node-ml

A Collection of Machine Learning algorithms built for use with NodeJS

Installation

npm install node-ml

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

[
  [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

[
  1,
  -1
]

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

Training the SLP

SingleLayerPerceptron(inputs, outputs, learningRate)

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
    });
});

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

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.