public static double L_LogLossD(int Y[][],double Rpred[][]){
  int N=Y.length;
  int L=Y[0].length;
  return L_LogLoss(Y,Rpred,Math.log((double)N)) / ((double)N * (double)L);
}

public static double L_LogLossD ( int Y [ ] [ ] , double Rpred [ ] [ ] ) { int N = Y . length ; int L = Y [ 0 ] . length ; return L_LogLoss ( Y , Rpred , Math . log ( ( double ) N ) ) / ( ( double ) N * ( double ) L ) ; }
l_logloss - the log loss between real - valued confidences rpred and true predictions y with a maximum penalty based on the number of examples d [ important note : earlier versions of meka only normalised by n , and not n * l as here ] .
