Anomaly detection library in golang, implemented via Gaussian distribution

1. Choose feature x(i) that might be indicative of anomalous examples
2. Estimate parameters by calculating the mean and standard deviation
3. Given a new example x, compute p(x) via the Gaussian normal distribution formaula
4. Anomaly if p(x) < k

Example

import (
	"goanomaly"
	"log"
	"math/big"
)

// ==============================================================

// get a set of data
dataSet := fakeFixedData()

// init the AnomalyDetection object
anomalyDetection := goanomaly.NewAnomalyDetection(dataSet...)

// Call EventIsAnomalous with a new data point, which you can test against, and a threshold 
anomaly, result := anomalyDetection.EventIsAnomalous(*big.NewFloat(5050), big.NewFloat(0.001))

if anomaly {
	log.Println("Data point", 5050, "is anomalous")
}

// ==============================================================

// helper function to fake fixed data
func fakeFixedData() []big.Float {

	var dataSet []big.Float

	baseInt := big.NewFloat(5000)
	
	// fake sample data
	var increment big.Float
	for i := 0; i < 999; i++ {
		baseInt = big.NewFloat(5000)
		if i > 200 && i < 800 {
			increment = *baseInt.Add(baseInt, deviationMaxFloat)
			dataSet = append(dataSet, increment)		

		} else {
			increment = *baseInt.Add(baseInt, higherDeviationFloat)
			dataSet = append(dataSet, increment)			
		}
	}
	return dataSet

}

Important

The Gaussian distribution anomaly detection usually does not keep into consideration the relation between the features. For instance if you want to have a relation between say CPU load(x1) and Memory usage(x2) then you should create a 3rd feature in this way: (x1)/(x2) and add it to the dataset

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LICENSE

Copyright (c) 2015-2016 Sec51.com info@sec51.com

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