Changeset 1185:c8d0179a32a2 in lemon-main

Timestamp:

10/17/18 19:22:52 (6 years ago)

Author:

Alpar Juttner <alpar@…>

Branch:

default

Parents:

1173:57167d92e96c (diff), 1184:3c00344f49c9 (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent.

Phase:

public

Message:

Merge bugfixes #610,#611,#612,#614

Files:

• CMakeLists.txt (modified) (1 diff)
• CMakeLists.txt (modified) (3 diffs)
• lemon/random.h (modified) (1 diff)
• lemon/random.h (modified) (7 diffs)

CMakeLists.txt

@@ -87 +87 @@
 IF(LEMON_ENABLE_GLPK) 
   FIND_PACKAGE(GLPK 4.33)
+  IF(GLPK_FOUND)
+    SET(LEMON_HAVE_LP TRUE)
+    SET(LEMON_HAVE_MIP TRUE)
+    SET(LEMON_HAVE_GLPK TRUE)
+  ENDIF(GLPK_FOUND)
 ENDIF(LEMON_ENABLE_GLPK)
 IF(LEMON_ENABLE_ILOG)
   FIND_PACKAGE(ILOG)
+  IF(ILOG_FOUND)
+    SET(LEMON_HAVE_LP TRUE)
+    SET(LEMON_HAVE_MIP TRUE)
+    SET(LEMON_HAVE_CPLEX TRUE)
+  ENDIF(ILOG_FOUND)
 ENDIF(LEMON_ENABLE_ILOG)
 IF(LEMON_ENABLE_COIN)
   FIND_PACKAGE(COIN)
+  IF(COIN_FOUND)
+    SET(LEMON_HAVE_LP TRUE)
+    SET(LEMON_HAVE_MIP TRUE)
+    SET(LEMON_HAVE_CLP TRUE)
+    SET(LEMON_HAVE_CBC TRUE)
+  ENDIF(COIN_FOUND)
 ENDIF(LEMON_ENABLE_COIN)
 IF(LEMON_ENABLE_SOPLEX)
   FIND_PACKAGE(SOPLEX)
+  IF(SOPLEX_FOUND)
+    SET(LEMON_HAVE_LP TRUE)
+    SET(LEMON_HAVE_SOPLEX TRUE)
+  ENDIF(SOPLEX_FOUND)
 ENDIF(LEMON_ENABLE_SOPLEX)
-
-IF(GLPK_FOUND)
-  SET(LEMON_HAVE_LP TRUE)
-  SET(LEMON_HAVE_MIP TRUE)
-  SET(LEMON_HAVE_GLPK TRUE)
-ENDIF(GLPK_FOUND)
-IF(ILOG_FOUND)
-  SET(LEMON_HAVE_LP TRUE)
-  SET(LEMON_HAVE_MIP TRUE)
-  SET(LEMON_HAVE_CPLEX TRUE)
-ENDIF(ILOG_FOUND)
-IF(COIN_FOUND)
-  SET(LEMON_HAVE_LP TRUE)
-  SET(LEMON_HAVE_MIP TRUE)
-  SET(LEMON_HAVE_CLP TRUE)
-  SET(LEMON_HAVE_CBC TRUE)
-ENDIF(COIN_FOUND)
-IF(SOPLEX_FOUND)
-  SET(LEMON_HAVE_LP TRUE)
-  SET(LEMON_HAVE_SOPLEX TRUE)
-ENDIF(SOPLEX_FOUND)
 
 IF(ILOG_FOUND)

CMakeLists.txt

@@ -4 +4 @@
   CMAKE_POLICY(SET CMP0048 OLD) 
 ENDIF(POLICY CMP0048)
+
+IF(POLICY CMP0043) 
+  CMAKE_POLICY(SET CMP0043 OLD) 
+ENDIF(POLICY CMP0043)
 
 IF(POLICY CMP0026)
@@ -234 +238 @@
     FORCE )
 
+SET_DIRECTORY_PROPERTIES(PROPERTIES
+  COMPILE_DEFINITIONS_DEBUG "LEMON_ENABLE_DEBUG"
+  COMPILE_DEFINITIONS_MAINTAINER "LEMON_ENABLE_DEBUG"
+)
 
 INCLUDE(CheckTypeSize)
@@ -262 +270 @@
 
 ENABLE_TESTING()
+
+
+INCLUDE(CheckCXXCompilerFlag)
+CHECK_CXX_COMPILER_FLAG("-std=c++11" LEMON_CXX11)
+IF(LEMON_CXX11)
+  SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
+ENDIF()
+
 
 IF(${CMAKE_BUILD_TYPE} STREQUAL "Maintainer")

lemon/random.h

@@ -340 +340 @@
           num = rnd() & mask;
         } while (num > max);
-        return num;
+        return static_cast<Result>(num);
       }
     };

lemon/random.h

@@ -111 +111 @@
       static const Word loMask = (1u << 31) - 1;
       static const Word hiMask = ~loMask;
-
 
       static Word tempering(Word rnd) {
@@ -244 +243 @@
     private:
 
-
       void fillState() {
         static const Word mask[2] = { 0x0ul, RandomTraits<Word>::mask };
@@ -252 +250 @@
         current = state + length;
 
-        register Word *curr = state + length - 1;
-        register long num;
+        Word *curr = state + length - 1;
+        long num;
 
         num = length - shift;
@@ -272 +270 @@
       }
 
-
       Word *current;
       Word state[length];
@@ -297 +294 @@
     template <typename Result, typename Word,
               int rest = std::numeric_limits<Result>::digits, int shift = 0,
-              bool last = rest <= std::numeric_limits<Word>::digits>
+              bool last = (rest <= std::numeric_limits<Word>::digits)>
     struct IntConversion {
       static const int bits = std::numeric_limits<Word>::digits;
@@ -466 +463 @@
     };
 
-  }
+    /// \ingroup misc
+    ///
+    /// \brief Mersenne Twister random number generator
+    ///
+    /// The Mersenne Twister is a twisted generalized feedback
+    /// shift-register generator of Matsumoto and Nishimura. The period
+    /// of this generator is \f$ 2^{19937} - 1\f$ and it is
+    /// equi-distributed in 623 dimensions for 32-bit numbers. The time
+    /// performance of this generator is comparable to the commonly used
+    /// generators.
+    ///
+    /// This is a template implementation of both 32-bit and
+    /// 64-bit architecture optimized versions. The generators differ
+    /// sligthly in the initialization and generation phase so they
+    /// produce two completly different sequences.
+    ///
+    /// \alert Do not use this class directly, but instead one of \ref
+    /// Random, \ref Random32 or \ref Random64.
+    ///
+    /// The generator gives back random numbers of serveral types. To
+    /// get a random number from a range of a floating point type, you
+    /// can use one form of the \c operator() or the \c real() member
+    /// function. If you want to get random number from the {0, 1, ...,
+    /// n-1} integer range, use the \c operator[] or the \c integer()
+    /// method. And to get random number from the whole range of an
+    /// integer type, you can use the argumentless \c integer() or
+    /// \c uinteger() functions. Finally, you can get random bool with
+    /// equal chance of true and false or with given probability of true
+    /// result using the \c boolean() member functions.
+    ///
+    /// Various non-uniform distributions are also supported: normal (Gauss),
+    /// exponential, gamma, Poisson, etc.; and a few two-dimensional
+    /// distributions, too.
+    ///
+    ///\code
+    /// // The commented code is identical to the other
+    /// double a = rnd();                     // [0.0, 1.0)
+    /// // double a = rnd.real();             // [0.0, 1.0)
+    /// double b = rnd(100.0);                // [0.0, 100.0)
+    /// // double b = rnd.real(100.0);        // [0.0, 100.0)
+    /// double c = rnd(1.0, 2.0);             // [1.0, 2.0)
+    /// // double c = rnd.real(1.0, 2.0);     // [1.0, 2.0)
+    /// int d = rnd[100000];                  // 0..99999
+    /// // int d = rnd.integer(100000);       // 0..99999
+    /// int e = rnd[6] + 1;                   // 1..6
+    /// // int e = rnd.integer(1, 1 + 6);     // 1..6
+    /// int b = rnd.uinteger<int>();          // 0 .. 2^31 - 1
+    /// int c = rnd.integer<int>();           // - 2^31 .. 2^31 - 1
+    /// bool g = rnd.boolean();               // P(g = true) = 0.5
+    /// bool h = rnd.boolean(0.8);            // P(h = true) = 0.8
+    ///\endcode
+    ///
+    /// LEMON provides a global instance of the random number generator:
+    /// \ref lemon::rnd "rnd". In most cases, it is a good practice
+    /// to use this global generator to get random numbers.
+    ///
+    /// \sa \ref Random, \ref Random32 or \ref Random64.
+    template<class Word>
+    class Random {
+    private:
+
+      _random_bits::RandomCore<Word> core;
+      _random_bits::BoolProducer<Word> bool_producer;
+
+
+    public:
+
+      ///\name Initialization
+      ///
+      /// @{
+
+      /// \brief Default constructor
+      ///
+      /// Constructor with constant seeding.
+      Random() { core.initState(); }
+
+      /// \brief Constructor with seed
+      ///
+      /// Constructor with seed. The current number type will be converted
+      /// to the architecture word type.
+      template <typename Number>
+      Random(Number seed) {
+        _random_bits::Initializer<Number, Word>::init(core, seed);
+      }
+
+      /// \brief Constructor with array seeding
+      ///
+      /// Constructor with array seeding. The given range should contain
+      /// any number type and the numbers will be converted to the
+      /// architecture word type.
+      template <typename Iterator>
+      Random(Iterator begin, Iterator end) {
+        typedef typename std::iterator_traits<Iterator>::value_type Number;
+        _random_bits::Initializer<Number, Word>::init(core, begin, end);
+      }
+
+      /// \brief Copy constructor
+      ///
+      /// Copy constructor. The generated sequence will be identical to
+      /// the other sequence. It can be used to save the current state
+      /// of the generator and later use it to generate the same
+      /// sequence.
+      Random(const Random& other) {
+        core.copyState(other.core);
+      }
+
+      /// \brief Assign operator
+      ///
+      /// Assign operator. The generated sequence will be identical to
+      /// the other sequence. It can be used to save the current state
+      /// of the generator and later use it to generate the same
+      /// sequence.
+      Random& operator=(const Random& other) {
+        if (&other != this) {
+          core.copyState(other.core);
+        }
+        return *this;
+      }
+
+      /// \brief Seeding random sequence
+      ///
+      /// Seeding the random sequence. The current number type will be
+      /// converted to the architecture word type.
+      template <typename Number>
+      void seed(Number seed) {
+        _random_bits::Initializer<Number, Word>::init(core, seed);
+      }
+
+      /// \brief Seeding random sequence
+      ///
+      /// Seeding the random sequence. The given range should contain
+      /// any number type and the numbers will be converted to the
+      /// architecture word type.
+      template <typename Iterator>
+      void seed(Iterator begin, Iterator end) {
+        typedef typename std::iterator_traits<Iterator>::value_type Number;
+        _random_bits::Initializer<Number, Word>::init(core, begin, end);
+      }
+
+      /// \brief Seeding from file or from process id and time
+      ///
+      /// By default, this function calls the \c seedFromFile() member
+      /// function with the <tt>/dev/urandom</tt> file. If it does not success,
+      /// it uses the \c seedFromTime().
+      /// \return Currently always \c true.
+      bool seed() {
+#ifndef LEMON_WIN32
+        if (seedFromFile("/dev/urandom", 0)) return true;
+#endif
+        if (seedFromTime()) return true;
+        return false;
+      }
+
+      /// \brief Seeding from file
+      ///
+      /// Seeding the random sequence from file. The linux kernel has two
+      /// devices, <tt>/dev/random</tt> and <tt>/dev/urandom</tt> which
+      /// could give good seed values for pseudo random generators (The
+      /// difference between two devices is that the <tt>random</tt> may
+      /// block the reading operation while the kernel can give good
+      /// source of randomness, while the <tt>urandom</tt> does not
+      /// block the input, but it could give back bytes with worse
+      /// entropy).
+      /// \param file The source file
+      /// \param offset The offset, from the file read.
+      /// \return \c true when the seeding successes.
+#ifndef LEMON_WIN32
+      bool seedFromFile(const std::string& file = "/dev/urandom", int offset = 0)
+#else
+        bool seedFromFile(const std::string& file = "", int offset = 0)
+#endif
+      {
+        std::ifstream rs(file.c_str());
+        const int size = 4;
+        Word buf[size];
+        if (offset != 0 && !rs.seekg(offset)) return false;
+        if (!rs.read(reinterpret_cast<char*>(buf), sizeof(buf))) return false;
+        seed(buf, buf + size);
+        return true;
+      }
+
+      /// \brief Seeding from process id and time
+      ///
+      /// Seeding from process id and time. This function uses the
+      /// current process id and the current time for initialize the
+      /// random sequence.
+      /// \return Currently always \c true.
+      bool seedFromTime() {
+#ifndef LEMON_WIN32
+        timeval tv;
+        gettimeofday(&tv, 0);
+        seed(getpid() + tv.tv_sec + tv.tv_usec);
+#else
+        seed(bits::getWinRndSeed());
+#endif
+        return true;
+      }
+
+      /// @}
+
+      ///\name Uniform Distributions
+      ///
+      /// @{
+
+      /// \brief Returns a random real number from the range [0, 1)
+      ///
+      /// It returns a random real number from the range [0, 1). The
+      /// default Number type is \c double.
+      template <typename Number>
+      Number real() {
+        return _random_bits::RealConversion<Number, Word>::convert(core);
+      }
+
+      double real() {
+        return real<double>();
+      }
+
+      /// \brief Returns a random real number from the range [0, 1)
+      ///
+      /// It returns a random double from the range [0, 1).
+      double operator()() {
+        return real<double>();
+      }
+
+      /// \brief Returns a random real number from the range [0, b)
+      ///
+      /// It returns a random real number from the range [0, b).
+      double operator()(double b) {
+        return real<double>() * b;
+      }
+
+      /// \brief Returns a random real number from the range [a, b)
+      ///
+      /// It returns a random real number from the range [a, b).
+      double operator()(double a, double b) {
+        return real<double>() * (b - a) + a;
+      }
+
+      /// \brief Returns a random integer from a range
+      ///
+      /// It returns a random integer from the range {0, 1, ..., b - 1}.
+      template <typename Number>
+      Number integer(Number b) {
+        return _random_bits::Mapping<Number, Word>::map(core, b);
+      }
+
+      /// \brief Returns a random integer from a range
+      ///
+      /// It returns a random integer from the range {a, a + 1, ..., b - 1}.
+      template <typename Number>
+      Number integer(Number a, Number b) {
+        return _random_bits::Mapping<Number, Word>::map(core, b - a) + a;
+      }
+
+      /// \brief Returns a random integer from a range
+      ///
+      /// It returns a random integer from the range {0, 1, ..., b - 1}.
+      template <typename Number>
+      Number operator[](Number b) {
+        return _random_bits::Mapping<Number, Word>::map(core, b);
+      }
+
+      /// \brief Returns a random non-negative integer
+      ///
+      /// It returns a random non-negative integer uniformly from the
+      /// whole range of the current \c Number type. The default result
+      /// type of this function is <tt>unsigned int</tt>.
+      template <typename Number>
+      Number uinteger() {
+        return _random_bits::IntConversion<Number, Word>::convert(core);
+      }
+
+      unsigned int uinteger() {
+        return uinteger<unsigned int>();
+      }
+
+      /// \brief Returns a random integer
+      ///
+      /// It returns a random integer uniformly from the whole range of
+      /// the current \c Number type. The default result type of this
+      /// function is \c int.
+      template <typename Number>
+      Number integer() {
+        static const int nb = std::numeric_limits<Number>::digits +
+          (std::numeric_limits<Number>::is_signed ? 1 : 0);
+        return _random_bits::IntConversion<Number, Word, nb>::convert(core);
+      }
+
+      int integer() {
+        return integer<int>();
+      }
+
+      /// \brief Returns a random bool
+      ///
+      /// It returns a random bool. The generator holds a buffer for
+      /// random bits. Every time when it become empty the generator makes
+      /// a new random word and fill the buffer up.
+      bool boolean() {
+        return bool_producer.convert(core);
+      }
+
+      /// @}
+
+      ///\name Non-uniform Distributions
+      ///
+      ///@{
+
+      /// \brief Returns a random bool with given probability of true result.
+      ///
+      /// It returns a random bool with given probability of true result.
+      bool boolean(double p) {
+        return operator()() < p;
+      }
+
+      /// Standard normal (Gauss) distribution
+
+      /// Standard normal (Gauss) distribution.
+      /// \note The Cartesian form of the Box-Muller
+      /// transformation is used to generate a random normal distribution.
+      double gauss()
+      {
+        double V1,V2,S;
+        do {
+          V1=2*real<double>()-1;
+          V2=2*real<double>()-1;
+          S=V1*V1+V2*V2;
+        } while(S>=1);
+        return std::sqrt(-2*std::log(S)/S)*V1;
+      }
+      /// Normal (Gauss) distribution with given mean and standard deviation
+
+      /// Normal (Gauss) distribution with given mean and standard deviation.
+      /// \sa gauss()
+      double gauss(double mean,double std_dev)
+      {
+        return gauss()*std_dev+mean;
+      }
+
+      /// Lognormal distribution
+
+      /// Lognormal distribution. The parameters are the mean and the standard
+      /// deviation of <tt>exp(X)</tt>.
+      ///
+      double lognormal(double n_mean,double n_std_dev)
+      {
+        return std::exp(gauss(n_mean,n_std_dev));
+      }
+      /// Lognormal distribution
+
+      /// Lognormal distribution. The parameter is an <tt>std::pair</tt> of
+      /// the mean and the standard deviation of <tt>exp(X)</tt>.
+      ///
+      double lognormal(const std::pair<double,double> &params)
+      {
+        return std::exp(gauss(params.first,params.second));
+      }
+      /// Compute the lognormal parameters from mean and standard deviation
+
+      /// This function computes the lognormal parameters from mean and
+      /// standard deviation. The return value can direcly be passed to
+      /// lognormal().
+      std::pair<double,double> lognormalParamsFromMD(double mean,
+                                                     double std_dev)
+      {
+        double fr=std_dev/mean;
+        fr*=fr;
+        double lg=std::log(1+fr);
+        return std::pair<double,double>(std::log(mean)-lg/2.0,std::sqrt(lg));
+      }
+      /// Lognormal distribution with given mean and standard deviation
+
+      /// Lognormal distribution with given mean and standard deviation.
+      ///
+      double lognormalMD(double mean,double std_dev)
+      {
+        return lognormal(lognormalParamsFromMD(mean,std_dev));
+      }
+
+      /// Exponential distribution with given mean
+
+      /// This function generates an exponential distribution random number
+      /// with mean <tt>1/lambda</tt>.
+      ///
+      double exponential(double lambda=1.0)
+      {
+        return -std::log(1.0-real<double>())/lambda;
+      }
+
+      /// Gamma distribution with given integer shape
+
+      /// This function generates a gamma distribution random number.
+      ///
+      ///\param k shape parameter (<tt>k>0</tt> integer)
+      double gamma(int k)
+      {
+        double s = 0;
+        for(int i=0;i<k;i++) s-=std::log(1.0-real<double>());
+        return s;
+      }
+
+      /// Gamma distribution with given shape and scale parameter
+
+      /// This function generates a gamma distribution random number.
+      ///
+      ///\param k shape parameter (<tt>k>0</tt>)
+      ///\param theta scale parameter
+      ///
+      double gamma(double k,double theta=1.0)
+      {
+        double xi,nu;
+        const double delta = k-std::floor(k);
+        const double v0=E/(E-delta);
+        do {
+          double V0=1.0-real<double>();
+          double V1=1.0-real<double>();
+          double V2=1.0-real<double>();
+          if(V2<=v0)
+            {
+              xi=std::pow(V1,1.0/delta);
+              nu=V0*std::pow(xi,delta-1.0);
+            }
+          else
+            {
+              xi=1.0-std::log(V1);
+              nu=V0*std::exp(-xi);
+            }
+        } while(nu>std::pow(xi,delta-1.0)*std::exp(-xi));
+        return theta*(xi+gamma(int(std::floor(k))));
+      }
+
+      /// Weibull distribution
+
+      /// This function generates a Weibull distribution random number.
+      ///
+      ///\param k shape parameter (<tt>k>0</tt>)
+      ///\param lambda scale parameter (<tt>lambda>0</tt>)
+      ///
+      double weibull(double k,double lambda)
+      {
+        return lambda*pow(-std::log(1.0-real<double>()),1.0/k);
+      }
+
+      /// Pareto distribution
+
+      /// This function generates a Pareto distribution random number.
+      ///
+      ///\param k shape parameter (<tt>k>0</tt>)
+      ///\param x_min location parameter (<tt>x_min>0</tt>)
+      ///
+      double pareto(double k,double x_min)
+      {
+        return exponential(gamma(k,1.0/x_min))+x_min;
+      }
+
+      /// Poisson distribution
+
+      /// This function generates a Poisson distribution random number with
+      /// parameter \c lambda.
+      ///
+      /// The probability mass function of this distribusion is
+      /// \f[ \frac{e^{-\lambda}\lambda^k}{k!} \f]
+      /// \note The algorithm is taken from the book of Donald E. Knuth titled
+      /// ''Seminumerical Algorithms'' (1969). Its running time is linear in the
+      /// return value.
+
+      int poisson(double lambda)
+      {
+        const double l = std::exp(-lambda);
+        int k=0;
+        double p = 1.0;
+        do {
+          k++;
+          p*=real<double>();
+        } while (p>=l);
+        return k-1;
+      }
+
+      ///@}
+
+      ///\name Two-Dimensional Distributions
+      ///
+      ///@{
+
+      /// Uniform distribution on the full unit circle
+
+      /// Uniform distribution on the full unit circle.
+      ///
+      dim2::Point<double> disc()
+      {
+        double V1,V2;
+        do {
+          V1=2*real<double>()-1;
+          V2=2*real<double>()-1;
+
+        } while(V1*V1+V2*V2>=1);
+        return dim2::Point<double>(V1,V2);
+      }
+      /// A kind of two-dimensional normal (Gauss) distribution
+
+      /// This function provides a turning symmetric two-dimensional distribution.
+      /// Both coordinates are of standard normal distribution, but they are not
+      /// independent.
+      ///
+      /// \note The coordinates are the two random variables provided by
+      /// the Box-Muller method.
+      dim2::Point<double> gauss2()
+      {
+        double V1,V2,S;
+        do {
+          V1=2*real<double>()-1;
+          V2=2*real<double>()-1;
+          S=V1*V1+V2*V2;
+        } while(S>=1);
+        double W=std::sqrt(-2*std::log(S)/S);
+        return dim2::Point<double>(W*V1,W*V2);
+      }
+      /// A kind of two-dimensional exponential distribution
+
+      /// This function provides a turning symmetric two-dimensional distribution.
+      /// The x-coordinate is of conditionally exponential distribution
+      /// with the condition that x is positive and y=0. If x is negative and
+      /// y=0 then, -x is of exponential distribution. The same is true for the
+      /// y-coordinate.
+      dim2::Point<double> exponential2()
+      {
+        double V1,V2,S;
+        do {
+          V1=2*real<double>()-1;
+          V2=2*real<double>()-1;
+          S=V1*V1+V2*V2;
+        } while(S>=1);
+        double W=-std::log(S)/S;
+        return dim2::Point<double>(W*V1,W*V2);
+      }
+
+      ///@}
+    };
+
+
+  };
 
   /// \ingroup misc
@@ -472 +1008 @@
   /// \brief Mersenne Twister random number generator
   ///
-  /// The Mersenne Twister is a twisted generalized feedback
-  /// shift-register generator of Matsumoto and Nishimura. The period
-  /// of this generator is \f$ 2^{19937} - 1 \f$ and it is
-  /// equi-distributed in 623 dimensions for 32-bit numbers. The time
-  /// performance of this generator is comparable to the commonly used
-  /// generators.
+  /// This class implements either the 32-bit or the 64-bit version of
+  /// the Mersenne Twister random number generator algorithm
+  /// depending on the system architecture.
+  /// 
+  /// For the API description, see its base class
+  /// \ref _random_bits::Random.
   ///
-  /// This implementation is specialized for both 32-bit and 64-bit
-  /// architectures. The generators differ sligthly in the
-  /// initialization and generation phase so they produce two
-  /// completly different sequences.
+  /// \sa \ref _random_bits::Random
+  typedef _random_bits::Random<unsigned long> Random;
+
+  /// \ingroup misc
   ///
-  /// The generator gives back random numbers of serveral types. To
-  /// get a random number from a range of a floating point type you
-  /// can use one form of the \c operator() or the \c real() member
-  /// function. If you want to get random number from the {0, 1, ...,
-  /// n-1} integer range use the \c operator[] or the \c integer()
-  /// method. And to get random number from the whole range of an
-  /// integer type you can use the argumentless \c integer() or \c
-  /// uinteger() functions. After all you can get random bool with
-  /// equal chance of true and false or given probability of true
-  /// result with the \c boolean() member functions.
+  /// \brief Mersenne Twister random number generator (32-bit version)
   ///
-  ///\code
-  /// // The commented code is identical to the other
-  /// double a = rnd();                     // [0.0, 1.0)
-  /// // double a = rnd.real();             // [0.0, 1.0)
-  /// double b = rnd(100.0);                // [0.0, 100.0)
-  /// // double b = rnd.real(100.0);        // [0.0, 100.0)
-  /// double c = rnd(1.0, 2.0);             // [1.0, 2.0)
-  /// // double c = rnd.real(1.0, 2.0);     // [1.0, 2.0)
-  /// int d = rnd[100000];                  // 0..99999
-  /// // int d = rnd.integer(100000);       // 0..99999
-  /// int e = rnd[6] + 1;                   // 1..6
-  /// // int e = rnd.integer(1, 1 + 6);     // 1..6
-  /// int b = rnd.uinteger<int>();          // 0 .. 2^31 - 1
-  /// int c = rnd.integer<int>();           // - 2^31 .. 2^31 - 1
-  /// bool g = rnd.boolean();               // P(g = true) = 0.5
-  /// bool h = rnd.boolean(0.8);            // P(h = true) = 0.8
-  ///\endcode
+  /// This class implements the 32-bit version of the Mersenne Twister
+  /// random number generator algorithm. It is recommended to be used
+  /// when someone wants to make sure that the \e same pseudo random
+  /// sequence will be generated on every platfrom.
   ///
-  /// LEMON provides a global instance of the random number
-  /// generator which name is \ref lemon::rnd "rnd". Usually it is a
-  /// good programming convenience to use this global generator to get
-  /// random numbers.
-  class Random {
-  private:
-
-    // Architecture word
-    typedef unsigned long Word;
-
-    _random_bits::RandomCore<Word> core;
-    _random_bits::BoolProducer<Word> bool_producer;
-
-
-  public:
-
-    ///\name Initialization
-    ///
-    /// @{
-
-    /// \brief Default constructor
-    ///
-    /// Constructor with constant seeding.
-    Random() { core.initState(); }
-
-    /// \brief Constructor with seed
-    ///
-    /// Constructor with seed. The current number type will be converted
-    /// to the architecture word type.
-    template <typename Number>
-    Random(Number seed) {
-      _random_bits::Initializer<Number, Word>::init(core, seed);
-    }
-
-    /// \brief Constructor with array seeding
-    ///
-    /// Constructor with array seeding. The given range should contain
-    /// any number type and the numbers will be converted to the
-    /// architecture word type.
-    template <typename Iterator>
-    Random(Iterator begin, Iterator end) {
-      typedef typename std::iterator_traits<Iterator>::value_type Number;
-      _random_bits::Initializer<Number, Word>::init(core, begin, end);
-    }
-
-    /// \brief Copy constructor
-    ///
-    /// Copy constructor. The generated sequence will be identical to
-    /// the other sequence. It can be used to save the current state
-    /// of the generator and later use it to generate the same
-    /// sequence.
-    Random(const Random& other) {
-      core.copyState(other.core);
-    }
-
-    /// \brief Assign operator
-    ///
-    /// Assign operator. The generated sequence will be identical to
-    /// the other sequence. It can be used to save the current state
-    /// of the generator and later use it to generate the same
-    /// sequence.
-    Random& operator=(const Random& other) {
-      if (&other != this) {
-        core.copyState(other.core);
-      }
-      return *this;
-    }
-
-    /// \brief Seeding random sequence
-    ///
-    /// Seeding the random sequence. The current number type will be
-    /// converted to the architecture word type.
-    template <typename Number>
-    void seed(Number seed) {
-      _random_bits::Initializer<Number, Word>::init(core, seed);
-    }
-
-    /// \brief Seeding random sequence
-    ///
-    /// Seeding the random sequence. The given range should contain
-    /// any number type and the numbers will be converted to the
-    /// architecture word type.
-    template <typename Iterator>
-    void seed(Iterator begin, Iterator end) {
-      typedef typename std::iterator_traits<Iterator>::value_type Number;
-      _random_bits::Initializer<Number, Word>::init(core, begin, end);
-    }
-
-    /// \brief Seeding from file or from process id and time
-    ///
-    /// By default, this function calls the \c seedFromFile() member
-    /// function with the <tt>/dev/urandom</tt> file. If it does not success,
-    /// it uses the \c seedFromTime().
-    /// \return Currently always \c true.
-    bool seed() {
-#ifndef LEMON_WIN32
-      if (seedFromFile("/dev/urandom", 0)) return true;
+  /// For the API description, see its base class
+  /// \ref _random_bits::Random.
+  ///
+  /// \sa \ref _random_bits::Random
+  typedef _random_bits::Random<unsigned int> Random32;
+
+  /// \ingroup misc
+  ///
+  /// \brief Mersenne Twister random number generator (64-bit version)
+  ///
+  /// This class implements the 64-bit version of the Mersenne Twister
+  /// random number generator algorithm. (Even though it runs
+  /// on 32-bit architectures, too.) It is recommended to be used when
+  /// someone wants to make sure that the \e same pseudo random sequence
+  /// will be generated on every platfrom.
+  ///
+  /// For the API description, see its base class
+  /// \ref _random_bits::Random.
+  ///
+  /// \sa \ref _random_bits::Random
+  typedef _random_bits::Random<unsigned long long> Random64;
+
+  extern Random rnd;
+  
+}
+
 #endif
-      if (seedFromTime()) return true;
-      return false;
-    }
-
-    /// \brief Seeding from file
-    ///
-    /// Seeding the random sequence from file. The linux kernel has two
-    /// devices, <tt>/dev/random</tt> and <tt>/dev/urandom</tt> which
-    /// could give good seed values for pseudo random generators (The
-    /// difference between two devices is that the <tt>random</tt> may
-    /// block the reading operation while the kernel can give good
-    /// source of randomness, while the <tt>urandom</tt> does not
-    /// block the input, but it could give back bytes with worse
-    /// entropy).
-    /// \param file The source file
-    /// \param offset The offset, from the file read.
-    /// \return \c true when the seeding successes.
-#ifndef LEMON_WIN32
-    bool seedFromFile(const std::string& file = "/dev/urandom", int offset = 0)
-#else
-    bool seedFromFile(const std::string& file = "", int offset = 0)
-#endif
-    {
-      std::ifstream rs(file.c_str());
-      const int size = 4;
-      Word buf[size];
-      if (offset != 0 && !rs.seekg(offset)) return false;
-      if (!rs.read(reinterpret_cast<char*>(buf), sizeof(buf))) return false;
-      seed(buf, buf + size);
-      return true;
-    }
-
-    /// \brief Seding from process id and time
-    ///
-    /// Seding from process id and time. This function uses the
-    /// current process id and the current time for initialize the
-    /// random sequence.
-    /// \return Currently always \c true.
-    bool seedFromTime() {
-#ifndef LEMON_WIN32
-      timeval tv;
-      gettimeofday(&tv, 0);
-      seed(getpid() + tv.tv_sec + tv.tv_usec);
-#else
-      seed(bits::getWinRndSeed());
-#endif
-      return true;
-    }
-
-    /// @}
-
-    ///\name Uniform Distributions
-    ///
-    /// @{
-
-    /// \brief Returns a random real number from the range [0, 1)
-    ///
-    /// It returns a random real number from the range [0, 1). The
-    /// default Number type is \c double.
-    template <typename Number>
-    Number real() {
-      return _random_bits::RealConversion<Number, Word>::convert(core);
-    }
-
-    double real() {
-      return real<double>();
-    }
-
-    /// \brief Returns a random real number from the range [0, 1)
-    ///
-    /// It returns a random double from the range [0, 1).
-    double operator()() {
-      return real<double>();
-    }
-
-    /// \brief Returns a random real number from the range [0, b)
-    ///
-    /// It returns a random real number from the range [0, b).
-    double operator()(double b) {
-      return real<double>() * b;
-    }
-
-    /// \brief Returns a random real number from the range [a, b)
-    ///
-    /// It returns a random real number from the range [a, b).
-    double operator()(double a, double b) {
-      return real<double>() * (b - a) + a;
-    }
-
-    /// \brief Returns a random integer from a range
-    ///
-    /// It returns a random integer from the range {0, 1, ..., b - 1}.
-    template <typename Number>
-    Number integer(Number b) {
-      return _random_bits::Mapping<Number, Word>::map(core, b);
-    }
-
-    /// \brief Returns a random integer from a range
-    ///
-    /// It returns a random integer from the range {a, a + 1, ..., b - 1}.
-    template <typename Number>
-    Number integer(Number a, Number b) {
-      return _random_bits::Mapping<Number, Word>::map(core, b - a) + a;
-    }
-
-    /// \brief Returns a random integer from a range
-    ///
-    /// It returns a random integer from the range {0, 1, ..., b - 1}.
-    template <typename Number>
-    Number operator[](Number b) {
-      return _random_bits::Mapping<Number, Word>::map(core, b);
-    }
-
-    /// \brief Returns a random non-negative integer
-    ///
-    /// It returns a random non-negative integer uniformly from the
-    /// whole range of the current \c Number type. The default result
-    /// type of this function is <tt>unsigned int</tt>.
-    template <typename Number>
-    Number uinteger() {
-      return _random_bits::IntConversion<Number, Word>::convert(core);
-    }
-
-    unsigned int uinteger() {
-      return uinteger<unsigned int>();
-    }
-
-    /// \brief Returns a random integer
-    ///
-    /// It returns a random integer uniformly from the whole range of
-    /// the current \c Number type. The default result type of this
-    /// function is \c int.
-    template <typename Number>
-    Number integer() {
-      static const int nb = std::numeric_limits<Number>::digits +
-        (std::numeric_limits<Number>::is_signed ? 1 : 0);
-      return _random_bits::IntConversion<Number, Word, nb>::convert(core);
-    }
-
-    int integer() {
-      return integer<int>();
-    }
-
-    /// \brief Returns a random bool
-    ///
-    /// It returns a random bool. The generator holds a buffer for
-    /// random bits. Every time when it become empty the generator makes
-    /// a new random word and fill the buffer up.
-    bool boolean() {
-      return bool_producer.convert(core);
-    }
-
-    /// @}
-
-    ///\name Non-uniform Distributions
-    ///
-    ///@{
-
-    /// \brief Returns a random bool with given probability of true result.
-    ///
-    /// It returns a random bool with given probability of true result.
-    bool boolean(double p) {
-      return operator()() < p;
-    }
-
-    /// Standard normal (Gauss) distribution
-
-    /// Standard normal (Gauss) distribution.
-    /// \note The Cartesian form of the Box-Muller
-    /// transformation is used to generate a random normal distribution.
-    double gauss()
-    {
-      double V1,V2,S;
-      do {
-        V1=2*real<double>()-1;
-        V2=2*real<double>()-1;
-        S=V1*V1+V2*V2;
-      } while(S>=1);
-      return std::sqrt(-2*std::log(S)/S)*V1;
-    }
-    /// Normal (Gauss) distribution with given mean and standard deviation
-
-    /// Normal (Gauss) distribution with given mean and standard deviation.
-    /// \sa gauss()
-    double gauss(double mean,double std_dev)
-    {
-      return gauss()*std_dev+mean;
-    }
-
-    /// Lognormal distribution
-
-    /// Lognormal distribution. The parameters are the mean and the standard
-    /// deviation of <tt>exp(X)</tt>.
-    ///
-    double lognormal(double n_mean,double n_std_dev)
-    {
-      return std::exp(gauss(n_mean,n_std_dev));
-    }
-    /// Lognormal distribution
-
-    /// Lognormal distribution. The parameter is an <tt>std::pair</tt> of
-    /// the mean and the standard deviation of <tt>exp(X)</tt>.
-    ///
-    double lognormal(const std::pair<double,double> &params)
-    {
-      return std::exp(gauss(params.first,params.second));
-    }
-    /// Compute the lognormal parameters from mean and standard deviation
-
-    /// This function computes the lognormal parameters from mean and
-    /// standard deviation. The return value can direcly be passed to
-    /// lognormal().
-    std::pair<double,double> lognormalParamsFromMD(double mean,
-                                                   double std_dev)
-    {
-      double fr=std_dev/mean;
-      fr*=fr;
-      double lg=std::log(1+fr);
-      return std::pair<double,double>(std::log(mean)-lg/2.0,std::sqrt(lg));
-    }
-    /// Lognormal distribution with given mean and standard deviation
-
-    /// Lognormal distribution with given mean and standard deviation.
-    ///
-    double lognormalMD(double mean,double std_dev)
-    {
-      return lognormal(lognormalParamsFromMD(mean,std_dev));
-    }
-
-    /// Exponential distribution with given mean
-
-    /// This function generates an exponential distribution random number
-    /// with mean <tt>1/lambda</tt>.
-    ///
-    double exponential(double lambda=1.0)
-    {
-      return -std::log(1.0-real<double>())/lambda;
-    }
-
-    /// Gamma distribution with given integer shape
-
-    /// This function generates a gamma distribution random number.
-    ///
-    ///\param k shape parameter (<tt>k>0</tt> integer)
-    double gamma(int k)
-    {
-      double s = 0;
-      for(int i=0;i<k;i++) s-=std::log(1.0-real<double>());
-      return s;
-    }
-
-    /// Gamma distribution with given shape and scale parameter
-
-    /// This function generates a gamma distribution random number.
-    ///
-    ///\param k shape parameter (<tt>k>0</tt>)
-    ///\param theta scale parameter
-    ///
-    double gamma(double k,double theta=1.0)
-    {
-      double xi,nu;
-      const double delta = k-std::floor(k);
-      const double v0=E/(E-delta);
-      do {
-        double V0=1.0-real<double>();
-        double V1=1.0-real<double>();
-        double V2=1.0-real<double>();
-        if(V2<=v0)
-          {
-            xi=std::pow(V1,1.0/delta);
-            nu=V0*std::pow(xi,delta-1.0);
-          }
-        else
-          {
-            xi=1.0-std::log(V1);
-            nu=V0*std::exp(-xi);
-          }
-      } while(nu>std::pow(xi,delta-1.0)*std::exp(-xi));
-      return theta*(xi+gamma(int(std::floor(k))));
-    }
-
-    /// Weibull distribution
-
-    /// This function generates a Weibull distribution random number.
-    ///
-    ///\param k shape parameter (<tt>k>0</tt>)
-    ///\param lambda scale parameter (<tt>lambda>0</tt>)
-    ///
-    double weibull(double k,double lambda)
-    {
-      return lambda*pow(-std::log(1.0-real<double>()),1.0/k);
-    }
-
-    /// Pareto distribution
-
-    /// This function generates a Pareto distribution random number.
-    ///
-    ///\param k shape parameter (<tt>k>0</tt>)
-    ///\param x_min location parameter (<tt>x_min>0</tt>)
-    ///
-    double pareto(double k,double x_min)
-    {
-      return exponential(gamma(k,1.0/x_min))+x_min;
-    }
-
-    /// Poisson distribution
-
-    /// This function generates a Poisson distribution random number with
-    /// parameter \c lambda.
-    ///
-    /// The probability mass function of this distribusion is
-    /// \f[ \frac{e^{-\lambda}\lambda^k}{k!} \f]
-    /// \note The algorithm is taken from the book of Donald E. Knuth titled
-    /// ''Seminumerical Algorithms'' (1969). Its running time is linear in the
-    /// return value.
-
-    int poisson(double lambda)
-    {
-      const double l = std::exp(-lambda);
-      int k=0;
-      double p = 1.0;
-      do {
-        k++;
-        p*=real<double>();
-      } while (p>=l);
-      return k-1;
-    }
-
-    ///@}
-
-    ///\name Two Dimensional Distributions
-    ///
-    ///@{
-
-    /// Uniform distribution on the full unit circle
-
-    /// Uniform distribution on the full unit circle.
-    ///
-    dim2::Point<double> disc()
-    {
-      double V1,V2;
-      do {
-        V1=2*real<double>()-1;
-        V2=2*real<double>()-1;
-
-      } while(V1*V1+V2*V2>=1);
-      return dim2::Point<double>(V1,V2);
-    }
-    /// A kind of two dimensional normal (Gauss) distribution
-
-    /// This function provides a turning symmetric two-dimensional distribution.
-    /// Both coordinates are of standard normal distribution, but they are not
-    /// independent.
-    ///
-    /// \note The coordinates are the two random variables provided by
-    /// the Box-Muller method.
-    dim2::Point<double> gauss2()
-    {
-      double V1,V2,S;
-      do {
-        V1=2*real<double>()-1;
-        V2=2*real<double>()-1;
-        S=V1*V1+V2*V2;
-      } while(S>=1);
-      double W=std::sqrt(-2*std::log(S)/S);
-      return dim2::Point<double>(W*V1,W*V2);
-    }
-    /// A kind of two dimensional exponential distribution
-
-    /// This function provides a turning symmetric two-dimensional distribution.
-    /// The x-coordinate is of conditionally exponential distribution
-    /// with the condition that x is positive and y=0. If x is negative and
-    /// y=0 then, -x is of exponential distribution. The same is true for the
-    /// y-coordinate.
-    dim2::Point<double> exponential2()
-    {
-      double V1,V2,S;
-      do {
-        V1=2*real<double>()-1;
-        V2=2*real<double>()-1;
-        S=V1*V1+V2*V2;
-      } while(S>=1);
-      double W=-std::log(S)/S;
-      return dim2::Point<double>(W*V1,W*V2);
-    }
-
-    ///@}
-  };
-
-
-  extern Random rnd;
-
-}
-
-#endif