maqqju / node-bayes

:chart_with_upwards_trend: Naive Bayes classifier for Node

node-bayes

node-bayes is a Naive Bayes classifier for Node.js. Built-in support for numeric attributes and Laplace smoothing.

Getting Started

npm install node-bayes

var bayes = require('node-bayes');

Example Usage

var TRAINING_COLUMNS = ['weather', 'temperature', 'humidity', 'wind', 'play?'];
var TRAINING_DATA_SIMPLE = [
    ['Sunny','Hot','High','Weak','No'],
    ['Sunny','Hot','High','Strong','No'],
    ['Overcast','Hot','High','Weak','Yes'],
    ['Rain','Mild','High','Weak','Yes'],
    ['Rain','Cool','Normal','Weak','Yes'],
    ['Rain','Cool','Normal','Strong','No'],
    ['Overcast','Cool','Normal','Strong','Yes'],
    ['Sunny','Mild','High','Weak','No'],
    ['Sunny','Cool','Normal','Weak','Yes'],
    ['Rain','Mild','Normal','Weak','Yes'],
    ['Sunny','Mild','Normal','Strong','Yes'],
    ['Overcast','Mild','High','Strong','Yes'],
    ['Overcast','Hot','Normal','Weak','Yes'],
    ['Rain','Mild','High','Strong','No']
];
var TRAINING_DATA = [
    ['Sunny',85,85,'Weak','No'],
    ['Sunny',80,90,'Strong','No'],
    ['Overcast',83,86,'Weak','Yes'],
    ['Rain',70,96,'Weak','Yes'],
    ['Rain',68,80,'Weak','Yes'],
    ['Rain',65,70,'Strong','No'],
    ['Overcast',64,65,'Strong','Yes'],
    ['Sunny',72,95,'Weak','No'],
    ['Sunny',69,70,'Weak','Yes'],
    ['Rain',75,80,'Weak','Yes'],
    ['Sunny',75,70,'Strong','Yes'],
    ['Overcast',72,90,'Strong','Yes'],
    ['Overcast',81,75,'Weak','Yes']
];

// Non-numeric attributes
var cls = new bayes.NaiveBayes({
  columns: TRAINING_COLUMNS,
  data: TRAINING_DATA_SIMPLE,
  verbose: true
});
cls.train();
var answer = cls.predict(['Sunny', 'Cool', 'High', 'Strong']);
console.log(answer);

// Numeric attributes
var cls = new bayes.NaiveBayes({
  columns: TRAINING_COLUMNS,
  data: TRAINING_DATA,
  verbose: true
});
cls.train();
var answer = cls.predict(['Sunny', 66, 90, 'Strong']);
console.log(answer);

Options

Option	Type	Description
data*	type[]	Your dataset, an array of arrays. Every sample must have consistent size and type signature.
columns*	string[]	Column names of your dataset. Must be unique.
labelIndex	integer	Index of your class label. Defaults to rightmost column.
verbose	boolean	If set to true, returns full probability breakdown for each class value. Defaults to false.
eagerTraining	boolean	If set to true, runs train() on predict() if there are dirty samples.
columnTypes	string[]	ColumnTypes of your dataset. Used for validation of samples in your dataset. If not supplied, the column types of the first sample is inspected automatically.
stripWhitespace	boolean	If set to true, whitespace in column names are stripped. Defaults to true.

Running Tests

npm test

Thanks

node-bayes © 2016+, Yos Riady. Released under the MIT License.
Authored and maintained by Yos Riady with help from contributors (list).

yos.io  ·  GitHub @yosriady