diff options
Diffstat (limited to 'lib/stdlib/test/rand_SUITE.erl')
| -rw-r--r-- | lib/stdlib/test/rand_SUITE.erl | 65 |
1 files changed, 52 insertions, 13 deletions
diff --git a/lib/stdlib/test/rand_SUITE.erl b/lib/stdlib/test/rand_SUITE.erl index 0aa39f87aa..c264a65521 100644 --- a/lib/stdlib/test/rand_SUITE.erl +++ b/lib/stdlib/test/rand_SUITE.erl @@ -27,6 +27,7 @@ -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]). @@ -52,7 +53,8 @@ all() -> groups() -> [{basic_stats, [parallel], - [basic_stats_uniform_1, basic_stats_uniform_2, basic_stats_normal]}, + [basic_stats_uniform_1, basic_stats_uniform_2, + basic_stats_standard_normal, basic_stats_normal]}, {reference_jump, [parallel], [reference_jump_state, reference_jump_procdict]}]. @@ -301,12 +303,35 @@ basic_stats_uniform_2(Config) when is_list(Config) -> || Alg <- algs()], ok. -basic_stats_normal(Config) when is_list(Config) -> +basic_stats_standard_normal(Config) when is_list(Config) -> ct:timetrap({minutes,6}), %% valgrind needs a lot of time - io:format("Testing normal~n",[]), - [basic_normal_1(?LOOP, rand:seed_s(Alg), 0, 0) || Alg <- algs()], + 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}) -> + io:format("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), @@ -346,19 +371,33 @@ basic_uniform_2(0, {#{type:=Alg}, _}, Sum, A) -> abs(?LOOP div 100 - Max) < 1000 orelse ct:fail({max, Alg, Max}), ok. -basic_normal_1(N, S0, Sum, Sq) when N > 0 -> - {X,S} = rand:normal_s(S0), - basic_normal_1(N-1, S, X+Sum, X*X+Sq); -basic_normal_1(0, {#{type:=Alg}, _}, Sum, SumSq) -> - Mean = Sum / ?LOOP, - StdDev = math:sqrt((SumSq - (Sum*Sum/?LOOP))/(?LOOP - 1)), - io:format("~.12w: Average: ~7.4f StdDev ~6.4f~n", [Alg, Mean, StdDev]), +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(Mean) < 0.005 orelse ct:fail({average, Alg, Mean}), - abs(StdDev - 1.0) < 0.005 orelse ct:fail({stddev, Alg, StdDev}), + 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. |