Anomaly Detection Module

Anomaly Detection / Anomaly Likelihood Functionality Added!

As part of the Network API development effort, it was decided that the production of a Java™ version of the Python NuPIC Network API Demo(s) (PNA) would be one of its downstream validating deliverables. One of the PNA Demo's dependencies was the as of yet uncompleted Anomaly/AnomalyLikelihood computation classes. As such, a giant segue (read sidetrack :-P) was born!

So as a result we now have a major piece of functionality added to the "Algorithms" package which contains the classes organized around CLA Classification.

Usage

There are 3 explicit modes in which anomaly functionality can be used:

1. Static call to obtain anomaly score.
   Ex.

        
int[] currentlyActiveColumns = { 3, 5, 7 };    // Array containing col indexes    
int[] predictedActiveColumns = { 2, 5, 6 };       
double score = Anomaly.computeRawAnomalyScore(currentlyActiveColumns, predictedActiveColumns);     

1. Factory instantiation of Anomaly instance. (Mode.PURE)
   Ex.

Map<String, Object> params = new HashMap<>();    
params.put(KEY_MODE, Mode.PURE);      // May be Mode.LIKELIHOOD    
params.put(KEY_USE_MOVING_AVG, true);    
params.put(KEY_WINDOW_SIZE, 10);    
params.put(KEY_IS_WEIGHTED, true);    
    
Anomaly anomalyComputer = Anomaly.create(params);    
double score = anomalyComputer.compute(currentlyActiveColumns, predictedActiveColumns, 0, 0);     

1. Factory instantiation of Anomaly instance. (Mode.LIKELIHOOD)
   Ex.

Map<String, Object> params = new HashMap<>();    
params.put(KEY_MODE, Mode.LIKELIHOOD);     
params.put(KEY_USE_MOVING_AVG, true);    
params.put(KEY_WINDOW_SIZE, 10);    
params.put(KEY_IS_WEIGHTED, true);    

// Collect timestamp, input value (double), score (double) in a List\<Sample\>    
List\<Sample\> samples = ...      // Where Sample = Sample(timestamp, value, score)    
Anomaly an = Anomaly.create(params);    
AnomalyLikelihoodMetrics metrics = an.estimateAnomalyLikelihoods(samples, 10, 0);    
    
// Above **AnomalyLikelihoodMetrics** contains many statistical goodies.    

To update the anomaly computer with new Samples, call:

AnomalyLikelihoodMetrics metrics2 = an.updateAnomalyLikelihoods(\<new samples\>, metrics.getParams());    

For more information regarding usage, please see the tests.

Be sure and keep up on the new Network API package development, monitor #HtmJavaDevUpdates