Beispiel #1
0
func bench(requests, concurrency int, image string) {
	start := time.Now()

	timings := make([]float64, requests)
	completeCh := make(chan time.Duration)
	current := 0
	go func() {
		for timing := range completeCh {
			timings = append(timings, timing.Seconds())
			current++
			percent := float64(current) / float64(requests) * 100
			fmt.Printf("[%3.f%%] %d/%d containers started\n", percent, current, requests)
		}
	}()
	session(requests, concurrency, image, completeCh)
	close(completeCh)

	total := time.Since(start)
	p50th, _ := stats.Median(timings)
	p90th, _ := stats.Percentile(timings, 90)
	p99th, _ := stats.Percentile(timings, 99)

	fmt.Println("")
	fmt.Printf("Time taken for tests: %s\n", total.String())
	fmt.Printf("Time per container: %vms [50th] | %vms [90th] | %vms [99th]\n", int(p50th*1000), int(p90th*1000), int(p99th*1000))
}
Beispiel #2
0
// Finalize calculation of the risk using available datapoints
func riskFinalize(op opContext, rs *slib.RRAServiceRisk) error {
	var (
		rvals []float64
		err   error
	)
	for _, x := range rs.Scenarios {
		// If the scenario had no data, don't include it in the
		// final scoring
		if x.NoData {
			continue
		}
		rvals = append(rvals, x.Score)
	}

	// Note the highest business impact value that was determined from
	// the RRA. This can be used as an indication of the business impact
	// for the service.
	rs.Risk.Impact = rs.UsedRRAAttrib.Impact
	rs.Risk.ImpactLabel, err = slib.ImpactLabelFromValue(rs.Risk.Impact)
	if err != nil {
		return err
	}

	if len(rvals) == 0 {
		// This can occur if we have no metric data, including no valid
		// information in the RRA
		logf("error in risk calculation: %q has no valid scenarios", rs.RRA.Name)
		rs.Risk.Median = 0.0
		rs.Risk.Average = 0.0
		rs.Risk.WorstCase = 0.0
		rs.Risk.MedianLabel = "unknown"
		rs.Risk.AverageLabel = "unknown"
		rs.Risk.WorstCaseLabel = "unknown"
		rs.Risk.DataClass, err = slib.DataValueFromLabel(rs.RRA.DefData)
		return nil
	}
	rs.Risk.Median, err = stats.Median(rvals)
	if err != nil {
		return err
	}
	rs.Risk.MedianLabel = slib.NormalLabelFromValue(rs.Risk.Median)
	rs.Risk.Average, err = stats.Mean(rvals)
	if err != nil {
		return err
	}
	rs.Risk.AverageLabel = slib.NormalLabelFromValue(rs.Risk.Average)
	rs.Risk.WorstCase, err = stats.Max(rvals)
	if err != nil {
		return err
	}
	rs.Risk.WorstCaseLabel = slib.NormalLabelFromValue(rs.Risk.WorstCase)

	rs.Risk.DataClass, err = slib.DataValueFromLabel(rs.RRA.DefData)
	if err != nil {
		return err
	}
	return nil
}
Beispiel #3
0
func main() {

	d := stats.LoadRawData([]interface{}{1.1, "2", 3.0, 4, "5"})

	a, _ := stats.Min(d)
	fmt.Println(a) // 1.1

	a, _ = stats.Max(d)
	fmt.Println(a) // 5

	a, _ = stats.Sum([]float64{1.1, 2.2, 3.3})
	fmt.Println(a) // 6.6

	a, _ = stats.Mean([]float64{1, 2, 3, 4, 5})
	fmt.Println(a) // 3

	a, _ = stats.Median([]float64{1, 2, 3, 4, 5, 6, 7})
	fmt.Println(a) // 4

	m, _ := stats.Mode([]float64{5, 5, 3, 3, 4, 2, 1})
	fmt.Println(m) // [5 3]

	a, _ = stats.PopulationVariance([]float64{1, 2, 3, 4, 5})
	fmt.Println(a) // 2

	a, _ = stats.SampleVariance([]float64{1, 2, 3, 4, 5})
	fmt.Println(a) // 2.5

	a, _ = stats.MedianAbsoluteDeviationPopulation([]float64{1, 2, 3})
	fmt.Println(a) // 1

	a, _ = stats.StandardDeviationPopulation([]float64{1, 2, 3})
	fmt.Println(a) // 0.816496580927726

	a, _ = stats.StandardDeviationSample([]float64{1, 2, 3})
	fmt.Println(a) // 1

	a, _ = stats.Percentile([]float64{1, 2, 3, 4, 5}, 75)
	fmt.Println(a) // 4

	a, _ = stats.PercentileNearestRank([]float64{35, 20, 15, 40, 50}, 75)
	fmt.Println(a) // 40

	c := []stats.Coordinate{
		{1, 2.3},
		{2, 3.3},
		{3, 3.7},
		{4, 4.3},
		{5, 5.3},
	}

	r, _ := stats.LinearRegression(c)
	fmt.Println(r) // [{1 2.3800000000000026} {2 3.0800000000000014} {3 3.7800000000000002} {4 4.479999999999999} {5 5.179999999999998}]

	r, _ = stats.ExponentialRegression(c)
	fmt.Println(r) // [{1 2.5150181024736638} {2 3.032084111136781} {3 3.6554544271334493} {4 4.406984298281804} {5 5.313022222665875}]

	r, _ = stats.LogarithmicRegression(c)
	fmt.Println(r) // [{1 2.1520822363811702} {2 3.3305559222492214} {3 4.019918836568674} {4 4.509029608117273} {5 4.888413396683663}]

	s, _ := stats.Sample([]float64{0.1, 0.2, 0.3, 0.4}, 3, false)
	fmt.Println(s) // [0.2,0.4,0.3]

	s, _ = stats.Sample([]float64{0.1, 0.2, 0.3, 0.4}, 10, true)
	fmt.Println(s) // [0.2,0.2,0.4,0.1,0.2,0.4,0.3,0.2,0.2,0.1]

	q, _ := stats.Quartile([]float64{7, 15, 36, 39, 40, 41})
	fmt.Println(q) // {15 37.5 40}

	iqr, _ := stats.InterQuartileRange([]float64{102, 104, 105, 107, 108, 109, 110, 112, 115, 116, 118})
	fmt.Println(iqr) // 10

	mh, _ := stats.Midhinge([]float64{1, 3, 4, 4, 6, 6, 6, 6, 7, 7, 7, 8, 8, 9, 9, 10, 11, 12, 13})
	fmt.Println(mh) // 7.5

	tr, _ := stats.Trimean([]float64{1, 3, 4, 4, 6, 6, 6, 6, 7, 7, 7, 8, 8, 9, 9, 10, 11, 12, 13})
	fmt.Println(tr) // 7.25

	o, _ := stats.QuartileOutliers([]float64{-1000, 1, 3, 4, 4, 6, 6, 6, 6, 7, 8, 15, 18, 100})
	fmt.Printf("%+v\n", o) //  {Mild:[15 18] Extreme:[-1000 100]}

	gm, _ := stats.GeometricMean([]float64{10, 51.2, 8})
	fmt.Println(gm) // 15.999999999999991

	hm, _ := stats.HarmonicMean([]float64{1, 2, 3, 4, 5})
	fmt.Println(hm) // 2.18978102189781

	a, _ = stats.Round(2.18978102189781, 3)
	fmt.Println(a) // 2.189
}
Beispiel #4
0
func main() {
	flag.Parse()
	n := *concurrency
	m := *total / n

	fmt.Printf("concurrency: %d\nrequests per client: %d\n\n", n, m)

	args := prepareArgs()

	b, _ := proto.Marshal(args)
	fmt.Printf("message size: %d bytes\n\n", len(b))

	var wg sync.WaitGroup
	wg.Add(n * m)

	var trans uint64
	var transOK uint64

	d := make([][]int64, n, n)

	//it contains warmup time but we can ignore it
	totalT := time.Now().UnixNano()
	for i := 0; i < n; i++ {
		dt := make([]int64, 0, m)
		d = append(d, dt)

		go func(i int) {
			conn, err := grpc.Dial(*host, grpc.WithInsecure())
			if err != nil {
				log.Fatalf("did not connect: %v", err)
			}
			c := NewHelloClient(conn)

			//warmup
			for j := 0; j < 5; j++ {
				c.Say(context.Background(), args)
			}

			for j := 0; j < m; j++ {
				t := time.Now().UnixNano()
				reply, err := c.Say(context.Background(), args)
				t = time.Now().UnixNano() - t

				d[i] = append(d[i], t)

				if err == nil && *(reply.Field1) == "OK" {
					atomic.AddUint64(&transOK, 1)
				}

				atomic.AddUint64(&trans, 1)
				wg.Done()
			}

			conn.Close()

		}(i)

	}

	wg.Wait()
	totalT = time.Now().UnixNano() - totalT
	totalT = totalT / 1000000
	fmt.Printf("took %d ms for %d requests", totalT, n*m)

	totalD := make([]int64, 0, n*m)
	for _, k := range d {
		totalD = append(totalD, k...)
	}
	totalD2 := make([]float64, 0, n*m)
	for _, k := range totalD {
		totalD2 = append(totalD2, float64(k))
	}

	mean, _ := stats.Mean(totalD2)
	median, _ := stats.Median(totalD2)
	max, _ := stats.Max(totalD2)
	min, _ := stats.Min(totalD2)

	fmt.Printf("sent     requests    : %d\n", n*m)
	fmt.Printf("received requests    : %d\n", atomic.LoadUint64(&trans))
	fmt.Printf("received requests_OK : %d\n", atomic.LoadUint64(&transOK))
	fmt.Printf("throughput  (TPS)    : %d\n", int64(n*m)*1000/totalT)
	fmt.Printf("mean: %.f ns, median: %.f ns, max: %.f ns, min: %.f ns\n", mean, median, max, min)
	fmt.Printf("mean: %d ms, median: %d ms, max: %d ms, min: %d ms\n", int64(mean/1000000), int64(median/1000000), int64(max/1000000), int64(min/1000000))

}
Beispiel #5
0
func main() {
	flag.Parse()
	n := *concurrency
	m := *total / n

	fmt.Printf("concurrency: %d\nrequests per client: %d\n\n", n, m)

	serviceMethodName := "Hello.Say"
	args := prepareArgs()

	b := make([]byte, 1024*1024)
	i, _ := args.MarshalTo(b)
	fmt.Printf("message size: %d bytes\n\n", i)

	var wg sync.WaitGroup
	wg.Add(n * m)

	var trans uint64
	var transOK uint64

	d := make([][]int64, n, n)

	//it contains warmup time but we can ignore it
	totalT := time.Now().UnixNano()
	for i := 0; i < n; i++ {
		dt := make([]int64, 0, m)
		d = append(d, dt)

		go func(i int) {
			s := &rpcx.DirectClientSelector{Network: "tcp", Address: *host}
			client := rpcx.NewClient(s)
			client.ClientCodecFunc = codec.NewProtobufClientCodec

			var reply BenchmarkMessage

			//warmup
			for j := 0; j < 5; j++ {
				client.Call(serviceMethodName, args, &reply)
			}

			for j := 0; j < m; j++ {
				t := time.Now().UnixNano()
				err := client.Call(serviceMethodName, args, &reply)
				t = time.Now().UnixNano() - t

				d[i] = append(d[i], t)

				if err == nil && reply.Field1 == "OK" {
					atomic.AddUint64(&transOK, 1)
				}

				atomic.AddUint64(&trans, 1)
				wg.Done()
			}

			client.Close()

		}(i)

	}

	wg.Wait()
	totalT = time.Now().UnixNano() - totalT
	totalT = totalT / 1000000
	fmt.Printf("took %d ms for %d requests", totalT, n*m)

	totalD := make([]int64, 0, n*m)
	for _, k := range d {
		totalD = append(totalD, k...)
	}
	totalD2 := make([]float64, 0, n*m)
	for _, k := range totalD {
		totalD2 = append(totalD2, float64(k))
	}

	mean, _ := stats.Mean(totalD2)
	median, _ := stats.Median(totalD2)
	max, _ := stats.Max(totalD2)
	min, _ := stats.Min(totalD2)

	fmt.Printf("sent     requests    : %d\n", n*m)
	fmt.Printf("received requests    : %d\n", atomic.LoadUint64(&trans))
	fmt.Printf("received requests_OK : %d\n", atomic.LoadUint64(&transOK))
	fmt.Printf("throughput  (TPS)    : %d\n", int64(n*m)*1000/totalT)
	fmt.Printf("mean: %.f ns, median: %.f ns, max: %.f ns, min: %.f ns\n", mean, median, max, min)
	fmt.Printf("mean: %d ms, median: %d ms, max: %d ms, min: %d ms\n", int64(mean/1000000), int64(median/1000000), int64(max/1000000), int64(min/1000000))

}
Beispiel #6
0
func main() {
	t := time.Now()
	fmt.Println(t.Format(time.RFC3339))

	rand.Seed(1)

	// Read in data
	readData()

	// Set one level with all row criteria,
	// this is used to start the set creation
	levelOne = fullOneLevel()

	//levels = fullTwoLevel()
	outputRowCriteria(levels)

	// experiment variables
	rand_numSets = 1000
	rand_maxSetMembers = 5
	maxExperiments = 1

	var expMin []float64
	var expMax []float64
	scoreCutoff = -0.89
	rowThreshhold = 2
	zScore = 2.58
	for experiment := 1; experiment <= maxExperiments; experiment++ {
		// experiment variables, changes per experiment
		rand_numSets += 0
		rand_maxSetMembers += 0
		scoreCutoff += -0.00
		zScore += 0.0

		// Setup experiment variables
		var scores []scoreResult
		var minScore float64 = -100
		var maxScore float64 = 0
		levels = fullFourLevel() //randLevels()
		fmt.Printf("sets count: %d, max set members: %d, level 1 count: %d, rowThreshhold: %d, scoreCutoff: %f, zScore: %f\n", len(levels), rand_maxSetMembers+2, len(levelOne), rowThreshhold, scoreCutoff, zScore)

		for dataSetId := 1; dataSetId <= datasets; dataSetId++ {
			s := levelEval(dataSetId)
			sort.Sort(scoreResults(s))

			// s contains a list of scores for one dataset, sorted
			// this is were we can get some info on that data
			//outputScoreList(s)

			if len(s) > 0 {
				//var sEval = evaluateScores(s)
				// pick the top score
				var sEval = s[0]
				scores = append(scores, sEval)
				fmt.Printf("%d, %f \n", sEval.dataSetId, sEval.score)

				if minScore < sEval.score {
					minScore = sEval.score
				}
				if maxScore > sEval.score {
					maxScore = sEval.score
				}
			}

			// For all score in this set write out the median and standard deviation
			var set []float64
			for _, scoreItem := range s {
				if scoreItem.score < 0.0 {
					set = append(set, scoreItem.score)
				}
			}

			var median, _ = stats.Median(set)
			var sd, _ = stats.StandardDeviation(set)
			var min, _ = stats.Min(set)
			var max, _ = stats.Max(set)
			fmt.Printf("dataset: %d, median: %f, sd: %f, min: %f, max: %f, len: %d\n", dataSetId, median, sd, min, max, len(set))

		}

		expMin = append(expMin, minScore)
		expMax = append(expMax, maxScore)

		//scoreCutoff = (minScore * (percentRofMin / 100.0)) + minScore
		//fmt.Printf(" scoreCutoff: %f \n", scoreCutoff)

		outputScores(scores)
		// Write output file
		outputResults(scores)

		// Compare to training truth data
		// compareTrainingDataWithResults()
	}

	t = time.Now()
	fmt.Println(t.Format(time.RFC3339))

	// Output min max scores per experiment
	for _, each := range expMin {
		fmt.Printf("min: %f, ", each)
	}
	fmt.Println()
	for _, each := range expMax {
		fmt.Printf("max: %f, ", each)
	}
}
Beispiel #7
0
func (c *cmdReport2) getFeatures(geneSnpChan chan SNPArr) {
	w, err := os.Create(c.prefix + ".detectable.gene.csv")
	if err != nil {
		log.Fatalln(err)
	}
	defer w.Close()
	w.WriteString("patric_id,genome,figfam,sample,pi,depth\n")

	fn := func(txn *lmdb.Txn) error {
		dbi, err := txn.OpenDBI("feature", 0)
		if err != nil {
			return err
		}

		for gs := range geneSnpChan {
			if len(gs.Arr) < 100 {
				continue
			}

			k := gs.Key
			v, err := txn.Get(dbi, k)
			if err != nil {
				return err
			}
			f := Feature{}
			if err := msgpack.Unmarshal(v, &f); err != nil {
				return err
			}

			seqLen := f.End - f.Start + 1

			// calculate median of depth
			depthArr := []float64{}
			piArr := []float64{}
			for _, snp := range gs.Arr {
				pos := snp.Position - f.Start
				if f.IsComplementaryStrand() {
					pos = seqLen - 1 - pos
				}
				if (pos+1)%3 == 0 {
					depthArr = append(depthArr, float64(len(snp.Bases)))
					piArr = append(piArr, snp.Pi())
				}

			}
			depthMedian, _ := stats.Median(depthArr)
			sort.Float64s(piArr)
			piMean, _ := stats.Mean(piArr[10 : len(piArr)-10])

			w.WriteString(fmt.Sprintf("%s,%s,%s,%s,%g,%g\n",
				f.PatricID,
				f.TaxID,
				f.FigfamID,
				c.prefix,
				piMean,
				depthMedian))
		}
		return nil
	}

	err = c.featureDB.View(fn)
	if err != nil {
		log.Panicln(err)
	}
}