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 = { ['_count'] = 100, ['_sets'] = 10, }

-- access function for the number of items in each sets -- @param number new count of items in each set -- @return number of items in each set function p.count( num ) if num then p._count = num end return p._count end

-- access function for the number of sets -- @param number new count of sets -- @return number of sets function p.sets( num ) if num then p._sets = num end return p._sets end

-- 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 -- @return table of runtime for the given function function p.runner(func, ...) -- measure the function local time = {} for i=1,p._sets do		time[1+#time] = os.clock for j=1,p._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.\n' messages['load-result'] = 'Relative load is estimated to $1.\n' 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 function f(formatstring, nums) local formatted = {} for _,v in ipairs(nums) do			formatted[1+#formatted] = string.format( formatstring, v ) end return formatted end local strings = { g('call-result'):numParams(unpack(f('%.1e', timing[1]))):plain, g('mean-result'):numParams(unpack(f('%.1e', timing[2]))):plain, g('overall-result'):numParams(unpack(f('%.1e', timing[3]))):plain, g('load-result'):numParams(unpack(f('%.1f', timing[4]))):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 -- @return string describing the result __call = function (self, func, ...) -- start the clock local tick = os.clock -- a dummy function that is used for a baseline measure function dummy return nil end local baseline = self.stats( self.runner(dummy, ...) ) local actual = self.stats( self.runner(func, ...) ) local combined = self.combine(baseline, actual) local tack = os.clock return self.report({{ combined[1] / p._count }, combined, { tack - tick }, {actual[1]/baseline[1]}}) end }

-- install the metatable setmetatable(p, mt)

-- done return p