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Robust Random Number Generation

By : thebol
Date : March 29 2020, 07:55 AM
will help you For C++, Boost.Random is probably what you're looking for. It has support for MT (among many other algorithms), and can collect entropy via the nondet_random class. Check it out! :-)
code :

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Near seeds in random number generation may give similar random numbers?

By : Jirka Hauser
Date : March 29 2020, 07:55 AM
wish of those help There will definitely be a correlation between the seed and the random numbers generated, by definition. The question is whether the randomization algorithm is sufficient to produce results that seem uncorrelated, and you should study up on the methods for evaluating randomness to answer that question.
You are right to be concerned though. Here are the results from Microsoft's C++ rand function with seed values from 0 to 9:
code :
   38  7719 21238  2437  8855 11797  8365 32285 10450 30612
   41 18467  6334 26500 19169 15724 11478 29358 26962 24464
   45 29216 24198 17795 29484 19650 14590 26431 10705 18316
   48  7196  9294  9091  7031 23577 17702 23503 27217 12168
   51 17945 27159   386 17345 27504 20815 20576 10960  6020
   54 28693 12255 24449 27660 31430 23927 17649 27472 32640
   58  6673 30119 15745  5206  2589 27040 14722 11216 26492
   61 17422 15215  7040 15521  6516 30152 11794 27727 20344
   64 28170   311 31103 25835 10443   497  8867 11471 14195
   68  6151 18175 22398  3382 14369  3609  5940 27982  8047

D std.random different behavior between integer and decimal uniform random number generation

By : mohit bhesaniya
Date : March 29 2020, 07:55 AM
wish helps you The following is based on a very quick review of the docs page:
I don't see where it's indicated that the results of uniform!int and uniform!double should even be remotely close, nor any specification on how the bits from the random number generator get converted to the requested type.

Fast Parallel Random Number Generation from /dev/{random,urandom}

By : sbrok
Date : March 29 2020, 07:55 AM
I wish did fix the issue. To consolidate a few comments...
Making multiple calls to a getRand() function which reads from /dev/urandom is slow and should be avoided, as it uses system calls which add a lot of overhead. It is better to read much larger chunks from /dev/urandom and buffer them, or to use /dev/urandom to seed a software PRNG.

Efficient random number generation with C++11 <random>

By : Russell
Date : March 29 2020, 07:55 AM
help you fix your problem One thing you can do is to have a permanent distribution object so that you only create the param_type object each time like this:
code :
template<typename Integral>
Integral randint(Integral min, Integral max)
    using param_type =
        typename std::uniform_int_distribution<Integral>::param_type;

    // only create these once (per thread)
    thread_local static std::mt19937 eng {std::random_device{}()};
    thread_local static std::uniform_int_distribution<Integral> dist;

    // presumably a param_type is cheaper than a uniform_int_distribution
    return dist(eng, param_type{min, max});

Why is this giving me a linker error? (random number generation using <random>)

By : Gowtham Vasudevan
Date : March 29 2020, 07:55 AM
I hope this helps you . Non-static member variables of a class are constructed when you construct an instance of the class. The static member variables, on the other hand, are same for all instances of the class. They need to be defined explicitly.
You need to define the static member variables in the enclosing namespace. Add
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