Archive for October, 2016

CHRONO + RANDOM = ?

Monday, October 24th, 2016

Being a quick sketch combining <chrono> and <random> functionality, with cryptarithmetic interludes…

At CppCon this year there were several good talks about randomness and time calculations in C++. On randomness: Walter Brown’s What C++ Programmers Need to Know About Header <random> and Cheinan Marks’ I Just Wanted a Random Integer! were both excellent talks. And Howard Hinnant gave several great talks: A <chrono> Tutorial, and Welcome to the Time Zone, a followup to his talk from last year, A C++ Approach to Dates and Times.

CHRONO + RANDOM = HORRID ?

That’s perhaps a little unfair, but recently I ran into the need to compute a random period of time. I think this is a common use case for things like backoff schemes for network retransmission. And it seemed to me that the interaction of <chrono> and <random> was not quite as good as it could be:

 system_clock::duration minTime = 0s; system_clock::duration maxTime = 5s; uniform_int_distribution<> d(minTime.count(), maxTime.count()); // 'gen' here is a Mersenne twister engine auto nextTransmissionWindow = system_clock::duration(d(gen));

This code gets more complex when you start computing an exponential backoff. Relatively straightforward, but clumsy, especially if you want a floating-point base for your exponent calculation: system_clock::duration has an integral representation, so in all likelihood you end up having to cast multiple times, using either static_cast or duration_cast. That’s a bit messy.

I remembered some code from another talk: Andy Bond’s AAAARGH!? Adopting Almost Always Auto Reinforces Good Habits!? in which he presented a function to make a uniform distribution by inferring its argument type, useful in generic code. Something like the following:

 template , typename D = std::uniform_int_distribution> inline auto make_uniform_distribution(const A& a, const B& b = std::numeric_limits::max()) -> std::enable_if_t::value, D> { return D(a, b); }

Of course, the standard also provides uniform_real_distribution, so we can provide another template and overload the function for real numbers:

 template , typename D = std::uniform_real_distribution> inline auto make_uniform_distribution(const A& a, const B& b = B{1}) -> std::enable_if_t::value, D> { return D(a, b); }

And with these two in hand, it’s easy to write a uniform_duration_distribution that uses the correct distribution for its underlying representation (using a home-made type trait to constrain it to duration types).

 template struct is_duration : std::false_type {}; template struct is_duration> : std::true_type {};   template ::value>> class uniform_duration_distribution { public: using result_type = Duration;   explicit uniform_duration_distribution( const Duration& a = Duration::zero(), const Duration& b = Duration::max()) : m_a(a), m_b(b) {}   void reset() {}   template result_type operator()(Generator& g) { auto d = make_uniform_distribution(m_a.count(), m_b.count()); return result_type(d(g)); }   result_type a() const { return m_a; } result_type b() const { return m_b; } result_type min() const { return m_a; } result_type max() const { return m_b; }   private: result_type m_a; result_type m_b; };

Having written this, we can once again overload make_uniform_distribution to provide for duration types:

 template , typename D = uniform_duration_distribution> inline auto make_uniform_distribution(const A& a, const B& b = B::max()) -> D { return D(a, b); }

And now we can compute a random duration more expressively and tersely, and, I think, in the spirit of the existing functionality that exists in <chrono> for manipulating durations.

 auto d = make_uniform_distribution(0s, 5000ms); auto nextTransmissionWindow = d(gen);

CHRONO + RANDOM = DREAMY

I leave it as an exercise for the reader to solve these cryptarithmetic puzzles. As for the casting problems, for now, I’m living with them.