# Blog Archives

## Using Function Pointers for Callbacks in C++

You plan to call some function func1, and at runtime you need it to invoke another function func2. For one reason or another, however, you cannot simply hardcode the name of func2 within func1. func2 may not be known definitively at compile time, or perhaps func1 belongs to a third-party API that you can’t change and recompile. In either case, you need a callback function.

In a situation such as that shown in below code, a function pointer is a good idea if updateProgress and longOperation shouldn’t knowanything about each other. For example, a function that updates the progress by displaying it to the user—either in a user interface (UI) dialog box, in a console window, or somewhere else—does not care about the context in which it is invoked. Similarly, the longOperation function may be part of some data loading API that doesn’t care whether it’s invoked from a graphical UI, a console window, or by a background process.

The first thing you will want to do is determine what the signature of the function is you plan to call and create a typedef for it. typedef is your friend when it comes to function pointers, because their syntax is ugly. Consider howyou would declare a function pointer variable f that contains the address of a function that takes a single integer argument and returns a boolean. It would look like this:

bool (*f)(int); // f is the variable name

One could argue, convincingly, that this is no big deal and that I’m just a whiner. But what if you want a vector of such function pointers?

vector<bool (*)(int)> vf;

Or an array of them?

bool (*af[10])(int);

Function pointers do not look like ordinary C++ variable declarations whose format is often a (qualified) type name followed by a variable name. This is why they can make for messy reading.

Thus, in below code, I used a typedef like this:

typedef bool (*FuncPtrBoolInt)(int);

Once that was out of the way, I was free to declare function pointers that have the signature of returning bool and accepting a single integer argument as I would any other sort of parameter, like so:

void longOperation(FuncPtrBoolInt f) {

// …

Now, all longOperation needs to do is call f like it would any function:

f (l/1000000);

In this way, f can be any function that accepts an integer argument and returns bool. Consider a caller of longOperation that doesn’t care about the progress. It can pass in a function pointer of a no-op function:

bool whoCares(int i) {return(true);}

//…

longOperation(whoCares);

More importantly, which function to pass to longOperation can be determined dynamically at runtime.

#include <iostream> // An example of a callback function bool updateProgress(int pct) { std::cout << pct << "% complete...\n"; return(true); } // A typedef to make for easier reading typedef bool (*FuncPtrBoolInt)(int); // A function that runs for a while void longOperation(FuncPtrBoolInt f) { for (long l = 0; l < 100000000; l++) if (l % 10000000 == 0) f(l / 1000000); } int main( ) { longOperation(updateProgress); // ok }

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## Generating Random Numbers

To be precise, random number generation functions, including rand, return pseudo-random numbers as opposed to truly random numbers, so whenever I say random, I actually mean pseudo-random. Before using the rand function you need to seed (i.e., initialize) the random number generator with a call to srand. This assures that subsequent calls to rand won’t produce the same sequence of numbers each time the program is run. The simplest way to seed the random number generator is to pass the result from a call to clock from the header as an unsigned int. Reseeding a random number generator causes number generation to be less random.

The rand function is limited in many ways. To begin with, it only generates integers, and only does so using a uniform distribution. Furthermore, the specific random number generation algorithm used is implementation specific and, thus, random number sequences are not reproducible from system to system given the same seed. This is a problem for certain kinds of applications, as well as when testing and debugging.

If you want to generate some random floating-point numbers in the interval of [0.0, 1.0) with a uniform distribution. The C++ standard provides the C runtime function rand in the header that returns a random number in the range of 0 to RAND_MAX inclusive. The RAND_MAX macro represents the highest value returnable by the rand function. A demonstration of

using rand to generate random floating-point numbers below.

#include <cstdlib> #include <ctime> #include <iostream> using namespace std; double doubleRand( ) { return double(rand( )) / (double(RAND_MAX) + 1.0); } int main( ) { srand(static_cast(clock( ))); cout << "expect 5 numbers within the interval [0.0, 1.0)" << endl; for (int i=0; i < 5; i++) { cout << doubleRand( ) << "\n"; } cout << endl; }

The program above should produce output similar to:

expect 5 numbers within the interval [0.0, 1.0)

0.010437

0.740997

0.34906

0.369293

0.544373

A much more sophisticated alternative to rand is the Boost Random library by Jens Maurer. The Boost Random library provides several high-quality random number generation functions for both integer and floating-point types, and support for numerous kinds

of distributions. Below code demonstrates how you can produce random floating- point numbers in the interval [0,1).

#include <boost/random.hpp> #include <iostream> #include <cstdlib> using namespace std; using namespace boost; typedef boost::mt19937 BaseGenerator; typedef boost::uniform_real Distribution; typedef boost::variate_generator<basegenerator, distribution=""> Generator; double boostDoubleRand( ) { static BaseGenerator base; static Distribution dist; static Generator rng(base, dist); return rng( ); } int main( ) { cout << "expect 5 numbers within the interval [0,1)" << endl; for (int i=0; i < 5; i++) { cout << boostDoubleRand( ) << "\n"; } cout << endl; }

The main advantage of the Boost Random library, is that the pseudo-random number generation algorithm has guaranteed and reproducible randomness properties based on the precise algorithm chosen. In above code I use the Mersenne Twister generator (mt19937) because it offers a good blend of performance and randomness.

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## Implementing Fixed-Point Numbers in C++

A fixed-point number, like a floating-point number, is an approximate representation of a real number. A floating-point number is stored as a mantissa (m), and an exponent (e), to form the equation m * be, where b is some constant. A fixed-point number is almost the same but the exponent is also a constant. This constant is passed to the basic_fixed_real in below class template as a template parameter.

By representing e as a constant, it allows fixed-point numbers to be represented internally as integers and for the arithmetic operations on them to be performed using integer arithmetic. This can often improve the speed of basic arithmetic operations

especially addition and subtraction. Fixed-point representations are less flexible than floating-point numbers, as they can only represent a narrow range of values. The fixed_real type in below function has a range that can only represent values from –2,097,151 to +2,097,151 with a precision of 1/1,024.

Implementing addition and subtraction of fixed-point numbers is straightforward enough: I simply add or subtract the underlying representation. To perform division and multiplication, I need an extra step of shifting the mantissa left or right to adjust for the binary point.

Below class template provides the implementation of a fixed-point real number, where the number of places to the right of the binary point is a template parameter. For instance basic_fixed_real<10> has 10 binary digits to the right of the binary point, allowing it to represent numbers up to a precision of 1/1,024.

#include <iostream> using namespace std; template<int E> struct BasicFixedReal { typedef BasicFixedReal self; static const int factor = 1 << (E - 1); BasicFixedReal( ) : m(0) { } BasicFixedReal(double d) : m(static_cast(d * factor)) { } self& operator+=(const self& x) { m += x.m; return *this; } self& operator-=(const self& x) { m -= x.m; return *this; } self& operator*=(const self& x) { m *= x.m; m >>= E; return *this; } self& operator/=(const self& x) { m /= x.m; m *= factor; return *this; } self& operator*=(int x) { m *= x; return *this; } self& operator/=(int x) { m /= x; return *this; } self operator-( ) { return self(-m); } double toDouble( ) const { return double(m) / factor; } // friend functions friend self operator+(self x, const self& y) { return x += y; } friend self operator-(self x, const self& y) { return x -= y; } friend self operator*(self x, const self& y) { return x *= y; } friend self operator/(self x, const self& y) { return x /= y; } // comparison operators friend bool operator==(const self& x, const self& y) { return x.m == y.m; } friend bool operator!=(const self& x, const self& y) { return x.m != y.m; } friend bool operator>(const self& x, const self& y) { return x.m > y.m; } friend bool operator<(const self& x, const self& y) { return x.m < y.m; } friend bool operator>=(const self& x, const self& y) { return x.m >= y.m; } friend bool operator<=(const self& x, const self& y) { return x.m <= y.m; } private: int m; }; typedef BasicFixedReal<10> FixedReal; int main( ) { FixedReal x(0); for (int i=0; i < 100; ++i) { x += FixedReal(0.0625); } cout << x.toDouble( ) << endl; }

The program outputs:

6.25

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