X[1].transpose() ) ;
	return Promise.all( [ Z, X[1], X[2] ] ) ;
})
.then( function(X) { 
	return Promise.all( [ X[0].mulp( X[2] ), X[1], X[2] ] ) ;
})
.then( function(X) { 
	console.log( "Theta", X[0] ) ;
        var predicted = X[1].mul( X[0] ) ;
	console.log( "Predicted", predicted ) ;
	
})
.catch( function(err) {
	console.log( "Fail", err ) ;
});

create a matrix

Create an uninitialized array (not too useful really)

	var lalg = require('lalg');
	var A = new lalg.Array( 10, 5 ) ;

Create an randomly initialized array

	var lalg = require('lalg');
	var A = lalg.rand( 10, 5 ) ;

Create two arrays one initialized to all 0.0, the other to all 1.0

	var lalg = require('lalg');
	var Zeros = lalg.zeros( 10, 5 ) ;
	var Ones = lalg.ones( 10, 5 ) ;

Create the identity matrix ( identity is square, only need 1 arg )

	var lalg = require('lalg');
	var Identity5 = lalg.eye( 5 ) ;

scalar functions

Add a value to each element in the matrix Multiply each element in the matrix by a single value. NB add() also takes vectors and matrices as args too (see later).

	var lalg = require('lalg');
	var A = lalg.zeros( 5 ) ;
	var T = A.add( 10 ) ;   // now all elements in T are equal to 10

Multiply each element in the matrix by a single value. NB mul() also takes vectors and numbers (be careful with vectors, they must be compatible shapes)

	var lalg = require('lalg');
	var A = lalg.ones( 5 ) ;
	var T = A.mul( 10 ) ;   // now all elements in T are equal to 10