class TMVA::MethodMLP: public TMVA::MethodANNBase, public TMVA::IFitterTarget, public TMVA::ConvergenceTest

 standard constructor

 constructor from a weight file

 destructor
 nothing to be done

 MLP can handle classification with 2 classes and regression with one regression-target

 default initializations

 define the options (their key words) that can be set in the option string
 know options:
 TrainingMethod  <string>     Training method
    available values are:         BP   Back-Propagation <default>
                                  GA   Genetic Algorithm (takes a LONG time)

 LearningRate    <float>      NN learning rate parameter
 DecayRate       <float>      Decay rate for learning parameter
 TestRate        <int>        Test for overtraining performed at each #th epochs

 BPMode          <string>     Back-propagation learning mode
    available values are:         sequential <default>
                                  batch

 BatchSize       <int>        Batch size: number of events/batch, only set if in Batch Mode,
                                          -1 for BatchSize=number_of_events

 process user options

 initialize learning rates of synapses, used only by backpropagation

 calculate the estimator that training is attempting to minimize

 train network with BFGS algorithm

 minimize estimator / train network with backpropagation algorithm

 train network over a single epoch/cyle of events

 Input:
   index: the array to shuffle
   n: the size of the array
 Output:
   index: the shuffled indexes
 This method is used for sequential training

 decay synapse weights
 in last 10 epochs, lower learning rate even more to find a good minimum

 fast per-event training

 train network over a single event
 this uses the new event model

 get the desired output of this event

 update the network based on how closely
 the output matched the desired output

 update the network based on how closely
 the output matched the desired output

 have each neuron calculate its delta by backpropagation

 create genetics class similar to GeneticCut
 give it vector of parameter ranges (parameters = weights)
 link fitness function of this class to ComputeEstimator
 instantiate GA (see MethodCuts)
 run it
 then this should exist for GA, Minuit and random sampling

 interface to the estimate

 this function is called by GeneticANN for GA optimization

 update synapse error fields and adjust the weights (if in sequential mode)

 just adjust the synapse weights (should be called in batch mode)

 write specific classifier response

 get help message text

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