RhodeCode

    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 true.

    bool seed() {

#ifndef 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 True when the seeding successes.

#ifndef 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 true.

    bool seedFromTime() {

#ifndef WIN32

      timeval tv;

      gettimeofday(&tv, 0);

      seed(getpid() + tv.tv_sec + tv.tv_usec);

#else

      FILETIME time;

      GetSystemTimeAsFileTime(&time);

      seed(GetCurrentProcessId() + time.dwHighDateTime + time.dwLowDateTime);

#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>();

    }

    /// @}

    ///\name Uniform distributions

    ///

    /// @{

    /// \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).

    }

    /// \brief Returns a random real number from the range [a, b)

    ///

    /// It returns a random real number from the range [a, b).

    }

    /// \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

    ///

    /// It returns a random bool with given probability of true result.

    bool boolean(double p) {

      return operator()() < p;

    }

    /// Standard Gauss distribution

    /// Standard 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;

    }