Implement base algorithms Sigmoid, SigmoidNorm, Sine, Polynomial, Gaussian, Exponential and Linear.

[deil87]

Spark as far as I see has perceptron with only sigmoid activation functions…. 

From the source code:
*Each layer has sigmoid activation function, output layer has softmax.

• Number of inputs has to be equal to the size of feature vectors.
• Number of outputs has to be equal to the total number of labels.

In paper you tried “Training regression models (as components of probabilistic classifiers) is fast and straightforward. We use several activation functions in simple perceptrons, namely Sigmoid, SigmoidNorm, Sine, Polynomial, Gaussian, Exponential and Linear. “ - so I believe we need to use Deeplearning4J implementations for these base classifiers

Or we can fork spark

[deil87]

Take a look at Layer.scala def multiLayerPerceptron() {}

[deil87]

No...those are simple one neuron perceptrons.