path: root/lib/stdlib/doc/src/random.xml

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  <header>
    <copyright>
      <year>1996</year><year>2016</year>
      <holder>Ericsson AB. All Rights Reserved.</holder>
    </copyright>
    <legalnotice>
      Licensed under the Apache License, Version 2.0 (the "License");
      you may not use this file except in compliance with the License.
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    <title>random</title>
    <prepared>Joe Armstrong</prepared>
    <responsible>Bjarne Dacker</responsible>
    <docno>1</docno>
    <approved>Bjarne D&auml;cker</approved>
    <checked></checked>
    <date>96-09-09</date>
    <rev>A</rev>
    <file>random.sgml</file>
  </header>
  <module>random</module>
  <modulesummary>Pseudo random number generation</modulesummary>
  <description>
    <p>Random number generator. The method is attributed to
      B.A. Wichmann and I.D.Hill, in 'An efficient and portable
      pseudo-random number generator', Journal of Applied
      Statistics. AS183. 1982. Also Byte March 1987. </p>
    <p>The current algorithm is a modification of the version attributed
      to Richard A O'Keefe in the standard Prolog library.</p>
    <p>Every time a random number is requested, a state is used to calculate
      it, and a new state produced. The state can either be implicit (kept
      in the process dictionary) or be an explicit argument and return value.
      In this implementation, the state (the type <c>ran()</c>) consists of a
      tuple of three integers.</p>
    <p>It should be noted that this random number generator is not cryptographically 
      strong. If a strong cryptographic random number generator is needed for
      example <c>crypto:strong_rand_bytes/1</c> could be used instead.</p>
      <note><p>The new and improved <seealso
      marker="stdlib:rand">rand</seealso> module should be used
      instead of this module.</p></note>
  </description>
  <datatypes>
    <datatype>
      <name name="ran"/>
      <desc><p>The state.</p></desc>
    </datatype>
  </datatypes>
  <funcs>
    <func>
      <name name="seed" arity="0"/>
      <fsummary>Seeds random number generation with default values</fsummary>
      <desc>
        <p>Seeds random number generation with default (fixed) values
          in the process dictionary, and returns the old state.</p>
      </desc>
    </func>
   <func>
      <name name="seed" arity="3"/>
      <fsummary>Seeds random number generator</fsummary>
      <desc>
        <p>Seeds random number generation with integer values in the process
          dictionary, and returns the old state.</p>
        <p>One easy way of obtaining a unique value to seed with is to:</p>
        <code type="none">
random:seed(erlang:phash2([node()]),
            erlang:monotonic_time(),
            erlang:unique_integer())</code>
        <p>See <seealso marker="erts:erlang#phash2/1">
          erlang:phash2/1</seealso>, <seealso marker="erts:erlang#node/0">
          node/0</seealso>, <seealso marker="erts:erlang#monotonic_time/0">
          erlang:monotonic_time/0</seealso>, and
          <seealso marker="erts:erlang#unique_integer/0">
          erlang:unique_integer/0</seealso>) for details.</p>
      </desc>
    </func>
    <func>
      <name name="seed" arity="1"/>
      <fsummary>Seeds random number generator</fsummary>
      <desc>
        <p>
	<c>seed({<anno>A1</anno>, <anno>A2</anno>, <anno>A3</anno>})</c> is equivalent to <c>seed(<anno>A1</anno>, <anno>A2</anno>, <anno>A3</anno>)</c>.
	</p>
      </desc>
    </func>
    <func>
      <name name="seed0" arity="0"/>
      <fsummary>Return default state for random number generation</fsummary>
      <desc>
        <p>Returns the default state.</p>
      </desc>
    </func>
    <func>
      <name name="uniform" arity="0"/>
      <fsummary>Return a random float</fsummary>
      <desc>
        <p>Returns a random float uniformly distributed between <c>0.0</c>
          and <c>1.0</c>, updating the state in the process dictionary.</p>
      </desc>
    </func>
    <func>
      <name name="uniform" arity="1"/>
      <fsummary>Return a random integer</fsummary>
      <desc>
        <p>Given an integer <c><anno>N</anno> >= 1</c>, <c>uniform/1</c> returns a
          random integer uniformly distributed between <c>1</c> and
          <c><anno>N</anno></c>, updating the state in the process dictionary.</p>
      </desc>
    </func>
    <func>
      <name name="uniform_s" arity="1"/>
      <fsummary>Return a random float</fsummary>
      <desc>
        <p>Given a state, <c>uniform_s/1</c>returns a random float uniformly
          distributed between <c>0.0</c> and <c>1.0</c>, and a new state.</p>
      </desc>
    </func>
    <func>
      <name name="uniform_s" arity="2"/>
      <fsummary>Return a random integer</fsummary>
      <desc>
        <p>Given an integer <c><anno>N</anno> >= 1</c> and a state, <c>uniform_s/2</c>
          returns a random integer uniformly distributed between <c>1</c> and
          <c><anno>N</anno></c>, and a new state.</p>
      </desc>
    </func>
  </funcs>

  <section>
    <title>Note</title>
    <p>Some of the functions use the process dictionary variable
      <c>random_seed</c> to remember the current seed.</p>
    <p>If a process calls <c>uniform/0</c> or <c>uniform/1</c> without
      setting a seed first, <c>seed/0</c> is called automatically.</p>
    <p>The implementation changed in R15. Upgrading to R15 will break
      applications that expect a specific output for a given seed. The output
      is still deterministic number series, but different compared to releases
      older than R15. The seed <c>{0,0,0}</c> will, for example, no longer
      produce a flawed series of only zeros.</p>
  </section>
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