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Diffstat (limited to 'test/minitest/test_minitest_benchmark.rb')
-rw-r--r-- | test/minitest/test_minitest_benchmark.rb | 104 |
1 files changed, 104 insertions, 0 deletions
diff --git a/test/minitest/test_minitest_benchmark.rb b/test/minitest/test_minitest_benchmark.rb new file mode 100644 index 0000000000..089d0b3ce0 --- /dev/null +++ b/test/minitest/test_minitest_benchmark.rb @@ -0,0 +1,104 @@ +############################################################ +# This file is imported from a different project. +# DO NOT make modifications in this repo. +# File a patch instead and assign it to Ryan Davis +############################################################ + +require 'minitest/autorun' +require 'minitest/benchmark' + +## +# Used to verify data: +# http://www.wolframalpha.com/examples/RegressionAnalysis.html + +class TestMiniTestBenchmark < MiniTest::Unit::TestCase + def test_cls_bench_exp + assert_equal [2, 4, 8, 16, 32], self.class.bench_exp(2, 32, 2) + end + + def test_cls_bench_linear + assert_equal [2, 4, 6, 8, 10], self.class.bench_linear(2, 10, 2) + end + + def test_cls_benchmark_methods + assert_equal [], self.class.benchmark_methods + + c = Class.new(MiniTest::Unit::TestCase) do + def bench_blah + end + end + + assert_equal ["bench_blah"], c.benchmark_methods + end + + def test_cls_bench_range + assert_equal [1, 10, 100, 1_000, 10_000], self.class.bench_range + end + + def test_fit_exponential_clean + x = [1.0, 2.0, 3.0, 4.0, 5.0] + y = x.map { |n| 1.1 * Math.exp(2.1 * n) } + + assert_fit :exponential, x, y, 1.0, 1.1, 2.1 + end + + def test_fit_exponential_noisy + x = [1.0, 1.9, 2.6, 3.4, 5.0] + y = [12, 10, 8.2, 6.9, 5.9] + + # verified with Numbers and R + assert_fit :exponential, x, y, 0.95, 13.81148, -0.1820 + end + + def test_fit_linear_clean + # y = m * x + b where m = 2.2, b = 3.1 + x = (1..5).to_a + y = x.map { |n| 2.2 * n + 3.1 } + + assert_fit :linear, x, y, 1.0, 3.1, 2.2 + end + + def test_fit_linear_noisy + x = [ 60, 61, 62, 63, 65] + y = [3.1, 3.6, 3.8, 4.0, 4.1] + + # verified in numbers and R + assert_fit :linear, x, y, 0.8315, -7.9635, 0.1878 + end + + def test_fit_power_clean + # y = A x ** B, where B = b and A = e ** a + # if, A = 1, B = 2, then + + x = [1.0, 2.0, 3.0, 4.0, 5.0] + y = [1.0, 4.0, 9.0, 16.0, 25.0] + + assert_fit :power, x, y, 1.0, 1.0, 2.0 + end + + def test_fit_power_noisy + # from www.engr.uidaho.edu/thompson/courses/ME330/lecture/least_squares.html + x = [10, 12, 15, 17, 20, 22, 25, 27, 30, 32, 35] + y = [95, 105, 125, 141, 173, 200, 253, 298, 385, 459, 602] + + # verified in numbers + assert_fit :power, x, y, 0.90, 2.6217, 1.4556 + + # income to % of households below income amount + # http://library.wolfram.com/infocenter/Conferences/6461/PowerLaws.nb + x = [15000, 25000, 35000, 50000, 75000, 100000] + y = [0.154, 0.283, 0.402, 0.55, 0.733, 0.843] + + # verified in numbers + assert_fit :power, x, y, 0.96, 3.119e-5, 0.8959 + end + + def assert_fit msg, x, y, fit, exp_a, exp_b + a, b, rr = send "fit_#{msg}", x, y + + assert_operator rr, :>=, fit + assert_in_delta exp_a, a + assert_in_delta exp_b, b + end +end + |