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-rw-r--r--lib/stdlib/doc/src/rand.xml118
-rw-r--r--lib/stdlib/src/rand.erl261
-rw-r--r--lib/stdlib/test/rand_SUITE.erl217
3 files changed, 568 insertions, 28 deletions
diff --git a/lib/stdlib/doc/src/rand.xml b/lib/stdlib/doc/src/rand.xml
index 89fb858823..21f680a0ee 100644
--- a/lib/stdlib/doc/src/rand.xml
+++ b/lib/stdlib/doc/src/rand.xml
@@ -133,8 +133,9 @@
variable <c>rand_seed</c> to remember the current state.</p>
<p>If a process calls
- <seealso marker="#uniform-0"><c>uniform/0</c></seealso> or
- <seealso marker="#uniform-1"><c>uniform/1</c></seealso> without
+ <seealso marker="#uniform-0"><c>uniform/0</c></seealso>,
+ <seealso marker="#uniform-1"><c>uniform/1</c></seealso> or
+ <seealso marker="#uniform_real-0"><c>uniform_real/0</c></seealso> without
setting a seed first, <seealso marker="#seed-1"><c>seed/1</c></seealso>
is called automatically with the default algorithm and creates a
non-constant seed.</p>
@@ -168,10 +169,17 @@ R3 = rand:uniform(),</pre>
S0 = rand:seed_s(exrop),
{R4, S1} = rand:uniform_s(S0),</pre>
+ <p>Textbook basic form Box-Muller standard normal deviate</p>
+
+ <pre>
+R5 = rand:uniform_real(),
+R6 = rand:uniform(),
+SND0 = math:sqrt(-2 * math:log(R5)) * math:cos(math:pi() * R6)</pre>
+
<p>Create a standard normal deviate:</p>
<pre>
-{SND0, S2} = rand:normal_s(S1),</pre>
+{SND1, S2} = rand:normal_s(S1),</pre>
<p>Create a normal deviate with mean -3 and variance 0.5:</p>
@@ -414,7 +422,8 @@ tests. We suggest to use a sign test to extract a random Boolean value.</pre>
This function may return exactly <c>0.0</c> which can be
fatal for certain applications. If that is undesired
you can use <c>(1.0 - rand:uniform())</c> to get the
- interval <c>0.0 &lt; <anno>X</anno> =&lt; 1.0</c>.
+ interval <c>0.0 &lt; <anno>X</anno> =&lt; 1.0</c>, or instead use
+ <seealso marker="#uniform_real-0"><c>uniform_real/0</c></seealso>.
</p>
<p>
If neither endpoint is desired you can test and re-try
@@ -432,6 +441,42 @@ end.</pre>
</func>
<func>
+ <name name="uniform_real" arity="0"/>
+ <fsummary>Return a random float.</fsummary>
+ <desc><marker id="uniform_real-0"/>
+ <p>
+ Returns a random float
+ uniformly distributed in the value range
+ <c>DBL_MIN =&lt; <anno>X</anno> &lt; 1.0</c>
+ and updates the state in the process dictionary.
+ </p>
+ <p>
+ Conceptually, a random real number <c>R</c> is generated
+ from the interval <c>0 =&lt; R &lt; 1</c> and then the
+ closest rounded down normalized number
+ in the IEEE 754 Double precision format
+ is returned.
+ </p>
+ <note>
+ <p>
+ The generated numbers from this function has got better
+ granularity for small numbers than the regular
+ <seealso marker="#uniform-0"><c>uniform/0</c></seealso>
+ because all bits in the mantissa are random.
+ This property, in combination with the fact that exactly zero
+ is never returned is useful for algoritms doing for example
+ <c>1.0 / <anno>X</anno></c> or <c>math:log(<anno>X</anno>)</c>.
+ </p>
+ </note>
+ <p>
+ See
+ <seealso marker="#uniform_real_s-1"><c>uniform_real_s/1</c></seealso>
+ for more explanation.
+ </p>
+ </desc>
+ </func>
+
+ <func>
<name name="uniform" arity="1"/>
<fsummary>Return a random integer.</fsummary>
<desc><marker id="uniform-1"/>
@@ -460,7 +505,8 @@ end.</pre>
This function may return exactly <c>0.0</c> which can be
fatal for certain applications. If that is undesired
you can use <c>(1.0 - rand:uniform(State))</c> to get the
- interval <c>0.0 &lt; <anno>X</anno> =&lt; 1.0</c>.
+ interval <c>0.0 &lt; <anno>X</anno> =&lt; 1.0</c>, or instead use
+ <seealso marker="#uniform_real_s-1"><c>uniform_real_s/1</c></seealso>.
</p>
<p>
If neither endpoint is desired you can test and re-try
@@ -478,6 +524,68 @@ end.</pre>
</func>
<func>
+ <name name="uniform_real_s" arity="1"/>
+ <fsummary>Return a random float.</fsummary>
+ <desc>
+ <p>
+ Returns, for a specified state, a random float
+ uniformly distributed in the value range
+ <c>DBL_MIN =&lt; <anno>X</anno> &lt; 1.0</c>
+ and updates the state in the process dictionary.
+ </p>
+ <p>
+ Conceptually, a random real number <c>R</c> is generated
+ from the interval <c>0 =&lt; R &lt; 1</c> and then the
+ closest rounded down normalized number
+ in the IEEE 754 Double precision format
+ is returned.
+ </p>
+ <note>
+ <p>
+ The generated numbers from this function has got better
+ granularity for small numbers than the regular
+ <seealso marker="#uniform_s-1"><c>uniform_s/1</c></seealso>
+ because all bits in the mantissa are random.
+ This property, in combination with the fact that exactly zero
+ is never returned is useful for algoritms doing for example
+ <c>1.0 / <anno>X</anno></c> or <c>math:log(<anno>X</anno>)</c>.
+ </p>
+ </note>
+ <p>
+ The concept implicates that the probability to get
+ exactly zero is extremely low; so low that this function
+ is in fact guaranteed to never return zero. The smallest
+ number that it might return is <c>DBL_MIN</c>, which is
+ 2.0^(-1022).
+ </p>
+ <p>
+ The value range stated at the top of this function
+ description is technically correct, but
+ <c>0.0 =&lt; <anno>X</anno> &lt; 1.0</c>
+ is a better description of the generated numbers'
+ statistical distribution. Except that exactly 0.0
+ is never returned, which is not possible to observe
+ statistically.
+ </p>
+ <p>
+ For example; for all sub ranges
+ <c>N*2.0^(-53) =&lt; X &lt; (N+1)*2.0^(-53)</c>
+ where
+ <c>0 =&lt; integer(N) &lt; 2.0^53</c>
+ the probability is the same.
+ Compare that with the form of the numbers generated by
+ <seealso marker="#uniform_s-1"><c>uniform_s/1</c></seealso>.
+ </p>
+ <p>
+ Having to generate extra random bits for
+ small numbers costs a little performance.
+ This function is about 20% slower than the regular
+ <seealso marker="#uniform_s-1"><c>uniform_s/1</c></seealso>
+ </p>
+ </desc>
+ </func>
+
+ <func>
<name name="uniform_s" arity="2"/>
<fsummary>Return a random integer.</fsummary>
<desc>
diff --git a/lib/stdlib/src/rand.erl b/lib/stdlib/src/rand.erl
index 7a8a5e6d4a..362e98006e 100644
--- a/lib/stdlib/src/rand.erl
+++ b/lib/stdlib/src/rand.erl
@@ -21,8 +21,8 @@
%% Multiple PRNG module for Erlang/OTP
%% Copyright (c) 2015-2016 Kenji Rikitake
%%
-%% exrop (xoroshiro116+) added and statistical distribution
-%% improvements by the Erlang/OTP team 2017
+%% exrop (xoroshiro116+) added, statistical distribution
+%% improvements and uniform_real added by the Erlang/OTP team 2017
%% =====================================================================
-module(rand).
@@ -30,10 +30,14 @@
-export([seed_s/1, seed_s/2, seed/1, seed/2,
export_seed/0, export_seed_s/1,
uniform/0, uniform/1, uniform_s/1, uniform_s/2,
+ uniform_real/0, uniform_real_s/1,
jump/0, jump/1,
normal/0, normal/2, normal_s/1, normal_s/3
]).
+%% Debug
+-export([make_float/3, float2str/1, bc64/1]).
+
-compile({inline, [exs64_next/1, exsplus_next/1,
exs1024_next/1, exs1024_calc/2,
exrop_next/1, exrop_next_s/2,
@@ -60,6 +64,10 @@
%% N i evaluated 3 times
(?BSL((Bits), (X), (N)) bor ((X) bsr ((Bits)-(N))))).
+-define(
+ BC(V, N),
+ bc((V), ?BIT((N) - 1), N)).
+
%%-define(TWO_POW_MINUS53, (math:pow(2, -53))).
-define(TWO_POW_MINUS53, 1.11022302462515657e-16).
@@ -84,14 +92,21 @@
%% The 'bits' field indicates how many bits the integer
%% returned from 'next' has got, i.e 'next' shall return
%% an random integer in the range 0..(2^Bits - 1).
-%% At least 53 bits is required for the floating point
-%% producing fallbacks. This field is only used when
-%% the 'uniform' or 'uniform_n' fields are not defined.
+%% At least 55 bits is required for the floating point
+%% producing fallbacks, but 56 bits would be more future proof.
%%
%% The fields 'next', 'uniform' and 'uniform_n'
-%% implement the algorithm. If 'uniform' or 'uinform_n'
+%% implement the algorithm. If 'uniform' or 'uniform_n'
%% is not present there is a fallback using 'next' and either
-%% 'bits' or the deprecated 'max'.
+%% 'bits' or the deprecated 'max'. The 'next' function
+%% must generate a word with at least 56 good random bits.
+%%
+%% The 'weak_low_bits' field indicate how many bits are of
+%% lesser quality and they will not be used by the floating point
+%% producing functions, nor by the range producing functions
+%% when more bits are needed, to avoid weak bits in the middle
+%% of the generated bits. The lowest bits from the range
+%% functions still have the generator's quality.
%%
-type alg_handler() ::
#{type := alg(),
@@ -148,11 +163,7 @@
%% For ranges larger than the algorithm bit size
uniform_range(Range, #{next:=Next, bits:=Bits} = Alg, R, V) ->
- WeakLowBits =
- case Alg of
- #{weak_low_bits:=WLB} -> WLB;
- #{} -> 0
- end,
+ WeakLowBits = maps:get(weak_low_bits, Alg, 0),
%% Maybe waste the lowest bit(s) when shifting in new bits
Shift = Bits - WeakLowBits,
ShiftMask = bnot ?MASK(WeakLowBits),
@@ -297,7 +308,7 @@ uniform_s({#{bits:=Bits, next:=Next} = Alg, R0}) ->
{(V bsr (Bits - 53)) * ?TWO_POW_MINUS53, {Alg, R1}};
uniform_s({#{max:=Max, next:=Next} = Alg, R0}) ->
{V, R1} = Next(R0),
- %% Old broken algorithm with non-uniform density
+ %% Old algorithm with non-uniform density
{V / (Max + 1), {Alg, R1}}.
@@ -317,7 +328,7 @@ uniform_s(N, {#{bits:=Bits, next:=Next} = Alg, R0})
?uniform_range(N, Alg, R1, V, MaxMinusN, I);
uniform_s(N, {#{max:=Max, next:=Next} = Alg, R0})
when is_integer(N), 1 =< N ->
- %% Old broken algorithm with skewed probability
+ %% Old algorithm with skewed probability
%% and gap in ranges > Max
{V, R1} = Next(R0),
if
@@ -328,6 +339,189 @@ uniform_s(N, {#{max:=Max, next:=Next} = Alg, R0})
{trunc(F * N) + 1, {Alg, R1}}
end.
+%% uniform_real/0: returns a random float X where 0.0 < X =< 1.0,
+%% updating the state in the process dictionary.
+
+-spec uniform_real() -> X :: float().
+uniform_real() ->
+ {X, Seed} = uniform_real_s(seed_get()),
+ _ = seed_put(Seed),
+ X.
+
+%% uniform_real_s/1: given a state, uniform_s/1
+%% returns a random float X where 0.0 < X =< 1.0,
+%% and a new state.
+%%
+%% This function does not use the same form of uniformity
+%% as the uniform_s/1 function.
+%%
+%% Instead, this function does not generate numbers with equal
+%% distance in the interval, but rather tries to keep all mantissa
+%% bits random also for small numbers, meaning that the distance
+%% between possible numbers decreases when the numbers
+%% approaches 0.0, as does the possibility for a particular
+%% number. Hence uniformity is preserved.
+%%
+%% To generate 56 bits at the time instead of 53 is actually
+%% a speed optimization since the probability to have to
+%% generate a second word decreases by 1/2 for every extra bit.
+%%
+%% This function generates normalized numbers, so the smallest number
+%% that can be generated is 2^-1022 with the distance 2^-1074
+%% to the next to smallest number, compared to 2^-53 for uniform_s/1.
+%%
+%% This concept of uniformity should work better for applications
+%% where you need to calculate 1.0/X or math:log(X) since those
+%% operations benefits from larger precision approaching 0.0,
+%% and that this function does not return 0.0 nor denormalized
+%% numbers very close to 0.0. The log() operation in The Box-Muller
+%% transformation for normal distribution is an example of this.
+%%
+%%-define(TWO_POW_MINUS55, (math:pow(2, -55))).
+%%-define(TWO_POW_MINUS110, (math:pow(2, -110))).
+%%-define(TWO_POW_MINUS55, 2.7755575615628914e-17).
+%%-define(TWO_POW_MINUS110, 7.7037197775489436e-34).
+%%
+-spec uniform_real_s(State :: state()) -> {X :: float(), NewState :: state()}.
+uniform_real_s({#{bits:=Bits, next:=Next} = Alg, R0}) ->
+ %% Generate a 56 bit number without using the weak low bits.
+ %%
+ %% Be sure to use only 53 bits when multiplying with
+ %% math:pow(2.0, -N) to avoid rounding which would make
+ %% "even" floats more probable than "odd".
+ %%
+ {V1, R1} = Next(R0),
+ M1 = V1 bsr (Bits - 56),
+ if
+ ?BIT(55) =< M1 ->
+ %% We have 56 bits - waste 3
+ {(M1 bsr 3) * math:pow(2.0, -53), {Alg, R1}};
+ ?BIT(54) =< M1 ->
+ %% We have 55 bits - waste 2
+ {(M1 bsr 2) * math:pow(2.0, -54), {Alg, R1}};
+ ?BIT(53) =< M1 ->
+ %% We have 54 bits - waste 1
+ {(M1 bsr 1) * math:pow(2.0, -55), {Alg, R1}};
+ ?BIT(52) =< M1 ->
+ %% We have 53 bits - use all
+ {M1 * math:pow(2.0, -56), {Alg, R1}};
+ true ->
+ %% Need more bits
+ {V2, R2} = Next(R1),
+ uniform_real_s(Alg, Next, M1, -56, R2, V2, Bits)
+ end;
+uniform_real_s({#{max:=_, next:=Next} = Alg, R0}) ->
+ %% Generate a 56 bit number.
+ %% Ignore the weak low bits for these old algorithms,
+ %% just produce something reasonable.
+ %%
+ %% Be sure to use only 53 bits when multiplying with
+ %% math:pow(2.0, -N) to avoid rounding which would make
+ %% "even" floats more probable than "odd".
+ %%
+ {V1, R1} = Next(R0),
+ M1 = ?MASK(56, V1),
+ if
+ ?BIT(55) =< M1 ->
+ %% We have 56 bits - waste 3
+ {(M1 bsr 3) * math:pow(2.0, -53), {Alg, R1}};
+ ?BIT(54) =< M1 ->
+ %% We have 55 bits - waste 2
+ {(M1 bsr 2) * math:pow(2.0, -54), {Alg, R1}};
+ ?BIT(53) =< M1 ->
+ %% We have 54 bits - waste 1
+ {(M1 bsr 1) * math:pow(2.0, -55), {Alg, R1}};
+ ?BIT(52) =< M1 ->
+ %% We have 53 bits - use all
+ {M1 * math:pow(2.0, -56), {Alg, R1}};
+ true ->
+ %% Need more bits
+ {V2, R2} = Next(R1),
+ uniform_real_s(Alg, Next, M1, -56, R2, V2, 56)
+ end.
+
+uniform_real_s(Alg, _Next, M0, -1064, R1, V1, Bits) -> % 19*56
+ %% This is a very theoretical bottom case.
+ %% The odds of getting here is about 2^-1008,
+ %% through a white box test case, or thanks to
+ %% a malfunctioning PRNG producing 18 56-bit zeros in a row.
+ %%
+ %% Fill up to 53 bits, we have at most 52
+ B0 = (53 - ?BC(M0, 52)), % Missing bits
+ {(((M0 bsl B0) bor (V1 bsr (Bits - B0))) * math:pow(2.0, -1064 - B0)),
+ {Alg, R1}};
+uniform_real_s(Alg, Next, M0, BitNo, R1, V1, Bits) ->
+ if
+ %% Optimize the most probable.
+ %% Fill up to 53 bits.
+ ?BIT(51) =< M0 ->
+ %% We have 52 bits in M0 - need 1
+ {(((M0 bsl 1) bor (V1 bsr (Bits - 1)))
+ * math:pow(2.0, BitNo - 1)),
+ {Alg, R1}};
+ ?BIT(50) =< M0 ->
+ %% We have 51 bits in M0 - need 2
+ {(((M0 bsl 2) bor (V1 bsr (Bits - 2)))
+ * math:pow(2.0, BitNo - 2)),
+ {Alg, R1}};
+ ?BIT(49) =< M0 ->
+ %% We have 50 bits in M0 - need 3
+ {(((M0 bsl 3) bor (V1 bsr (Bits - 3)))
+ * math:pow(2.0, BitNo - 3)),
+ {Alg, R1}};
+ M0 == 0 ->
+ M1 = V1 bsr (Bits - 56),
+ if
+ ?BIT(55) =< M1 ->
+ %% We have 56 bits - waste 3
+ {(M1 bsr 3) * math:pow(2.0, BitNo - 53), {Alg, R1}};
+ ?BIT(54) =< M1 ->
+ %% We have 55 bits - waste 2
+ {(M1 bsr 2) * math:pow(2.0, BitNo - 54), {Alg, R1}};
+ ?BIT(53) =< M1 ->
+ %% We have 54 bits - waste 1
+ {(M1 bsr 1) * math:pow(2.0, BitNo - 55), {Alg, R1}};
+ ?BIT(52) =< M1 ->
+ %% We have 53 bits - use all
+ {M1 * math:pow(2.0, BitNo - 56), {Alg, R1}};
+ BitNo =:= -1008 ->
+ %% Endgame
+ %% For the last round we can not have 14 zeros or more
+ %% at the top of M1 because then we will underflow,
+ %% so we need at least 43 bits
+ if
+ ?BIT(42) =< M1 ->
+ %% We have 43 bits - get the last bits
+ uniform_real_s(Alg, Next, M1, BitNo - 56, R1);
+ true ->
+ %% Would underflow 2^-1022 - start all over
+ %%
+ %% We could just crash here since the odds for
+ %% the PRNG being broken is much higher than
+ %% for a good PRNG generating this many zeros
+ %% in a row. Maybe we should write an error
+ %% report or call this a system limit...?
+ uniform_real_s({Alg, R1})
+ end;
+ true ->
+ %% Need more bits
+ uniform_real_s(Alg, Next, M1, BitNo - 56, R1)
+ end;
+ true ->
+ %% Fill up to 53 bits
+ B0 = 53 - ?BC(M0, 49), % Number of bits we need to append
+ {(((M0 bsl B0) bor (V1 bsr (Bits - B0)))
+ * math:pow(2.0, BitNo - B0)),
+ {Alg, R1}}
+ end.
+%%
+uniform_real_s(#{bits:=Bits} = Alg, Next, M0, BitNo, R0) ->
+ {V1, R1} = Next(R0),
+ uniform_real_s(Alg, Next, M0, BitNo, R1, V1, Bits);
+uniform_real_s(#{max:=_} = Alg, Next, M0, BitNo, R0) ->
+ {V1, R1} = Next(R0),
+ uniform_real_s(Alg, Next, M0, BitNo, R1, ?MASK(56, V1), 56).
+
%% jump/1: given a state, jump/1
%% returns a new state which is equivalent to that
%% after a large number of call defined for each algorithm.
@@ -1025,3 +1219,42 @@ normal_fi(Indx) ->
1.0214971439701471e-02,8.6165827693987316e-03,7.0508754713732268e-03,
5.5224032992509968e-03,4.0379725933630305e-03,2.6090727461021627e-03,
1.2602859304985975e-03}).
+
+%%%bitcount64(0) -> 0;
+%%%bitcount64(V) -> 1 + bitcount(V, 64).
+%%%
+%%%-define(
+%%% BITCOUNT(V, N),
+%%% bitcount(V, N) ->
+%%% if
+%%% (1 bsl ((N) bsr 1)) =< (V) ->
+%%% ((N) bsr 1) + bitcount((V) bsr ((N) bsr 1), ((N) bsr 1));
+%%% true ->
+%%% bitcount((V), ((N) bsr 1))
+%%% end).
+%%%?BITCOUNT(V, 64);
+%%%?BITCOUNT(V, 32);
+%%%?BITCOUNT(V, 16);
+%%%?BITCOUNT(V, 8);
+%%%?BITCOUNT(V, 4);
+%%%?BITCOUNT(V, 2);
+%%%bitcount(_, 1) -> 0.
+
+bc64(V) -> ?BC(V, 64).
+
+%% Linear from high bit - higher probability first gives faster execution
+bc(V, B, N) when B =< V -> N;
+bc(V, B, N) -> bc(V, B bsr 1, N - 1).
+
+make_float(S, E, M) ->
+ <<F/float>> = <<S:1, E:11, M:52>>,
+ F.
+
+float2str(N) ->
+ <<S:1, E:11, M:52>> = <<(float(N))/float>>,
+ lists:flatten(
+ io_lib:format(
+ "~c~c.~13.16.0bE~b",
+ [case S of 1 -> $-; 0 -> $+ end,
+ case E of 0 -> $0; _ -> $1 end,
+ M, E - 16#3ff])).
diff --git a/lib/stdlib/test/rand_SUITE.erl b/lib/stdlib/test/rand_SUITE.erl
index f69d42551e..ef4f9faad9 100644
--- a/lib/stdlib/test/rand_SUITE.erl
+++ b/lib/stdlib/test/rand_SUITE.erl
@@ -29,11 +29,14 @@
basic_stats_uniform_1/1, basic_stats_uniform_2/1,
basic_stats_standard_normal/1,
basic_stats_normal/1,
+ uniform_real_conv/1,
plugin/1, measure/1,
reference_jump_state/1, reference_jump_procdict/1]).
-export([test/0, gen/1]).
+-export([uniform_real_gen/1, uniform_gen/2]).
+
-include_lib("common_test/include/ct.hrl").
-define(LOOP, 1000000).
@@ -46,7 +49,7 @@ all() ->
[seed, interval_int, interval_float,
api_eq,
reference,
- {group, basic_stats},
+ {group, basic_stats}, uniform_real_conv,
plugin, measure,
{group, reference_jump}
].
@@ -101,7 +104,7 @@ seed_1(Alg) ->
_ = rand:uniform(),
S00 = get(rand_seed),
erase(),
- _ = rand:uniform(),
+ _ = rand:uniform_real(),
false = S00 =:= get(rand_seed), %% hopefully
%% Choosing algo and seed
@@ -228,11 +231,13 @@ interval_float(Config) when is_list(Config) ->
interval_float_1(0) -> ok;
interval_float_1(N) ->
X = rand:uniform(),
+ Y = rand:uniform_real(),
if
- 0.0 =< X, X < 1.0 ->
+ 0.0 =< X, X < 1.0, 0.0 < Y, Y < 1.0 ->
ok;
true ->
- io:format("X=~p 0=<~p<1.0~n", [X,X]),
+ io:format("X=~p 0.0=<~p<1.0~n", [X,X]),
+ io:format("Y=~p 0.0<~p<1.0~n", [Y,Y]),
exit({X, rand:export_seed()})
end,
interval_float_1(N-1).
@@ -334,7 +339,13 @@ basic_stats_normal(Config) when is_list(Config) ->
IntendedMeanVariancePairs).
basic_uniform_1(N, S0, Sum, A0) when N > 0 ->
- {X,S} = rand:uniform_s(S0),
+ {X,S} =
+ case N band 1 of
+ 0 ->
+ rand:uniform_s(S0);
+ 1 ->
+ rand:uniform_real_s(S0)
+ end,
I = trunc(X*100),
A = array:set(I, 1+array:get(I,A0), A0),
basic_uniform_1(N-1, S, Sum+X, A);
@@ -400,6 +411,137 @@ normal_s(Mean, Variance, State0) ->
rand:normal_s(Mean, Variance, State0).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% White box test of the conversion to float
+
+uniform_real_conv(Config) when is_list(Config) ->
+ [begin
+%% ct:pal("~13.16.0bx~3.16.0b: ~p~n", [M,E,Gen]),
+ uniform_real_conv_check(M, E, Gen)
+ end || {M, E, Gen} <- uniform_real_conv_data()],
+ uniform_real_scan(0),
+ uniform_real_scan(3).
+
+uniform_real_conv_data() ->
+ [{16#fffffffffffff, -1, [16#3ffffffffffffff]},
+ {16#fffffffffffff, -1, [16#3ffffffffffffe0]},
+ {16#ffffffffffffe, -1, [16#3ffffffffffffdf]},
+ %%
+ {16#0000000000000, -1, [16#200000000000000]},
+ {16#fffffffffffff, -2, [16#1ffffffffffffff]},
+ {16#fffffffffffff, -2, [16#1fffffffffffff0]},
+ {16#ffffffffffffe, -2, [16#1ffffffffffffef]},
+ %%
+ {16#0000000000000, -2, [16#100000000000000]},
+ {16#fffffffffffff, -3, [16#0ffffffffffffff]},
+ {16#fffffffffffff, -3, [16#0fffffffffffff8]},
+ {16#ffffffffffffe, -3, [16#0fffffffffffff7]},
+ %%
+ {16#0000000000000, -3, [16#080000000000000]},
+ {16#fffffffffffff, -4, [16#07fffffffffffff]},
+ {16#fffffffffffff, -4, [16#07ffffffffffffc]},
+ {16#ffffffffffffe, -4, [16#07ffffffffffffb]},
+ %%
+ {16#0000000000000, -4, [16#040000000000000]},
+ {16#fffffffffffff, -5, [16#03fffffffffffff,16#3ffffffffffffff]},
+ {16#fffffffffffff, -5, [16#03ffffffffffffe,16#200000000000000]},
+ {16#ffffffffffffe, -5, [16#03fffffffffffff,16#1ffffffffffffff]},
+ {16#ffffffffffffe, -5, [16#03fffffffffffff,16#100000000000000]},
+ %%
+ {16#0000000000001, -56, [16#000000000000007,16#00000000000007f]},
+ {16#0000000000001, -56, [16#000000000000004,16#000000000000040]},
+ {16#0000000000000, -57, [16#000000000000003,16#20000000000001f]},
+ {16#0000000000000, -57, [16#000000000000000,16#200000000000000]},
+ {16#fffffffffffff, -58, [16#000000000000003,16#1ffffffffffffff]},
+ {16#fffffffffffff, -58, [16#000000000000000,16#1fffffffffffff0]},
+ {16#ffffffffffffe, -58, [16#000000000000000,16#1ffffffffffffef]},
+ {16#ffffffffffffe, -58, [16#000000000000000,16#1ffffffffffffe0]},
+ %%
+ {16#0000000000000, -58, [16#000000000000000,16#10000000000000f]},
+ {16#0000000000000, -58, [16#000000000000000,16#100000000000000]},
+ {2#11001100000000000000000000000000000000000011000000011, % 53 bits
+ -1022,
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, % 18 zeros
+ 2#1100110000000000000000000000000000000000001 bsl 2, % 43 bits
+ 2#1000000011 bsl (56-10+2)]}, % 10 bits
+ {0, -1, % 0.5 after retry
+ [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, % 18 zeros
+ 2#111111111111111111111111111111111111111111 bsl 2, % 42 bits - retry
+ 16#200000000000003]}]. % 0.5
+
+-define(UNIFORM_REAL_SCAN_PATTERN, (16#19000000000009)). % 53 bits
+-define(UNIFORM_REAL_SCAN_NUMBER, (1021)).
+
+uniform_real_scan_template(K) ->
+ <<0:?UNIFORM_REAL_SCAN_NUMBER,
+ ?UNIFORM_REAL_SCAN_PATTERN:53,K:2,0:1>>.
+
+uniform_real_scan(K) ->
+ Templ = uniform_real_scan_template(K),
+ N = ?UNIFORM_REAL_SCAN_NUMBER,
+ uniform_real_scan(Templ, N, K).
+
+uniform_real_scan(Templ, N, K) when 0 =< N ->
+ <<_:N/bits,T/bits>> = Templ,
+ Data = uniform_real_scan_data(T, K),
+ uniform_real_conv_check(
+ ?UNIFORM_REAL_SCAN_PATTERN, N - 1 - ?UNIFORM_REAL_SCAN_NUMBER, Data),
+ uniform_real_scan(Templ, N - 1, K);
+uniform_real_scan(_, _, _) ->
+ ok.
+
+uniform_real_scan_data(Templ, K) ->
+ case Templ of
+ <<X:56, T/bits>> ->
+ B = rand:bc64(X),
+ [(X bsl 2) bor K |
+ if
+ 53 =< B ->
+ [];
+ true ->
+ uniform_real_scan_data(T, K)
+ end];
+ _ ->
+ <<X:56, _/bits>> = <<Templ/bits, 0:56>>,
+ [(X bsl 2) bor K]
+ end.
+
+uniform_real_conv_check(M, E, Gen) ->
+ <<F/float>> = <<0:1, (E + 16#3ff):11, M:52>>,
+ try uniform_real_gen(Gen) of
+ F -> F;
+ FF ->
+ ct:pal(
+ "~s =/= ~s: ~s~n",
+ [rand:float2str(FF), rand:float2str(F),
+ [["16#",integer_to_list(G,16),$\s]||G<-Gen]]),
+ ct:fail({neq, FF, F})
+ catch
+ Error:Reason ->
+ ct:pal(
+ "~w:~p ~s: ~s~n",
+ [Error, Reason, rand:float2str(F),
+ [["16#",integer_to_list(G,16),$\s]||G<-Gen]]),
+ ct:fail({Error, Reason, F, erlang:get_stacktrace()})
+ end.
+
+
+uniform_real_gen(Gen) ->
+ State = rand_state(Gen),
+ {F, {#{type := rand_SUITE_list},[]}} = rand:uniform_real_s(State),
+ F.
+
+uniform_gen(Range, Gen) ->
+ State = rand_state(Gen),
+ {N, {#{type := rand_SUITE_list},[]}} = rand:uniform_s(Range, State),
+ N.
+
+%% Loaded dice for white box tests
+rand_state(Gen) ->
+ {#{type => rand_SUITE_list, bits => 58, weak_low_bits => 1,
+ next => fun ([H|T]) -> {H, T} end},
+ Gen}.
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Test that the user can write algorithms.
plugin(Config) when is_list(Config) ->
@@ -520,6 +662,21 @@ do_measure(_Config) ->
end,
Algs),
%%
+ ct:pal("~nRNG uniform integer half range performance~n",[]),
+ _ =
+ measure_1(
+ fun (State) -> half_range(State) end,
+ fun (State, Range, Mod) ->
+ measure_loop(
+ fun (St0) ->
+ ?CHECK_UNIFORM_RANGE(
+ Mod:uniform_s(Range, St0), Range,
+ X, St1)
+ end,
+ State)
+ end,
+ Algs),
+ %%
ct:pal("~nRNG uniform integer half range + 1 performance~n",[]),
_ =
measure_1(
@@ -630,7 +787,8 @@ do_measure(_Config) ->
Algs),
%%
ct:pal("~nRNG uniform float performance~n",[]),
- _ = measure_1(
+ _ =
+ measure_1(
fun (_) -> 0 end,
fun (State, _, Mod) ->
measure_loop(
@@ -641,8 +799,22 @@ do_measure(_Config) ->
end,
Algs),
%%
+ ct:pal("~nRNG uniform_real float performance~n",[]),
+ _ =
+ measure_1(
+ fun (_) -> 0 end,
+ fun (State, _, Mod) ->
+ measure_loop(
+ fun (St0) ->
+ ?CHECK_UNIFORM(Mod:uniform_real_s(St0), X, St)
+ end,
+ State)
+ end,
+ Algs),
+ %%
ct:pal("~nRNG normal float performance~n",[]),
- _ = measure_1(
+ [TMarkNormalFloat|_] =
+ measure_1(
fun (_) -> 0 end,
fun (State, _, Mod) ->
measure_loop(
@@ -652,10 +824,36 @@ do_measure(_Config) ->
State)
end,
Algs),
+ %% Just for fun try an implementation of the Box-Muller
+ %% transformation for creating normal distribution floats
+ %% to compare with our Ziggurat implementation.
+ %% Generates two numbers per call that we add so they
+ %% will not be optimized away. Hence the benchmark time
+ %% is twice what it should be.
+ TwoPi = 2 * math:pi(),
+ _ =
+ measure_1(
+ fun (_) -> 0 end,
+ fun (State, _, Mod) ->
+ measure_loop(
+ fun (State0) ->
+ {U1, State1} = Mod:uniform_real_s(State0),
+ {U2, State2} = Mod:uniform_s(State1),
+ R = math:sqrt(-2.0 * math:log(U1)),
+ T = TwoPi * U2,
+ Z0 = R * math:cos(T),
+ Z1 = R * math:sin(T),
+ ?CHECK_NORMAL({Z0 + Z1, State2}, X, State3)
+ end,
+ State)
+ end,
+ exrop, TMarkNormalFloat),
ok.
+-define(LOOP_MEASURE, (?LOOP div 5)).
+
measure_loop(Fun, State) ->
- measure_loop(Fun, State, ?LOOP).
+ measure_loop(Fun, State, ?LOOP_MEASURE).
%%
measure_loop(Fun, State, N) when 0 < N ->
measure_loop(Fun, Fun(State), N-1);
@@ -693,7 +891,8 @@ measure_1(RangeFun, Fun, Alg, TMark) ->
end,
io:format(
"~.12w: ~p ns ~p% [16#~.16b]~n",
- [Alg, (Time * 1000 + 500) div ?LOOP, Percent, Range]),
+ [Alg, (Time * 1000 + 500) div ?LOOP_MEASURE,
+ Percent, Range]),
Parent ! {self(), Time},
normal
end),