%maven nz.ac.waikato.cms.moa:moa:2018.6.0

import moa.classifiers.trees.HoeffdingTree;
import moa.streams.generators.RandomRBFGenerator;

HoeffdingTree learner = new HoeffdingTree();
RandomRBFGenerator stream = new RandomRBFGenerator();

stream.prepareForUse();
learner.setModelContext(stream.getHeader());
learner.prepareForUse();

%maven org.knowm.xchart:xchart:3.5.2
import org.knowm.xchart.*;
import moa.core.TimingUtils;
import com.yahoo.labs.samoa.instances.Instance;

int numInstances = 1000000;
int sampleSize = 1000;
boolean isTesting = true;
double[] xData = new double[numInstances/sampleSize];
double[] yData = new double[numInstances/sampleSize];

int numberSamplesCorrect = 0;
int numberSamples = 0;
boolean preciseCPUTiming = TimingUtils.enablePreciseTiming();
long evaluateStartTime = TimingUtils.getNanoCPUTimeOfCurrentThread();
while (stream.hasMoreInstances() && numberSamples < numInstances) {
    Instance trainInst = stream.nextInstance().getData();
    if (isTesting) {
            if (learner.correctlyClassifies(trainInst)){
                    numberSamplesCorrect++;
            }
    }
    if (numberSamples % sampleSize == 0){
        xData[numberSamples / sampleSize] = numberSamples / sampleSize;
        yData[numberSamples / sampleSize] = 100.0 * (double) numberSamplesCorrect/ (double) numberSamples;
    }
    numberSamples++;
    learner.trainOnInstance(trainInst);
}
double accuracy = 100.0 * (double) numberSamplesCorrect/ (double) numberSamples;
double time = TimingUtils.nanoTimeToSeconds(TimingUtils.getNanoCPUTimeOfCurrentThread()- evaluateStartTime);
System.out.println(numberSamples + " instances processed with " + accuracy + "% accuracy in "+time+" seconds.");

XYChart chart = QuickChart.getChart("Prequential Evaluation", "#Instances", "Accuracy", "y(x)", xData, yData);
BitmapEncoder.getBufferedImage(chart);

import moa.DoTask;
DoTask.main("EvaluatePrequential -l trees.HoeffdingTree -i 1000000".split(" "));