sN.MLPtrain: Trains a multilayer perceptron with 1 hidden layer
In simpleNeural: An Easy to Use Multilayer Perceptron

Description Usage Arguments Value References Examples

Trains a multilayer perceptron with 1 hidden layer and a sigmoid activation function, using backpropagation and gradient descent. Don't forget to normalize the data first - sN.normalizeDF(), provided in the package, can be used to do so.

1 2	sN.MLPtrain(X, y, hidden_layer_size = 5, it = 50, lambda = 0.5, alpha = 0.5)

`X`	Matrix of predictors
`y`	Vector of output (the ANN learns y=ANN(X)). Classes should be assigned an integer number, starting at 0 for the first class.
`hidden_layer_size`	Number of units in the hidden layer
`it`	Number of iterations for the gradient descent. The default value of 50 may be a little low in some cases. 100 to 1000 are generally sensible values.
`lambda`	Penalization for model coefficients (regularization parameter)
`alpha`	Speed multiplier (learning rate) for gradient descent

The coefficients of the MLP, in a list (Theta1 between input and hidden layers, Theta2 between hidden and output layers)

M.W Gardner, S.R Dorling, Artificial neural networks (the multilayer perceptron)- a review of applications in the atmospheric sciences, Atmospheric Environment, Volume 32, Issues 14-15, 1 August 1998, Pages 2627-2636, ISSN 1352-2310, doi: 10.1016/S1352-2310(97)00447-0 [http://www.sciencedirect.com/science/article/pii/S1352231097004470]

Jain, A.K.; Jianchang Mao; Mohiuddin, K.M., "Artificial neural networks: a tutorial," Computer , vol.29, no.3, pp.31,44, Mar 1996. doi: 10.1109/2.485891 [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=485891&isnumber=10412]

Rumelhart, David E., Geoffrey E. Hinton, and R. J. Williams. "Learning Internal Representations by Error Propagation". David E. Rumelhart, James L. McClelland, and the PDP research group (editors). Parallel distributed processing: Explorations in the microstructure of cognition, Volume 1: Foundations. MIT Press, 1986.

# NB: the provided examples are just here to help use the package's functions.
# In real use cases you should perform a proper validation (cross-validation,
# external validation data...)
data(UCI.BCD.Wisconsin);
X=as.matrix(sN.normalizeDF(as.data.frame(UCI.BCD.Wisconsin[,3:32])));
y=as.matrix(UCI.BCD.Wisconsin[,2]);
myMLP=sN.MLPtrain(X=X,y=y,hidden_layer_size=20,it=50,lambda=0.5,alpha=0.5);
myPrediction=sN.MLPpredict(nnModel=myMLP,X=X,raw=TRUE);
#library('verification');
#roc.area(y,myPrediction[,2]);