class TMVA::MethodPDERS: public TMVA::MethodBase

 standard constructor for the PDERS method

 construct MethodPDERS through from file

 PDERS can handle classification with 2 classes and regression with one or more regression-targets

 default initialisation routine called by all constructors

 destructor

 define the options (their key words) that can be set in the option string
 know options:
 VolumeRangeMode   <string>  Method to determine volume range
    available values are:        MinMax
                                 Unscaled
                                 RMS
                                 kNN
                                 Adaptive <default>

 KernelEstimator   <string>  Kernel estimation function
    available values are:        Box <default>
                                 Sphere
                                 Teepee
                                 Gauss
                                 Sinc3
                                 Sinc5
                                 Sinc7
                                 Sinc9
                                 Sinc11
                                 Lanczos2
                                 Lanczos3
                                 Lanczos5
                                 Lanczos8
                                 Trim

 DeltaFrac         <float>   Ratio of #EventsMin/#EventsMax for MinMax and RMS volume range
 NEventsMin        <int>     Minimum number of events for adaptive volume range
 NEventsMax        <int>     Maximum number of events for adaptive volume range
 MaxVIterations    <int>     Maximum number of iterations for adaptive volume range
 InitialScale      <float>   Initial scale for adaptive volume range
 GaussSigma        <float>   Width with respect to the volume size of Gaussian kernel estimator

 process the options specified by the user

 this is a dummy training: the preparation work to do is the construction
 of the binary tree as a pointer chain. It is easier to directly save the
 trainingTree in the weight file, and to rebuild the binary tree in the
 test phase from scratch

 init the size of a volume element using a defined fraction of the
 volume containing the entire events

 compute also average RMS values required for adaptive Gaussian

 create binary search trees for signal and background

 defines volume dimensions

 Interface to RootFinder

 count number of events in rescaled volume

 normalization factors so we can work with radius 1 hyperspheres

 normalization factors so we can work with radius 1 hyperspheres

 from the normalized euclidean distance calculate the distance
 for a certain kernel

 Calculating the normalization factor only once (might need a reset at some point.
 Can the method be restarted with different params?)

 We use Euclidian metric here. Might not be best or most efficient.

 NormSinc

 Lanczos Filter

 statistical error estimate for RS estimator

 write weights to xml file

 read weight info from file

 write training sample (TTree) to file

 read training sample from file

 static pointer to this object

 update static this pointer

 write specific classifier response

 get help message text

 typical length of text line:
         "|--------------------------------------------------------------|"

 accessors

 ranking of input variables

 This is a workaround for OSx where static thread_local data members are
 not supported. The C++ solution would indeed be the following: