class TestMinitestBenchmark

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 121
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

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 21
def bench_blah
end

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 9
def test_cls_bench_exp
  assert_equal [2, 4, 8, 16, 32], Minitest::Benchmark.bench_exp(2, 32, 2)
end

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 13
def test_cls_bench_linear
  assert_equal [2, 4, 6, 8, 10], Minitest::Benchmark.bench_linear(2, 10, 2)
end

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 28
def test_cls_bench_range
  assert_equal [1, 10, 100, 1_000, 10_000], Minitest::Benchmark.bench_range
end

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 17
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

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 63
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

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 70
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

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 32
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

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 39
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

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 78
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

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 86
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

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 47
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

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 54
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

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 94
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

# File minitest-5.13.0/test/minitest/test_minitest_benchmark.rb, line 104
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