Since Matlab uses JRE pretty extensively, it is very easy to write Matlab code which is equivalent to the java code.
This is the original java code.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | int numInstances = 10000;Classifierlearner = new HoeffdingTree();RandomRBFGenerator stream = new RandomRBFGenerator();stream.prepareForUse();learner.setModelContext(stream.getHeader());learner.prepareForUse();int numberSamplesCorrect = 0;int numberSamples = 0;boolean isTesting = true;while (stream.hasMoreInstances() && numberSamples < numInstances) { Instance trainInst = stream.nextInstance(); if (isTesting) { if (learner.correctlyClassifies(trainInst)) { numberSamplesCorrect++; } } numberSamples++; learner.trainOnInstance(trainInst);}double accuracy = 100.0 * (double) numberSamplesCorrect / (double) numberSamples;System.out.println(numberSamples + " instances processed with " + accuracy + "% accuracy "); |
And here is the corresponding Matlab code.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | clear; close all;clc;javaclasspath('C:\Users\Ninad\Desktop\moa-release-2014 .11\moa.jar');import moa.classifiers.trees.HoeffdingTree;import moa.streams.generators.RandomRBFGenerator;numInstances =10000;learner=HoeffdingTree;stream=RandomRBFGenerator;stream.prepareForUse;learner.setModelContext(stream.getHeader);learner.prepareForUse;numberSamplesCorrect=0;numberSamples=0;isTesting=true;while(stream.hasMoreInstances && numberSamples<numInstances) trainInst=stream.nextInstance; if(isTesting) if(learner.correctlyClassifies(trainInst)) numberSamplesCorrect=numberSamplesCorrect+1; end end numberSamples=numberSamples+1; learner.trainOnInstance(trainInst);endaccuracy=numberSamplesCorrect/numberSamples;fprintf('%d instances processed with %f accuracy\n',numberSamples,accuracy); |