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author | Raimo Niskanen <[email protected]> | 2017-09-14 15:57:16 +0200 |
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committer | Raimo Niskanen <[email protected]> | 2017-10-18 12:04:24 +0200 |
commit | 0248865dea315253618e733d77177b5a80679e20 (patch) | |
tree | 5f09f35f52aa65c20f62056ee46b135325194d76 /lib/stdlib/test | |
parent | 52449634868e38087d0c93a0145fcfd9d2de394b (diff) | |
download | otp-0248865dea315253618e733d77177b5a80679e20.tar.gz otp-0248865dea315253618e733d77177b5a80679e20.tar.bz2 otp-0248865dea315253618e733d77177b5a80679e20.zip |
Implement uniform_real/0 and uniform_real_s/1
Diffstat (limited to 'lib/stdlib/test')
-rw-r--r-- | lib/stdlib/test/rand_SUITE.erl | 210 |
1 files changed, 203 insertions, 7 deletions
diff --git a/lib/stdlib/test/rand_SUITE.erl b/lib/stdlib/test/rand_SUITE.erl index f69d42551e..0c7d8d842e 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( @@ -652,6 +824,30 @@ 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. measure_loop(Fun, State) -> |