Merge pull request #13 from Feuerlabs/uw-boundary-stats-subset
adjust get_statistics to allow for requesting specific stats to calculate
diff --git a/src/bear.erl b/src/bear.erl
index 04593e7..b211a54 100644
--- a/src/bear.erl
+++ b/src/bear.erl
@@ -41,32 +41,7 @@
-compile([native]).
-get_statistics(Values) when length(Values) < ?STATS_MIN ->
- [
- {min, 0.0},
- {max, 0.0},
- {arithmetic_mean, 0.0},
- {geometric_mean, 0.0},
- {harmonic_mean, 0.0},
- {median, 0.0},
- {variance, 0.0},
- {standard_deviation, 0.0},
- {skewness, 0.0},
- {kurtosis, 0.0},
- {percentile,
- [
- {50, 0.0},
- {75, 0.0},
- {90, 0.0},
- {95, 0.0},
- {99, 0.0},
- {999, 0.0}
- ]
- },
- {histogram, [{0, 0}]},
- {n, 0}
- ];
-get_statistics(Values) ->
+get_statistics([_,_,_,_,_|_] = Values) ->
Scan_res = scan_values(Values),
Scan_res2 = scan_values2(Values, Scan_res),
Variance = variance(Scan_res, Scan_res2),
@@ -94,7 +69,86 @@
},
{histogram, get_histogram(Values, Scan_res, Scan_res2)},
{n, Scan_res#scan_result.n}
- ].
+ ];
+get_statistics(Values) when is_list(Values) ->
+ [
+ {min, 0.0},
+ {max, 0.0},
+ {arithmetic_mean, 0.0},
+ {geometric_mean, 0.0},
+ {harmonic_mean, 0.0},
+ {median, 0.0},
+ {variance, 0.0},
+ {standard_deviation, 0.0},
+ {skewness, 0.0},
+ {kurtosis, 0.0},
+ {percentile,
+ [
+ {50, 0.0},
+ {75, 0.0},
+ {90, 0.0},
+ {95, 0.0},
+ {99, 0.0},
+ {999, 0.0}
+ ]
+ },
+ {histogram, [{0, 0}]},
+ {n, 0}
+ ].
+
+get_statistics_subset(Values, Items) ->
+ Length = length(Values),
+ if Length < ?STATS_MIN ->
+ [I || {K,_} = I <- get_statistics([]),
+ lists:member(K, Items) orelse K==percentiles];
+ true ->
+ SortedValues = lists:sort(Values),
+ Steps = calc_steps(Items),
+ Scan_res = if Steps > 1 -> scan_values(Values);
+ true -> []
+ end,
+ Scan_res2 = if Steps > 2 -> scan_values2(Values, Scan_res);
+ true -> []
+ end,
+ report_subset(Items, Length,
+ SortedValues, Scan_res, Scan_res2)
+ end.
+
+calc_steps(Items) ->
+ lists:foldl(fun({I,_},Acc) ->
+ erlang:max(level(I), Acc);
+ (I,Acc) ->
+ erlang:max(level(I), Acc)
+ end, 1, Items).
+
+level(standard_deviation) -> 3;
+level(variance ) -> 3;
+level(skewness ) -> 3;
+level(kurtosis ) -> 3;
+level(histogram ) -> 3;
+level(arithmetic_mean ) -> 2;
+level(geometric_mean ) -> 2;
+level(harmonic_mean ) -> 2;
+level(_) -> 1.
+
+report_subset(Items, N, SortedValues, Scan_res, Scan_res2) ->
+ lists:map(
+ fun(min) -> {min, hd(SortedValues)};
+ (max) -> {max, lists:last(SortedValues)};
+ (arithmetic_mean) -> {arithmetic_mean, arithmetic_mean(Scan_res)};
+ (harmonic_mean) -> {harmonic_mean, harmonic_mean(Scan_res)};
+ (geometric_mean) -> {geometric_mean, geometric_mean(Scan_res)};
+ (median) -> {median, percentile(SortedValues,
+ #scan_result{n = N}, 0.5)};
+ (variance) -> {variance, variance(Scan_res, Scan_res2)};
+ (standard_deviation=I) -> {I, std_deviation(Scan_res, Scan_res2)};
+ (skewness) -> {skewness, skewness(Scan_res, Scan_res2)};
+ (kurtosis) -> {kurtosis, kurtosis(Scan_res, Scan_res2)};
+ ({percentile,Ps}) -> {percentile, percentiles(Ps, N, SortedValues)};
+ (histogram) ->
+ {histogram, get_histogram(SortedValues, Scan_res, Scan_res2)};
+ (n) -> {n, N}
+ end, Items).
get_statistics(Values, _) when length(Values) < ?STATS_MIN ->
0.0;
@@ -446,3 +500,36 @@
end
end.
+
+percentiles(Ps, N, Values) ->
+ Items = [{P, perc(P, N)} || P <- Ps],
+ pick_items(Values, 1, Items).
+
+pick_items([H|_] = L, P, [{Tag,P}|Ps]) ->
+ [{Tag,H} | pick_items(L, P, Ps)];
+pick_items([_|T], P, Ps) ->
+ pick_items(T, P+1, Ps);
+pick_items([], _, Ps) ->
+ [{Tag,undefined} || {Tag,_} <- Ps].
+
+perc(P, Len) when is_integer(P), 0 =< P, P =< 100 ->
+ V = round(P * Len / 100),
+ erlang:max(1, V);
+perc(P, Len) when is_integer(P), 100 =< P, P =< 1000 ->
+ V = round(P * Len / 1000),
+ erlang:max(1, V);
+perc(P, Len) when is_float(P), 0 =< P, P =< 1 ->
+ erlang:max(1, round(P * Len)).
+
+
+test_values() ->
+ [1,1,1,1,1,1,1,
+ 2,2,2,2,2,2,2,
+ 3,3,3,3,3,3,3,3,3,3,3,3,3,3,
+ 4,4,4,4,4,4,4,4,4,4,4,4,4,4,
+ 5,5,5,5,5,5,5,5,5,5,5,5,5,5,
+ 6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,
+ 7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,
+ 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,
+ 9,9,9,9,9,9,9].
+
diff --git a/test/bear_test.erl b/test/bear_test.erl
index 2cca076..fc37c6b 100644
--- a/test/bear_test.erl
+++ b/test/bear_test.erl
@@ -235,3 +235,28 @@
?assertEqual([{2.0,5},{2.0,5},{2.0,5},{2.0,5}], bear:tied_rank_worker([], [{2.0,5}], {[1,2,3], 5})),
?assertEqual([{2.0,5},{2.0,5},{2.0,5},{2.0,5},{2.0,5},{2.0,5}],
bear:tied_rank_worker([{2.0,5},{2.0,5}], [{2.0,5}], {[1,2,3], 5})).
+
+subset_test() ->
+ Stats = bear:get_statistics(bear:test_values()),
+ match_values(Stats).
+
+full_subset_test() ->
+ Stats = bear:get_statistics(bear:test_values()),
+ match_values2(Stats).
+
+match_values([H|T]) ->
+ Res = bear:get_statistics_subset(bear:test_values(), [mk_item(H)]),
+ Res = [H],
+ match_values(T);
+match_values([]) ->
+ ok.
+
+mk_item({percentile, Ps}) ->
+ {percentile, [P || {P,_} <- Ps]};
+mk_item({K, _}) ->
+ K.
+
+match_values2(Stats) ->
+ Items = [mk_item(I) || I <- Stats],
+ Stats = bear:get_statistics_subset(bear:test_values(), Items),
+ ok.
