/* Data Analysis with Java
* John R. Hubbard
* May 28, 2017
*/
package dawj.ch07;
import java.util.List;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.evaluation.Prediction;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ConverterUtils;
import weka.core.converters.ConverterUtils.DataSource;
public class TestWekaBayes {
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? " *": "");
}
}
}
/*
run:
1. 1 1
2. 1 1
3. 1 1
4. 1 1
5. 1 1
6. 0 1 *
7. 1 1
8. 0 0
9. 0 0
10. 0 1 *
11. 1 1
12. 1 1
13. 1 1
14. 1 1
15. 0 0
16. 1 1
*/
