mojo-resource tutorial

[bvandorf]

Hi,

In the mojo-resource tutorial, you show how to predict from a java application, during my testing I was unable to predict anything other than the default prediction shown in the tutorial even by specifically setting the testRow variable.

https://github.com/h2oai/h2o-tutorials/tree/master/tutorials/mojo-resource

`

    URL mojoURL = Main.class.getResource("irisgbm.zip");
    MojoReaderBackend reader = MojoReaderBackendFactory.createReaderBackend(mojoURL, 
    MojoReaderBackendFactory.CachingStrategy.MEMORY);
    MojoModel model = ModelMojoReader.readFrom(reader);
    EasyPredictModelWrapper modelWrapper = new EasyPredictModelWrapper(model);

    RowData testRow = new RowData();
    for (int i = 0; i < args.length; i++)
    {
        testRow.put("C"+i, Double.valueOf(args[i]));
    }
    if (testRow.size() == 0)
    {
        System.out.println("Add test values to testRow");
        testRow.put("C0", 5.1);
        testRow.put("C1", 3.5);
        testRow.put("C2", 1.4);
        testRow.put("C3", 0.2);
    }
    System.out.println("C0: " + testRow.get("C0"));
    System.out.println("C1: " + testRow.get("C1"));
    System.out.println("C2: " + testRow.get("C2"));
    System.out.println("C3: " + testRow.get("C3"));
    System.out.println("");

    MultinomialModelPrediction prediction = (MultinomialModelPrediction)modelWrapper.predict(testRow);

    for (int i = 0; i < prediction.classProbabilities.length; i++)
        System.out.println(modelWrapper.getResponseDomainValues()[i] + ": "+ prediction.classProbabilities[i]);
    System.out.println("Prediction: " + prediction.label);
    System.out.println("Prediction Index: " + prediction.labelIndex);

`