TRandom

class TRandom : public TNamed

    public:

               TRandom TRandom(UInt_t seed = 65539)
               TRandom TRandom(const TRandom&)
          virtual void ~TRandom()
         virtual Int_t Binomial(Int_t ntot, Double_t prob)
        static TClass* Class()
      virtual Double_t Exp(Double_t tau)
      virtual Double_t Gaus(Double_t mean = 0, Double_t sigma = 1)
        virtual UInt_t GetSeed()
        virtual UInt_t Integer(UInt_t imax)
       virtual TClass* IsA() const
      virtual Double_t Landau(Double_t mean = 0, Double_t sigma = 1)
         virtual Int_t Poisson(Double_t mean)
      virtual Double_t PoissonD(Double_t mean)
          virtual void Rannor(Float_t& a, Float_t& b)
          virtual void Rannor(Double_t& a, Double_t& b)
          virtual void ReadRandom(const char* filename)
      virtual Double_t Rndm(Int_t i = 0)
          virtual void RndmArray(Int_t n, Float_t* array)
          virtual void RndmArray(Int_t n, Double_t* array)
          virtual void SetSeed(UInt_t seed = 65539)
          virtual void ShowMembers(TMemberInspector& insp, char* parent)
          virtual void Streamer(TBuffer& b)
                  void StreamerNVirtual(TBuffer& b)
      virtual Double_t Uniform(Double_t x1 = 1)
      virtual Double_t Uniform(Double_t x1, Double_t x2)
          virtual void WriteRandom(const char* filename)

Data Members

    protected:

      UInt_t fSeed  Random number generator seed

See also

TRandom2, TRandom3

Class Description

 TRandom

 basic Random number generator class (periodicity = 10**8).

 The following basic Random generators are provided:
 ===================================================
   -Exp(tau)
   -Integer(imax)
   -Gaus(mean,sigma)
   -Rndm()
   -Uniform(x1)
   -Landau(mpv,sigma)
   -Poisson(mean)
   -Binomial(ntot,prob)

 Random numbers distributed according to 1-d, 2-d or 3-d distributions
 =====================================================================
 contained in TF1, TF2 or TF3 objects.
 For example, to get a random number distributed following abs(sin(x)/x)*sqrt(x)
 you can do:
   TF1 *f1 = new TF1("f1","abs(sin(x)/x)*sqrt(x)",0,10);
   double r = f1->GetRandom();
 The technique of using a TF1,2 or 3 function is very powerful.
 It is also more precise than using the basic functions (except Rndm).
 With a TF1 function, for example, the real integral of the function
 is correctly calculated in the specified range of the function.
 Getting a number from a TF1 function is also very fast.
 The following table shows some timings (in microsecons/call)
 for basic functions and TF1 functions.
 The left column is with the compiler, the right column with CINT.
 Numbers have been obtained on a Pentium 233Mhz running Linux.

                          g++        CINT
   Rndm..............    0.330       4.15
   Gaus..............    2.220       6.77
   Landau............   21.590      46.82
   Binomial(5,0.5)...    0.890       5.34
   Binomial(15,0.5)..    0.920       5.36
   Poisson(3)........    2.170       5.93
   Poisson(10).......    4.160       7.95
   Poisson(70).......   21.510      25.27
   Poisson(100)......    2.910       6.72
   GausTF1...........    2.070       4.73
   LandauTF1.........    2.100       4.73

  Note that the time to generate a number from an arbitrary TF1 function
  is independent of the complexity of the function.
  For Landau distribution, it is recommended to use the TF1 technique.

  TH1::FillRandom(TH1 *) or TH1::FillRandom(const char *tf1name)
  ==============================================================
  can be used to fill an histogram (1-d, 2-d, 3-d from an existing histogram
  or from an existing function.

  Note this interesting feature when working with objects
  =======================================================
  You can use several TRandom objects, each with their "independent"
  random sequence. For example, one can imagine
     TRandom *eventGenerator = new TRandom();
     TRandom *tracking       = new TRandom();
  eventGenerator can be used to generate the event kinematics.
  tracking can be used to track the generated particles with random numbers
  independent from eventGenerator.
  This very interesting feature gives the possibility to work with simple
  and very fast random number generators without worrying about
  random number periodicity as it was the case with Fortran.
  One can use TRandom::SetSeed to modify the seed of one generator.

  a TRandom object may be written to a Root file
  ==============================================
    -as part of another object
    -or with its own key (example gRandom->Write("Random");

  The small program below has been used to get the values in the table above.
   #ifndef __CINT__
      #include "TROOT.h"
   #include "TF1.h"
   #include "TRandom.h"
   #include "TStopwatch.h"
   void rand();

   //______________________________________________________________________________
   int main()
   {
     TROOT simple("simple","Test of random numbers");

     rand();
   }
   #endif
   void rand() {
     int i, N = 1000000;
     double cpn = 1000000./N;
     double x;
     TStopwatch sw;
     sw.Start();
     for (i=0;i<N;i++) {
        x = gRandom->Rndm(i);
     }
     printf("Rndm.............. %8.3f microseconds/calln",sw.CpuTime()*cpn);
     sw.Start();
     for (i=0;i<N;i++) {
        x = gRandom->Gaus(0,1);
     }
     printf("Gaus.............. %8.3fn",sw.CpuTime()*cpn);
     sw.Start();
     for (i=0;i<N;i++) {
        x = gRandom->Landau(0,1);
     }
     printf("Landau............ %8.3fn",sw.CpuTime()*cpn);
     sw.Start();
     for (i=0;i<N;i++) {
        x = gRandom->Binomial(5,0.5);
     }
     printf("Binomial(5,0.5)... %8.3fn",sw.CpuTime()*cpn);
     sw.Start();
     for (i=0;i<N;i++) {
        x = gRandom->Binomial(15,0.5);
     }
     printf("Binomial(15,0.5).. %8.3fn",sw.CpuTime()*cpn);
     sw.Start();
     for (i=0;i<N;i++) {
        x = gRandom->Poisson(3);
     }
     printf("Poisson(3)........ %8.3fn",sw.CpuTime()*cpn);
     sw.Start();
     for (i=0;i<N;i++) {
        x = gRandom->Poisson(10);
     }
     printf("Poisson(10)....... %8.3fn",sw.CpuTime()*cpn);
     sw.Start();
     for (i=0;i<N;i++) {
        x = gRandom->Poisson(70);
     }
     printf("Poisson(70)....... %8.3fn",sw.CpuTime()*cpn);
     sw.Start();
     for (i=0;i<N;i++) {
        x = gRandom->Poisson(100);
     }
     printf("Poisson(100)...... %8.3fn",sw.CpuTime()*cpn);

     TF1 *f1 = new TF1("f1","gaus",-4,4);
     f1->SetParameters(1,0,1);
     sw.Start();
     for (i=0;i<N;i++) {
        x = f1->GetRandom();
     }
     printf("GausTF1........... %8.3fn",sw.CpuTime()*cpn);

     TF1 *f2 = new TF1("f2","landau",-5,15);
     f2->SetParameters(1,0,1);
     sw.Start();
     for (i=0;i<N;i++) {
        x = f2->GetRandom();
     }
     printf("LandauTF1......... %8.3fn",sw.CpuTime()*cpn);

   }

*-*-*-*-*-*-*-*-*-*-*default constructor*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
*-*                  ===================

*-*-*-*-*-*-*-*-*-*-*default destructor*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
*-*                  ==================

 Generates a random integer N according to the binomial law
 Coded from Los Alamos report LA-5061-MS

 N is binomially distributed between 0 and ntot inclusive
 with mean prob*ntot.
 prob is between 0 and 1.

 Note: This function should not be used when ntot is large (say >100).
 The normal approximation is then recommended instead
 (with mean =*ntot+0.5 and standard deviation sqrt(ntot*prob*(1-prob)).

 returns an exponential deviate.

          exp( -t/tau )

      Return a number distributed following a gaussian with mean and sigma

  returns a random integer on [ 0, imax-1 ].

  Generate a random number following a Landau distribution
  with mpv(most probable value) and sigma
  Converted by Rene Brun from CERNLIB routine ranlan(G110)

 Generates a random integer N according to a Poisson law.
 Coded from Los Alamos report LA-5061-MS
 Prob(N) = exp(-mean)*mean^N/Factorial(N)

 Generates a random number according to a Poisson law.
 Coded from Los Alamos report LA-5061-MS
 Prob(N) = exp(-mean)*mean^N/Factorial(N)

 This function is a variant of TRandom::Poisson returning a double
 instead of an integer.

      Return 2 numbers distributed following a gaussian with mean=0 and sigma=1

      Return 2 numbers distributed following a gaussian with mean=0 and sigma=1

 Reads saved random generator status from filename

  Machine independent random number generator.
  Produces uniformly-distributed floating points between 0 and 1.
  Identical sequence on all machines of >= 32 bits.
  Periodicity = 10**8
  Universal version (Fred James 1985).
  generates a number in ]0,1]

 Return an array of n random numbers uniformly distributed in ]0,1]

 Return an array of n random numbers uniformly distributed in ]0,1]

  Set the random generator seed
  if seed is zero, the seed is set to the current  machine clock
  Note that the machine clock is returned with a precision of 1 second.
  If one calls SetSeed(0) within a loop and the loop time is less than 1s,
  all generated numbers will be identical!

 returns a uniform deviate on the interval  ]0, x1].

 returns a uniform deviate on the interval ]x1, x2].

 Writes random generator status to filename

Inline Functions

             UInt_t GetSeed()
            TClass* Class()
            TClass* IsA() const
               void ShowMembers(TMemberInspector& insp, char* parent)
               void Streamer(TBuffer& b)
               void StreamerNVirtual(TBuffer& b)
            TRandom TRandom(const TRandom&)