func updateComplexity(v *Web, pop neat.Population) {
// Build complexity slice
x := make([]float64, len(pop.Genomes))
for i, g := range pop.Genomes {
x[i] = float64(g.Complexity())
}
var b neat.Genome
max := -1.0
for _, g := range pop.Genomes {
if g.Fitness > max {
b = g
max = g.Fitness
}
}
// Append the record
min, _ := stats.Min(x)
max, _ = stats.Max(x)
mean, _ := stats.Mean(x)
v.complexity = append(v.complexity, [4]float64{
min,
mean,
max,
float64(b.Complexity()),
})
}
func (hist *History) PrintSummary() {
nanos := []float64{}
for _, duration := range hist.values {
nanos = append(nanos, float64(duration))
}
fmt.Printf("%10s", hist.name)
fmt.Printf(" %10s", time.Duration(stats.Min(nanos)))
for _, p := range percentiles {
nano := time.Duration(stats.Percentile(nanos, p))
fmt.Printf(" %10s", nano)
}
fmt.Printf(" %10s", time.Duration(stats.Max(nanos)))
fmt.Println()
}
func updateFitness(v *Web, pop neat.Population) {
// Build fitness slice
x := make([]float64, len(pop.Genomes))
for i, g := range pop.Genomes {
x[i] = g.Fitness
}
// Append the record
min, _ := stats.Min(x)
max, _ := stats.Max(x)
mean, _ := stats.Mean(x)
v.fitness = append(v.fitness, [3]float64{
min,
mean,
max,
})
}
//fuzzyMap uses a word-similarity algorithm to match a value to its dictionary key, then assigns it the dictionary value
func fuzzyMap(datum string, mappings []datasources.Setting) string {
var distances []float64
result := ""
for _, mapping := range mappings {
d := matchr.Levenshtein(datum, mapping.Label)
distances = append(distances, float64(d))
}
minDistance, err := stats.Min(distances)
if err != nil {
log.Fatal("Error finding minimum: ", err)
}
for i, distance := range distances {
if int(minDistance) == int(distance) {
result = mappings[i].Value
break
}
}
return result
}
func FprintSummary(out io.Writer, hists ...*History) {
fmt.Fprintf(out, "%10s", "")
fmt.Fprintf(out, " %10s", "MIN")
for _, p := range percentiles {
fmt.Fprintf(out, " %9d%%", int(p))
}
fmt.Fprintf(out, " %10s", "MAX")
fmt.Fprintln(out)
for _, hist := range hists {
nanos := []float64{}
for _, duration := range hist.values {
nanos = append(nanos, float64(duration))
}
fmt.Fprintf(out, "%10s", hist.name)
fmt.Fprintf(out, " %10s", time.Duration(stats.Min(nanos)))
for _, p := range percentiles {
fmt.Fprintf(out, " %10s", time.Duration(stats.Percentile(nanos, p)))
}
fmt.Fprintf(out, " %10s", time.Duration(stats.Max(nanos)))
fmt.Fprintln(out)
}
}
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
}
func main() {
verbose := flag.Bool("v", false, "verbose output")
flag.Parse()
file, err := os.Open("delta_data.bin")
check(err)
defer file.Close()
buffer := bufio.NewReader(file)
sizes := make([]float64, 0)
speeds := make([]float64, 0)
encode := qpc.NewHistory("encode")
decode := qpc.NewHistory("decode")
server := physics.NewState(901)
client := physics.NewState(901)
// initialize the base state
for i := 0; i < 6; i += 1 {
server.ReadNext(buffer)
client.IncFrame()
client.Current().Assign(server.Current())
}
frame := 6
for {
err = server.ReadNext(buffer)
if err == io.EOF {
break
}
check(err)
frame += 1
runtime.GC()
// Server side
encode.Start()
snapshot := server.Encode()
encode.Stop()
// ===
runtime.GC()
// Client side
decode.Start()
client.IncFrame()
client.Decode(snapshot)
decode.Stop()
// ===
size := float64(len(snapshot)*8) / 1000.0
sizes = append(sizes, size)
speed := size * 60.0
speeds = append(speeds, speed)
equal := server.Current().Equals(client.Current())
if *verbose {
if !equal {
fmt.Print("! ")
}
fmt.Printf("%04d %8.3fkbps %10s %10s\n", frame, speed, encode.Last(), decode.Last())
} else {
if equal {
fmt.Print(".")
} else {
fmt.Print("X")
}
}
}
fmt.Println()
fmt.Printf("#%d %.3fkbps ±%.3fkbps\n", len(sizes), stats.Mean(speeds), stats.StdDevS(speeds))
fmt.Println()
fmt.Printf("MIN %10.3f kbps\n", stats.Min(speeds))
for _, p := range []float64{5, 10, 25, 50, 75, 90, 95} {
fmt.Printf("P%02.f %10.3f kbps\n", p, stats.Percentile(speeds, p))
}
fmt.Printf("MAX %10.3f kbps\n", stats.Max(speeds))
fmt.Println()
fmt.Printf("TOTAL %10.3f kb\n", stats.Sum(sizes))
fmt.Printf(" AVG %10.3f kb per frame\n", stats.Mean(sizes))
fmt.Printf(" AVG %10.3f bits per cube\n", stats.Mean(sizes)*1000/float64(len(sizes)))
fmt.Println()
fmt.Println("TIMING:")
qpc.PrintSummary(encode, decode)
}
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))
}
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))
}
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)
}
}
//apply transforms an array of data
func apply(data []string, transformation templates.Transformation) ([]string, []Mapping) {
p := transformation.Parameters
var wg sync.WaitGroup
var mapping []Mapping
switch transformation.Operation {
case "toDate":
if len(p) != 2 {
log.Fatal("toDate transformation requires 2 parameters: current format, new format")
}
oldFormat := p[0]
newFormat := p[1]
for i, x := range data {
y, err := time.Parse(oldFormat, x)
if err != nil {
log.Print("Error parsing date with index ", i, " with format: ", oldFormat)
} else {
data[i] = y.Format(newFormat)
}
}
case "setNull":
for i, x := range data {
if arrayPos(x, p) != -1 {
data[i] = ""
}
}
case "standardize":
if len(p) != 1 {
log.Fatal("standardize transformation requires 1 parameter: type (min-max|z-score)")
}
stype := p[0]
switch stype {
case "min-max":
newData := strArrToFloatArr(data)
min, err := stats.Min(newData)
if err != nil {
log.Fatal("Error finding minimum of data: ", err)
}
max, err := stats.Max(newData)
if err != nil {
log.Fatal("Error finding maximum of data: ", err)
}
srange := max - min
for i, x := range newData {
data[i] = floatToString((x - min) / srange)
}
case "z-score":
newData := strArrToFloatArr(data)
mean, err := stats.Mean(newData)
if err != nil {
log.Fatal("Error finding mean of data: ", err)
}
sd, err := stats.StandardDeviation(newData)
if err != nil {
log.Fatal("Error finding standard deviation of data: ", err)
}
for i, x := range newData {
data[i] = floatToString((x - mean) / sd)
}
case "decimal":
newData := strArrToFloatArr(data)
max, err := stats.Max(newData)
if err != nil {
log.Fatal("Error finding maximum of data: ", err)
}
min, err := stats.Min(newData)
if err != nil {
log.Fatal("Error finding minimum of data: ", err)
}
var maxAbs float64
if math.Abs(max) > math.Abs(min) {
maxAbs = math.Abs(max)
} else {
maxAbs = math.Abs(min)
}
c := math.Ceil(math.Log10(maxAbs))
for i, x := range newData {
data[i] = floatToString(x / math.Pow10(int(c)))
}
}
case "binPercent":
table := NewPivotTable(data)
intP := strArrToIntArr(p)
sort.Ints(intP)
ps := NewPercentileService(*table, intP)
mapping = ps.CreateMappings()
ps.Bin(mapping, data)
case "fuzzyMap":
if len(p) != 3 {
log.Fatal("fuzzyMap transformation requires 3 parameters: datasource GUID, match, put")
}
dsGUID := p[0]
ds := datasources.NewDatasourceService(database.GetDatabase())
dsObj, err := ds.GetDatasource(dsGUID)
if err != nil {
log.Fatal("Error finding Datasource: ", err)
}
distinctValues := getDistinctValues(data)
for i, datum := range distinctValues {
wg.Add(1)
go func(i int, datum string, dsObj datasources.Datasource) {
result := fuzzyMap(datum, dsObj.Settings)
fuzzyMapping := NewMapping(datum, result)
mapping = append(mapping, *fuzzyMapping)
defer wg.Done()
}(i, datum, dsObj)
}
wg.Wait()
data = applyMappings(mapping, data)
}
return data, mapping
}