开源软件名称（OpenSource Name）：

junku901/machine_learning

开源软件地址(OpenSource Url)：

https://github.com/junku901/machine_learning

开源编程语言(OpenSource Language)：

JavaScript 100.0%

开源软件介绍(OpenSource Introduction)：

machine_learning

Machine learning library for node.js. You can also use this library in browser.

Demo in Browser!

API Documentation

Installation

Node.js

$ npm install machine_learning

To use this library in browser, include machine_learning.min.js file.

<script src="/js/machine_learning.min.js"></script>

Demo in Browser!

Here is the API Documentation. (Still in progress)

Features

Logistic Regression
MLP (Multi-Layer Perceptron)
SVM (Support Vector Machine)
KNN (K-nearest neighbors)
K-means clustering
3 Optimization Algorithms (Hill-Climbing, Simulated Annealing, Genetic Algorithm)
Decision Tree
NMF (non-negative matrix factorization)

Implementation Details

SVM is using Sequential Minimal Optimization (SMO) for its training algorithm.

For Decision Tree, Classification And Regression Tree (CART) was used for its building algorithm.

Usage

Logistic Regression

var ml = require('machine_learning');
var x = [[1,1,1,0,0,0],
         [1,0,1,0,0,0],
         [1,1,1,0,0,0],
         [0,0,1,1,1,0],
         [0,0,1,1,0,0],
         [0,0,1,1,1,0]];
var y = [[1, 0],
         [1, 0],
         [1, 0],
         [0, 1],
         [0, 1],
         [0, 1]];

var classifier = new ml.LogisticRegression({
    'input' : x,
    'label' : y,
    'n_in' : 6,
    'n_out' : 2
});

classifier.set('log level',1);

var training_epochs = 800, lr = 0.01;

classifier.train({
    'lr' : lr,
    'epochs' : training_epochs
});

x = [[1, 1, 0, 0, 0, 0],
     [0, 0, 0, 1, 1, 0],
     [1, 1, 1, 1, 1, 0]];

console.log("Result : ",classifier.predict(x));

MLP (Multi-Layer Perceptron)

var ml = require('machine_learning');
var x = [[0.4, 0.5, 0.5, 0.,  0.,  0.],
         [0.5, 0.3,  0.5, 0.,  0.,  0.],
         [0.4, 0.5, 0.5, 0.,  0.,  0.],
         [0.,  0.,  0.5, 0.3, 0.5, 0.],
         [0.,  0.,  0.5, 0.4, 0.5, 0.],
         [0.,  0.,  0.5, 0.5, 0.5, 0.]];
var y = [[1, 0],
         [1, 0],
         [1, 0],
         [0, 1],
         [0, 1],
         [0, 1]];

var mlp = new ml.MLP({
    'input' : x,
    'label' : y,
    'n_ins' : 6,
    'n_outs' : 2,
    'hidden_layer_sizes' : [4,4,5]
});

mlp.set('log level',1); // 0 : nothing, 1 : info, 2 : warning.

mlp.train({
    'lr' : 0.6,
    'epochs' : 20000
});

a = [[0.5, 0.5, 0., 0., 0., 0.],
     [0., 0., 0., 0.5, 0.5, 0.],
     [0.5, 0.5, 0.5, 0.5, 0.5, 0.]];

console.log(mlp.predict(a));

SVM (Support Vector Machine)

var ml = require('machine_learning');
var x = [[0.4, 0.5, 0.5, 0.,  0.,  0.],
         [0.5, 0.3,  0.5, 0.,  0.,  0.01],
         [0.4, 0.8, 0.5, 0.,  0.1,  0.2],
         [1.4, 0.5, 0.5, 0.,  0.,  0.],
         [1.5, 0.3,  0.5, 0.,  0.,  0.],
         [0., 0.9, 1.5, 0.,  0.,  0.],
         [0., 0.7, 1.5, 0.,  0.,  0.],
         [0.5, 0.1,  0.9, 0.,  -1.8,  0.],
         [0.8, 0.8, 0.5, 0.,  0.,  0.],
         [0.,  0.9,  0.5, 0.3, 0.5, 0.2],
         [0.,  0.,  0.5, 0.4, 0.5, 0.],
         [0.,  0.,  0.5, 0.5, 0.5, 0.],
         [0.3, 0.6, 0.7, 1.7,  1.3, -0.7],
         [0.,  0.,  0.5, 0.3, 0.5, 0.2],
         [0.,  0.,  0.5, 0.4, 0.5, 0.1],
         [0.,  0.,  0.5, 0.5, 0.5, 0.01],
         [0.2, 0.01, 0.5, 0.,  0.,  0.9],
         [0.,  0.,  0.5, 0.3, 0.5, -2.3],
         [0.,  0.,  0.5, 0.4, 0.5, 4],
         [0.,  0.,  0.5, 0.5, 0.5, -2]];

var y =  [-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,1,1,1,1,1,1,1,1,1,1];

var svm = new ml.SVM({
    x : x,
    y : y
});

svm.train({
    C : 1.1, // default : 1.0. C in SVM.
    tol : 1e-5, // default : 1e-4. Higher tolerance --> Higher precision
    max_passes : 20, // default : 20. Higher max_passes --> Higher precision
    alpha_tol : 1e-5, // default : 1e-5. Higher alpha_tolerance --> Higher precision

    kernel : { type: "polynomial", c: 1, d: 5}
    // default : {type : "gaussian", sigma : 1.0}
    // {type : "gaussian", sigma : 0.5}
    // {type : "linear"} // x*y
    // {type : "polynomial", c : 1, d : 8} // (x*y + c)^d
    // Or you can use your own kernel.
    // kernel : function(vecx,vecy) { return dot(vecx,vecy);}
});

console.log("Predict : ",svm.predict([1.3,  1.7,  0.5, 0.5, 1.5, 0.4]));

KNN (K-nearest neighbors)

var ml = require('machine_learning');

var data = [[1,0,1,0,1,1,1,0,0,0,0,0,1,0],
            [1,1,1,1,1,1,1,0,0,0,0,0,1,0],
            [1,1,1,0,1,1,1,0,1,0,0,0,1,0],
            [1,0,1,1,1,1,1,1,0,0,0,0,1,0],
            [1,1,1,1,1,1,1,0,0,0,0,0,1,1],
            [0,0,1,0,0,1,0,0,1,0,1,1,1,0],
            [0,0,0,0,0,0,1,1,1,0,1,1,1,0],
            [0,0,0,0,0,1,1,1,0,1,0,1,1,0],
            [0,0,1,0,1,0,1,1,1,1,0,1,1,1],
            [0,0,0,0,0,0,1,1,1,1,1,1,1,1],
            [1,0,1,0,0,1,1,1,1,1,0,0,1,0]
           ];

var result = [23,12,23,23,45,70,123,73,146,158,64];

var knn = new ml.KNN({
    data : data,
    result : result
});

var y = knn.predict({
    x : [0,0,0,0,0,0,0,1,1,1,1,1,1,1],
    k : 3,

    weightf : {type : 'gaussian', sigma : 10.0},
    // default : {type : 'gaussian', sigma : 10.0}
    // {type : 'none'}. weight == 1
    // Or you can use your own weight f
    // weightf : function(distance) {return 1./distance}

    distance : {type : 'euclidean'}
    // default : {type : 'euclidean'}
    // {type : 'pearson'}
    // Or you can use your own distance function
    // distance : function(vecx, vecy) {return Math.abs(dot(vecx,vecy));}
});

console.log(y);

K-means clustering

var ml = require('machine_learning');

var data = [[1,0,1,0,1,1,1,0,0,0,0,0,1,0],
            [1,1,1,1,1,1,1,0,0,0,0,0,1,0],
            [1,1,1,0,1,1,1,0,1,0,0,0,1,0],
            [1,0,1,1,1,1,1,1,0,0,0,0,1,0],
            [1,1,1,1,1,1,1,0,0,0,0,0,1,1],
            [0,0,1,0,0,1,0,0,1,0,1,1,1,0],
            [0,0,0,0,0,0,1,1,1,0,1,1,1,0],
            [0,0,0,0,0,1,1,1,0,1,0,1,1,0]<