Sets our main struct and passes it to the parent class
Creates a new random number generator initialized with seed.
Creates a new random number generator initialized with seed. Since 2.4
Creates a new random number generator initialized with a seed taken either from /dev/urandom (if existing) or from the current time (as a fallback).
Copies a GRand into a new one with the same exact state as before. This way you can take a snapshot of the random number generator for replaying later. Since 2.4
Frees the memory allocated for the GRand.
Get the main Gtk struct
the main Gtk struct as a void*
Returns the next random gdouble from rand_ equally distributed over the range [0..1).
Returns the next random gdouble from rand_ equally distributed over the range [begin..end).
Returns the next random guint32 from rand_ equally distributed over the range [0..2^32-1].
Returns the next random gint32 from rand_ equally distributed over the range [begin..end-1].
Sets the seed for the random number generator GRand to seed.
Initializes the random number generator by an array of longs. Array can be of arbitrary size, though only the first 624 values are taken. This function is useful if you have many low entropy seeds, or if you require more then 32bits of actual entropy for your application. Since 2.4
Returns a random gdouble equally distributed over the range [0..1).
Returns a random gdouble equally distributed over the range [begin..end).
Return a random guint32 equally distributed over the range [0..2^32-1].
Returns a random gint32 equally distributed over the range [begin..end-1].
Sets the seed for the global random number generator, which is used by the g_random_* functions, to seed.
the main Gtk struct
The following functions allow you to use a portable, fast and good pseudo-random number generator (PRNG). It uses the Mersenne Twister PRNG, which was originally developed by Makoto Matsumoto and Takuji Nishimura. Further information can be found at
http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html.
If you just need a random number, you simply call the g_random_* functions, which will create a globally used GRand and use the according g_rand_* functions internally. Whenever you need a stream of reproducible random numbers, you better create a GRand yourself and use the g_rand_* functions directly, which will also be slightly faster. Initializing a GRand with a certain seed will produce exactly the same series of random numbers on all platforms. This can thus be used as a seed for e.g. games.
The g_rand*_range functions will return high quality equally distributed random numbers, whereas for example the (g_random_int()%max) approach often doesn't yield equally distributed numbers.
GLib changed the seeding algorithm for the pseudo-random number generator Mersenne Twister, as used by GRand and GRandom. This was necessary, because some seeds would yield very bad pseudo-random streams. Also the pseudo-random integers generated by g_rand*_int_range() will have a slightly better equal distribution with the new version of GLib.
The original seeding and generation algorithms, as found in GLib 2.0.x, can be used instead of the new ones by setting the environment variable G_RANDOM_VERSION to the value of '2.0'. Use the GLib-2.0 algorithms only if you have sequences of numbers generated with Glib-2.0 that you need to reproduce exactly.