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Edit File: test_minitest_benchmark.rb
require "minitest/autorun" require "minitest/benchmark" ## # Used to verify data: # http://www.wolframalpha.com/examples/RegressionAnalysis.html class TestMinitestBenchmark < Minitest::Test def test_cls_bench_exp assert_equal [2, 4, 8, 16, 32], Minitest::Benchmark.bench_exp(2, 32, 2) end def test_cls_bench_linear assert_equal [2, 4, 6, 8, 10], Minitest::Benchmark.bench_linear(2, 10, 2) end def test_cls_runnable_methods assert_equal [], Minitest::Benchmark.runnable_methods c = Class.new(Minitest::Benchmark) do def bench_blah end end assert_equal ["bench_blah"], c.runnable_methods end def test_cls_bench_range assert_equal [1, 10, 100, 1_000, 10_000], Minitest::Benchmark.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_logarithmic_clean x = [1.0, 2.0, 3.0, 4.0, 5.0] y = x.map { |n| 1.1 + 2.1 * Math.log(n) } assert_fit :logarithmic, x, y, 1.0, 1.1, 2.1 end def test_fit_logarithmic_noisy x = [1.0, 2.0, 3.0, 4.0, 5.0] # Generated with # y = x.map { |n| jitter = 0.999 + 0.002 * rand; (Math.log(n) ) * jitter } y = [0.0, 0.6935, 1.0995, 1.3873, 1.6097] assert_fit :logarithmic, x, y, 0.95, 0, 1 end def test_fit_constant_clean x = (1..5).to_a y = [5.0, 5.0, 5.0, 5.0, 5.0] assert_fit :linear, x, y, nil, 5.0, 0 end def test_fit_constant_noisy x = (1..5).to_a y = [1.0, 1.2, 1.0, 0.8, 1.0] # verified in numbers and R assert_fit :linear, x, y, nil, 1.12, -0.04 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 = [15_000, 25_000, 35_000, 50_000, 75_000, 100_000] 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 bench = Minitest::Benchmark.new :blah a, b, rr = bench.send "fit_#{msg}", x, y assert_operator rr, :>=, fit if fit assert_in_delta exp_a, a assert_in_delta exp_b, b end end describe "my class Bench" do klass = self it "should provide bench methods" do klass.must_respond_to :bench end end