// Class: ReadKNN
// Automatically generated by MethodBase::MakeClass
//

/* configuration options =====================================================

#GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-

Method         : KNN::KNN
TMVA Release   : 4.1.4         [262404]
ROOT Release   : 5.34/05       [336389]
Creator        : karavdina
Date           : Wed Mar 27 10:31:01 2013
Host           : Linux pool253.a1.kph 3.7.9-205.fc18.x86_64 #1 SMP Sun Feb 24 20:10:02 UTC 2013 x86_64 x86_64 x86_64 GNU/Linux
Dir            : /panda/karavdina/BkgSeparation
Training events: 10413
Analysis type  : [Classification]


#OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*-

# Set by User:
H: "True" [Print method-specific help message]
CreateMVAPdfs: "True" [Create PDFs for classifier outputs (signal and background)]
nkNN: "30" [Number of k-nearest neighbors]
ScaleFrac: "8.000000e-01" [Fraction of events used to compute variable width]
SigmaFact: "1.000000e+00" [Scale factor for sigma in Gaussian kernel]
Kernel: "Gaus" [Use polynomial (=Poln) or Gaussian (=Gaus) kernel]
Trim: "False" [Use equal number of signal and background events]
UseKernel: "False" [Use polynomial kernel weight]
UseWeight: "True" [Use weight to count kNN events]
# Default:
V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
VerbosityLevel: "Default" [Verbosity level]
VarTransform: "None" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
BalanceDepth: "6" [Binary tree balance depth]
UseLDA: "False" [Use local linear discriminant - experimental feature]
##


#VAR -*-*-*-*-*-*-*-*-*-*-*-* variables *-*-*-*-*-*-*-*-*-*-*-*-

NVar 9
x                             x                             x                             x                                                               'D'    [-19.8703174591,19.7482700348]
y                             y                             y                             y                                                               'D'    [-17.0589866638,16.8706817627]
z                             z                             z                             z                                                               'D'    [-0.0387658625841,0.514511883259]
p                             p                             p                             p                                                               'D'    [14.9999980927,21.2431297302]
theta                         theta                         theta                         theta                                                           'D'    [0.000339014193742,0.0198132172227]
phi                           phi                           phi                           phi                                                             'D'    [-3.14069318771,3.14107322693]
px                            px                            px                            px                                                              'D'    [-0.222319915891,0.326948046684]
py                            py                            py                            py                                                              'D'    [-0.234180748463,0.166489973664]
pz                            pz                            pz                            pz                                                              'D'    [14.9989938736,21.2421226501]
NSpec 0


============================================================================ */

#include <vector>
#include <cmath>
#include <string>
#include <iostream>

#ifndef IClassifierReader__def
#define IClassifierReader__def

class IClassifierReader {

 public:

   // constructor
   IClassifierReader() : fStatusIsClean( true ) {}
   virtual ~IClassifierReader() {}

   // return classifier response
   virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;

   // returns classifier status
   bool IsStatusClean() const { return fStatusIsClean; }

 protected:

   bool fStatusIsClean;
};

#endif

class ReadKNN : public IClassifierReader {

 public:

   // constructor
   ReadKNN( std::vector<std::string>& theInputVars ) 
      : IClassifierReader(),
        fClassName( "ReadKNN" ),
        fNvars( 9 ),
        fIsNormalised( false )
   {      
      // the training input variables
      const char* inputVars[] = { "x", "y", "z", "p", "theta", "phi", "px", "py", "pz" };

      // sanity checks
      if (theInputVars.size() <= 0) {
         std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
         fStatusIsClean = false;
      }

      if (theInputVars.size() != fNvars) {
         std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
                   << theInputVars.size() << " != " << fNvars << std::endl;
         fStatusIsClean = false;
      }

      // validate input variables
      for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
         if (theInputVars[ivar] != inputVars[ivar]) {
            std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
                      << " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
            fStatusIsClean = false;
         }
      }

      // initialize min and max vectors (for normalisation)
      fVmin[0] = -19.8703174591064;
      fVmax[0] = 19.74827003479;
      fVmin[1] = -17.0589866638184;
      fVmax[1] = 16.8706817626953;
      fVmin[2] = -0.0387658625841141;
      fVmax[2] = 0.51451188325882;
      fVmin[3] = 14.9999980926514;
      fVmax[3] = 21.2431297302246;
      fVmin[4] = 0.000339014193741605;
      fVmax[4] = 0.0198132172226906;
      fVmin[5] = -3.14069318771362;
      fVmax[5] = 3.14107322692871;
      fVmin[6] = -0.222319915890694;
      fVmax[6] = 0.326948046684265;
      fVmin[7] = -0.234180748462677;
      fVmax[7] = 0.166489973664284;
      fVmin[8] = 14.9989938735962;
      fVmax[8] = 21.2421226501465;

      // initialize input variable types
      fType[0] = 'D';
      fType[1] = 'D';
      fType[2] = 'D';
      fType[3] = 'D';
      fType[4] = 'D';
      fType[5] = 'D';
      fType[6] = 'D';
      fType[7] = 'D';
      fType[8] = 'D';

      // initialize constants
      Initialize();

   }

   // destructor
   virtual ~ReadKNN() {
      Clear(); // method-specific
   }

   // the classifier response
   // "inputValues" is a vector of input values in the same order as the 
   // variables given to the constructor
   double GetMvaValue( const std::vector<double>& inputValues ) const;

 private:

   // method-specific destructor
   void Clear();

   // common member variables
   const char* fClassName;

   const size_t fNvars;
   size_t GetNvar()           const { return fNvars; }
   char   GetType( int ivar ) const { return fType[ivar]; }

   // normalisation of input variables
   const bool fIsNormalised;
   bool IsNormalised() const { return fIsNormalised; }
   double fVmin[9];
   double fVmax[9];
   double NormVariable( double x, double xmin, double xmax ) const {
      // normalise to output range: [-1, 1]
      return 2*(x - xmin)/(xmax - xmin) - 1.0;
   }

   // type of input variable: 'F' or 'I'
   char   fType[9];

   // initialize internal variables
   void Initialize();
   double GetMvaValue__( const std::vector<double>& inputValues ) const;

   // private members (method specific)
   // not implemented for class: "ReadKNN"
};
   inline double ReadKNN::GetMvaValue( const std::vector<double>& inputValues ) const
   {
      // classifier response value
      double retval = 0;

      // classifier response, sanity check first
      if (!IsStatusClean()) {
         std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
                   << " because status is dirty" << std::endl;
         retval = 0;
      }
      else {
         if (IsNormalised()) {
            // normalise variables
            std::vector<double> iV;
            int ivar = 0;
            for (std::vector<double>::const_iterator varIt = inputValues.begin();
                 varIt != inputValues.end(); varIt++, ivar++) {
               iV.push_back(NormVariable( *varIt, fVmin[ivar], fVmax[ivar] ));
            }
            retval = GetMvaValue__( iV );
         }
         else {
            retval = GetMvaValue__( inputValues );
         }
      }

      return retval;
   }