weka Standard Deviation - Java Machine Learning AI

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Description

weka Standard Deviation

Demo Code


import weka.core.Instances;
import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.Writer;
import java.util.Random;

import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.misc.VFI;
import weka.classifiers.trees.J48;

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

    BufferedReader reader = new BufferedReader(new FileReader("a.arff"));
    Instances data = new Instances(reader);
    reader.close();/*from  w w w  . jav a  2 s .  c o m*/
    // setting class attribute
    data.setClassIndex(data.numAttributes() - 1);

    int i = data.numInstances();
    int j = data.numAttributes() - 1;

    File file = new File("tableauc.csv");
    Writer output = null;
    output = new BufferedWriter(new FileWriter(file));
    output.write("%missing,auc,correct,fmeasure,aucsd,correctsd,fmeasuresd\n");

    Random randomGenerator = new Random();
    int numBlock = data.numInstances() * (data.numAttributes() - 1);

    for (int perc = 0; perc < 101; perc = perc + 2) {
      Instances mdata = new Instances(data);
      int numMissing = perc * numBlock / 100;
      double y1[] = new double[10];
      double y2[] = new double[10];
      double y3[] = new double[10];

      for (int k = 0; k < numMissing; k++) {
        int r = randomGenerator.nextInt(i);
        int c = randomGenerator.nextInt(j);
        mdata.instance(r).setMissing(c);
        mdata.instance(r).setMissing(c);
      }
      for (int p = 0; p < 10; p++) {
        Instances traindata = mdata.trainCV(10, p);
        Instances testdata = mdata.testCV(10, p);

        Classifier cModel = (Classifier) new J48();
        cModel.buildClassifier(traindata);
        Evaluation eTest1 = new Evaluation(testdata);
        eTest1.evaluateModel(cModel, testdata);
        y1[p] = eTest1.areaUnderROC(0);
        y2[p] = eTest1.correct();
        y3[p] = eTest1.fMeasure(0);
      }
      double auc = 0, corr = 0, fm = 0;
      for (int a = 0; a < 10; a++) {
        auc += y1[a];
        corr += y2[a];
        fm += y3[a];
      }

      double aucsd = StandardDeviation(y1, 10);
      double corrsd = StandardDeviation(y2, 10);
      double fmsd = StandardDeviation(y3, 10);
      output.write(perc + "," + auc / 10 + "," + corr + "," + fm / 10 + "," + aucsd + "," + corrsd + "," + fmsd + "\n");

    }
    output.close();
  }

  public static double StandardDeviation(double[] values, int NumAmount)

  {
    double sum = 0, mean = 0, sq_diff_sum = 0;
    for (int i = 0; i < NumAmount; i++) {
      sum += values[i];
      mean = sum / NumAmount;
      sq_diff_sum = 0;
    }

    for (int i = 0; i < NumAmount; ++i) {
      double diff = values[i] - mean;
      sq_diff_sum += diff * diff;

    }
    double deviance = sq_diff_sum / NumAmount;
    return Math.sqrt(deviance);
  }

}

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