Use weka AttributeStats - Java Machine Learning AI

Java examples for Machine Learning AI:weka

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Description

Use weka AttributeStats

Demo Code

import weka.core.AttributeStats;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.experiment.Stats;

public class Attributes {
    public static void main(String[] args) throws Exception {
        DataSource source = new DataSource("weather.arff");
        Instances data = source.getDataSet();
        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }//from  ww  w  .  j  a  v  a 2 s.  c  om
        System.out.println("class index:" + data.classIndex());
        int numAttr = data.numAttributes() - 1;
        System.out.println("the number of attributes:" + numAttr);
        for (int i = 0; i < numAttr; i++) {
            if (data.attribute(i).isNominal()) {
                System.out.println("the" + i + " th attribute is numeric");
                int n = data.attribute(i).numValues();
                System.out.println("the " + i + " th attribute has:" + n
                        + " values");
            }
            AttributeStats as = data.attributeStats(i);
            int dc = as.distinctCount;
            System.out.println("the " + i + "th attribute has " + dc
                    + " distinct value");
            if (data.attribute(i).isNumeric()) {
                System.out.println("the " + i + "th attribute is numeric");
                Stats s = as.numericStats;
                System.out.println("the " + i + "th attribute has min:"
                        + s.min + " max:" + s.max);

            }
        }
        int numInst = data.numInstances();
        for (int j = 0; j < numInst; j++) {
            Instance instance = data.instance(j);
            double cv = instance.classValue();
            //System.out.println("cv:"+cv);
            System.out.println(instance.classAttribute().value((int) cv));
        }
    }

}

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