import weka.classifiers.Classifier;
import weka.classifiers.lazy.IB1;
import weka.classifiers.Evaluation;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
/**
 *  * WekaAPISampleClient is a simple demonstration of the Weka API
 *   * It builds a nearest-neighbor model of Fisher's Iris dataset
 *    * Caution: greater care should be taken in evaluation than is shown below (more on this point in class)
 *     * @author ALVAREZ
 *      *
 *       */
public class WekaAPISampleClient {

	/**
 * 	 * @param args
 * 	 	 */
	public static void main(String[] args) throws Exception {
		/**
 * 		 * prepare instance data
 * 		 		 */
		String dataFileName = "iris.arff";	// appropriate path needed here
		Instances data = (new DataSource(dataFileName)).getDataSet();
		data.setClassIndex(data.numAttributes() - 1);
		/**
 * 		 * build classifier model
 * 		 		 */
		Classifier classy = new IB1();
		classy.buildClassifier(data);
		/**
 * 		 * evaluate classifier model
 * 		 		 */
		Evaluation eval = new Evaluation(data);
		eval.evaluateModel(classy, data);	// evaluates on training data - very bad idea
		System.out.println("1-NN performance on " + dataFileName + ":\n" + eval.toSummaryString());
	}

}