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https://github.com/miketheprogrammer/node-ml

A Collection of Machine Learning algorithms built for use with NodeJS
https://github.com/miketheprogrammer/node-ml

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A Collection of Machine Learning algorithms built for use with NodeJS

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README 

        
node-ml

=======

( Development is current suspended ).



A Collection of Machine Learning algorithms built for use with NodeJS



[![build status](https://secure.travis-ci.org/miketheprogrammer/node-ml.png)](http://travis-ci.org/miketheprogrammer/node-ml)



Installation

========

````javascript

npm install node-ml

````



Basic API Knowledge

========



Models are Instantiated with a TrainingSet as an argument



Models are trained via .train()



Models are acted upon by the perceive or predict functions. These functions currently do the same thing, the wording is indicative of the nature of the result, and the action the model has taken on the data.



Models are designed to be trained at the start of a node instance, not during runtime. 



Models all inherit from EventEmitter



Models support either Callbacks or Events



callbacks are of the structure function( err, result ) 



Models support 3 events : trained, response, error



Callbacks override Events, if you specify a callback and event will not be received.



The Single Layer Perceptron

========



With the single layer perceptron is it possible to solve Linearly Seperable Problems. This makes the SLP a fast tool for solving

simple classification problems.



The SLP takes as input a list of 1x2 vectors as in

````javascript

[

  [1,1],

  [-1,-1]

]

````

We must also provide the SLP a list of expected outputs for each vector. Currently the system only supports 1 | -1

These outputs define the side of the line the elements fall on. It is not important which value you give to which inputs.

Just that these inputs correspond in a linear way to the outputs.



So for the above input we prove

````javascript

[

  1,

  -1

]

````



Now the SLP will solve for where [1,1] === 1 and [-1,-1] === -1



Training the SLP

---------------

SingleLayerPerceptron(inputs, outputs, learningRate)

````javascript

slp = new SingleLayerPerceptron(inputs, outputs, 0.001);

slp.train(function(trainedModel) { 

    trainedModel.perceive([1,1], function(result) {

      console.log(result);

      //should print out 1

    }); 

    trainedModel.perceive([-1,-1], function(result) {

      console.log(result);

      //should print out -1

    });

});

````



Also Events are supported

--------------

current events are: trained, response, error



trained is fired when a model completes training



response is fired when a model completed a perception or prediction phase



error is fired ... well on an error.



````javascript

    slp = new SingleLayerPerceptron(inputs, outputs, 0.001);

    slp.on('error', function(err) {

        t.same(1,1);

    });

    slp.on('trained', function(trainedModel) {

        t.same(true,(trainedModel != undefined));



        trainedModel.perceive([1,1]);

        trainedModel.perceive([-1,-1]);

    });

    slp.on('response', function(response) {

        perceivedTestCount -= 1;

        var result = response.out;

        var input = response.in;

        var expectedIndex;

        for (var i in perceivedTestInput ) {

            if (perceivedTestInput[i].toString() == input.toString())

                expectedIndex = i;

            

        }

        var expected = perceivedTestOutput[expectedIndex];

        t.same(expected, result);



        if (perceivedTestCount == 0 )

            t.end();

        

    });



    slp.train();

````



Even Better remember the above trained model is a Line seperating a 2d dimension space from -1 to 1 

We can input any value in this range and get an output however this limited training set is a bad choice but heres some outputs



Obtained From Running examples/singlelayerperceptron2.js

````javascript

Input: 1,1

1

Input: -1,1

-1

Input: 1,-1

1

Input: -.5,1

1

Input: .5,-1

-1

Input: .2,.45634

1

Input: .2,-.45634

-1

Input: -.4,-.4

-1

Input: -1,-1

-1

````



The Multi Layer Perceptron

========

With the Multi Layer Perceptron it is possible to Classify linearly non seperable data set. Meaning that the data fits to a polynomial function.



Refer to examples.



The Linear Regression Model

========



With Linear Regression we can predict outcomes based on an input.



Refer to examples.



The KMeans Classifier

========



This implementation of the KMeans classifier is an N-Dimensional

classification algorithm. It works by:



1. Generating Random K Centroids.



2. Assigning a K centroid to a point p in Training set T

   such that the Cost(p) with respect to k is minimized;

   Cost(p) is defined as the distances from P to each K



3. Move the centroids to the Mean of each cluster assigned to them



4. Repeat until movement no longer occurs.



Refer to examples for Usage:



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