Is there a way to import a RapidMiner MLP-ANN in OpenCV?

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I trained and validated a MLP Model in RapidMiner Studio. My Input Values are already normalized to [-1, 1]. As far as I understood, the MLP is already defined by its weights. As you can see here, the ANN has one Hidden Layer: http://i.stack.imgur.com/qhVP0.png

Now I'm trying to import this in OpenCV, as I don't want to retrain the whole model. I got all weights per Node + Bias from RapidMiner.

OpenCV offers the function CvANN_MLP::load(), where I am able to load a XML or YML file. I tried to modify an existing YML config for my needs. As you can see, I already defined the dimensions of the layers/data.

I have 23 Inputs, 15 Hidden Nodes and 5 Outputs. So i got (23 + 1) * 15 = 360 weights for the Hidden-Layer and (15 + 1) * 5 = 80 weights for the Output-Layer.

My main questions are:

• Is this even possible?
• What is the correct order for the values?
• Where are the Bias-Values located?
• What exactly does the output-scaling do?
• How to determine the predicted label from the Response Matrix in OpenCV? (Is it the Index of the largest Value?)

I already tried to import the modified file and the program compiled as well. It computes something, but I don't think / am not really sure it worked.

Here is my YML File:

%YAML:1.0
mlp: !!opencv-ml-ann-mlp
   layer_sizes: !!opencv-matrix
      rows: 1
      cols: 3
      dt: i
      data: [ 23, 15, 5 ]
   activation_function: SIGMOID_SYM
   f_param1: 0.6666666666666666
   f_param2: 1.7159000000000000
   min_val: -0.9500000000000000
   max_val: 0.9500000000000000
   min_val1: -0.9800000000000000
   max_val1: 0.9800000000000000
   training_params:
      train_method: RPROP
      dw0: 0.1000000000000000
      dw_plus: 1.2000000000000000
      dw_minus: 0.5000000000000000
      dw_min: 1.1920928955078125e-07
      dw_max: 50.
      term_criteria: { epsilon:9.9999997764825821e-03, iterations:1000 }
   input_scale: [ 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1 ]
   output_scale: [ 1, -1, 1, -1, 1, -1, 1, -1, 1, -1 ]
   inv_output_scale: [ 1, -1, 1, -1, 1, -1, 1, -1, 1, -1 ]
   weights:
      - [ 10.063, 8.812, 3.996, 19.716, -10.239, 2.720, -21.349, 16.635, 0.797, -0.906, -3.003, -5.882, -2.595, -0.957, -4.255, -2.008, 2.978, 17.362, 2.246, 9.740, 0.491, 3.492, 23.299, 10.214,
          31.730, -23.089, 0.369, -72.193, 2.193, -9.687, 4.192, -26.858, 2.780, 5.791, 0.348, -3.331, 2.822, -15.520, -9.149, -16.861, -10.512, -17.079, -14.414, -14.371, -0.278, 10.420, -3.733, -1.921,
          -0.198, 50.929, 0.355, 3.136, 4.892, 0.496, -10.206, -2.844, 0.606, 1.570, -3.054, 6.012, 1.654, -2.043, -2.194, -3.776, -4.745, -6.988, -4.795, -0.397, -2.280, -7.741, -12 ...