1 #include <iostream> 2 #include <fstream> 3 #include <ctime>            // std::time 4  5 #include <boost/random/linear_congruential.hpp> 6 #include <boost/random/uniform_int.hpp> 7 #include <boost/random/uniform_real.hpp> 8 #include <boost/random/variate_generator.hpp> 9 #include <boost/generator_iterator.hpp>10 11 // This is a typedef for a random number generator.12 // Try boost::mt19937 or boost::ecuyer1988 instead of boost::minstd_rand13 typedef boost::minstd_rand base_generator_type;14 15 // This is a reproducible simulation experiment.  See main().16 void experiment(base_generator_type & generator)17 {18   // Define a uniform random number distribution of integer values between19   // 1 and 6 inclusive.20   typedef boost::uniform_int<> distribution_type;21   typedef boost::variate_generator<base_generator_type&, distribution_type> gen_type;22   gen_type die_gen(generator, distribution_type(1, 6));23 24   // If you want to use an STL iterator interface, use iterator_adaptors.hpp.25   boost::generator_iterator<gen_type> die(&die_gen);26   for(int i = 0; i < 10; i++)27     std::cout << *die++ << " ";28   std::cout << ‘\n‘;29 }30 31 int main()32 {33   // Define a random number generator and initialize it with a reproducible34   // seed.35   base_generator_type generator(42);36 37   std::cout << "10 samples of a uniform distribution in [0..1):\n";38 39   // Define a uniform random number distribution which produces "double"40   // values between 0 and 1 (0 inclusive, 1 exclusive).41   boost::uniform_real<> uni_dist(0,1);42   boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);43 44   std::cout.setf(std::ios::fixed);45   // You can now retrieve random numbers from that distribution by means46   // of a STL Generator interface, i.e. calling the generator as a zero-47   // argument function.48   for(int i = 0; i < 10; i++)49     std::cout << uni() << ‘\n‘;50 51   /*52    * Change seed to something else.53    *54    * Caveat: std::time(0) is not a very good truly-random seed.  When55    * called in rapid succession, it could return the same values, and56    * thus the same random number sequences could ensue.  If not the same57    * values are returned, the values differ only slightly in the58    * lowest bits.  A linear congruential generator with a small factor59    * wrapped in a uniform_smallint (see experiment) will produce the same60    * values for the first few iterations.   This is because uniform_smallint61    * takes only the highest bits of the generator, and the generator itself62    * needs a few iterations to spread the initial entropy from the lowest bits63    * to the whole state.64    */65   generator.seed(static_cast<unsigned int>(std::time(0)));66 67   std::cout << "\nexperiment: roll a die 10 times:\n";68 69   // You can save a generator‘s state by copy construction.70   base_generator_type saved_generator = generator;71 72   // When calling other functions which take a generator or distribution73   // as a parameter, make sure to always call by reference (or pointer).74   // Calling by value invokes the copy constructor, which means that the75   // sequence of random numbers at the caller is disconnected from the76   // sequence at the callee.77   experiment(generator);78 79   std::cout << "redo the experiment to verify it:\n";80   experiment(saved_generator);81 82   // After that, both generators are equivalent83   assert(generator == saved_generator);84 85   // as a degenerate case, you can set min = max for uniform_int86   boost::uniform_int<> degen_dist(4,4);87   boost::variate_generator<base_generator_type&, boost::uniform_int<> > deg(generator, degen_dist);88   std::cout << deg() << " " << deg() << " " << deg() << std::endl;89   90   {91     // You can save the generator state for future use.  You can read the92     // state back in at any later time using operator>>.93     std::ofstream file("rng.saved", std::ofstream::trunc);94     file << generator;95   }96 97   getchar();98   return 0;99 }