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# This class was taken entirely from Mauricio Fernandez's blog at
# http://eigenclass.org/hiki.rb?adaptative+benchmark
#
class AdaptiveBenchmark
def initialize(len, runs)
@field_len = len
@min_runs = runs
end
def self.bm(len = 10, min_runs = 10)
puts "#{" " * len} \tstddev \truns\tinterval\tconfidence"
yield new(len, min_runs)
end
REPORT_OPT = {:precision => 0.1, :confidence => 0.95}
def report(name, options = {})
old_sync, $stdout.sync = $stdout.sync, true
opt = REPORT_OPT.clone.update(options)
sample_avg = sample_variance = 0
tms_to_total = lambda{|tms| tms.utime + tms.stime }
take_sample = lambda do |i|
GC.start
t0 = tms_to_total[Process.times]
yield
exec_time = tms_to_total[Process.times] - t0
new_sample_avg = sample_avg + (exec_time - sample_avg) / i
if i == 1
sample_avg = new_sample_avg
next
end
sample_variance = (1 - 1.0/i) * sample_variance + (i+1) * (new_sample_avg - sample_avg) ** 2
sample_avg = new_sample_avg
end
(1..@min_runs).each{|i| take_sample[i] }
population_variance = 1.0 * @min_runs / (@min_runs - 1) * sample_variance
a = opt[:precision] * sample_avg
num_runs = (sample_variance / (a ** 2 * (1 - opt[:confidence]))).ceil
total_runs = @min_runs+num_runs
(@min_runs+1..total_runs+1).each{ |i| take_sample[i] }
population_variance = 1.0 * total_runs / (total_runs - 1) * sample_variance
puts "%-#{@field_len}s %8.6f\t%8.6f\t%-6d\t%8.6f\t%3d%%" % [name, sample_avg, Math.sqrt(population_variance), total_runs, a, opt[:confidence] * 100]
ensure
$stdout.sync = old_sync
end
end
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