diff options
Diffstat (limited to 'ruby_1_9_3/lib/minitest/benchmark.rb')
| -rw-r--r-- | ruby_1_9_3/lib/minitest/benchmark.rb | 372 |
1 files changed, 0 insertions, 372 deletions
diff --git a/ruby_1_9_3/lib/minitest/benchmark.rb b/ruby_1_9_3/lib/minitest/benchmark.rb deleted file mode 100644 index 77c0afafb7..0000000000 --- a/ruby_1_9_3/lib/minitest/benchmark.rb +++ /dev/null @@ -1,372 +0,0 @@ -###################################################################### -# This file is imported from the minitest project. -# DO NOT make modifications in this repo. They _will_ be reverted! -# File a patch instead and assign it to Ryan Davis. -###################################################################### - -require 'minitest/unit' -require 'minitest/spec' - -class MiniTest::Unit - attr_accessor :runner - - 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 |x| - # @obj.algorithm - # 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 |x| - # @obj.algorithm - # 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 |x| - # @obj.algorithm - # 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 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 |x| - # @obj.algorithm - # 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: http://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 - - ## - # 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 - - 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 - # @obj.fast_algorithm - # 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 - # @obj.zoom_algorithm! - # 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 - # @obj.algorithm - # end - # end - - def self.bench_performance_exponential name, threshold = 0.99, &work - bench name do - assert_performance_exponential threshold, &work - end - end -end |