java.io.ObjectInputStream.readObject C# (CSharp) Method

        public virtual global::java.lang.Object readObject()
        {
            global::net.sf.jni4net.jni.JNIEnv @__env = this.Env;
            using(new global::net.sf.jni4net.jni.LocalFrame(@__env, 10)){
            return global::net.sf.jni4net.utils.Convertor.FullJ2C<global::java.lang.Object>(@__env, @__env.CallObjectMethodPtr(this, global::java.io.ObjectInputStream.j4n_readObject15));
            }
        }

    public static void Main(String[] args)
    {
        try
        {
            // Load the model
            java.io.ObjectInputStream   stream       = new java.io.ObjectInputStream(new java.io.FileInputStream("iris_j48.model"));
            weka.classifiers.Classifier qhClassifier = (weka.classifiers.Classifier)stream.readObject();
            stream.close();

            // This model was trained on 66% of instances from the iris dataset. Test the model on remaining 34% instances.
            weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("iris.arff"));
            insts.setClassIndex(insts.numAttributes() - 1);
            int percentSplit = 66;
            int trainSize    = insts.numInstances() * percentSplit / 100;
            int testSize     = insts.numInstances() - trainSize;
            int numCorrect   = 0;
            for (int i = trainSize; i < insts.numInstances(); i++)
            {
                weka.core.Instance currentInst    = insts.instance(i);
                double             predictedClass = qhClassifier.classifyInstance(currentInst);
                if (predictedClass == insts.instance(i).classValue())
                {
                    numCorrect++;
                }
            }
            Console.WriteLine(numCorrect + " out of " + testSize + " correct (" + (double)((double)numCorrect / (double)testSize * 100.0) + "%)");
        }
        catch (java.lang.Exception e)
        {
            e.printStackTrace();
        }
    }