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Weka adaption for streaming processes in streams.
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/**
 * 
 */
package streams.weka;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import weka.classifiers.AbstractClassifier;
import weka.core.Attribute;
import weka.core.Instance;
import weka.core.Instances;

/**
 * @author chris
 * 
 */
public class RandomGuess extends AbstractClassifier {

	/** The unique class ID */
	private static final long serialVersionUID = -5074227619992360588L;

	static Logger log = LoggerFactory.getLogger(RandomGuess.class);

	boolean regression = false;
	Double min = 0.0;
	Double max = 1.0;

	int numClasses = 0;
	String[] classes;

	/**
	 * @see weka.classifiers.Classifier#buildClassifier(weka.core.Instances)
	 */
	@Override
	public void buildClassifier(Instances data) throws Exception {

		Attribute labelAttribute = data.classAttribute();
		if (labelAttribute.isNumeric()) {
			regression = true;
			for (int i = 0; i < data.size(); i++) {
				Instance ex = data.instance(i);
				Double val = ex.value(labelAttribute);
				min = Math.min(min, val);
				max = Math.max(max, val);
			}
			// log.info("Creating regression RandomGuess model for [{},{}]",
			// min,
			// max);
		} else {
			numClasses = labelAttribute.numValues();
			classes = new String[numClasses];
			// log.info("Creating RandomGuess model with {} classes",
			// numClasses);

			for (int i = 0; i < numClasses; i++) {
				String str = labelAttribute.value(i);
				classes[i] = str;
			}
		}
	}

	/**
	 * @see weka.classifiers.AbstractClassifier#classifyInstance(weka.core.Instance)
	 */
	@Override
	public double classifyInstance(Instance instance) throws Exception {

		if (regression) {
			double ret = Math.random() * (max - min);
			// log.info("Random regression value: {}", ret);
			return ret;
		}

		double[] dist = this.distributionForInstance(instance);
		int max = 0;
		for (int i = 1; i < dist.length; i++) {
			if (dist[max] < dist[i]) {
				max = i;
			}
		}

		// log.info("Random prediction value: {} ({})", classes[max], max);
		return (double) max;
	}

	/*
	 */
	@Override
	public double[] distributionForInstance(Instance instance) throws Exception {

		double sum = 0.0;
		double[] dist = new double[numClasses];

		for (int i = 0; i < classes.length; i++) {
			dist[i] = Math.random();
			sum += dist[i];
		}

		for (int i = 0; i < dist.length; i++) {
			dist[i] = dist[i] / sum;
		}

		return dist;
	}
}