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https://github.com/yandongliu/learningjs

javascript implementation of logistic regression/c4.5 decision tree
https://github.com/yandongliu/learningjs

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javascript implementation of logistic regression/c4.5 decision tree

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README 

        
LearningJS: A Javascript Implementation of Logistic Regression and C4.5 Decision Tree Algorithms

==========

Author: Yandong Liu. Email: yandongl @ cs.cmu.edu



# Update

I've made some update on the data loading logic so now it reads in csv-format file. Previous version is still accessible but it's no longer supported.



# Introduction

Javascript implementation of several machine learning algorithms including Decision Tree and Logistic Regression this far. More to come.



# Online Demo

Here's a online [demo](http://www.cs.cmu.edu/~yandongl/learningjs/decision-tree-demo.html) with visualization and a few datasets.



# Data format

Input files need to be in CSV-format with 1st line being feature names. One of the features has to be called 'label'. E.g.  


outlook, temp, humidity, wind, label

text, real, text, text, feature_type

'Sunny',80,'High', 'Weak', 'No'

'Sunny',82,'High', 'Strong', 'No'

'Overcast',73,'High', 'Weak', 'Yes' 



There's also an optional 2nd line for feature types and the 'label' column for 2nd line has to be called 'feature_type'. This is useful if feature types are mixed. For Logistic Regression, all features should be real numbers. E.g.


label,a,b,c,d,e,f,g,h,i,j,k,l,m

1,1,0.72694,1.4742,0.32396,0.98535,1,0.83592,0.0046566,0.0039465,0.04779,0.12795,0.016108,0.0052323

2,2,0.74173,1.5257,0.36116,0.98152,0.99825,0.79867,0.0052423,0.0050016,0.02416,0.090476,0.0081195,0.002708

3,3,0.76722,1.5725,0.38998,0.97755,1,0.80812,0.0074573,0.010121,0.011897,0.057445,0.0032891,0.00092068

1,4,0.73797,1.4597,0.35376,0.97566,1,0.81697,0.0068768,0.0086068,0.01595,0.065491,0.0042707,0.0011544




# Usage

Data loading: data_util.js provides three methods:



 * `loadTextFile`: the csv-format file will be loaded from disk and columns are parsed as strings unless 2nd line specifies feature types.

 * `loadRealFile`: the csv-format file will be loaded from disk and columns are parsed as real numbers.

 * `loadString`: a big string will be chopped into lines and columns are parsed as strings unless 2nd line specifies feature types.



In the loading callback function you will obtain a data object D on which you can apply the learning methods. Note that only Decision Tree supports both real and categorical features. Logistic Regression works on real features only.  



```javascript



loadString(content, function(D) {

  var tree = new learningjs.tree();

  tree.train(D, function(model, err){

    if(err) {

      console.log(err);

    } else {

      model.calcAccuracy(D.data, D.targets, function(acc, correct, total){

        console.log( 'training: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');

      });

    }

  });

}); 

```



# Use in Nodejs

Similarly you need to import the lib and do the same:



```javascript 

var learningjs = require('learningjs.js');

var data_util = require('data_util.js');

var tree = new learningjs.tree();

data_util.loadRealFile(fn_csv, function(D) {



  //normalize data

  data_util.normalize(D.data, D.nfeatures); 



  //logistic regression. following params are optional

  D.optimizer = 'sgd'; //default choice. other choice is 'gd'

  D.learning_rate = 0.005;

  D.l2_weight = 0.0;

  D.iterations = 1000; //increase number of iterations for better performance



  new learningjs.logistic().train(D, function(model, err){

    if(err) {

      console.log(err);

    } else {

      model.calcAccuracy(D.data, D.targets, function(acc, correct, total){

        console.log('training: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');

      });

      data_util.loadRealFile(fn_test, function(T) {

        model.calcAccuracy(T.data, T.targets, function(acc, correct, total){

          console.log('    test: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');

        });

      });

    }

  });

}); 

```



# License

MIT