use weka unsupervised Normalize - Java Machine Learning AI

Java examples for Machine Learning AI:weka

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Description

use weka unsupervised Normalize

Demo Code

import java.io.File;

import weka.classifiers.Evaluation;
import weka.classifiers.functions.LinearRegression;
import weka.core.Instances;
import weka.core.converters.ArffSaver;
import weka.core.converters.ConverterUtils.DataSource;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Normalize;

public class Normalization {
    public static void main(String[] args) throws Exception {
        DataSource source = new DataSource("house.arff");
        Instances dataset = source.getDataSet();
        dataset.setClassIndex(dataset.numAttributes() - 1);
        /**//  ww w.  j  a  va  2 s  .  c om
         * normalize all the attribute values between 0 and 1
         */
        Normalize normalize = new Normalize();
        normalize.setInputFormat(dataset);
        Instances newdata = Filter.useFilter(dataset, normalize);
        /**
         * linear regression model
         */
        LinearRegression lr = new LinearRegression();
        lr.buildClassifier(newdata);
        Evaluation lreval = new Evaluation(newdata);
        lreval.evaluateModel(lr, newdata);
        System.out.println(lreval.toSummaryString());
        /**
         * store newdata in new arff file
         */

        ArffSaver saver = new ArffSaver();
        saver.setInstances(newdata);
        saver.setFile(new File("housenormlize.arff"));
        saver.writeBatch();

    }
}

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