class TMLPAnalyzer: public TObject

 Destructor

 Returns the number of layers.

 Returns the number of neurons in given layer.

 Returns the formula used as input for neuron (idx) in
 the first layer.

 Returns the name of any neuron from the input layer

 Returns the name of any neuron from the output layer

 Gives some information about the network in the terminal.

 Collect information about what is usefull in the network.
 This method has to be called first when analyzing a network.
 Fills the two analysis trees.

 Draws the distribution (on the test sample) of the
 impact on the network output of a small variation of
 the ith input.

 Draws the distribution (on the test sample) of the
 impact on the network output of a small variation of
 each input.
 DrawDInputs() draws something that approximates the distribution of the
 derivative of the NN w.r.t. each input. That quantity is recognized as
 one of the measures to determine key quantities in the network.

 What is done is to vary one input around its nominal value and to see
 how the NN changes. This is done for each entry in the sample and produces
 a distribution.

 What you can learn from that is:
 - is variable a really useful, or is my network insensitive to it ?
 - is there any risk of big systematic ? Is the network extremely sensitive
   to small variations of any of my inputs ?

 As you might understand, this is to be considered with care and can serve
 as input for an "educated guess" when optimizing the network.

 Draws the distribution of the neural network (using ith neuron).
 Two distributions are drawn, for events passing respectively the "signal"
 and "background" cuts. Only the test sample is used.

 Create a profile of the difference of the MLP output minus the
 true value for a given output node outnode, vs the true value for
 outnode, for all test data events. This method is mainly useful
 when doing regression analysis with the MLP (i.e. not classification,
 but continuous truth values).
 The resulting TProfile histogram is returned.
 It is not drawn if option "goff" is specified.
 Options are passed to TProfile::Draw

 Creates TProfiles of the difference of the MLP output minus the
 true value vs the true value, one for each output, filled with the
 test data events. This method is mainly useful when doing regression
 analysis with the MLP (i.e. not classification, but continuous truth
 values).
 The returned THStack contains all the TProfiles. It is drawn unless
 the option "goff" is specified.
 Options are passed to TProfile::Draw.

 Creates a profile of the difference of the MLP output outnode minus
 the true value of outnode vs the input value innode, for all test
 data events.
 The resulting TProfile histogram is returned.
 It is not drawn if option "goff" is specified.
 Options are passed to TProfile::Draw

 Creates a profile of the difference of the MLP output outnode minus the
 true value of outnode vs the input value, stacked for all inputs, for
 all test data events.
 The returned THStack contains all the TProfiles. It is drawn unless
 the option "goff" is specified.
 Options are passed to TProfile::Draw.