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Merge pull request #15 from rodo/master
Add unit test on uncovered function, move test data from src/ to test/
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joewilliams committed Nov 6, 2013
2 parents 5ed737e + 3994adf commit 7d1ee8e0072341c5d61034560d889450a5bf468f
Showing 2 changed files with 118 additions and 45 deletions.
@@ -524,15 +524,3 @@ perc(P, Len) when is_integer(P), 100 =< P, P =< 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].
@@ -30,7 +30,7 @@

-include_lib("eunit/include/eunit.hrl").

-define(PRECISION, 1.0e15).
-define(PRECISION_DIGIT, 6).

get_statistics_1_empty_test() ->
%% get_statistics/1
@@ -54,28 +54,29 @@ get_statistics_1_empty_test() ->
get_statistics_1_regular_test() ->
%% get_statistics/1
%% Non empty set of values
Percentile = [{50, 5},{75, 8},{90, 9},{95, 10},{99, 10},{999, 10}],
Stats = bear:get_statistics(lists:seq(1,10)),
Percentile = [{50, -10},{75, 23},{90, 43},{95, 46},{99, 50},{999, 50}],
Stats = bear:get_statistics(sample1()),

{geometric_mean, Geometric} = lists:keyfind(geometric_mean, 1, Stats),
{harmonic_mean, Harmonic} = lists:keyfind(harmonic_mean, 1, Stats),
{variance, Variance} = lists:keyfind(variance, 1, Stats),
{standard_deviation, StandardDeviation} = lists:keyfind(standard_deviation, 1, Stats),
{kurtosis, Kurtosis} = lists:keyfind(kurtosis, 1, Stats),

?assertEqual({min, 1}, lists:keyfind(min, 1, Stats)),
?assertEqual({max, 10}, lists:keyfind(max, 1, Stats)),
?assertEqual({arithmetic_mean, 5.5}, lists:keyfind(arithmetic_mean, 1, Stats)),
?assertEqual(4528728688116766, erlang:trunc(?PRECISION * Geometric)),
?assertEqual(3414171521474055, erlang:trunc(?PRECISION * Harmonic)),
?assertEqual({median, 5}, lists:keyfind(median, 1, Stats)),
?assertEqual(9166666666666666, erlang:trunc(?PRECISION * Variance)),
?assertEqual(3027650354097491, erlang:trunc(?PRECISION * StandardDeviation)),
?assertEqual({skewness, 0.0}, lists:keyfind(skewness, 1, Stats)),
?assertEqual(-1561636363636363, erlang:trunc(?PRECISION * Kurtosis)),
{skewness, Skewness} = lists:keyfind(skewness, 1, Stats),

?assertEqual({min, -49}, lists:keyfind(min, 1, Stats)),
?assertEqual({max, 50}, lists:keyfind(max, 1, Stats)),
?assertEqual({arithmetic_mean, -1.66}, lists:keyfind(arithmetic_mean, 1, Stats)),
?assertEqual(true, approx(4.08326, Geometric)),
?assertEqual(true, approx(54.255629738, Harmonic)),
?assertEqual({median, -10}, lists:keyfind(median, 1, Stats)),
?assertEqual(true, approx(921.0453061, Variance)),
?assertEqual(true, approx(30.348728, StandardDeviation)),
?assertEqual(true, approx(0.148722, Skewness)),
?assertEqual(true, approx(-1.2651687, Kurtosis)),
?assertEqual({percentile, Percentile}, lists:keyfind(percentile, 1, Stats)),
?assertEqual({histogram, [{6,6},{11,4},{16,0}]}, lists:keyfind(histogram, 1, Stats)),
?assertEqual({n, 10}, lists:keyfind(n, 1, Stats)).
?assertEqual({histogram, [{-20,16},{11,16},{41,12},{71,6}]}, lists:keyfind(histogram, 1, Stats)),
?assertEqual({n, 50}, lists:keyfind(n, 1, Stats)).

get_statistics_2_1_test() ->
%% get_statistics/2
@@ -152,26 +153,33 @@ update_bin_1_test() ->
C = bear:update_bin(4, [4], Dict),
?assertEqual(1, dict:fetch(4, C)).

get_covariance_test() ->
get_covariance_exceptions_test() ->
%% Array 1 is too short
?assertEqual(0.0, bear:get_covariance([], [2,1,2,3,4,5,6])),
%% Array 2 is too short
?assertEqual(0.0, bear:get_covariance([1,2,3,4,5,6], [])),
%% diffenrent arry length
?assertEqual(0.0, bear:get_covariance([1,2,3,4,5,6], [1,2,3,4,5,6,7])),
?assertEqual(0.0, bear:get_covariance([1,2,3,4,5,6], [1,2,3,4,5,6,7])).

get_covariance_regular_test() ->
%% Usual case
?assertEqual(-30944444444444444, erlang:trunc(?PRECISION * bear:get_covariance([11,2,3,41,5,9], [34,2,23,4,5,6]))).
%% Result is not the same as R compute, R use an unbiased estimate
%% http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Covariance
?assertEqual(true, approx(170.813599, bear:get_covariance(sample1(),sample2()))).

ranks_of_test() ->
?assertEqual([4.0,3.0,1.0,2.0], bear:ranks_of([3,4,15,6])).

get_pearson_correlation_test() ->
get_pearson_correlation_exceptions_test() ->
?assertEqual(0.0, bear:get_pearson_correlation([], 42)),
?assertEqual(0.0, bear:get_pearson_correlation(42, [])),
?assertEqual(0.0, bear:get_pearson_correlation(lists:seq(1,10), lists:seq(1,11))),
?assertEqual(1.0, bear:get_pearson_correlation(lists:seq(1,10), lists:seq(1,10))),
?assertEqual(1.0, bear:get_pearson_correlation(lists:seq(0,10), lists:seq(5,15))),
?assertEqual(1.0, bear:get_pearson_correlation(lists:seq(40,60,2), lists:seq(10,20))).
?assertEqual(1.0, bear:get_pearson_correlation(lists:seq(0,10), lists:seq(5,15))).

get_pearson_correlation_regular_test() ->
%% Target is calculate by R
?assertEqual(true, approx(0.2068785, bear:get_pearson_correlation(sample1(), sample2()))).

get_pearson_correlation_nullresult_test() ->
%% The two series do not correlate
@@ -193,25 +201,33 @@ get_bin_count_test() ->
?assertEqual(3, bear:get_bin_count(9, 15, 3)),
?assertEqual(4, bear:get_bin_count(10.2, 20.2, 4)).

get_kendall_correlation_test()->
get_kendall_correlation_exceptions_test()->
?assertEqual(0.0, bear:get_kendall_correlation([], [])),
?assertEqual(0.0, bear:get_kendall_correlation([], [1,2,3,4,5,6,7])),
?assertEqual(0.0, bear:get_kendall_correlation([1,2,3,4,5,6,7],[])),
?assertEqual(0.0, bear:get_kendall_correlation(lists:seq(1,10),lists:seq(1,11))),
?assertEqual(1.0, bear:get_kendall_correlation([1,2,3,4,5,6,7], [2,3,4,5,6,7,9])).
?assertEqual(0.0, bear:get_kendall_correlation(lists:seq(1,10),lists:seq(1,11))).

get_kendall_correlation_regular_test()->
Kendall = bear:get_kendall_correlation(sample1(order), sample2(order)),
?assertEqual(true, approx(0.9787755, Kendall)).

get_spearman_correlation_test()->
kendall_correlation_test()->
Kendall = bear:kendall_correlation(sample1(order), sample2(order)),
?assertEqual(true, approx(0.9787755, Kendall)).

get_spearman_correlation_exceptions_test()->
?assertEqual(0.0, bear:get_spearman_correlation([], [])),
?assertEqual(0.0, bear:get_spearman_correlation([], [1,2,3,4,5,6,7])),
?assertEqual(0.0, bear:get_spearman_correlation([1,2,3,4,5,6,7],[])),
?assertEqual(0.0, bear:get_spearman_correlation(lists:seq(1,10),lists:seq(1,11))),
?assertEqual(1.0, bear:get_spearman_correlation([1,2,3,4,5,6,7], [2,3,4,5,6,7,9])).
?assertEqual(0.0, bear:get_spearman_correlation(lists:seq(1,10),lists:seq(1,11))).

get_spearman_correlation_regular_test()->
?assertEqual(true, approx(0.997888, bear:get_spearman_correlation(sample1(order), sample2(order)))).

math_log_test() ->
?assertEqual(1, bear:math_log(0)),
?assertEqual(1.0, bear:math_log(0.0)),
?assertEqual(3737669618283368, erlang:trunc(?PRECISION * bear:math_log(42))).
?assertEqual(true, approx(3.737669618283368, bear:math_log(42))).

inverse_test() ->
?assertEqual(0, bear:inverse(0)),
@@ -236,12 +252,25 @@ tied_rank_worker_test() ->
?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})).

perc_test() ->
?assertEqual(14, bear:perc(36, 40)),
?assertEqual(5, bear:perc(900, 5)),
?assertEqual(5, bear:perc(0.9, 5)).

get_statistics_subset_nev_test() ->
%% Not enough values case
?assertEqual([], bear:get_statistics_subset([1,2], [])).

get_statistics_subset_regular_test() ->
%% Regular case
?assertEqual([{max, 50},{min, -49}], bear:get_statistics_subset(sample1(), [max,min])).

subset_test() ->
Stats = bear:get_statistics(bear:test_values()),
Stats = bear:get_statistics(test_values()),
match_values(Stats).

full_subset_test() ->
Stats = bear:get_statistics(bear:test_values()),
Stats = bear:get_statistics(test_values()),
match_values2(Stats).

negative_test() ->
@@ -255,7 +284,7 @@ negative2_test() ->
[{min, -10}] = bear:get_statistics_subset(Values, [min]).

match_values([H|T]) ->
Res = bear:get_statistics_subset(bear:test_values(), [mk_item(H)]),
Res = bear:get_statistics_subset(test_values(), [mk_item(H)]),
Res = [H],
match_values(T);
match_values([]) ->
@@ -268,5 +297,61 @@ mk_item({K, _}) ->

match_values2(Stats) ->
Items = [mk_item(I) || I <- Stats],
Stats = bear:get_statistics_subset(bear:test_values(), Items),
Stats = bear:get_statistics_subset(test_values(), Items),
ok.

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].

negative_values() ->
%% All values are negative
[-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].

between(Value, Low, High) ->
(Value >= Low) and (Value =< High).

approx(Target, Value) ->
High = Target + math:pow(10, - ?PRECISION_DIGIT),
Low = Target - math:pow(10, - ?PRECISION_DIGIT),
case (Value > Low) and (Value < High) of
true -> true;
_ -> Value
end.

check_sample_test() ->
?assertEqual(50, length(sample1())),
?assertEqual(50, length(sample1(order))),
?assertEqual(50, length(sample2())),
?assertEqual(50, length(sample2(order))).

sample1(X) when X == order ->
lists:sort(sample1()).

sample2(X) when X == order ->
lists:sort(sample2()).

sample1() ->
%% datas from file bear/samples/data.csv
%% first column X
[-16,-18,-47,22,-18,36,25,49,-24,15,36,-10,-21,43,-35,1,-24,10,33,-21,-18,-36,-36,-43,-37,-10,23,50,31,-49,43,46,22,-43,12,-47,15,-14,6,-31,46,-8,0,-46,-16,-22,6,10,38,-11].

sample2() ->
%% datas from file bear/samples/data.csv
%% second column Y
[33,20,-35,16,-19,8,25,3,4,10,36,-20,-41,43,28,39,-30,3,-47,-23,17,-6,-50,16,-26,-49,8,-31,24,16,32,27,-19,-32,-17,1,-37,25,-50,-32,-42,-22,25,18,-34,-37,7,-13,16,10].

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