From 9bd79c2cefb349a00958e35801acb724f0dcae15 Mon Sep 17 00:00:00 2001
From: Hiroshi SHIBATA <hsbt@ruby-lang.org>
Date: Tue, 18 May 2021 17:31:34 +0900
Subject: Removed minitest/benchmark

---
 tool/lib/minitest/benchmark.rb                | 418 --------------------------
 tool/test/minitest/test_minitest_benchmark.rb | 130 --------
 2 files changed, 548 deletions(-)
 delete mode 100644 tool/lib/minitest/benchmark.rb
 delete mode 100644 tool/test/minitest/test_minitest_benchmark.rb

diff --git a/tool/lib/minitest/benchmark.rb b/tool/lib/minitest/benchmark.rb
deleted file mode 100644
index 547b516c4b..0000000000
--- a/tool/lib/minitest/benchmark.rb
+++ /dev/null
@@ -1,418 +0,0 @@
-# encoding: utf-8
-# frozen_string_literal: true
-
-require 'minitest/unit'
-
-class MiniTest::Unit # :nodoc:
-  def run_benchmarks # :nodoc:
-    _run_anything :benchmark
-  end
-
-  def benchmark_suite_header suite # :nodoc:
-    "\n#{suite}\t#{suite.bench_range.join("\t")}"
-  end
-
-  class TestCase
-    ##
-    # Returns a set of ranges stepped exponentially from +min+ to
-    # +max+ by powers of +base+. Eg:
-    #
-    #   bench_exp(2, 16, 2) # => [2, 4, 8, 16]
-
-    def self.bench_exp min, max, base = 10
-      min = (Math.log10(min) / Math.log10(base)).to_i
-      max = (Math.log10(max) / Math.log10(base)).to_i
-
-      (min..max).map { |m| base ** m }.to_a
-    end
-
-    ##
-    # Returns a set of ranges stepped linearly from +min+ to +max+ by
-    # +step+. Eg:
-    #
-    #   bench_linear(20, 40, 10) # => [20, 30, 40]
-
-    def self.bench_linear min, max, step = 10
-      (min..max).step(step).to_a
-    rescue LocalJumpError # 1.8.6
-      r = []; (min..max).step(step) { |n| r << n }; r
-    end
-
-    ##
-    # Returns the benchmark methods (methods that start with bench_)
-    # for that class.
-
-    def self.benchmark_methods # :nodoc:
-      public_instance_methods(true).grep(/^bench_/).map { |m| m.to_s }.sort
-    end
-
-    ##
-    # Returns all test suites that have benchmark methods.
-
-    def self.benchmark_suites
-      TestCase.test_suites.reject { |s| s.benchmark_methods.empty? }
-    end
-
-    ##
-    # Specifies the ranges used for benchmarking for that class.
-    # Defaults to exponential growth from 1 to 10k by powers of 10.
-    # Override if you need different ranges for your benchmarks.
-    #
-    # See also: ::bench_exp and ::bench_linear.
-
-    def self.bench_range
-      bench_exp 1, 10_000
-    end
-
-    ##
-    # Runs the given +work+, gathering the times of each run. Range
-    # and times are then passed to a given +validation+ proc. Outputs
-    # the benchmark name and times in tab-separated format, making it
-    # easy to paste into a spreadsheet for graphing or further
-    # analysis.
-    #
-    # Ranges are specified by ::bench_range.
-    #
-    # Eg:
-    #
-    #   def bench_algorithm
-    #     validation = proc { |x, y| ... }
-    #     assert_performance validation do |n|
-    #       @obj.algorithm(n)
-    #     end
-    #   end
-
-    def assert_performance validation, &work
-      range = self.class.bench_range
-
-      io.print "#{__name__}"
-
-      times = []
-
-      range.each do |x|
-        GC.start
-        t0 = Time.now
-        instance_exec(x, &work)
-        t = Time.now - t0
-
-        io.print "\t%9.6f" % t
-        times << t
-      end
-      io.puts
-
-      validation[range, times]
-    end
-
-    ##
-    # Runs the given +work+ and asserts that the times gathered fit to
-    # match a constant rate (eg, linear slope == 0) within a given
-    # +threshold+. Note: because we're testing for a slope of 0, R^2
-    # is not a good determining factor for the fit, so the threshold
-    # is applied against the slope itself. As such, you probably want
-    # to tighten it from the default.
-    #
-    # See http://www.graphpad.com/curvefit/goodness_of_fit.htm for
-    # more details.
-    #
-    # Fit is calculated by #fit_linear.
-    #
-    # Ranges are specified by ::bench_range.
-    #
-    # Eg:
-    #
-    #   def bench_algorithm
-    #     assert_performance_constant 0.9999 do |n|
-    #       @obj.algorithm(n)
-    #     end
-    #   end
-
-    def assert_performance_constant threshold = 0.99, &work
-      validation = proc do |range, times|
-        a, b, rr = fit_linear range, times
-        assert_in_delta 0, b, 1 - threshold
-        [a, b, rr]
-      end
-
-      assert_performance validation, &work
-    end
-
-    ##
-    # Runs the given +work+ and asserts that the times gathered fit to
-    # match a exponential curve within a given error +threshold+.
-    #
-    # Fit is calculated by #fit_exponential.
-    #
-    # Ranges are specified by ::bench_range.
-    #
-    # Eg:
-    #
-    #   def bench_algorithm
-    #     assert_performance_exponential 0.9999 do |n|
-    #       @obj.algorithm(n)
-    #     end
-    #   end
-
-    def assert_performance_exponential threshold = 0.99, &work
-      assert_performance validation_for_fit(:exponential, threshold), &work
-    end
-
-    ##
-    # Runs the given +work+ and asserts that the times gathered fit to
-    # match a logarithmic curve within a given error +threshold+.
-    #
-    # Fit is calculated by #fit_logarithmic.
-    #
-    # Ranges are specified by ::bench_range.
-    #
-    # Eg:
-    #
-    #   def bench_algorithm
-    #     assert_performance_logarithmic 0.9999 do |n|
-    #       @obj.algorithm(n)
-    #     end
-    #   end
-
-    def assert_performance_logarithmic threshold = 0.99, &work
-      assert_performance validation_for_fit(:logarithmic, threshold), &work
-    end
-
-    ##
-    # Runs the given +work+ and asserts that the times gathered fit to
-    # match a straight line within a given error +threshold+.
-    #
-    # Fit is calculated by #fit_linear.
-    #
-    # Ranges are specified by ::bench_range.
-    #
-    # Eg:
-    #
-    #   def bench_algorithm
-    #     assert_performance_linear 0.9999 do |n|
-    #       @obj.algorithm(n)
-    #     end
-    #   end
-
-    def assert_performance_linear threshold = 0.99, &work
-      assert_performance validation_for_fit(:linear, threshold), &work
-    end
-
-    ##
-    # Runs the given +work+ and asserts that the times gathered curve
-    # fit to match a power curve within a given error +threshold+.
-    #
-    # Fit is calculated by #fit_power.
-    #
-    # Ranges are specified by ::bench_range.
-    #
-    # Eg:
-    #
-    #   def bench_algorithm
-    #     assert_performance_power 0.9999 do |x|
-    #       @obj.algorithm
-    #     end
-    #   end
-
-    def assert_performance_power threshold = 0.99, &work
-      assert_performance validation_for_fit(:power, threshold), &work
-    end
-
-    ##
-    # Takes an array of x/y pairs and calculates the general R^2 value.
-    #
-    # See: https://en.wikipedia.org/wiki/Coefficient_of_determination
-
-    def fit_error xys
-      y_bar  = sigma(xys) { |x, y| y } / xys.size.to_f
-      ss_tot = sigma(xys) { |x, y| (y    - y_bar) ** 2 }
-      ss_err = sigma(xys) { |x, y| (yield(x) - y) ** 2 }
-
-      1 - (ss_err / ss_tot)
-    end
-
-    ##
-    # To fit a functional form: y = ae^(bx).
-    #
-    # Takes x and y values and returns [a, b, r^2].
-    #
-    # See: http://mathworld.wolfram.com/LeastSquaresFittingExponential.html
-
-    def fit_exponential xs, ys
-      n     = xs.size
-      xys   = xs.zip(ys)
-      sxlny = sigma(xys) { |x,y| x * Math.log(y) }
-      slny  = sigma(xys) { |x,y| Math.log(y)     }
-      sx2   = sigma(xys) { |x,y| x * x           }
-      sx    = sigma xs
-
-      c = n * sx2 - sx ** 2
-      a = (slny * sx2 - sx * sxlny) / c
-      b = ( n * sxlny - sx * slny ) / c
-
-      return Math.exp(a), b, fit_error(xys) { |x| Math.exp(a + b * x) }
-    end
-
-    ##
-    # To fit a functional form: y = a + b*ln(x).
-    #
-    # Takes x and y values and returns [a, b, r^2].
-    #
-    # See: http://mathworld.wolfram.com/LeastSquaresFittingLogarithmic.html
-
-    def fit_logarithmic xs, ys
-      n     = xs.size
-      xys   = xs.zip(ys)
-      slnx2 = sigma(xys) { |x,y| Math.log(x) ** 2 }
-      slnx  = sigma(xys) { |x,y| Math.log(x)      }
-      sylnx = sigma(xys) { |x,y| y * Math.log(x)  }
-      sy    = sigma(xys) { |x,y| y                }
-
-      c = n * slnx2 - slnx ** 2
-      b = ( n * sylnx - sy * slnx ) / c
-      a = (sy - b * slnx) / n
-
-      return a, b, fit_error(xys) { |x| a + b * Math.log(x) }
-    end
-
-
-    ##
-    # Fits the functional form: a + bx.
-    #
-    # Takes x and y values and returns [a, b, r^2].
-    #
-    # See: http://mathworld.wolfram.com/LeastSquaresFitting.html
-
-    def fit_linear xs, ys
-      n   = xs.size
-      xys = xs.zip(ys)
-      sx  = sigma xs
-      sy  = sigma ys
-      sx2 = sigma(xs)  { |x|   x ** 2 }
-      sxy = sigma(xys) { |x,y| x * y  }
-
-      c = n * sx2 - sx**2
-      a = (sy * sx2 - sx * sxy) / c
-      b = ( n * sxy - sx * sy ) / c
-
-      return a, b, fit_error(xys) { |x| a + b * x }
-    end
-
-    ##
-    # To fit a functional form: y = ax^b.
-    #
-    # Takes x and y values and returns [a, b, r^2].
-    #
-    # See: http://mathworld.wolfram.com/LeastSquaresFittingPowerLaw.html
-
-    def fit_power xs, ys
-      n       = xs.size
-      xys     = xs.zip(ys)
-      slnxlny = sigma(xys) { |x, y| Math.log(x) * Math.log(y) }
-      slnx    = sigma(xs)  { |x   | Math.log(x)               }
-      slny    = sigma(ys)  { |   y| Math.log(y)               }
-      slnx2   = sigma(xs)  { |x   | Math.log(x) ** 2          }
-
-      b = (n * slnxlny - slnx * slny) / (n * slnx2 - slnx ** 2);
-      a = (slny - b * slnx) / n
-
-      return Math.exp(a), b, fit_error(xys) { |x| (Math.exp(a) * (x ** b)) }
-    end
-
-    ##
-    # Enumerates over +enum+ mapping +block+ if given, returning the
-    # sum of the result. Eg:
-    #
-    #   sigma([1, 2, 3])                # => 1 + 2 + 3 => 7
-    #   sigma([1, 2, 3]) { |n| n ** 2 } # => 1 + 4 + 9 => 14
-
-    def sigma enum, &block
-      enum = enum.map(&block) if block
-      enum.inject { |sum, n| sum + n }
-    end
-
-    ##
-    # Returns a proc that calls the specified fit method and asserts
-    # that the error is within a tolerable threshold.
-
-    def validation_for_fit msg, threshold
-      proc do |range, times|
-        a, b, rr = send "fit_#{msg}", range, times
-        assert_operator rr, :>=, threshold
-        [a, b, rr]
-      end
-    end
-  end
-end
-
-class MiniTest::Spec
-  ##
-  # This is used to define a new benchmark method. You usually don't
-  # use this directly and is intended for those needing to write new
-  # performance curve fits (eg: you need a specific polynomial fit).
-  #
-  # See ::bench_performance_linear for an example of how to use this.
-
-  def self.bench name, &block
-    define_method "bench_#{name.gsub(/\W+/, '_')}", &block
-  end
-
-  ##
-  # Specifies the ranges used for benchmarking for that class.
-  #
-  #   bench_range do
-  #     bench_exp(2, 16, 2)
-  #   end
-  #
-  # See Unit::TestCase.bench_range for more details.
-
-  def self.bench_range &block
-    return super unless block
-
-    meta = (class << self; self; end)
-    meta.send :define_method, "bench_range", &block
-  end
-
-  ##
-  # Create a benchmark that verifies that the performance is linear.
-  #
-  #   describe "my class" do
-  #     bench_performance_linear "fast_algorithm", 0.9999 do |n|
-  #       @obj.fast_algorithm(n)
-  #     end
-  #   end
-
-  def self.bench_performance_linear name, threshold = 0.99, &work
-    bench name do
-      assert_performance_linear threshold, &work
-    end
-  end
-
-  ##
-  # Create a benchmark that verifies that the performance is constant.
-  #
-  #   describe "my class" do
-  #     bench_performance_constant "zoom_algorithm!" do |n|
-  #       @obj.zoom_algorithm!(n)
-  #     end
-  #   end
-
-  def self.bench_performance_constant name, threshold = 0.99, &work
-    bench name do
-      assert_performance_constant threshold, &work
-    end
-  end
-
-  ##
-  # Create a benchmark that verifies that the performance is exponential.
-  #
-  #   describe "my class" do
-  #     bench_performance_exponential "algorithm" do |n|
-  #       @obj.algorithm(n)
-  #     end
-  #   end
-
-  def self.bench_performance_exponential name, threshold = 0.99, &work
-    bench name do
-      assert_performance_exponential threshold, &work
-    end
-  end
-end
diff --git a/tool/test/minitest/test_minitest_benchmark.rb b/tool/test/minitest/test_minitest_benchmark.rb
deleted file mode 100644
index 550fb3dc59..0000000000
--- a/tool/test/minitest/test_minitest_benchmark.rb
+++ /dev/null
@@ -1,130 +0,0 @@
-# encoding: utf-8
-# frozen_string_literal: false
-
-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_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 = [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 if fit
-    assert_in_delta exp_a, a
-    assert_in_delta exp_b, b
-  end
-end
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