Generate random numbers using C 11 random library

Question

As the title suggests  I am trying to figure out a way of generating random numbers using the new C  11  lt random gt  library  I have tried it with this code   std  default random engine generator  std  uniform real distribution lt double gt  uniform distance 1  10 001     The problem with the code I have is that every time I compile and run it  it always generates the same numbers  So my question is what other functions in the random library can accomplish this while being truly random   For my particular use case  I was trying to get a value within the range  1  10

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My  random  library provide a high convenient wrapper around C  11 random classes  You can do almost all things with a simple  get  method   Examples    Random number in a range  auto val   Random  get -10  10      Integer auto val   Random  get 10 f  -10 f      Float point  Random boolean  auto val   Random  get lt bool gt        50  to generate true auto val   Random  get lt bool gt   0 7      70  to generate true  Random value from a std  initilizer list  auto val   Random  get    1  3  5  7  9         val   1 or 3 or     Random iterator from iterator range or all container  auto it   Random  get  vec begin    vec end         it   random iterator auto it   Random  get  vec       return random iterator    And even more things   Check out the github page   https   github com effolkronium random

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Here is some resource you can read about pseudo-random number generator   https   en wikipedia org wiki Pseudorandom number generator  Basically  random numbers in computer need a seed  this number can be the current system time    Replace  std  default random engine generator    By  std  default random engine generator  lt some seed number gt

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You ve got two common situations  The first is that you want random numbers and aren t too fussed about the quality or execution speed  In that case  use the following macro   define uniform    rand    RAND MAX   1 0     that gives you p in the range 0 to 1 - epsilon  unless RAND MAX is bigger than the precision of a double  but worry about that when you come to it    int x    int   uniform     N    Now gives a random integer on 0 to N -1   If you need other distributions  you have to transform p  Or sometimes it s easier to call uniform   several times   If you want repeatable behaviour  seed with a constant  otherwise seed with a call to time     Now if you are bothered about quality or run time performance  rewrite uniform    But otherwise don t touch the code  Always keep uniform   on 0 to 1 minus epsilon  Now you can wrap the C   random number library to create a better uniform    but that s a sort of medium-level option  If you are bothered about the characteristics of the RNG  then it s also worth investing a bit of time to understand how the underlying methods work  then provide one  So you ve got complete control of the code  and you can guarantee that with the same seed  the sequence will always be exactly the same  regardless of platform or which version of C   you are linking to

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Stephan T  Lavavej  stl  from Microsoft did a talk at Going Native about how to use the new C  11 random functions and why not to use rand    In it  he included a slide that basically solves your question  I ve copied the code from that slide below   You can see his full talk here  http   channel9 msdn com Events GoingNative 2013 rand-Considered-Harmful   include  lt random gt   include  lt iostream gt   int main         std  random device rd      std  mt19937 mt rd         std  uniform real distribution lt double gt  dist 1 0  10 0        for  int i 0  i lt 16    i          std  cout  lt  lt  dist mt   lt  lt    n       We use random device once to seed the random number generator named mt  random device   is slower than mt19937  but it does not need to be seeded because it requests random data from your operating system  which will source from various locations  like RdRand for example      Looking at this question   answer  it appears that uniform real distribution returns a number in the range  a  b   where you want  a  b   To do that  our uniform real distibution should actually look like   std  uniform real distribution lt double gt  dist 1  std  nextafter 10  DBL MAX

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Here s something that I just wrote along those lines     include  lt random gt   include  lt chrono gt   include  lt thread gt   using namespace std                                                                       RANDOM BACKOFF TIME                                                                  class backoff time t     public      random device                      rd      mt19937                            mt      uniform real distribution lt double gt   dist       backoff time t     rd    mt rd     dist 0 5  1 5          double rand           return dist mt             thread local backoff time t backoff time    int main int argc  char   argv       double x1   backoff time rand       double x2   backoff time rand       double x3   backoff time rand       double x4   backoff time rand       return 0

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I red all the stuff above  about 40 other pages with  c   in it like this and watched the video from Stephan T  Lavavej  STL   and still wasn t sure how random numbers works in praxis so I took a full Sunday to figure out what its all about  and how it works and can be used   In my opinion STL is right about  not using srand anymore  and he explained it well in the video 2   He also recommend to use   a  void random device uniform   -- for encrypted generation but slower  from my example   b  the examples with mt19937 -- faster  ability to create seeds  not encrypted    I pulled out all claimed c  11 books I have access to and found f e  that german Authors like Breymann  2015  still use a clone of   srand  time  0      srand  static cast lt unsigned int gt  time nullptr     or srand  static cast lt unsigned int gt  time NULL     or   just with  lt random gt  instead of  lt time gt  and  lt cstdlib gt   includings - so be careful to learn just from one book       Meaning - that shouldn t be used since c  11 because      Programs often need a source of random numbers  Prior to the new   standard  both C and C   relied on a simple C library function named   rand  That function produces pseudorandom integers that are uniformly   distributed in the range from 0 to a system- dependent maximum value   that is at least 32767    The rand function has several problems  Many  if not most  programs   need random numbers in a different range from the one produced by   rand  Some applications require random floating-point numbers  Some   programs need numbers that reflect a nonuniform distribution    Programmers often introduce nonrandomness when they try to transform   the range  type  or distribution of the numbers generated by rand     quote from Lippmans C   primer fifth edition 2012      I finally found a the best explaination out of 20 books in Bjarne Stroustrups newer ones - and he should know his stuff - in  A tour of C   2019    Programming Principles and Practice Using C   2016  and  The C   Programming Language 4th edition 2014  and also some examples in  Lippmans C   primer fifth edition 2012    And it is really simple because a random number generator consists of two parts   1  an engine that produces a sequence of random or pseudo-random values   2  a distribution that maps those values into a mathematical distribution in a range     Despite the opinion of Microsofts STL guy  Bjarne Stroustrups writes      In    the standard library provides random number engines and   distributions    24 7   By default use the default random engine     which is chosen for wide applicability and low cost    The void die roll   Example is from Bjarne Stroustrups - good idea generating engine and distribution with using  more bout that here      To be able to make practical use of the  random number generators provided by the standard library in  lt random gt  here some executable code with different examples reduced to the least necessary that hopefully safe time and money for you guys        include  lt random gt        random engine  random distribution      include  lt iostream gt      cout      include  lt functional gt    to use bind      using namespace std        void space     for visibility reasons if you execute the stuff              cout  lt  lt    n   lt  lt  endl         for  int i   0  i  lt  20    i         cout  lt  lt                cout  lt  lt    n   lt  lt  endl             void uniform default                uniformly distributed from 0 to 6 inclusive         uniform int distribution lt size t gt  u  0  6           default random engine e      generates unsigned random integers      for  size t i   0  i  lt  10    i             u uses e as a source of numbers            each call returns a uniformly distributed value in the specified range         cout  lt  lt  u e   lt  lt                  void random device uniform                  space             cout  lt  lt   random device  amp  uniform int distribution   lt  lt  endl            random device engn           uniform int distribution lt size t gt  dist 1  6             for  int i 0  i lt 10    i           cout  lt  lt  dist engn   lt  lt                  void die roll                 space            cout  lt  lt   default random engine and Uniform int distribution   lt  lt  endl       using my engine   default random engine      using my distribution   uniform int distribution lt size t gt            my engine rd             my distribution one to six  1  6            auto die   bind one to six rd      the default engine    for  int i   0  i lt 10    i           for  int i   0  i  lt 10    i          cout  lt  lt  die    lt  lt                    void uniform default int                space           cout  lt  lt   uniform default int   lt  lt  endl          default random engine engn         uniform int distribution lt size t gt  dist 1  6            for  int i   0  i lt 10    i          cout  lt  lt  dist engn   lt  lt                  void mersenne twister engine seed                 space            cout  lt  lt   mersenne twister engine with seed 1234   lt  lt  endl             mt19937 dist  1234      for 32 bit systems         mt19937 64 dist  1234     for 64 bit systems          for  int i   0  i lt 10    i          cout  lt  lt  dist    lt  lt                   void random seed mt19937 2                 space            cout  lt  lt   mersenne twister split up in two with seed 1234   lt  lt  endl           mt19937 dist 1234           mt19937 engn dist            for  int i   0  i  lt  10    i          cout  lt  lt  dist    lt  lt                cout  lt  lt  endl           for  int j   0  j  lt  10    j          cout  lt  lt  engn    lt  lt                    int main                     uniform default                 random device uniform                die roll                random device uniform                mersenne twister engine seed                random seed mt19937 2            return 0          I think that adds it all up and like I said  it took me a bunch of reading and time to destill it to that examples - if you have further stuff about  number generation I am happy to hear about that via pm or in the comment section and will add it if necessary or edit this post  Bool

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