зеркало из https://github.com/github/ruby.git
131 строка
3.2 KiB
Ruby
131 строка
3.2 KiB
Ruby
# encoding: utf-8
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require 'minitest/autorun'
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require 'minitest/benchmark'
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##
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# Used to verify data:
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# http://www.wolframalpha.com/examples/RegressionAnalysis.html
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class TestMiniTestBenchmark < MiniTest::Unit::TestCase
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def test_cls_bench_exp
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assert_equal [2, 4, 8, 16, 32], self.class.bench_exp(2, 32, 2)
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end
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def test_cls_bench_linear
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assert_equal [2, 4, 6, 8, 10], self.class.bench_linear(2, 10, 2)
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end
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def test_cls_benchmark_methods
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assert_equal [], self.class.benchmark_methods
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c = Class.new(MiniTest::Unit::TestCase) do
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def bench_blah
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end
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end
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assert_equal ["bench_blah"], c.benchmark_methods
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end
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def test_cls_bench_range
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assert_equal [1, 10, 100, 1_000, 10_000], self.class.bench_range
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end
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def test_fit_exponential_clean
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x = [1.0, 2.0, 3.0, 4.0, 5.0]
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y = x.map { |n| 1.1 * Math.exp(2.1 * n) }
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assert_fit :exponential, x, y, 1.0, 1.1, 2.1
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end
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def test_fit_exponential_noisy
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x = [1.0, 1.9, 2.6, 3.4, 5.0]
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y = [12, 10, 8.2, 6.9, 5.9]
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# verified with Numbers and R
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assert_fit :exponential, x, y, 0.95, 13.81148, -0.1820
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end
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def test_fit_logarithmic_clean
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x = [1.0, 2.0, 3.0, 4.0, 5.0]
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y = x.map { |n| 1.1 + 2.1 * Math.log(n) }
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assert_fit :logarithmic, x, y, 1.0, 1.1, 2.1
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end
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def test_fit_logarithmic_noisy
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x = [1.0, 2.0, 3.0, 4.0, 5.0]
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# Generated with
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# y = x.map { |n| jitter = 0.999 + 0.002 * rand; (Math.log(n) ) * jitter }
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y = [0.0, 0.6935, 1.0995, 1.3873, 1.6097]
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assert_fit :logarithmic, x, y, 0.95, 0, 1
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end
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def test_fit_constant_clean
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x = (1..5).to_a
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y = [5.0, 5.0, 5.0, 5.0, 5.0]
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assert_fit :linear, x, y, nil, 5.0, 0
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end
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def test_fit_constant_noisy
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x = (1..5).to_a
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y = [1.0, 1.2, 1.0, 0.8, 1.0]
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# verified in numbers and R
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assert_fit :linear, x, y, nil, 1.12, -0.04
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end
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def test_fit_linear_clean
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# y = m * x + b where m = 2.2, b = 3.1
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x = (1..5).to_a
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y = x.map { |n| 2.2 * n + 3.1 }
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assert_fit :linear, x, y, 1.0, 3.1, 2.2
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end
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def test_fit_linear_noisy
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x = [ 60, 61, 62, 63, 65]
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y = [3.1, 3.6, 3.8, 4.0, 4.1]
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# verified in numbers and R
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assert_fit :linear, x, y, 0.8315, -7.9635, 0.1878
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end
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def test_fit_power_clean
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# y = A x ** B, where B = b and A = e ** a
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# if, A = 1, B = 2, then
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x = [1.0, 2.0, 3.0, 4.0, 5.0]
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y = [1.0, 4.0, 9.0, 16.0, 25.0]
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assert_fit :power, x, y, 1.0, 1.0, 2.0
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end
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def test_fit_power_noisy
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# from www.engr.uidaho.edu/thompson/courses/ME330/lecture/least_squares.html
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x = [10, 12, 15, 17, 20, 22, 25, 27, 30, 32, 35]
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y = [95, 105, 125, 141, 173, 200, 253, 298, 385, 459, 602]
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# verified in numbers
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assert_fit :power, x, y, 0.90, 2.6217, 1.4556
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# income to % of households below income amount
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# http://library.wolfram.com/infocenter/Conferences/6461/PowerLaws.nb
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x = [15000, 25000, 35000, 50000, 75000, 100000]
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y = [0.154, 0.283, 0.402, 0.55, 0.733, 0.843]
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# verified in numbers
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assert_fit :power, x, y, 0.96, 3.119e-5, 0.8959
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end
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def assert_fit msg, x, y, fit, exp_a, exp_b
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a, b, rr = send "fit_#{msg}", x, y
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assert_operator rr, :>=, fit if fit
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assert_in_delta exp_a, a
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assert_in_delta exp_b, b
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end
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end
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