Rudder is a meant to provide a machine learning framework and package. Using rudder you can extend a base record class and use annotations to define your features and labels, then pass those records into an analytics algorithm without having to write any additional connectors.

There will also be a few utilities that will come in and out of existence as this project grows and is refined.

Everything in this framework should be thread safe, if you find something that is not, please post an issue, a patch, or a unit test illustrating the issue.

Example of running clustering

class MyRecordClass  {
	@Feature
	double myFeature1
	@Feature
	double myFeature2
	@Feature
	double myFeature3
	@Label(setlabel="setLabel")
	String myLabel;	
	Object nonFeatureItem;
	String nonFeatureOrLabelString;
	
	void setLabel(String label);
	
	//Only use non-primitives in the set method signature
	void setLabel(Double doubleLabel);
	
	//Other stuff I want to do
	
	//This API uses equals often, so your object should override it
	boolean equals(Object o);
}

class MyClusterRunner {
	public static void main(String[] args) {
		double epsilon = 4d;  //Maximum distance between records in a cluster
		int minClusterSize = 3;  //Minimum number of records in a cluster
		IRudderList<MyRecordClass> myRecords = new RudderList<MyRecordClass>();
		//populate your object	
		DBScan dbscan = new DBScan(epsilon, minClusterSize);
		db.setSourceData(list);
		ArrayList<Cluster> clusters = db.getClusters();	
		//do stuff with your clusters i.e. print record label and cluster
		for(int i=0; i<clusters.size(); i++) {
			for(MyRecordClass r : clusters.getElements()) {
				System.out.println("Cluster " + i+ ": " + myRecords.getStringLabel(r));
			}
		}
	}
}