weka Naive Bayes Classifier - Java Machine Learning AI

Java examples for Machine Learning AI:weka

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Description

weka Naive Bayes Classifier

Demo Code

import java.io.BufferedReader;
import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.IOException;
import java.io.File;
import java.util.ArrayList;
import java.util.Enumeration;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.trees.RandomForest;
import weka.core.Attribute;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.SerializationHelper;
import weka.core.converters.ArffLoader;
import weka.core.converters.CSVSaver;
import weka.core.converters.Loader;

public class main_f {
    public static void main(String[] args) throws Exception {

        long st_time = System.currentTimeMillis();
        st_time = System.currentTimeMillis();

        Classifier classifier = (Classifier) SerializationHelper
                .read("NaiveBayes.model");


        ArffLoader testLoader = new ArffLoader();
        testLoader.setSource(new File("src/test.arff"));
        testLoader.setRetrieval(Loader.BATCH);
        Instances testDataSet = testLoader.getDataSet();

        Attribute testAttribute = testDataSet.attribute("class");
        testDataSet.setClass(testAttribute);

        int correct = 0;
        int incorrect = 0;
        FastVector attInfo = new FastVector();
        attInfo.addElement(new Attribute("Id"));
        attInfo.addElement(new Attribute("Category"));

        Instances outputInstances = new Instances("predict", attInfo,
                testDataSet.numInstances());

        Enumeration testInstances = testDataSet.enumerateInstances();
        int index = 1;
        while (testInstances.hasMoreElements()) {
            Instance instance = (Instance) testInstances.nextElement();
            double classification = classifier.classifyInstance(instance);
            Instance predictInstance = new Instance(
                    outputInstances.numAttributes());
            predictInstance.setValue(0, index++);
            predictInstance.setValue(1, (int) classification + 1);
            outputInstances.add(predictInstance);
        }//w  w  w .j av  a  2 s . com

        System.out.println("Correct Instance: " + correct);
        System.out.println("IncCorrect Instance: " + incorrect);
        double accuracy = (double) (correct)
                / (double) (correct + incorrect);
        System.out.println("Accuracy: " + accuracy);
        CSVSaver predictedCsvSaver = new CSVSaver();
        predictedCsvSaver.setFile(new File("predict.csv"));
        predictedCsvSaver.setInstances(outputInstances);
        predictedCsvSaver.writeBatch();

        System.out.println("Prediciton saved to predict.csv");

    }
}

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