Category Archives: C++

C++ London Meetup: FFIG and Why Iterators got it Wrong


This month’s C++ London meetup features two talks – the first on C++/Python integration and the second on C++ iterators.

Foreign Function Interface Generator (FFIG) – Jonathan Coe
Jonathan presented his hobby project, FFIG, largely written on the train during his commute (!).

I want to be able to write C++ code and call it from Python without doing any extra work

He explained that existing solutions (Boost::python and SWIG) generate interfaces that bind you to a specific binary Python implementation. Whilst a work in progress, FFIG’s generated libraries are Python library independent (meaning you can compile once and call from multiple Python libraries).

FFIG generates a C-API on top of your C++ code, which eliminates any classes/structs, but provides a library readily callable from Python. It also generates a Python (or Ruby) library that layers the objects over the C-API, to restore the original interface. A key lesson learned during development was to hide any ownership concerns from the Python layer.

Why Iterators Got It Wrong РArno Sch̦dl
Arno has developed a range-like library within his firm (Think-Cell), which addresses some features of iterators perceived as ugly:

// Iterators returned by begin() and end() are asymmetrical
auto it = collection.begin(); // start of a non-empty collection
std::cout << *it; // ok - prints the first element of the collection
auto it2 = collection.end(); // end of a non-empty collection
std::cout << *it2;// error - undefined behaviour, end() returns 1 past the last element!

// Algorithms may return a valid element or a sentinel "border" indicator
std::vector<int> v1{0, 1, 2, 3, 4}, v2{};
auto result1 = std::find(std::begin(v1), std::end(v1), 2);
std::cout << *result1; // ok
auto result2 = std::find(std::begin(v2), std::end(v2), 2);
std::cout << *result2; // error - de-referencing end again

The summary was that his library distinguished between elements and borders instead of using iterators. Once rolled out through the codebase, this made the code clearer and avoided any chance of undefined behaviour. However, I think the availability of Eric Niebler’s Range library (or simple higher-order functions) make the STL so easy to use that these concerns shouldn’t put off new developers.

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ACCU Meetup: Making Templates Easier, Roger Orr

Roger Orr gave this month’s ACCU presentation on Making Templates Easier in C++. He showed two techniques that people commonly use to tailor template implementations for specific types: Tag Dispatch and SFINAE (via enable_if).

With Tag Dispatch, you can switch to different implementations of a template function using traits classes based on one of the input parameter types (e.g. use std::iterator_traits to target a faster implementation for random access iterators). The downside is that you often have to duplicate code across the different implementations.

With SFINAE, you can use typedefs within a parameter type to disable particular overloads. E.g. STL containers have a ::value_type typedef, so you can use that to differentiate between collections and scalar inputs. The downside is that you sometimes have to add additional, defaulted template parameters to allow the compiler to distinguish between otherwise identical template definitions.

Roger then introduced constexpr if from C++17 and concepts from C++20.

The advantages of constexpr if are that it can be used both inside and outside templates and specialisations can be defined inline. Any code that would not compile can be put inside a constexpr if and will be discarded. This seems more straightforward than the recursive template solutions Roger showed earlier in the talk.

Concepts are intended to help define the requirements of a template in a way visible to the compiler as well as the developer. Reusing concept definitions should leave to a domain-specific language that helps within a project. Better still, use of a template parameter type that doesn’t satisfy concept requirements will generate a more helpful error message than if SFINAE were used to achieve the constraints.

The finale was an overview of the new SpaceShip operator, !

The video is now available on the SkillsMatter website.

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ACCU Meetup: Functional C++ (Phil Nash)

meetup-functional-cPhil Nash presented his ideas on functional C++ to a packed ACCU meeting a couple of weeks ago. He kindly provided the slides on his website.

For the uninitiated, the functional style is often quite a shock, but having written F# for some time, I’m in favour of “modelling computations as evaluations of expressions” as Phil presented it, or the declarative style as it’s often described. I wrote about Higher-Order Functions in C++ recently and Phil touched on that as well.

One of the highlights of the talk was the section on persistent data structures, which share as much of the previous state as possible whenever additional elements are added. For example, an associative binary tree could have a new element added, but retain links to the bulk of the original tree. There are challenges to stay balanced, but often the benefits can be worth it (e.g. a red-black, persistent tree that’s thread-safe because all the data is immutable). Phil also presented a Trie hybrid with hashing – a persistent tree structure, with performance similar to unordered_map, for which the hashing ensures no re-balancing is required.

The finale was a demonstration of pipelining for C++, based on std::optional (available from C++17). The recommendation was to watch Eric Niebler’s Ranges talk from CppCon 2015 for more details.

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Higher-Order functions in C++

The other day, I was writing some C++ and found that I was thinking about how to manipulate the data I had as if I was writing F#. It would have been convenient to turn a std::map into an array of tuples, which I could do in F# like this:

let f (xs : map<int,string) =
  let xs = xs |> Map.toArray
  // now treat xs as array of tuples...

There’s no function in STL to do this off the bat – instead, you have to roll your own (not that it’s much code, but it does break your through processes if you have to stop to code this sort of thing every time).

Of course, this is just one of many helpful F# higher-order functions that are provided in the F# Map module – and there are counterparts for each of the collection classes i.e. Array, Set, List etc. In C++, the nearest equivalent is the STL which provides both collection classes and a number of algorithms that operate on them. Better still, from C++11 onwards we have lambdas, which make using STL algorithms much easier. Even so, in most cases, the F# operations seem much more tailored to the sort of data transformation I see at work – our codebase is littered with map/filter/fold operations as people transform/select and accumulate data. Conversely, our C++ codebase is full of … for loops, evidence in my eyes that STL algorithms aren’t as immediately applicable. In fact, the ease of use of higher-order functions was one of the reasons that F# was quickly adopted in my workplace (along with immutability, strong-typing, conciseness, type inference and syntax checking).

I’ve written one-to-one C++ equivalents of the F# module functions that I use the most for Map and Vector – see below. Interestingly, I found that I really did have to ‘engage brain’ to write some of these, particularly Map.filter. For that one, you can’t use the erase-remove idiom because map keys are both const and strictly ordered (whereas for Vector, erase-remove_if implements filter neatly). A library of functions as per my code below would definitely be a productivity boost.

First, I’ve factored some common utilities into namespace Collection:

namespace MusingStudio
{
    namespace Collection
    {
        template <typename C, typename F>
        C& filter( C& collection, F keep_predicate )
        {
            auto erase_predicate = [&pred=keep_predicate]( auto&& x ){ return !pred( std::forward<decltype(x)>(x) ); };
            collection.erase( std::remove_if( collection.begin(), collection.end(), erase_predicate ), collection.end() );
            return collection;
        }
        
        // This form of filter always takes a copy and applies the filter to it
        // - sometimes you want to preserve the original collection
        template <typename C, typename F>
        C filter_copy( const C& collection, F keep_predicate )
        {
            C target;
            std::copy_if( collection.begin(), collection.end(), std::inserter( target, target.end() ), keep_predicate );
            return target;
        }
        
        template <typename C, typename F, typename A>
        A fold( const C& items, F f, A&& init )
        {
            A acc{ std::forward<A>(init) };
            for ( const auto& item : items )
            {
                f( acc, item );
            }
            return acc;
        }
                
        // F( T ) -> T and collection C is mutated
        template <typename C, typename F>
        C& transform( C& items, F f )
        {
            for ( auto& t : items )
            {
                t = f( t );
            }
            
            return items;
        }

    }
}

Next, here are the higher-order functions that I use for Map:

namespace MusingStudio
{
    namespace Map
    {
        // filter_copy takes a copy of the original collection then applies the filter
        template< typename K, typename V, typename F>
        std::map<K,V> filter_copy( const std::map<K,V>& items, F predicate )
        {
            return Collection::filter_copy( items, predicate );
        }
        
        template< typename K, typename V, typename F>
        std::map<K,V>& filter( std::map<K,V>& items, F predicate )
        {
            // NB the erase-remove_if idiom does not work for std::map
            // because the nodes must remain ordered by key.  This is enforced
            // by std::map<K,V> holding keys as const K.  So any assignment
            // to the key (to effecfively re-order the binary tree) fails to compile.
            // http://stackoverflow.com/questions/9515357/map-lambda-remove-if
            
            // instead, manually iterate over the collection, erasing items
            // for which predicate() returns false
            for ( auto it = items.begin(), itEnd = items.end(); it != itEnd; )
            {
                if ( predicate( *it ) )
                {
                    ++it; // ok - keep this item
                }
                else
                {
                    it = items.erase( it );
                }
            }
            
            return items;
        }
        
        template <typename K, typename V>
        auto to_vector( const std::map<K,V>& collection )
        {
            std::vector< std::pair<K,V> > items;
            
            for ( const auto& item : collection )
            {
                items.push_back( std::make_pair( item.first, item.second ) );
            }
            
            return items;
        }
        
        template <typename K, typename V>
        auto keys( const std::map<K,V>& collection )
        {
            std::set< K > items;
            
            for ( const auto& item : collection )
            {
                items.insert( item.first );
            }
            
            return items;
        }
        
        template <typename K, typename V>
        auto values( const std::map<K,V>& collection )
        {
            std::vector< V > items;
            
            for ( const auto& item : collection )
            {
                items.push_back( item.second );
            }
            
            return items;
        }
        
        template<typename K, typename V, typename F, typename A>
        A fold( const std::map<K,V>& items, F f, A&& init )
        {
            return Collection::fold( items, f, std::forward<A>(init) );
        }
        
        // F( std::pair<K,V> ) -> std::pair< L, U >
        // Construct a new std::map<L,U> mapping from (K,V) to (L,U)
        template <typename K, typename V, typename F>
        auto map( const std::map<K,V>& items, F f )
        {
            using KVP = typename std::map<K,V>::value_type;
            using RVP = decltype( f( KVP() ) );
            
            std::map< decltype( RVP().first ), decltype( RVP().second ) > result;
            
            for ( const KVP& kvp : items )
            {
                result.insert( f( kvp ) );
            }
            
            return result;
        }
        
        // F( K, V ) -> V and std::map<K,V> is mutated
        template <typename K, typename V, typename F>
        std::map<K,V>& transform( std::map<K,V>& items, F f )
        {
            using KVP = typename std::map<K,V>::value_type;
            
            for ( const KVP& kvp : items )
            {
                items[kvp.first] = f( kvp.first, kvp.second );
            }
            
            return items;
        }
    }
}

And here are the higher-order functions that I use for Vector:

namespace MusingStudio
{
    namespace Vector
    {
        template< typename T, typename F>
        std::vector<T>& filter( std::vector<T>& items, F predicate )
        {
            Collection::filter( items, predicate );
            return items;
        }
        
        template< typename T, typename F>
        std::vector<T> filter_copy( const std::vector<T>& items, F predicate )
        {
            return Collection::filter_copy( items, predicate );
        }
        
        // Requires F to have signature void( A&, T )
        template< typename T, typename F, typename A>
        A fold( const std::vector<T>& items, F f, A&& init )
        {
            return Collection::fold( items, f, std::forward<A>(init) );
        }
        
        template< typename T, typename P = std::less<T> >
        std::vector<T>& sort( std::vector<T>& items, P compare = P() )
        {
            std::sort( items.begin(), items.end(), compare );
            return items;
        }
        
        template< typename T, typename P = std::less<T> >
        std::vector<T> sort_copy( const std::vector<T>& items, P compare = P() )
        {
            std::vector<T> result( items );
            std::sort( result.begin(), result.end(), compare );
            return result;
        }
        
        // F( T ) -> U, construct a new vector<U>, mapping from T to U
        template <typename T, typename F>
        auto map( const std::vector<T>& items, F f )
        {
            using U = decltype( f(T()) );
            
            std::vector< U > result;
            std::transform( items.begin(), items.end(), std::inserter( result, result.end() ), f );
            return result;
        }
        
        // F( T ) -> T and std::vector<T> is mutated
        template <typename T, typename F>
        std::vector<T>& transform( std::vector<T>& items, F f )
        {
            return Collection::transform( items, f );
        }
    }
}

Here are some unit tests that show how much easier it is to use the Map/Vector functions instead of going directly to STL – I’d argue that this code is comparable to F# for conciseness (although F# code would still benefit from pipelining subsequent operations).

#include <iostream>

#include <gmock/gmock.h>
#include <Vector.hpp>
#include <Map.hpp>

using namespace testing;
using namespace MusingStudio;

TEST( Map, to_vector )
{
    using Mapped = std::map<int, std::string>;
    using Tuples = std::vector<std::pair<int,std::string> >;
    
    Mapped items{ { 1, "Hi" }, { 2, "Bye" } };
    
    EXPECT_EQ( (Tuples{ { 1, "Hi" }, { 2, "Bye" } }), 
      Map::to_vector( items ) );
}

TEST( Map, keys )
{
    using Mapped = std::map<int, std::string>;
    using Keys = std::set<int>;
    
    Mapped items{ { 1, "Hi" }, { 2, "Bye" } };
    
    EXPECT_EQ( (Keys{ 1, 2 }), 
      Map::keys( items ) );
}

TEST( Map, values )
{
    using Mapped = std::map<int, std::string>;
    using Values = std::vector<std::string>;
    
    Mapped items{ { 1, "Hi" }, { 2, "Bye" } };
    
    EXPECT_EQ( (Values{ "Hi", "Bye" }), 
      Map::values( items ) );
}

TEST( Map, filter )
{
    using Mapped = std::map<int, int>;
    
    Mapped items{ {1,1}, {2,4}, {3,9}, {4,16} };
    
    Mapped even_keys{ {2,4},{4,16} };
    auto lambda = []( const auto& keyvaluepair ){ return keyvaluepair.first % 2 == 0; };
    
    // Map::filter will mutate parameter 'items'
    EXPECT_EQ( even_keys, 
      Map::filter( items, lambda ) );
    EXPECT_EQ( 2, items.size() );
}

TEST( Map, filter_copy )
{
    using Mapped = std::map<int, int>;
    
    Mapped items{ {1,1}, {2,4}, {3,9}, {4,16} };
    
    Mapped even_keys{ {2,4},{4,16} };
    auto lambda = []( const auto& keyvaluepair ){ return keyvaluepair.first % 2 == 0; };
    
    // Map::filter_copy creates a copy, so parameter 'items' is untouched
    EXPECT_EQ( even_keys, 
      Map::filter_copy( items, lambda ) );
    EXPECT_EQ( 4, items.size() );
}

TEST( Map, fold )
{
    using Mapped = std::map<int, int>;
    
    Mapped items{ {1,2}, {3,4}, {5,6} };
    
    // Map::fold takes F( A&, pair<K,V> ) -> void
    EXPECT_EQ( 21, 
      Map::fold( items, 
        []( int& acc, const auto& kvp ){ acc += kvp.first + kvp.second; }, 0 ) );
}

TEST( Map, transform_mutable_values_only )
{
    using Transformed = std::map<int, int>;

    // Map::transform over the values, mutating them
    // Takes F(K,V) -> V i.e. the type of the return value must be V
    // "items" must be a named variable because parameter is non-const
    // (we will mutate it)
    Transformed items = { {1,1}, {2,2}, {3,3} };
    
    EXPECT_EQ( (Transformed{ {1,1}, {2,4}, {3,9} }),
      Map::transform( items, 
        []( int _, int v ){ return v*v; } ) );
}

TEST( Map, map_keys_and_values )
{
    using Mapped = std::map<int,double>;
    
    // Map::map over the pairs<key,value>
    // Takes F( pair<K,V> ) -> pair<K',V'> 
    // i.e. both key and value types can change
    auto lambda =
        []( const auto& kvp )
        {
            return std::make_pair( kvp.first + kvp.second,
                                   (double)kvp.second / (double)kvp.first );
        };
    
    // Map::map - new keys and values, not mutating the original collection, 
    // can be passed as unnamed temporary
    EXPECT_EQ( (Mapped{ {2,1}, {6,2} }),
      Map::map( std::map<int,int>{ {1,1}, {2,4} }, lambda ) );
}

TEST( Vector, filter )
{
    std::vector<int> items{ 1,2,3,4,5,4,3,2,1 };

    // Vector::filter will mutate the input collection
    EXPECT_EQ( (std::vector<int>{1,2,2,1}),
      Vector::filter( items,
        [](int i){ return 0 <= i && i <= 2; } ) );
    EXPECT_EQ( 4, items.size() );
}

TEST( Vector, filter_copy )
{
    std::vector<int> items{ 1,2,3,4,5,4,3,2,1 };
    auto untouched_size = items.size();
    
    // Vector::filter_copy creates a copy, so parameter 'items' is untouched
    EXPECT_EQ( (std::vector<int>{1,2,2,1}),
      Vector::filter_copy( items,
        [](int i){ return 0 <= i && i <= 2; } ) );
    EXPECT_EQ( untouched_size, items.size() );
}

TEST( Vector, fold )
{
    std::vector<int> items{ 1, 2, 3, 4, 5, -4, -6, -2, -1 };
    // Vector::fold takes F( A&, T ) -> void
    auto accumulate_squares = []( std::set<int>& acc, int i ){ acc.insert(i*i); };
    std::set<int> expected{1, 4, 9, 16, 25, 36};
    EXPECT_EQ( expected, 
      Vector::fold( items, accumulate_squares, std::set<int>{} ) );
}

TEST( Vector, sort )
{
    // Vector::sort mutates the input, hence input is non-const reference
    std::vector<int> items{1,2,1,3};
    EXPECT_EQ( (std::vector<int>{1,1,2,3}), 
      Vector::sort( items ) );
}

TEST( Vector, sort_copy )
{
    // Vector::sort_copy copies the input collection,
    // so collection parameter is const& (and can be an unnamed temporary)
    EXPECT_EQ( (std::vector<int>{1,1,2,3}), 
      Vector::sort_copy( std::vector<int>{1,2,1,3} ) );
}

TEST( Vector, map )
{
    // Vector::map takes F(T) -> U
    // Input collection is const and a new collection is returned
    EXPECT_EQ( (std::vector<double>{ 1.1, 2.1, 3.1 }),
      Vector::map( std::vector<int>{ 1,2,3 },
        []( int i ){ return (double)i + 0.1; } ) );
}

TEST( Vector, transform )
{
    // Vector::transform takes F(T) -> T
    // Input collection is mutated
    std::vector<int> items{ 1, 2, 3 };
    EXPECT_EQ( (std::vector<int>{ 2, 4, 6 }),
      Vector::transform( items, []( int i ){ return 2*i; } ) );
}

int main(int argc, char* argv[]) 
{    
    InitGoogleMock( &argc, argv );
    return RUN_ALL_TESTS();   
}

Notice that we can bypass immutability in C++, so whereas in F# Map::filter would always create a copy, it could be preferable in C++ to filter in-place. With that in mind, I’ve written both filter and filter_copy variations. There’s a similar dilemma for map operations – if you want free rein over the output types, then use Map::map or Vector::map. But if you want to transform the data in place (sticking to the existing types), use Map::transform or Vector::transform.

That covers the most popular functions for just Map and Vector, but it would be straight-forward to extend the library to cover List, Set and others. Similarly, I’d like to extend it to include higher-order functions like Choose, but I’ll need C++17’s std::optional for that.

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Stateful Lambdas (Jason Turner)

I just watched Jason Turner’s latest C++ Weekly video, where he uses C++14 generalised lambda capture to implement the fibonacci sequence:

screen-shot-2016-11-15-at-13-54-29

This is very cool – I hadn’t seen Fibonacci done this way before. I was interested in std::exchange too, introduced in C++14. As used here, std::exchange replaces the existing value of j with a new value and returns the original value, which is assigned to i. So this idiom allows you to update a value and store the previous value, all in a single expression.

Even better, Jason shows that this whole program is calculated at compile time in Clang – very impressive.

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How to calculate Fibonacci sequence for large numbers

Firstly, here’s a basic implementation of Fibonacci using recursion:

    using Numbers = std::vector<unsigned long long>;
    Numbers cache( 5000, 0 );
    
    unsigned long long fibonacciRecursiveImpl( size_t n, Numbers& cache )
    {
        if ( n == 0 ) return 0ULL;
        if ( n == 1 ) return 1ULL;
        
        if ( cache[n] != 0 ) return cache[n];
        
        auto num = fibonacciRecursiveImpl( n-1, cache ) + fibonacciRecursiveImpl( n-2, cache );
        cache[n] = num;
        return num;
    }
    
    unsigned long long fibonacciRecursive( size_t n )
    {
        return fibonacciRecursiveImpl( n, cache );
    }

This works fine for small numbers (e.g. up to 20). I was interested to know where it would fail first, data overflow (due to the size of the numbers involved) or stack overflow (due to the recursive approach)? I implemented this on a MacBook using Apple LLVM 7.0 and the C++14 flag, but no other special switches set. It turns out that the overflow problems kick in for n > 93, but there was no sign of stack overflows, even up for n ~ 2000.

Even if there was a stack overflow, you could still use recursion, but change to a tail recursive solution (many C++ compilers will eliminate tail calls, essentially turning this into an iterative solution):

    // Implement recursive approach with possibility for "Tail call elimination",
    // avoids any concerns about stack overflow
    unsigned long long fibonacciTailRecursiveImpl( size_t n, unsigned long long prevprev, unsigned long long prev )
    {
        if ( n == 0 ) return prevprev;
        if ( n == 1 ) return prev;
        
        return fibonacciTailRecursiveImpl( n-1, prev, prevprev + prev );
    }
    
    unsigned long long fibonacciTailRecursive( size_t n )
    {
        return fibonacciTailRecursiveImpl( n, 0, 1 );
    }

So how to avoid the data overflow for Fibonacci over n = 93? At that point, you need to introduce a large number type with its own internal representation of large integers and suitable operator+ implementation. I used one such, BigNum, for this HackerRank challenge. The implementation stores the large integer as a string, with each character taking values in the range [0,100]. This reduces the number of digit-by-digit additions by a factor of 10 compared to a straight decimal representation.

I replaced unsigned long long with BigNum in the recursive solution above and verified that it returns the correct answers for n ~ 2000, with no stack overflows. Here, I’ll show it in an iterative solution (if you don’t want to keep a cache around, this is highly memory efficient, because you only need to store the previous two values):

    BigNum fibonacciBigNumIterativeImpl( size_t n )
    {
        BigNum prevprev(0);
        BigNum prev(1);
        
        if ( n == 0 ) return prevprev;
        if ( n == 1 ) return prev;
        
        BigNum num(0);
        for ( size_t i = 2; i <= n; ++i )
        {
            num = prevprev + prev;
            prevprev = prev;
            prev = num;
        }
        return num;
    }
    
    std::string fibonacciBigNumIterative( size_t n )
    {
        auto result = fibonacciBigNumIterativeImpl( n );
        return result.toString();
    }

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How to approximate Pi using C++11 random number generators

The other day I learnt a method to approximate Pi if you have a random number generator for the range [0,1]. Consider a unit circle centred on (0,0) in 2D coordinates. Then one quarter of the area of the circle lies in the quadrant formed by x and y in range [0,1]. The area of the quarter circle is Pi * R^2/4, and here, R = 1 (it’s a unit circle). So we can generate a bunch of random 2D points, calculate the ratio between those that fall inside the circle and those in the outer unit square, then multiply by 4 to approximate Pi.
approximatepi
That sounds like a neat test case for the C++11 random number generators, so I thought I’d try it out. It turns out to work pretty well, if you’re prepared to use a sufficiently large number of random values.

    double approxPi( size_t points )
    {
        std::random_device rand_device;
        
        // mersenne_twister_engine is a random number engine 
        // based on Mersenne Twister algorithm.
        std::mt19937 generator( rand_device() );
        
        // We want random values uniformly distributed in [0,1]
        std::uniform_real_distribution<> unif_zero_one(0, 1);
        
        size_t points_inside{0};
        
        for (int i = 0; i < points; ++i )
        {
            auto x = unif_zero_one( generator );
            auto y = unif_zero_one( generator );
            double d = std::sqrt( x*x + y*y );
            
            if ( d <= 1.0 )
                ++points_inside;
        }
                
        return 4.0 * (static_cast<double>(points_inside) / static_cast<double>(points));
    }
}

void testApproximatePi()
{
    SHOULD_BE_APPROX( 3.14159, 0.3, approxPi( 100 ) );
    SHOULD_BE_APPROX( 3.14159, 0.1, approxPi( 1000 ) );
    SHOULD_BE_APPROX( 3.14159, 0.01, approxPi( 100000 ) );
    SHOULD_BE_APPROX( 3.14159, 0.001, approxPi( 10000000 ) );
    
    std::cout << "\n";
}

A typical run gives reasonable approximations once you get over 100,000 points:
screen-shot-2016-09-16-at-13-30-26

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