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2004-02-18 Sebastian Wilhelmi <seppi@seppi.de> * glib/glib-sections.txt: Add the new g_rand_* functions
204 lines
3.9 KiB
Plaintext
204 lines
3.9 KiB
Plaintext
<!-- ##### SECTION Title ##### -->
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Random Numbers
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<!-- ##### SECTION Short_Description ##### -->
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pseudo-random number generator.
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<!-- ##### SECTION Long_Description ##### -->
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<para>
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The following functions allow you to use a portable, fast and good
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pseudo-random number generator (PRNG). It uses the Mersenne Twister
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PRNG, which was originally developed by Makoto Matsumoto and Takuji
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Nishimura. Further information can be found at <ulink
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url="http://www.math.keio.ac.jp/~matumoto/emt.html"
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>www.math.keio.ac.jp/~matumoto/emt.html</ulink>.
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</para>
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<para>
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If you just need a random number, you simply call the
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<function>g_random_*</function> functions, which will create a globally
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used #GRand and use the according <function>g_rand_*</function> functions
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internally. Whenever you need a stream of reproducible random numbers, you
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better create a #GRand yourself and use the <function>g_rand_*</function>
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functions directly, which will also be slightly faster. Initializing a #GRand
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with a certain seed will produce exactly the same series of random numbers
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on all platforms. This can thus be used as a seed for e.g. games.
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</para>
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<para>
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The <function>g_rand*_range</function> functions will return high quality
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equally distributed random numbers, whereas for example the
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<literal>(g_random_int()%max)</literal> approach often doesn't
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yield equally distributed numbers.
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</para>
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<para>
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GLib changed the seeding algorithm for the pseudo-random number
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generator Mersenne Twister, as used by <structname>GRand</structname>
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and <structname>GRandom</structname>. This was necessary, because some
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seeds would yield very bad pseudo-random streams. Also the
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pseudo-random integers generated by
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<function>g_rand*_int_range()</function> will have a
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slightly better equal distribution with the new version of GLib.
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</para>
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<para>
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The original seeding and generation algorithms, as found in GLib 2.0.x,
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can be used instead of the new ones by setting the environment variable
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<envar>G_RANDOM_VERSION</envar> to the value of '2.0'. Use the
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GLib-2.0 algorithms only if you have sequences of numbers generated
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with Glib-2.0 that you need to reproduce exactly.
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</para>
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<!-- ##### SECTION See_Also ##### -->
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<para>
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</para>
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<!-- ##### STRUCT GRand ##### -->
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<para>
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The #GRand struct is an opaque data structure. It should only be
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accessed through the <function>g_rand_*</function> functions.
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</para>
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<!-- ##### FUNCTION g_rand_new_with_seed ##### -->
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@seed:
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@Returns:
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<!-- ##### FUNCTION g_rand_new_with_seed_array ##### -->
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<para>
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</para>
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@seed:
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@seed_length:
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@Returns:
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<!-- ##### FUNCTION g_rand_new ##### -->
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@Returns:
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<!-- ##### FUNCTION g_rand_copy ##### -->
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<para>
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</para>
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@rand_:
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@Returns:
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<!-- ##### FUNCTION g_rand_free ##### -->
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@rand_:
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<!-- ##### FUNCTION g_rand_set_seed ##### -->
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@rand_:
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@seed:
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<!-- ##### FUNCTION g_rand_set_seed_array ##### -->
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<para>
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</para>
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@rand_:
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@seed:
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@seed_length:
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<!-- ##### MACRO g_rand_boolean ##### -->
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<para>
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Returns a random #gboolean from @rand_. This corresponds to a unbiased
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coin toss.
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</para>
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@rand_: a #GRand.
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@Returns: a random #gboolean.
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<!-- ##### FUNCTION g_rand_int ##### -->
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@rand_:
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@Returns:
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<!-- ##### FUNCTION g_rand_int_range ##### -->
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@rand_:
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@begin:
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@end:
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@Returns:
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<!-- ##### FUNCTION g_rand_double ##### -->
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@rand_:
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@Returns:
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<!-- ##### FUNCTION g_rand_double_range ##### -->
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@rand_:
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@begin:
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@end:
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@Returns:
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<!-- ##### FUNCTION g_random_set_seed ##### -->
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@seed:
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<!-- ##### MACRO g_random_boolean ##### -->
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<para>
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Returns a random #gboolean. This corresponds to a unbiased coin toss.
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</para>
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@Returns: a random #gboolean.
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<!-- ##### FUNCTION g_random_int ##### -->
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@Returns:
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<!-- ##### FUNCTION g_random_int_range ##### -->
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@begin:
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@end:
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@Returns:
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<!-- ##### FUNCTION g_random_double ##### -->
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@Returns:
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<!-- ##### FUNCTION g_random_double_range ##### -->
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@begin:
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@end:
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@Returns:
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