%% %% %CopyrightBegin% %% %% Copyright Ericsson AB 2000-2017. 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, plugin/1, measure/1, reference_jump_state/1, reference_jump_procdict/1]). -export([test/0, gen/1]). -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}, plugin, measure, {group, reference_jump} ]. groups() -> [{basic_stats, [parallel], [basic_stats_uniform_1, basic_stats_uniform_2, basic_stats_standard_normal, basic_stats_normal]}, {reference_jump, [parallel], [reference_jump_state, reference_jump_procdict]}]. group(basic_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 -> io:format("Failed: ~p: ~p ~p~n", [Test, Reason, erlang:get_stacktrace()]) end end, Tests). algs() -> [exs64, exsplus, exsp, exrop, exs1024, exs1024s]. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% 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 -> ct:fail({Alg, Reason, erlang:get_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(), 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(), if 0.0 =< X, X < 1.0 -> ok; true -> io:format("X=~p 0=<~p<1.0~n", [X,X]), 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} = rand:uniform_s(S0), 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). %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% 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, 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. do_measure(_Config) -> Algos = try crypto:strong_rand_bytes(1) of <<_>> -> [crypto64, crypto] catch error:low_entropy -> []; error:undef -> [] end ++ algs(), %% ct:pal("RNG uniform integer performance~n",[]), TMark1 = measure_1( random, fun (_) -> 10000 end, undefined, fun (Range, State) -> {int, random:uniform_s(Range, State)} end), _ = [measure_1( Algo, fun (_) -> 10000 end, TMark1, fun (Range, State) -> {int, rand:uniform_s(Range, State)} end) || Algo <- Algos], %% ct:pal("~nRNG uniform integer half range performance~n",[]), HalfRangeFun = fun (State) -> half_range(State) end, TMark2 = measure_1( random, HalfRangeFun, undefined, fun (Range, State) -> {int, random:uniform_s(Range, State)} end), _ = [measure_1( Algo, HalfRangeFun, TMark2, fun (Range, State) -> {int, rand:uniform_s(Range, State)} end) || Algo <- Algos], %% ct:pal("~nRNG uniform integer half range + 1 performance~n",[]), HalfRangePlus1Fun = fun (State) -> half_range(State) + 1 end, TMark3 = measure_1( random, HalfRangePlus1Fun, undefined, fun (Range, State) -> {int, random:uniform_s(Range, State)} end), _ = [measure_1( Algo, HalfRangePlus1Fun, TMark3, fun (Range, State) -> {int, rand:uniform_s(Range, State)} end) || Algo <- Algos], %% ct:pal("~nRNG uniform integer full range - 1 performance~n",[]), FullRangeMinus1Fun = fun (State) -> (half_range(State) bsl 1) - 1 end, TMark4 = measure_1( random, FullRangeMinus1Fun, undefined, fun (Range, State) -> {int, random:uniform_s(Range, State)} end), _ = [measure_1( Algo, FullRangeMinus1Fun, TMark4, fun (Range, State) -> {int, rand:uniform_s(Range, State)} end) || Algo <- Algos], %% ct:pal("~nRNG uniform integer full range performance~n",[]), FullRangeFun = fun (State) -> half_range(State) bsl 1 end, TMark5 = measure_1( random, FullRangeFun, undefined, fun (Range, State) -> {int, random:uniform_s(Range, State)} end), _ = [measure_1( Algo, FullRangeFun, TMark5, fun (Range, State) -> {int, rand:uniform_s(Range, State)} end) || Algo <- Algos], %% ct:pal("~nRNG uniform integer full range + 1 performance~n",[]), FullRangePlus1Fun = fun (State) -> (half_range(State) bsl 1) + 1 end, TMark6 = measure_1( random, FullRangePlus1Fun, undefined, fun (Range, State) -> {int, random:uniform_s(Range, State)} end), _ = [measure_1( Algo, FullRangePlus1Fun, TMark6, fun (Range, State) -> {int, rand:uniform_s(Range, State)} end) || Algo <- Algos], %% ct:pal("~nRNG uniform integer double range performance~n",[]), DoubleRangeFun = fun (State) -> half_range(State) bsl 2 end, TMark7 = measure_1( random, DoubleRangeFun, undefined, fun (Range, State) -> {int, random:uniform_s(Range, State)} end), _ = [measure_1( Algo, DoubleRangeFun, TMark7, fun (Range, State) -> {int, rand:uniform_s(Range, State)} end) || Algo <- Algos], %% ct:pal("~nRNG uniform integer double range + 1 performance~n",[]), DoubleRangePlus1Fun = fun (State) -> (half_range(State) bsl 2) + 1 end, TMark8 = measure_1( random, DoubleRangePlus1Fun, undefined, fun (Range, State) -> {int, random:uniform_s(Range, State)} end), _ = [measure_1( Algo, DoubleRangePlus1Fun, TMark8, fun (Range, State) -> {int, rand:uniform_s(Range, State)} end) || Algo <- Algos], %% ct:pal("~nRNG uniform float performance~n",[]), TMark9 = measure_1( random, fun (_) -> 0 end, undefined, fun (_, State) -> {uniform, random:uniform_s(State)} end), _ = [measure_1( Algo, fun (_) -> 0 end, TMark9, fun (_, State) -> {uniform, rand:uniform_s(State)} end) || Algo <- Algos], %% ct:pal("~nRNG normal float performance~n",[]), io:format("~.12w: not implemented (too few bits)~n", [random]), _ = [measure_1( Algo, fun (_) -> 0 end, TMark9, fun (_, State) -> {normal, rand:normal_s(State)} end) || Algo <- Algos], ok. measure_1(Algo, RangeFun, TMark, Gen) -> Parent = self(), Seed = case Algo of crypto64 -> crypto64_seed(); crypto -> crypto:rand_seed_s(); random -> random:seed(os:timestamp()), get(random_seed); _ -> rand:seed_s(Algo) end, Range = RangeFun(Seed), Pid = spawn_link( fun() -> Fun = fun() -> measure_2(?LOOP, Range, Seed, Gen) end, {Time, ok} = timer:tc(Fun), Percent = case TMark of undefined -> 100; _ -> (Time * 100 + 50) div TMark end, io:format( "~.12w: ~p ns ~p% [16#~.16b]~n", [Algo, (Time * 1000 + 500) div ?LOOP, Percent, Range]), Parent ! {self(), Time}, normal end), receive {Pid, Msg} -> Msg end. measure_2(N, Range, State0, Fun) when N > 0 -> case Fun(Range, State0) of {int, {Random, State}} when is_integer(Random), Random >= 1, Random =< Range -> measure_2(N-1, Range, State, Fun); {uniform, {Random, State}} when is_float(Random), 0.0 =< Random, Random < 1.0 -> measure_2(N-1, Range, State, Fun); {normal, {Random, State}} when is_float(Random) -> measure_2(N-1, Range, State, Fun); Res -> exit({error, Res, State0}) end; measure_2(0, _, _, _) -> ok. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% 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 %% #include %% %% 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 %% #include %% %% 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