How to use LSTM with a 2d array with DeepLearning4j

Question

i am trying to learn how to use LSTM with deeplearning4j lib.

I created a dummy scenario where i want to get an output (3 classes) based on data that i collected.

I got the data from here (http://www.osservatoriodioropa.it/meteoropa/NOAAMO.TXT) if someone is curious :)

Back to the scenario. I created 2 matrix, one with features, other with classes that i want to output, just as a test.

When i try the classifier i got

Exception in thread "main" java.lang.IllegalStateException: 3D input expected to RNN layer expected, got 2

i think because the RnnOutputLayer expect a 3d matrix, but i am not able to understand how to populate it. How can i convert a 2d matrix into a 3d matrix correlating the previous event with the new one? The data are a time serie, and i want to relate the classification of the new day based on previous days as well. (I know that probably the data won't fit this scenario and that there are better way to do that, but that's just to learning how to use LSTM, not how to classify this specific dataset)

this is the code so far

public class Test {

    public static void main(String args[]) {
        int events = 5;
        int features = 6;
        int classes = 3;

        double[][] featureMatrix = new double[events][features];
        double[][] labelMatrix = new double[events][classes];

        for (int i = 0; i < events; i++) {
            for (int f = 0; f < features; f++) {
                featureMatrix[i][f] = getFeature(i, f);
            }
            for (int c = 0; c < classes; c++) {
                labelMatrix[i][c] = getResult(i, c);
            }
        }

        INDArray trainingIn = Nd4j.create(featureMatrix);
        INDArray trainingOut = Nd4j.create(labelMatrix);

        DataSet myData = new DataSet(trainingIn, trainingOut);
        MultiLayerNetwork multiLayerNetwork = createModel(features,classes);
        multiLayerNetwork.init();
        multiLayerNetwork.fit(myData);

    }

    private static double getFeature(int i, int f) {
        //dummy
        return 1.;
    }

    private static double getResult(int i, int c) {
        //dummy

        return 1.;
    }

    public static MultiLayerNetwork createModel(int inputNum, int outputNum) {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .trainingWorkspaceMode(ENABLED).inferenceWorkspaceMode(ENABLED)
                .seed(123456)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .updater(new RmsProp.Builder().learningRate(0.05).rmsDecay(0.002).build())
                .l2(0.0005)
                .weightInit(WeightInit.XAVIER)
                .activation(Activation.TANH)
                .list()

                .layer(new LSTM.Builder().name("1").nIn(inputNum).nOut(inputNum).build())
                .layer(new LSTM.Builder().name("2").nIn(inputNum).nOut(inputNum).build())

                .layer(new RnnOutputLayer.Builder().name("output").nIn(inputNum).nOut(outputNum)
                        .activation(Activation.IDENTITY).lossFunction(LossFunctions.LossFunction.MSE).build())
                .build();
        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();
        return net;
    }

}