%%
%% %CopyrightBegin%
%%
%% Copyright Ericsson AB 2000-2018. All Rights Reserved.
%%
%% Licensed under the Apache License, Version 2.0 (the "License");
%% you may not use this file except in compliance with the License.
%% You may obtain a copy of the License at
%%
%% http://www.apache.org/licenses/LICENSE-2.0
%%
%% Unless required by applicable law or agreed to in writing, software
%% distributed under the License is distributed on an "AS IS" BASIS,
%% WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
%% See the License for the specific language governing permissions and
%% limitations under the License.
%%
%% %CopyrightEnd%
-module(rand_SUITE).
-compile({nowarn_deprecated_function,[{random,seed,1},
{random,uniform_s,1},
{random,uniform_s,2}]}).
-export([all/0, suite/0, groups/0, group/1]).
-export([interval_int/1, interval_float/1, seed/1,
api_eq/1, reference/1,
basic_stats_uniform_1/1, basic_stats_uniform_2/1,
basic_stats_standard_normal/1,
basic_stats_normal/1,
stats_standard_normal_box_muller/1,
stats_standard_normal_box_muller_2/1,
stats_standard_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).
suite() ->
[{ct_hooks,[ts_install_cth]},
{timetrap,{minutes,3}}].
all() ->
[seed, interval_int, interval_float,
api_eq,
reference,
{group, basic_stats},
{group, distr_stats},
uniform_real_conv,
plugin, measure,
{group, reference_jump}
].
groups() ->
[{basic_stats, [parallel],
[basic_stats_uniform_1, basic_stats_uniform_2,
basic_stats_standard_normal]},
{distr_stats, [parallel],
[stats_standard_normal_box_muller,
stats_standard_normal_box_muller_2,
stats_standard_normal]},
{reference_jump, [parallel],
[reference_jump_state, reference_jump_procdict]}].
group(basic_stats) ->
%% valgrind needs a lot of time
[{timetrap,{minutes,10}}];
group(distr_stats) ->
%% valgrind needs a lot of time
[{timetrap,{minutes,10}}];
group(reference_jump) ->
%% valgrind needs a lot of time
[{timetrap,{minutes,10}}].
%% A simple helper to test without test_server during dev
test() ->
Tests = all(),
lists:foreach(
fun (Test) ->
try
ok = ?MODULE:Test([]),
io:format("~p: ok~n", [Test])
catch _:Reason:Stacktrace ->
io:format("Failed: ~p: ~p ~p~n",
[Test, Reason, Stacktrace])
end
end, Tests).
algs() ->
[exrop, exsp, exs1024s, exs64, exsplus, exs1024].
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Test that seed and seed_s and export_seed/0 is working.
seed(Config) when is_list(Config) ->
Algs = algs(),
Test = fun(Alg) ->
try seed_1(Alg)
catch _:Reason:Stacktrace ->
ct:fail({Alg, Reason, Stacktrace})
end
end,
[Test(Alg) || Alg <- Algs],
ok.
seed_1(Alg) ->
%% Check that uniform seeds automatically,
_ = rand:uniform(),
S00 = get(rand_seed),
erase(),
_ = rand:uniform_real(),
false = S00 =:= get(rand_seed), %% hopefully
%% Choosing algo and seed
S0 = rand:seed(Alg, {0, 0, 0}),
%% Check that (documented?) process_dict variable is correct
S0 = get(rand_seed),
S0 = rand:seed_s(Alg, {0, 0, 0}),
%% Check that process_dict should not be used for seed_s functionality
_ = rand:seed_s(Alg, {1, 0, 0}),
S0 = get(rand_seed),
%% Test export
ES0 = rand:export_seed(),
ES0 = rand:export_seed_s(S0),
S0 = rand:seed(ES0),
S0 = rand:seed_s(ES0),
%% seed/1 calls should be unique
S1 = rand:seed(Alg),
false = (S1 =:= rand:seed_s(Alg)),
%% Negative integers works
_ = rand:seed_s(Alg, {-1,-1,-1}),
%% Check that export_seed/1 returns 'undefined' if there is no seed
erase(rand_seed),
undefined = rand:export_seed(),
%% Other term do not work
{'EXIT', _} = (catch rand:seed_s(foobar, os:timestamp())),
{'EXIT', _} = (catch rand:seed_s(Alg, {asd, 1, 1})),
{'EXIT', _} = (catch rand:seed_s(Alg, {0, 234.1234, 1})),
{'EXIT', _} = (catch rand:seed_s(Alg, {0, 234, [1, 123, 123]})),
ok.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Check that both APIs are consistent with each other.
api_eq(_Config) ->
Algs = algs(),
Small = fun(Alg) ->
Seed = rand:seed(Alg),
io:format("Seed ~p~n",[rand:export_seed_s(Seed)]),
api_eq_1(Seed)
end,
_ = [Small(Alg) || Alg <- Algs],
ok.
api_eq_1(S00) ->
Check = fun(_, Seed) ->
{V0, S0} = rand:uniform_s(Seed),
V0 = rand:uniform(),
{V1, S1} = rand:uniform_s(1000000, S0),
V1 = rand:uniform(1000000),
{V2, S2} = rand:normal_s(S1),
V2 = rand:normal(),
S2
end,
S1 = lists:foldl(Check, S00, lists:seq(1, 200)),
S1 = get(rand_seed),
{V0, S2} = rand:uniform_s(S1),
V0 = rand:uniform(),
S2 = get(rand_seed),
Exported = rand:export_seed(),
Exported = rand:export_seed_s(S2),
S3 = lists:foldl(Check, S2, lists:seq(1, 200)),
S3 = get(rand_seed),
S4 = lists:foldl(Check, S3, lists:seq(1, 200)),
S4 = get(rand_seed),
%% Verify that we do not have loops
false = S1 =:= S2,
false = S2 =:= S3,
false = S3 =:= S4,
S2 = rand:seed(Exported),
S3 = lists:foldl(Check, S2, lists:seq(1, 200)),
ok.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Check that uniform/1 returns values within the proper interval.
interval_int(Config) when is_list(Config) ->
Algs = algs(),
Small = fun(Alg) ->
Seed = rand:seed(Alg),
io:format("Seed ~p~n",[rand:export_seed_s(Seed)]),
Max = interval_int_1(100000, 7, 0),
Max =:= 7 orelse exit({7, Alg, Max})
end,
_ = [Small(Alg) || Alg <- Algs],
%% Test large integers
Large = fun(Alg) ->
Seed = rand:seed(Alg),
io:format("Seed ~p~n",[rand:export_seed_s(Seed)]),
Max = interval_int_1(100000, 1 bsl 128, 0),
Max > 1 bsl 64 orelse exit({large, Alg, Max})
end,
[Large(Alg) || Alg <- Algs],
ok.
interval_int_1(0, _, Max) -> Max;
interval_int_1(N, Top, Max) ->
X = rand:uniform(Top),
if
0 < X, X =< Top ->
ok;
true ->
io:format("X=~p Top=~p 0<~p<~p~n", [X,Top,X,Top]),
exit({X, rand:export_seed()})
end,
interval_int_1(N-1, Top, max(X, Max)).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Check that uniform/0 returns values within the proper interval.
interval_float(Config) when is_list(Config) ->
Algs = algs(),
Test = fun(Alg) ->
_ = rand:seed(Alg),
interval_float_1(100000)
end,
[Test(Alg) || Alg <- Algs],
ok.
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 < Y, Y < 1.0 ->
ok;
true ->
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).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Check if each algorithm generates the proper sequence.
reference(Config) when is_list(Config) ->
[reference_1(Alg) || Alg <- algs()],
ok.
reference_1(Alg) ->
Refval = reference_val(Alg),
Testval = gen(Alg),
case Refval =:= Testval of
true -> ok;
false when Refval =:= not_implemented ->
exit({not_implemented,Alg});
false ->
io:format("Failed: ~p~n",[Alg]),
io:format("Length ~p ~p~n",[length(Refval), length(Testval)]),
io:format("Head ~p ~p~n",[hd(Refval), hd(Testval)]),
exit(wrong_value)
end.
gen(Algo) ->
State =
case Algo of
exs64 -> %% Printed with orig 'C' code and this seed
rand:seed_s({exs64, 12345678});
_ when Algo =:= exsplus; Algo =:= exsp; Algo =:= exrop ->
%% Printed with orig 'C' code and this seed
rand:seed_s({Algo, [12345678|12345678]});
_ when Algo =:= exs1024; Algo =:= exs1024s ->
%% Printed with orig 'C' code and this seed
rand:seed_s({Algo, {lists:duplicate(16, 12345678), []}});
_ ->
rand:seed(Algo, {100, 200, 300})
end,
Max = range(State),
gen(?LOOP, State, Max, []).
gen(N, State0, Max, Acc) when N > 0 ->
{Random, State} = rand:uniform_s(Max, State0),
case N rem (?LOOP div 100) of
0 -> gen(N-1, State, Max, [Random|Acc]);
_ -> gen(N-1, State, Max, Acc)
end;
gen(_, _, _, Acc) -> lists:reverse(Acc).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% This just tests the basics so we have not made any serious errors
%% when making the conversion from the original algorithms.
%% The algorithms must have good properties to begin with
%%
%% Check that the algorithms generate sound values.
basic_stats_uniform_1(Config) when is_list(Config) ->
ct:timetrap({minutes,15}), %% valgrind needs a lot of time
[basic_uniform_1(?LOOP, rand:seed_s(Alg), 0.0, array:new([{default, 0}]))
|| Alg <- algs()],
ok.
basic_stats_uniform_2(Config) when is_list(Config) ->
ct:timetrap({minutes,15}), %% valgrind needs a lot of time
[basic_uniform_2(?LOOP, rand:seed_s(Alg), 0, array:new([{default, 0}]))
|| Alg <- algs()],
ok.
basic_stats_standard_normal(Config) when is_list(Config) ->
ct:timetrap({minutes,6}), %% valgrind needs a lot of time
io:format("Testing standard normal~n",[]),
IntendedMean = 0,
IntendedVariance = 1,
[basic_normal_1(?LOOP, IntendedMean, IntendedVariance,
rand:seed_s(Alg), 0, 0)
|| Alg <- algs()],
ok.
basic_stats_normal(Config) when is_list(Config) ->
IntendedMeans = [-1.0e6, -50, -math:pi(), -math:exp(-1),
0.12345678, math:exp(1), 100, 1.0e6],
IntendedVariances = [1.0e-6, math:exp(-1), 1, math:pi(), 1.0e6],
IntendedMeanVariancePairs =
[{Mean, Variance} || Mean <- IntendedMeans,
Variance <- IntendedVariances],
ct:timetrap({minutes, 6 * length(IntendedMeanVariancePairs)}), %% valgrind needs a lot of time
lists:foreach(
fun ({IntendedMean, IntendedVariance}) ->
ct:pal(
"Testing normal(~.2f, ~.2f)~n",
[float(IntendedMean), float(IntendedVariance)]),
[basic_normal_1(?LOOP, IntendedMean, IntendedVariance,
rand:seed_s(Alg), 0, 0)
|| Alg <- algs()]
end,
IntendedMeanVariancePairs).
basic_uniform_1(N, S0, Sum, A0) when N > 0 ->
{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);
basic_uniform_1(0, {#{type:=Alg}, _}, Sum, A) ->
AverN = Sum / ?LOOP,
io:format("~.12w: Average: ~.4f~n", [Alg, AverN]),
Counters = array:to_list(A),
Min = lists:min(Counters),
Max = lists:max(Counters),
io:format("~.12w: Min: ~p Max: ~p~n", [Alg, Min, Max]),
%% Verify that the basic statistics are ok
%% be gentle we don't want to see to many failing tests
abs(0.5 - AverN) < 0.005 orelse ct:fail({average, Alg, AverN}),
abs(?LOOP div 100 - Min) < 1000 orelse ct:fail({min, Alg, Min}),
abs(?LOOP div 100 - Max) < 1000 orelse ct:fail({max, Alg, Max}),
ok.
basic_uniform_2(N, S0, Sum, A0) when N > 0 ->
{X,S} = rand:uniform_s(100, S0),
A = array:set(X-1, 1+array:get(X-1,A0), A0),
basic_uniform_2(N-1, S, Sum+X, A);
basic_uniform_2(0, {#{type:=Alg}, _}, Sum, A) ->
AverN = Sum / ?LOOP,
io:format("~.12w: Average: ~.4f~n", [Alg, AverN]),
Counters = tl(array:to_list(A)),
Min = lists:min(Counters),
Max = lists:max(Counters),
io:format("~.12w: Min: ~p Max: ~p~n", [Alg, Min, Max]),
%% Verify that the basic statistics are ok
%% be gentle we don't want to see to many failing tests
abs(50.5 - AverN) < 0.5 orelse ct:fail({average, Alg, AverN}),
abs(?LOOP div 100 - Min) < 1000 orelse ct:fail({min, Alg, Min}),
abs(?LOOP div 100 - Max) < 1000 orelse ct:fail({max, Alg, Max}),
ok.
basic_normal_1(N, IntendedMean, IntendedVariance, S0, StandardSum, StandardSq) when N > 0 ->
{X,S} = normal_s(IntendedMean, IntendedVariance, S0),
% We now shape X into a standard normal distribution (in case it wasn't already)
% in order to minimise the accumulated error on Sum / SumSq;
% otherwise said error would prevent us of making a fair judgment on
% the overall distribution when targeting large means and variances.
StandardX = (X - IntendedMean) / math:sqrt(IntendedVariance),
basic_normal_1(N-1, IntendedMean, IntendedVariance, S,
StandardX+StandardSum, StandardX*StandardX+StandardSq);
basic_normal_1(0, _IntendedMean, _IntendedVariance, {#{type:=Alg}, _}, StandardSum, StandardSumSq) ->
StandardMean = StandardSum / ?LOOP,
StandardVariance = (StandardSumSq - (StandardSum*StandardSum/?LOOP))/(?LOOP - 1),
StandardStdDev = math:sqrt(StandardVariance),
io:format("~.12w: Standardised Average: ~7.4f, Standardised StdDev ~6.4f~n",
[Alg, StandardMean, StandardStdDev]),
%% Verify that the basic statistics are ok
%% be gentle we don't want to see to many failing tests
abs(StandardMean) < 0.005 orelse ct:fail({average, Alg, StandardMean}),
abs(StandardStdDev - 1.0) < 0.005 orelse ct:fail({stddev, Alg, StandardStdDev}),
ok.
normal_s(Mean, Variance, State0) when Mean == 0, Variance == 1 ->
% Make sure we're also testing the standard normal interface
rand:normal_s(State0);
normal_s(Mean, Variance, State0) ->
rand:normal_s(Mean, Variance, State0).
-dialyzer({no_improper_lists, stats_standard_normal_box_muller/1}).
stats_standard_normal_box_muller(Config) when is_list(Config) ->
try math:erfc(1.0) of
_ ->
TwoPi = 2.0 * math:pi(),
NormalS =
fun
([S0]) ->
{U1, S1} = rand:uniform_real_s(S0),
R = math:sqrt(-2.0 * math:log(U1)),
{U2, S2} = rand:uniform_s(S1),
T = TwoPi * U2,
Z0 = R * math:cos(T),
Z1 = R * math:sin(T),
{Z0, [S2|Z1]};
([S|Z]) ->
{Z, [S]}
end,
State = [rand:seed(exrop)],
stats_standard_normal(NormalS, State, 3)
catch error:_ ->
{skip, "math:erfc/1 not supported"}
end.
-dialyzer({no_improper_lists, stats_standard_normal_box_muller_2/1}).
stats_standard_normal_box_muller_2(Config) when is_list(Config) ->
try math:erfc(1.0) of
_ ->
TwoPi = 2.0 * math:pi(),
NormalS =
fun
([S0]) ->
{U0, S1} = rand:uniform_s(S0),
U1 = 1.0 - U0,
R = math:sqrt(-2.0 * math:log(U1)),
{U2, S2} = rand:uniform_s(S1),
T = TwoPi * U2,
Z0 = R * math:cos(T),
Z1 = R * math:sin(T),
{Z0, [S2|Z1]};
([S|Z]) ->
{Z, [S]}
end,
State = [rand:seed(exrop)],
stats_standard_normal(NormalS, State, 3)
catch error:_ ->
{skip, "math:erfc/1 not supported"}
end.
stats_standard_normal(Config) when is_list(Config) ->
Retries = 7,
try math:erfc(1.0) of
_ ->
stats_standard_normal(
fun rand:normal_s/1, rand:seed_s(exrop), Retries)
catch error:_ ->
{skip, "math:erfc/1 not supported"}
end.
%%
stats_standard_normal(Fun, S, Retries) ->
%%%
%%% ct config:
%%% {rand_SUITE, [{stats_standard_normal,[{seconds, 8}, {std_devs, 4.0}]}]}.
%%%
Seconds = ct:get_config({?MODULE, ?FUNCTION_NAME, seconds}, 8),
StdDevs =
ct:get_config(
{?MODULE, ?FUNCTION_NAME, std_devs},
4.0), % probability erfc(4.0/sqrt(2)) (1/15787) to fail a bucket
%%%
ct:timetrap({seconds, Seconds + 120}),
%% Buckets is chosen to get a range where the the probability to land
%% in the top catch-all bucket is not vanishingly low, but with
%% these values it is about 1/25 of the probability for the low bucket
%% (closest to 0).
%%
%% Rounds is calculated so the expected value for the low
%% bucket will be at least TargetHits.
%%
InvDelta = 512,
Buckets = 4 * InvDelta, % 4 std devs range
TargetHits = 1024,
Sqrt2 = math:sqrt(2.0),
W = InvDelta * Sqrt2,
P0 = math:erf(1 / W),
Rounds = TargetHits * ceil(1.0 / P0),
Histogram = array:new({default, 0}),
ct:pal(
"Running standard normal test against ~w std devs for ~w seconds...",
[StdDevs, Seconds]),
StopTime = erlang:monotonic_time(second) + Seconds,
{PositiveHistogram, NegativeHistogram, Outlier, TotalRounds, NewS} =
stats_standard_normal(
InvDelta, Buckets, Histogram, Histogram, 0.0,
Fun, S, Rounds, StopTime, Rounds, 0),
Precision = math:sqrt(TotalRounds * P0) / StdDevs,
TopP = math:erfc(Buckets / W),
TopPrecision = math:sqrt(TotalRounds * TopP) / StdDevs,
OutlierProbability = math:erfc(Outlier / Sqrt2) * TotalRounds,
InvOP = 1.0 / OutlierProbability,
ct:pal(
"Total rounds: ~w, tolerance: 1/~.2f..1/~.2f, "
"outlier: ~.2f, probability 1/~.2f.",
[TotalRounds, Precision, TopPrecision, Outlier, InvOP]),
case
{bucket_error, TotalRounds,
check_histogram(
W, TotalRounds, StdDevs, PositiveHistogram, Buckets),
check_histogram(
W, TotalRounds, StdDevs, NegativeHistogram, Buckets)}
of
{_, _, [], []} when InvOP < 100 ->
{comment, {tp, TopPrecision, op, InvOP}};
{_, _, [], []} ->
%% If the probability for getting this Outlier is lower than
%% 1/100, then this is fishy!
stats_standard_normal(
Fun, NewS, Retries, {outlier_fishy, InvOP});
BucketErrors ->
stats_standard_normal(
Fun, NewS, Retries, BucketErrors)
end.
%%
stats_standard_normal(Fun, S, Retries, Failure) ->
case Retries - 1 of
0 ->
ct:fail(Failure);
NewRetries ->
ct:pal("Retry due to TC glitch: ~p", [Failure]),
stats_standard_normal(Fun, S, NewRetries)
end.
%%
stats_standard_normal(
InvDelta, Buckets, PositiveHistogram, NegativeHistogram, Outlier,
Fun, S, 0, StopTime, Rounds, TotalRounds) ->
case erlang:monotonic_time(second) of
Now when Now < StopTime ->
stats_standard_normal(
InvDelta, Buckets,
PositiveHistogram, NegativeHistogram, Outlier,
Fun, S, Rounds, StopTime, Rounds, TotalRounds + Rounds);
_ ->
{PositiveHistogram, NegativeHistogram,
Outlier, TotalRounds + Rounds, S}
end;
stats_standard_normal(
InvDelta, Buckets, PositiveHistogram, NegativeHistogram, Outlier,
Fun, S, Count, StopTime, Rounds, TotalRounds) ->
case Fun(S) of
{X, NewS} when 0.0 =< X ->
Bucket = min(Buckets, floor(X * InvDelta)),
stats_standard_normal(
InvDelta, Buckets,
increment_bucket(Bucket, PositiveHistogram),
NegativeHistogram, max(Outlier, X),
Fun, NewS, Count - 1, StopTime, Rounds, TotalRounds);
{MinusX, NewS} ->
X = -MinusX,
Bucket = min(Buckets, floor(X * InvDelta)),
stats_standard_normal(
InvDelta, Buckets,
PositiveHistogram,
increment_bucket(Bucket, NegativeHistogram), max(Outlier, X),
Fun, NewS, Count - 1, StopTime, Rounds, TotalRounds)
end.
increment_bucket(Bucket, Array) ->
array:set(Bucket, array:get(Bucket, Array) + 1, Array).
check_histogram(W, Rounds, StdDevs, Histogram, Buckets) ->
TargetP = 0.5 * math:erfc(Buckets / W),
P = 0.0,
N = 0,
check_histogram(
W, Rounds, StdDevs, Histogram, TargetP,
Buckets, Buckets, P, N).
%%
check_histogram(
_W, _Rounds, _StdDevs, _Histogram, _TargetP,
0, _PrevBucket, _PrevP, _PrevN) ->
[];
check_histogram(
W, Rounds, StdDevs, Histogram, TargetP,
Bucket, PrevBucket, PrevP, PrevN) ->
N = PrevN + array:get(Bucket, Histogram),
P = 0.5 * math:erfc(Bucket / W),
BucketP = P - PrevP,
if
BucketP < TargetP ->
check_histogram(
W, Rounds, StdDevs, Histogram, TargetP,
Bucket - 1, PrevBucket, PrevP, N);
true ->
Exp = BucketP * Rounds,
Var = Rounds * BucketP*(1.0 - BucketP),
Threshold = StdDevs * math:sqrt(Var),
LowerLimit = floor(Exp - Threshold),
UpperLimit = ceil(Exp + Threshold),
if
N < LowerLimit; UpperLimit < N ->
[#{bucket => {Bucket, PrevBucket}, n => N,
lower => LowerLimit, upper => UpperLimit} |
check_histogram(
W, Rounds, StdDevs, Histogram, TargetP,
Bucket - 1, Bucket, P, 0)];
true ->
check_histogram(
W, Rounds, StdDevs, Histogram, TargetP,
Bucket - 1, Bucket, P, 0)
end
end.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% 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),$\s]||G<-Gen]]),
ct:fail({neq, FF, F})
catch
Error:Reason:Stacktrace ->
ct:pal(
"~w:~p ~s: ~s~n",
[Error, Reason, rand:float2str(F),
[["16#",integer_to_list(G,16),$\s]||G<-Gen]]),
ct:fail({Error, Reason, F, 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) ->
try crypto:strong_rand_bytes(1) of
<<_>> ->
_ = lists:foldl(
fun(_, S0) ->
{V1, S1} = rand:uniform_s(10000, S0),
true = is_integer(V1),
{V2, S2} = rand:uniform_s(S1),
true = is_float(V2),
S2
end, crypto64_seed(), lists:seq(1, 200)),
ok
catch
error:low_entropy ->
{skip,low_entropy};
error:undef ->
{skip,no_crypto}
end.
%% Test implementation
crypto64_seed() ->
{#{type=>crypto64,
bits=>64,
next=>fun crypto64_next/1,
uniform=>fun crypto64_uniform/1,
uniform_n=>fun crypto64_uniform_n/2},
<<>>}.
%% Be fair and create bignums i.e. 64bits otherwise use 58bits
crypto64_next(<<Num:64, Bin/binary>>) ->
{Num, Bin};
crypto64_next(_) ->
crypto64_next(crypto:strong_rand_bytes((64 div 8)*100)).
crypto64_uniform({Api, Data0}) ->
{Int, Data} = crypto64_next(Data0),
{Int / (1 bsl 64), {Api, Data}}.
crypto64_uniform_n(N, {Api, Data0}) when N < (1 bsl 64) ->
{Int, Data} = crypto64_next(Data0),
{(Int rem N)+1, {Api, Data}};
crypto64_uniform_n(N, State0) ->
{F,State} = crypto64_uniform(State0),
{trunc(F * N) + 1, State}.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Not a test but measures the time characteristics of the different algorithms
measure(Config) ->
ct:timetrap({minutes,60}), %% valgrind needs a lot of time
case ct:get_timetrap_info() of
{_,{_,1}} -> % No scaling
do_measure(Config);
{_,{_,Scale}} ->
{skip,{will_not_run_in_scaled_time,Scale}}
end.
-define(CHECK_UNIFORM_RANGE(Gen, Range, X, St),
case (Gen) of
{(X), (St)} when is_integer(X), 1 =< (X), (X) =< (Range) ->
St
end).
-define(CHECK_UNIFORM(Gen, X, St),
case (Gen) of
{(X), (St)} when is_float(X), 0.0 =< (X), (X) < 1.0 ->
St
end).
-define(CHECK_UNIFORM_NZ(Gen, X, St),
case (Gen) of
{(X), (St)} when is_float(X), 0.0 < (X), (X) =< 1.0 ->
St
end).
-define(CHECK_NORMAL(Gen, X, St),
case (Gen) of
{(X), (St)} when is_float(X) ->
St
end).
do_measure(_Config) ->
Algs =
algs() ++
try crypto:strong_rand_bytes(1) of
<<_>> -> [crypto64, crypto_cache, crypto]
catch
error:low_entropy -> [];
error:undef -> []
end,
%%
ct:pal("~nRNG uniform integer range 10000 performance~n",[]),
_ =
measure_1(
fun (_) -> 10000 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 32 bit performance~n",[]),
_ =
measure_1(
fun (_) -> 1 bsl 32 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 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(
fun (State) -> half_range(State) + 1 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 full range - 1 performance~n",[]),
_ =
measure_1(
fun (State) -> (half_range(State) bsl 1) - 1 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 full range performance~n",[]),
_ =
measure_1(
fun (State) -> half_range(State) bsl 1 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 full range + 1 performance~n",[]),
_ =
measure_1(
fun (State) -> (half_range(State) bsl 1) + 1 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 double range performance~n",[]),
_ =
measure_1(
fun (State) ->
half_range(State) bsl 2
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 double range + 1 performance~n",[]),
_ =
measure_1(
fun (State) ->
(half_range(State) bsl 2) + 1
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 64 bit performance~n",[]),
_ =
measure_1(
fun (_) -> 1 bsl 64 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 float performance~n",[]),
_ =
measure_1(
fun (_) -> 0 end,
fun (State, _, Mod) ->
measure_loop(
fun (St0) ->
?CHECK_UNIFORM(Mod:uniform_s(St0), X, St)
end,
State)
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",[]),
[TMarkNormalFloat|_] =
measure_1(
fun (_) -> 0 end,
fun (State, _, Mod) ->
measure_loop(
fun (St0) ->
?CHECK_NORMAL(Mod:normal_s(St0), X, St1)
end,
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).
%%
measure_loop(Fun, State, N) when 0 < N ->
measure_loop(Fun, Fun(State), N-1);
measure_loop(_, _, _) ->
ok.
measure_1(RangeFun, Fun, Algs) ->
TMark = measure_1(RangeFun, Fun, hd(Algs), undefined),
[TMark] ++
[measure_1(RangeFun, Fun, Alg, TMark) || Alg <- tl(Algs)].
measure_1(RangeFun, Fun, Alg, TMark) ->
Parent = self(),
{Mod, State} =
case Alg of
crypto64 ->
{rand, crypto64_seed()};
crypto_cache ->
{rand, crypto:rand_seed_alg(crypto_cache)};
crypto ->
{rand, crypto:rand_seed_s()};
random ->
{random, random:seed(os:timestamp()), get(random_seed)};
_ ->
{rand, rand:seed_s(Alg)}
end,
Range = RangeFun(State),
Pid = spawn_link(
fun() ->
{Time, ok} = timer:tc(fun () -> Fun(State, Range, Mod) end),
Percent =
case TMark of
undefined -> 100;
_ -> (Time * 100 + 50) div TMark
end,
io:format(
"~.12w: ~p ns ~p% [16#~.16b]~n",
[Alg, (Time * 1000 + 500) div ?LOOP_MEASURE,
Percent, Range]),
Parent ! {self(), Time},
normal
end),
receive
{Pid, Msg} -> Msg
end.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% The jump sequence tests has two parts
%% for those with the functional API (jump/1)
%% and for those with the internal state
%% in process dictionary (jump/0).
-define(LOOP_JUMP, (?LOOP div 1000)).
%% Check if each algorithm generates the proper jump sequence
%% with the functional API.
reference_jump_state(Config) when is_list(Config) ->
[reference_jump_1(Alg) || Alg <- algs()],
ok.
reference_jump_1(Alg) ->
Refval = reference_jump_val(Alg),
Testval = gen_jump_1(Alg),
case Refval =:= Testval of
true -> ok;
false ->
io:format("Failed: ~p~n",[Alg]),
io:format("Length ~p ~p~n",[length(Refval), length(Testval)]),
io:format("Head ~p ~p~n",[hd(Refval), hd(Testval)]),
io:format("Vals ~p ~p~n",[Refval, Testval]),
exit(wrong_value)
end.
gen_jump_1(Algo) ->
State =
case Algo of
exs64 -> %% Test exception of not_implemented notice
try rand:jump(rand:seed_s(exs64))
catch
error:not_implemented -> not_implemented
end;
_ when Algo =:= exsplus; Algo =:= exsp; Algo =:= exrop ->
%% Printed with orig 'C' code and this seed
rand:seed_s({Algo, [12345678|12345678]});
_ when Algo =:= exs1024; Algo =:= exs1024s ->
%% Printed with orig 'C' code and this seed
rand:seed_s({Algo, {lists:duplicate(16, 12345678), []}});
_ -> % unimplemented
not_implemented
end,
case State of
not_implemented -> [not_implemented];
_ ->
Max = range(State),
gen_jump_1(?LOOP_JUMP, State, Max, [])
end.
gen_jump_1(N, State0, Max, Acc) when N > 0 ->
{_, State1} = rand:uniform_s(Max, State0),
{Random, State2} = rand:uniform_s(Max, rand:jump(State1)),
case N rem (?LOOP_JUMP div 100) of
0 -> gen_jump_1(N-1, State2, Max, [Random|Acc]);
_ -> gen_jump_1(N-1, State2, Max, Acc)
end;
gen_jump_1(_, _, _, Acc) -> lists:reverse(Acc).
%% Check if each algorithm generates the proper jump sequence
%% with the internal state in the process dictionary.
reference_jump_procdict(Config) when is_list(Config) ->
[reference_jump_0(Alg) || Alg <- algs()],
ok.
reference_jump_0(Alg) ->
Refval = reference_jump_val(Alg),
Testval = gen_jump_0(Alg),
case Refval =:= Testval of
true -> ok;
false ->
io:format("Failed: ~p~n",[Alg]),
io:format("Length ~p ~p~n",[length(Refval), length(Testval)]),
io:format("Head ~p ~p~n",[hd(Refval), hd(Testval)]),
exit(wrong_value)
end.
gen_jump_0(Algo) ->
Seed = case Algo of
exs64 -> %% Test exception of not_implemented notice
try
_ = rand:seed(exs64),
rand:jump()
catch
error:not_implemented -> not_implemented
end;
_ when Algo =:= exsplus; Algo =:= exsp; Algo =:= exrop ->
%% Printed with orig 'C' code and this seed
rand:seed({Algo, [12345678|12345678]});
_ when Algo =:= exs1024; Algo =:= exs1024s ->
%% Printed with orig 'C' code and this seed
rand:seed({Algo, {lists:duplicate(16, 12345678), []}});
_ -> % unimplemented
not_implemented
end,
case Seed of
not_implemented -> [not_implemented];
_ ->
Max = range(Seed),
gen_jump_0(?LOOP_JUMP, Max, [])
end.
gen_jump_0(N, Max, Acc) when N > 0 ->
_ = rand:uniform(Max),
_ = rand:jump(),
Random = rand:uniform(Max),
case N rem (?LOOP_JUMP div 100) of
0 -> gen_jump_0(N-1, Max, [Random|Acc]);
_ -> gen_jump_0(N-1, Max, Acc)
end;
gen_jump_0(_, _, Acc) -> lists:reverse(Acc).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Data
reference_val(exs64) ->
[16#3737ad0c703ff6c3,16#3868a78fe71adbbd,16#1f01b62b4338b605,16#50876a917437965f,
16#b2edfe32a10e27fc,16#995924551d8ebae1,16#9f1e6b94e94e0b58,16#27ec029eb0e94f8e,
16#bf654e6df7fe5c,16#b7d5ef7b79be65e3,16#4bdba4d1c159126b,16#a9c816fdc701292c,
16#a377b6c89d85ac8b,16#7abb5cd0e5847a6,16#62666f1fc00a0a90,16#1edc3c3d255a8113,
16#dfc764073767f18e,16#381783d577ca4e34,16#49693588c085ddcb,16#da6fcb16dd5163f3,
16#e2357a703475b1b7,16#aaa84c4924b5985a,16#b8fe07bb2bac1e49,16#23973ac0160ff064,
16#1afbc7b023f5d618,16#9f510f7b7caa2a0f,16#d5b0a57f7f5f1084,16#d8c49b66c5f99a29,
16#e920ac3b598b5213,16#1090d7e27e7a7c76,16#81171917168ee74f,16#f08489a3eb6988e,
16#396260c4f0b2ed46,16#4fd0a6a6caefd5b2,16#423dff07a3b888a,16#12718773ebd99987,
16#e50991e540807cb,16#8cfa03bbaa6679d6,16#55bdf86dfbb92dbf,16#eb7145378cce74a8,
16#71856c224c846595,16#20461588dae6e24d,16#c73b3e63ced74bac,16#775b11813dda0c78,
16#91f358e51068ede0,16#399955ef36766bc2,16#4489ee072e8a38b1,16#ba77759d52321ca0,
16#14f519eab5c53db8,16#1f754bd08e4f34c4,16#99e25ca29b2fcfeb,16#da11927c0d9837f8,
16#1eeb0f87009f5a87,16#a7c444d3b0db1089,16#49c7fbf0714849ad,16#4f2b693e7f8265cb,
16#80e1493cbaa8f256,16#186f345bcac2661e,16#330065ae0c698d26,16#5235ed0432c42e93,
16#429792e31ddb10bb,16#8769054bb6533cff,16#1ab382483444201f,16#2216368786fc7b9,
16#1efea1155216da0b,16#782dc868ba595452,16#2b80f6d159617f48,16#407fc35121b2fa1b,
16#90e8be6e618873d1,16#40ad4ec92a8abf8e,16#34e2890f583f435,16#838c0aef0a5d8427,
16#ed4238f4bd6cbcfa,16#7feed11f7a8bb9f0,16#2b0636a93e26c89d,16#481ad4bea5180646,
16#673e5ad861afe1cc,16#298eeb519d69e74d,16#eb1dd06d168c856,16#4770651519ee7ef9,
16#7456ebf1bcf608f1,16#d6200f6fbd61ce05,16#c0695dfab11ab6aa,16#5bff449249983843,
16#7aba88471474c9ac,16#d7e9e4a21c989e91,16#c5e02ee67ccb7ce1,16#4ea8a3a912246153,
16#f2e6db7c9ce4ec43,16#39498a95d46d2470,16#c5294fcb8cce8aa9,16#a918fe444719f3dc,
16#98225f754762c0c0,16#f0721204f2cb43f5,16#b98e77b099d1f2d1,16#691d6f75aee3386,
16#860c7b2354ec24fd,16#33e007bd0fbcb609,16#7170ae9c20fb3d0,16#31d46938fe383a60];
reference_val(exs1024) ->
[16#9c61311d0d4a01fd,16#ce963ef5803b703e,16#545dcffb7b644e1a,16#edd56576a8d778d5,
16#16bee799783c6b45,16#336f0b3caeb417fa,16#29291b8be26dedfa,16#1efed996d2e1b1a8,
16#c5c04757bd2dadf9,16#11aa6d194009c616,16#ab2b3e82bdb38a91,16#5011ee46fd2609eb,
16#766db7e5b701a9bb,16#d42cb2632c419f35,16#107c6a2667bf8557,16#3ffbf922cb306967,
16#1e71e3d024ac5131,16#6fdb368ec67a5f06,16#b0d8e72e7aa6d1c1,16#e5705a02dae89e3b,
16#9c24eb68c086a1d3,16#418de330f55f71f0,16#2917ddeb278bc8d2,16#aeba7fba67208f39,
16#10ceaf40f6af1d8d,16#47a6d06811d33132,16#603a661d6caf720a,16#a28bd0c9bcdacb3c,
16#f44754f006909762,16#6e25e8e67ccc43bc,16#174378ce374a549e,16#b5598ae9f57c4e50,
16#ca85807fbcd51dd,16#1816e58d6c3cc32a,16#1b4d630d3c8e96a6,16#c19b1e92b4efc5bd,
16#665597b20ddd721a,16#fdab4eb21b75c0ae,16#86a612dcfea0756c,16#8fc2da192f9a55f0,
16#d7c954eb1af31b5,16#6f5ee45b1b80101b,16#ebe8ea4e5a67cbf5,16#1cb952026b4c1400,
16#44e62caffe7452c0,16#b591d8f3e6d7cbcf,16#250303f8d77b6f81,16#8ef2199aae4c9b8d,
16#a16baa37a14d7b89,16#c006e4d2b2da158b,16#e6ec7abd54c93b31,16#e6b0d79ae2ab6fa7,
16#93e4b30e4ab7d4cd,16#42a01b6a4ef63033,16#9ab1e94fe94976e,16#426644e1de302a1f,
16#8e58569192200139,16#744f014a090107c1,16#15d056801d467c6c,16#51bdad3a8c30225f,
16#abfc61fb3104bd45,16#c610607122272df7,16#905e67c63116ebfc,16#1e4fd5f443bdc18,
16#1945d1745bc55a4c,16#f7cd2b18989595bb,16#f0d273b2c646a038,16#ee9a6fdc6fd5d734,
16#541a518bdb700518,16#6e67ab9a65361d76,16#bcfadc9bfe5b2e06,16#69fa334cf3c11496,
16#9657df3e0395b631,16#fc0d0442160108ec,16#2ee538da7b1f7209,16#8b20c9fae50a5a9e,
16#a971a4b5c2b3b6a,16#ff6241e32489438e,16#8fd6433f45255777,16#6e6c82f10818b0dc,
16#59a8fad3f6af616b,16#7eac34f43f12221c,16#6e429ec2951723ec,16#9a65179767a45c37,
16#a5f8127d1e6fdf35,16#932c50bc633d8d5c,16#f3bbea4e7ebecb8,16#efc3a2bbf6a8674,
16#451644a99971cb6,16#cf70776d652c150d,16#c1fe0dcb87a25403,16#9523417132b2452e,
16#8f98bc30d06b980e,16#bb4b288ecb8daa9a,16#59e54beb32f78045,16#f9ab1562456b9d66,
16#6435f4130304a793,16#b4bb94c2002e1849,16#49a86d1e4bade982,16#457d63d60ed52b95];
reference_val(exsplus) ->
[16#bc76c2e638db,16#15ede2ebb16c9fb,16#185ee2c27d6b88d,16#15d5ee9feafc3a5,
16#1862e91dfce3e6b,16#2c9744b0fb69e46,16#78b21bc01cef6b,16#2d16a2fae6c76ba,
16#13dfccb8ff86bce,16#1d9474c59e23f4d,16#d2f67dcd7f0dd6,16#2b6d489d51a0725,
16#1fa52ef484861d8,16#1ae9e2a38f966d4,16#2264ab1e193acca,16#23bbca085039a05,
16#2b6eea06a0af0e1,16#3ad47fa8866ea20,16#1ec2802d612d855,16#36c1982b134d50,
16#296b6a23f5b75e0,16#c5eeb600a9875c,16#2a3fd51d735f9d4,16#56fafa3593a070,
16#13e9d416ec0423e,16#28101a91b23e9dc,16#32e561eb55ce15a,16#94a7dbba66fe4a,
16#2e1845043bcec1f,16#235f7513a1b5146,16#e37af1bf2d63cb,16#2048033824a1639,
16#c255c750995f7,16#2c7542058e89ee3,16#204dfeefbdb62ba,16#f5a936ec63dd66,
16#33b3b7dbbbd8b90,16#c4f0f79026ffe9,16#20ffee2d37aca13,16#2274f931716be2c,
16#29b883902ba9df1,16#1a838cd5312717f,16#2edfc49ff3dc1d6,16#418145cbec84c2,
16#d2d8f1a17d49f,16#d41637bfa4cc6f,16#24437e03a0f5df8,16#3d1d87919b94a90,
16#20d6997b36769b6,16#16f9d7855cd87ca,16#821ef7e2a062a3,16#2c4d11dc4a2da70,
16#24a3b27f56ed26b,16#144b23c8b97387a,16#34a2ced56930d12,16#21cc0544113a017,
16#3e780771f634fb2,16#146c259c02e7e18,16#1d99e4cfad0ef1,16#fdf3dabefc6b3a,
16#7d0806e4d12dfb,16#3e3ae3580532eae,16#2456544200fbd86,16#f83aad4e88db85,
16#37c134779463b4d,16#21a20bf64b6e735,16#1c0585ac88b69f2,16#1b3fcea8dd30e56,
16#334bc301aefd97,16#37066eb7e80a946,16#15a19a6331b570f,16#35e67fa43c3f7d0,
16#152a4020145fb80,16#8d55139491dfbe,16#21d9cba585c059d,16#31475f363654635,
16#2567b17acb7a104,16#39201be3a7681c5,16#6bc675fd26b601,16#334b93232b1b1e3,
16#357c402cb732c6a,16#362e32efe4db46a,16#8edc7ae3da51e5,16#31573376785eac9,
16#6c6145ffa1169d,16#18ec2c393d45359,16#1f1a5f256e7130c,16#131cc2f49b8004f,
16#36f715a249f4ec2,16#1c27629826c50d3,16#914d9a6648726a,16#27f5bf5ce2301e8,
16#3dd493b8012970f,16#be13bed1e00e5c,16#ceef033b74ae10,16#3da38c6a50abe03,
16#15cbd1a421c7a8c,16#22794e3ec6ef3b1,16#26154d26e7ea99f,16#3a66681359a6ab6];
reference_val(exsp) ->
reference_val(exsplus);
reference_val(exs1024s) ->
reference_val(exs1024);
reference_val(exrop) ->
%% #include <stdint.h>
%% #include <stdio.h>
%%
%% uint64_t s[2];
%% uint64_t next(void);
%% /* Xoroshiro116+ PRNG here */
%%
%% int main(char *argv[]) {
%% int n;
%% uint64_t r;
%% s[0] = 12345678;
%% s[1] = 12345678;
%%
%% for (n = 1000000; n > 0; n--) {
%% r = next();
%% if ((n % 10000) == 0) {
%% printf("%llu,", (unsigned long long) (r + 1));
%% }
%% }
%% printf("\n");
%% }
[24691357,29089185972758626,135434857127264790,
277209758236304485,101045429972817342,
241950202080388093,283018380268425711,268233672110762489,
173241488791227202,245038518481669421,
253627577363613736,234979870724373477,115607127954560275,
96445882796968228,166106849348423677,
83614184550774836,109634510785746957,68415533259662436,
12078288820568786,246413981014863011,
96953486962147513,138629231038332640,206078430370986460,
11002780552565714,238837272913629203,
60272901610411077,148828243883348685,203140738399788939,
131001610760610046,30717739120305678,
262903815608472425,31891125663924935,107252017522511256,
241577109487224033,263801934853180827,
155517416581881714,223609336630639997,112175917931581716,
16523497284706825,201453767973653420,
35912153101632769,211525452750005043,96678037860996922,
70962216125870068,107383886372877124,
223441708670831233,247351119445661499,233235283318278995,
280646255087307741,232948506631162445,
%%
117394974124526779,55395923845250321,274512622756597759,
31754154862553492,222645458401498438,
161643932692872858,11771755227312868,93933211280589745,
92242631276348831,197206910466548143,
150370169849735808,229903773212075765,264650708561842793,
30318996509793571,158249985447105184,
220423733894955738,62892844479829080,112941952955911674,
203157000073363030,54175707830615686,
50121351829191185,115891831802446962,62298417197154985,
6569598473421167,69822368618978464,
176271134892968134,160793729023716344,271997399244980560,
59100661824817999,150500611720118722,
23707133151561128,25156834940231911,257788052162304719,
176517852966055005,247173855600850875,
83440973524473396,94711136045581604,154881198769946042,
236537934330658377,152283781345006019,
250789092615679985,78848633178610658,72059442721196128,
98223942961505519,191144652663779840,
102425686803727694,89058927716079076,80721467542933080,
8462479817391645,2774921106204163].
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
reference_jump_val(exsplus) ->
[82445318862816932, 145810727464480743, 16514517716894509, 247642377064868650,
162385642339156908, 251810707075252101, 82288275771998924, 234412731596926322,
49960883129071044, 200690077681656596, 213743196668671647, 131182800982967108,
144200072021941728, 263557425008503277, 194858522616874272, 185869394820993172,
80384502675241453, 262654144824057588, 90033295011291362, 4494510449302659,
226005372746479588, 116780561309220553, 47048528594475843, 39168929349768743,
139615163424415552, 55330632656603925, 237575574720486569, 102381140288455025,
18452933910354323, 150248612130579752, 269358096791922740, 61313433522002187,
160327361842676597, 185187983548528938, 57378981505594193, 167510799293984067,
105117045862954303, 176126685946302943, 123590876906828803, 69185336947273487,
9098689247665808, 49906154674145057, 131575138412788650, 161843880211677185,
30743946051071186, 187578920583823612, 45008401528636978, 122454158686456658,
111195992644229524, 17962783958752862, 13579507636941108, 130137843317798663,
144202635170576832, 132539563255093922, 159785575703967124, 187241848364816640,
183044737781926478, 12921559769912263, 83553932242922001, 96698298841984688,
281664320227537824, 224233030818578263, 77812932110318774, 169729351013291728,
164475402723178734, 242780633011249051, 51095111179609125, 19249189591963554,
221412426221439180, 265700202856282653, 265342254311932308, 241218503498385511,
255400887248486575, 212083616929812076, 227947034485840579, 268261881651571692,
104846262373404908, 49690734329496661, 213259196633566308, 186966479726202436,
282157378232384574, 11272948584603747, 166540426999573480, 50628164001018755,
65235580992800860, 230664399047956956, 64575592354687978, 40519393736078511,
108341851194332747, 115426411532008961, 120656817002338193, 234537867870809797,
12504080415362731, 45083100453836317, 270968267812126657, 93505647407734103,
252852934678537969, 258758309277167202, 74250882143432077, 141629095984552833];
reference_jump_val(exs1024) ->
[2655961906500790629, 17003395417078685063, 10466831598958356428, 7603399148503548021,
1650550950190587188, 12294992315080723704, 15743995773860389219, 5492181000145247327,
14118165228742583601, 1024386975263610703, 10124872895886669513, 6445624517813169301,
6238575554686562601, 14108646153524288915, 11804141635807832816, 8421575378006186238,
6354993374304550369, 838493020029548163, 14759355804308819469, 12212491527912522022,
16943204735100571602, 198964074252287588, 7325922870779721649, 15853102065526570574,
16294058349151823341, 6153379962047409781, 15874031679495957261, 17299265255608442340,
984658421210027171, 17408042033939375278, 3326465916992232353, 5222817718770538733,
13262385796795170510, 15648751121811336061, 6718721549566546451, 7353765235619801875,
16110995049882478788, 14559143407227563441, 4189805181268804683, 10938587948346538224,
1635025506014383478, 12619562911869525411, 17469465615861488695, 125252234176411528,
2004192558503448853, 13175467866790974840, 17712272336167363518, 1710549840100880318,
17486892343528340916, 5337910082227550967, 8333082060923612691, 6284787745504163856,
8072221024586708290, 6077032673910717705, 11495200863352251610, 11722792537523099594,
14642059504258647996, 8595733246938141113, 17223366528010341891, 17447739753327015776,
6149800490736735996, 11155866914574313276, 7123864553063709909, 15982886296520662323,
5775920250955521517, 8624640108274906072, 8652974210855988961, 8715770416136907275,
11841689528820039868, 10991309078149220415, 11758038663970841716, 7308750055935299261,
15939068400245256963, 6920341533033919644, 8017706063646646166, 15814376391419160498,
13529376573221932937, 16749061963269842448, 14639730709921425830, 3265850480169354066,
4569394597532719321, 16594515239012200038, 13372824240764466517, 16892840440503406128,
11260004846380394643, 2441660009097834955, 10566922722880085440, 11463315545387550692,
5252492021914937692, 10404636333478845345, 11109538423683960387, 5525267334484537655,
17936751184378118743, 4224632875737239207, 15888641556987476199, 9586888813112229805,
9476861567287505094, 14909536929239540332, 17996844556292992842, 2699310519182298856];
reference_jump_val(exsp) ->
reference_jump_val(exsplus);
reference_jump_val(exs1024s) ->
reference_jump_val(exs1024);
reference_jump_val(exs64) -> [not_implemented];
reference_jump_val(exrop) ->
%% #include <stdint.h>
%% #include <stdio.h>
%%
%% uint64_t s[2];
%% uint64_t next(void);
%% /* Xoroshiro116+ PRNG here */
%%
%% int main(char *argv[]) {
%% int n;
%% uint64_t r;
%% s[0] = 12345678;
%% s[1] = 12345678;
%% for (n = 1000; n > 0; n--) {
%% next();
%% jump();
%% r = next();
%% if ((n % 10) == 0) {
%% printf("%llu,", (unsigned long long) (r + 1));
%% }
%% }
%% printf("\n");
%% }
[60301713907476001,135397949584721850,4148159712710727,
110297784509908316,18753463199438866,
106699913259182846,2414728156662676,237591345910610406,
48519427605486503,38071665570452612,
235484041375354592,45428997361037927,112352324717959775,
226084403445232507,270797890380258829,
160587966336947922,80453153271416820,222758573634013699,
195715386237881435,240975253876429810,
93387593470886224,23845439014202236,235376123357642262,
22286175195310374,239068556844083490,
120126027410954482,250690865061862527,113265144383673111,
57986825640269127,206087920253971490,
265971029949338955,40654558754415167,185972161822891882,
72224917962819036,116613804322063968,
129103518989198416,236110607653724474,98446977363728314,
122264213760984600,55635665885245081,
42625530794327559,288031254029912894,81654312180555835,
261800844953573559,144734008151358432,
77095621402920587,286730580569820386,274596992060316466,
97977034409404188,5517946553518132,
%%
56460292644964432,252118572460428657,38694442746260303,
165653145330192194,136968555571402812,
64905200201714082,257386366768713186,22702362175273017,
208480936480037395,152926769756967697,
256751159334239189,130982960476845557,21613531985982870,
87016962652282927,130446710536726404,
188769410109327420,282891129440391928,251807515151187951,
262029034126352975,30694713572208714,
46430187445005589,176983177204884508,144190360369444480,
14245137612606100,126045457407279122,
169277107135012393,42599413368851184,130940158341360014,
113412693367677211,119353175256553456,
96339829771832349,17378172025472134,110141940813943768,
253735613682893347,234964721082540068,
85668779779185140,164542570671430062,18205512302089755,
282380693509970845,190996054681051049,
250227633882474729,171181147785250210,55437891969696407,
241227318715885854,77323084015890802,
1663590009695191,234064400749487599,222983191707424780,
254956809144783896,203898972156838252].
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% The old algorithms used a range 2^N - 1 for their reference val
%% tests, which was incorrect but works as long as you do not draw
%% the value 2^N, which is very unlikely. It was not possible
%% to simply correct the range to 2^N due to another incorrectness
%% in that the old algorithms changed to using the broken
%% (multiply a float approach with too few bits) approach for
%% ranges >= 2^N. This function digs out the range to use
%% for the reference tests for old and new algorithms.
range({#{bits:=Bits}, _}) -> 1 bsl Bits;
range({#{max:=Max}, _}) -> Max; %% Old incorrect range
range({_, _, _}) -> 51. % random
half_range({#{bits:=Bits}, _}) -> 1 bsl (Bits - 1);
half_range({#{max:=Max}, _}) -> (Max bsr 1) + 1;
half_range({#{}, _}) -> 1 bsl 63; % crypto
half_range({_, _, _}) -> 1 bsl 50. % random
