node-ml
A Collection of Machine Learning algorithms built for use with NodeJS
npm install node-ml
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Last Published By | |
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Version | 1.0.2 last updated 2 months ago |
Keywords | machine, learning, ml, classifier, clustering, bayes, k-means, logistic, regression, perceptron, neural, net |
Repository | http://github.com/miketheprogrammer/node-ml.git (git) |
Homepage | https://github.com/miketheprogrammer/node-ml |
Bugs | https://github.com/miketheprogrammer/node-ml/issues |
Dependencies | sylvester |
node-ml
( Development is current suspended ).
A Collection of Machine Learning algorithms built for use with NodeJS
Installation
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
[
[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
});
});
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.
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
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:
Generating Random K Centroids.
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
Move the centroids to the Mean of each cluster assigned to them
Repeat until movement no longer occurs.
Refer to examples for Usage: