erlang的随机数 及 random:uniform()函数

　　每次调用会更新进程字典里的random_seed变量，这样在同一个进程内每次调用random:uniform()时，随机数种子都不同，所以生成的随机数都不一样（调用完random:uniform()后，可以用get(random_seed)查看更新后的种子值）。

但是如果是不同的进程分别调用random:uniform()，因为随机种子更新的算法是一样的，所以每次各进程的随机数种子也是相同的，从而生成的随机数也是一样的，要想让不同进程生成的随机数不同，要手动为每个进程设置不同的种子，常用的是用erlang:now，比如：

random:seed(erlang:now()),random:uniform().

不过如果每个进程调用random:seed(erlang:now())太接近，种子值会比较接近，生成的随机数也会比较接近，更好的方法是用一个单独的进程来生成种子，保证每次的种子值相差比较大：

Seed = {random:uniform(99999), random:uniform(999999), random:uniform(999999)}

然后每次调用random:uniform()前从该种子生成进程获取最新的种子值，seed()之。

下面为random.erl 的源码：

%%
%% %CopyrightBegin%
%%
%% Copyright Ericsson AB 1996-2011. All Rights Reserved.
%%
%% The contents of this file are subject to the Erlang Public License,
%% Version 1.1, (the "License"); you may not use this file except in
%% compliance with the License. You should have received a copy of the
%% Erlang Public License along with this software. If not, it can be
%% retrieved online at http://www.erlang.org/. %%
%% Software distributed under the License is distributed on an "AS IS"
%% basis, WITHOUT WARRANTY OF ANY KIND, either express or implied. See
%% the License for the specific language governing rights and limitations
%% under the License.
%%
%% %CopyrightEnd%
%%
-module(random).

%% Reasonable random number generator.
%%  The method is attributed to B. A. Wichmann and I. D. Hill
%%  See "An efficient and portable pseudo-random number generator",
%%  Journal of Applied Statistics. AS183. 1982. Also Byte March 1987.

-export([seed/0, seed/1, seed/3, uniform/0, uniform/1,
uniform_s/1, uniform_s/2, seed0/0]).

-define(PRIME1, 30269).
-define(PRIME2, 30307).
-define(PRIME3, 30323).

%%-----------------------------------------------------------------------
%% The type of the state

-type ran() :: {integer(), integer(), integer()}.

%%-----------------------------------------------------------------------

-spec seed0() -> ran().

seed0() ->
{3172, 9814, 20125}.

%% seed()
%%  Seed random number generation with default values

-spec seed() -> ran().

seed() ->
case seed_put(seed0()) of
undefined -> seed0();
{_,_,_} = Tuple -> Tuple
end.

%% seed({A1, A2, A3})
%%  Seed random number generation

-spec seed({A1, A2, A3}) -> 'undefined' | ran() when
A1 :: integer(),
A2 :: integer(),
A3 :: integer().

seed({A1, A2, A3}) ->
seed(A1, A2, A3).

%% seed(A1, A2, A3)
%%  Seed random number generation

-spec seed(A1, A2, A3) -> 'undefined' | ran() when
A1 :: integer(),
A2 :: integer(),
A3 :: integer().

seed(A1, A2, A3) ->
seed_put({(abs(A1) rem (?PRIME1-1)) + 1,   % Avoid seed numbers that are
(abs(A2) rem (?PRIME2-1)) + 1,   % even divisors of the
(abs(A3) rem (?PRIME3-1)) + 1}). % corresponding primes.

-spec seed_put(ran()) -> 'undefined' | ran().

seed_put(Seed) ->
put(random_seed, Seed).

%% uniform()
%%  Returns a random float between 0 and 1.

-spec uniform() -> float().

uniform() ->
{A1, A2, A3} = case get(random_seed) of
undefined -> seed0();
Tuple -> Tuple
end,
B1 = (A1*171) rem ?PRIME1,
B2 = (A2*172) rem ?PRIME2,
B3 = (A3*170) rem ?PRIME3,
put(random_seed, {B1,B2,B3}),
R = B1/?PRIME1 + B2/?PRIME2 + B3/?PRIME3,
R - trunc(R).

%% uniform(N) -> I
%%  Given an integer N >= 1, uniform(N) returns a random integer
%%  between 1 and N.

-spec uniform(N) -> pos_integer() when
N :: pos_integer().

uniform(N) when is_integer(N), N >= 1 ->
trunc(uniform() * N) + 1.

%%% Functional versions

%% uniform_s(State) -> {F, NewState}
%%  Returns a random float between 0 and 1.

-spec uniform_s(State0) -> {float(), State1} when
State0 :: ran(),
State1 :: ran().

uniform_s({A1, A2, A3}) ->
B1 = (A1*171) rem ?PRIME1,
B2 = (A2*172) rem ?PRIME2,
B3 = (A3*170) rem ?PRIME3,
R = B1/?PRIME1 + B2/?PRIME2 + B3/?PRIME3,
{R - trunc(R), {B1,B2,B3}}.

%% uniform_s(N, State) -> {I, NewState}
%%  Given an integer N >= 1, uniform(N) returns a random integer
%%  between 1 and N.

-spec uniform_s(N, State0) -> {integer(), State1} when
N :: pos_integer(),
State0 :: ran(),
State1 :: ran().

uniform_s(N, State0) when is_integer(N), N >= 1 ->
{F, State1} = uniform_s(State0),
{trunc(F * N) + 1, State1}.

View Code
random:seed 由进程字典put存了随机数，random：uniform则get取了随机数，而它同时又put了新的随机数.

如果一开始直接调用，random:uniform/0, 则一开始 get(random_seed)为undefined，后面每次生产的种子的规则都是根据其业务规则生成。 但如果是 先调用random:seed/1 ,则它先生成了随机种子，put到random_seed 的进程字典中，后面依次调用random:uniform/0的时候，则从random_seed的进程字典中取出随机种子，则不是undefined，后面根据其业务规则，生成随机数.