Module:User:Litzsch/Timing

-- module timing individual functions -- © John Erling Blad, Creative Commons by Attribution 3.0

-- @var The table holding this modules exported members local p = {}

-- calculate the statistics for time series, and report mean and variance -- for some background on this calculation, see average and Standard deviation -- @param table timing is a sequence of time differences -- @return table of mean and variance function p.stats( timing ) local minVal = timing[1] local maxVal = timing[1] for i,v in ipairs(timing) do	end local sqr,mean=0,0 for i,v in ipairs(timing) do		minVal = v < minVal and v or minVal maxVal = v > maxVal and v or maxVal mean = v + mean sqr = math.pow(v,2) + sqr end mean = mean / #timing local var = (sqr / #timing) - math.pow(mean,2) return { mean, var, minVal, maxVal } end

-- runner that iterates a provided function while taking the time for each chunk of iterations -- @param function func that is the kernel of the iterations -- @param integer count that is the size of each chunk of iterations -- @param integer sets that is the number of chunks to process -- @return table of runtime for the given function function p.runner(func, count, sets) -- measure the function local time = {} for i=1,sets do		time[1+#time] = os.clock for j=1,count do			func end time[#time] = os.clock - time[#time] end return time end

-- combine the measurements into a new form -- for some background on this calculation, see Sum of normally distributed random variables -- @param table tick stats for the baseline -- @param table tack stats for the observed function -- @return table with the combined stats, shifted from variance to standard deviation function p.combine(tick, tack) -- adjust the actual measurement for the baseline return { tack[1] - tick[1], math.pow(tack[2] + tick[2], 0.5), tick[3], tick[4] } end

-- formatter for the result produced by the runner -- @param table timing as a mean and a standard deviation -- @return string describing the result function p.report( timing ) local messages = {} messages['call-result'] = 'Each call was running for about $1 seconds.\n' messages['mean-result'] = '\tMean runtime for each set was $1 seconds,\n\twith standard deviation of $2 seconds,\n\tminimum $3, maximum $4.\n' messages['overall-result'] = '\tTotal time spent was about $1 seconds.' local function g( key, ...) local msg = mw.message.new( 'timing-' .. key ) if msg:isBlank then msg = mw.message.newRawMessage( messages[key] ) end return msg end local strings = { g('call-result'):numParams(unpack(timing[1])):plain, g('mean-result'):numParams(unpack(timing[2])):plain, g('overall-result'):numParams(unpack(timing[3])):plain }	return table.concat(strings, '') end

-- metatable for the export local mt = { -- call-form of the table, with arguments as if it were runner -- @paramfunction func that is the kernel of the iterations -- @param integer count that is the size of each chunk of iterations, default is 100 -- @param integer sets that is the number of chunks to process, default is 10 -- @return string describing the result __call = function (self, func, count, sets) local tick = os.clock -- the defaults if not count then count = 100 end if not sets then sets = 10 end -- a dummy function that is used for a baseline measure function dummy return nil end local baseline = self.stats( self.runner(dummy, count, sets) ) local actual = self.stats( self.runner(func, count, sets) ) local combined = self.combine(baseline, actual) local tack = os.clock return self.report({{ combined[1] / count }, combined, { tack - tick }}) end }

-- install the metatable setmetatable(p, mt)

-- done return p