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62 lines
2.5 KiB
Markdown
62 lines
2.5 KiB
Markdown
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Title: Random Numbers
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SPDX-License-Identifier: LGPL-2.1-or-later
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SPDX-FileCopyrightText: 2000, 2002 Sebastian Wilhelmi
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SPDX-FileCopyrightText: 2013 Colin Walters
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# Random Numbers
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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).
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Do not use this API for cryptographic purposes such as key
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generation, nonces, salts or one-time pads.
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This PRNG is suitable for non-cryptographic use such as in games
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(shuffling a card deck, generating levels), generating data for
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a test suite, etc. If you need random data for cryptographic
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purposes, it is recommended to use platform-specific APIs such
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as `/dev/random` on UNIX, or
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[`CryptGenRandom()`](https://learn.microsoft.com/en-us/windows/win32/api/wincrypt/nf-wincrypt-cryptgenrandom)
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on Windows.
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[type@GLib.Rand] uses the Mersenne Twister PRNG, which was originally
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developed by Makoto Matsumoto and Takuji Nishimura. Further
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information can be found at
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[this page](http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html).
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If you just need a random number, you simply call the `g_random_*()`
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functions, which will create a globally used [type@GLib.Rand] and use the
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according `g_rand_*()` functions internally:
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* [func@GLib.random_int]
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* [func@GLib.random_int_range]
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* [func@GLib.random_double]
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* [func@GLib.random_double_range]
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* [func@GLib.random_set_seed]
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Whenever you need a stream of reproducible random numbers, you better create a
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[type@GLib.Rand] yourself and use the `g_rand_*()` functions directly, which
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will also be slightly faster. Initializing a [type@GLib.Rand] with a
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certain seed will produce exactly the same series of random
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numbers on all platforms. This can thus be used as a seed for
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e.g. games.
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The `g_rand*_range()` functions will return high quality equally
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distributed random numbers, whereas for example the
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`(g_random_int () % max)` approach often
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doesn’t yield equally distributed numbers.
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GLib changed the seeding algorithm for the pseudo-random number
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generator Mersenne Twister, as used by [type@GLib.Rand]. This was necessary,
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because some seeds would yield very bad pseudo-random streams.
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Also the pseudo-random integers generated by `g_rand*_int_range()`
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will have a slightly better equal distribution with the new
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version of GLib.
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The original seeding and generation algorithms, as found in
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GLib 2.0.x, can be used instead of the new ones by setting the
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environment variable `G_RANDOM_VERSION` to the value of `2.0`.
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Use the GLib-2.0 algorithms only if you have sequences of numbers
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generated with Glib-2.0 that you need to reproduce exactly.
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