weka.classifiers.evaluation.Prediction Java Examples

The following examples show how to use weka.classifiers.evaluation.Prediction. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: TestWekaBayes.java    From Java-Data-Analysis with MIT License 8 votes vote down vote up 
public static void main(String[] args) throws Exception {
//        ConverterUtils.DataSource source = new ConverterUtils.DataSource("data/AnonFruit.arff");
        DataSource source = new DataSource("data/AnonFruit.arff");
        Instances train = source.getDataSet();
        train.setClassIndex(3);  // target attribute: (Sweet)
        //build model
        NaiveBayes model=new NaiveBayes();
        model.buildClassifier(train);

        //use
        Instances test = train;
        Evaluation eval = new Evaluation(test);
        eval.evaluateModel(model,test);
        List <Prediction> predictions = eval.predictions();
        int k = 0;
        for (Instance instance : test) {
            double actual = instance.classValue();
            double prediction = eval.evaluateModelOnce(model, instance);
            System.out.printf("%2d.%4.0f%4.0f", ++k, actual, prediction);
            System.out.println(prediction != actual? " *": "");
        }
    }
Example #2
Source File: MLUtils.java    From meka with GNU General Public License v3.0 5 votes vote down vote up 
/** 
 * Convert to Weka (multi-target) Predictions. 
 * Note: currently only multi-label.
 * */
public static ArrayList<Prediction> toWekaPredictions(int y[], double p[]) {
	ArrayList<Prediction> predictions = new ArrayList<Prediction>();
	for(int i = 0; i < y.length; i++) {
		predictions.add(new NominalPrediction((double)y[i], new double[]{1.-p[i],p[i]}));
	}
	return predictions;
}