How to use RemoveOutliers method of stats Package

Best Syzkaller code snippet using stats.RemoveOutliers

main.go

Source:main.go

1package main2import (3	"encoding/json"4	"flag"5	"fmt"6	"io/ioutil"7	"log"8	"math"9	"net"10	"net/http"11	"strconv"12	"strings"13	"time"14	"github.com/adrg/strutil"15	"github.com/adrg/strutil/metrics"16	"github.com/gocolly/colly/v2"17	"github.com/montanaflynn/stats"18)19// this application can work as one of the agents' actuators20// based on the current intention, an agent can retrieve information about some topic21// sensors - beliefs (topic) - retrieve info from the web - sensor -> neural network - learn and improve an existent plan22// learn and improve an agent strategy, for instance23type Item struct {24	Title       string25	Description string26	Price       float6427	User        string28	// UserRating  string29	Amount int30	New    bool31}32const threshold float64 = 1.033func main() {34	c := colly.NewCollector(35		colly.UserAgent("Mozilla/5.0"),36	)37	itens := []Item{}38	c.WithTransport(&http.Transport{39		Proxy: http.ProxyFromEnvironment,40		DialContext: (&net.Dialer{41			Timeout:   30 * time.Second,42			KeepAlive: 30 * time.Second,43			DualStack: true,44		}).DialContext,45		MaxIdleConns:          100,46		IdleConnTimeout:       90 * time.Second,47		TLSHandshakeTimeout:   10 * time.Second,48		ExpectContinueTimeout: 1 * time.Second,49	})50	detailCollector := c.Clone()51	// this function main goal is to iterate over52	// the returned list and enter in the links53	// to retrieved the desired information54	c.OnHTML(`a.ui-search-item__group__element.ui-search-link`, func(e *colly.HTMLElement) {55		log.Println("Starting Scraper")56		productLink := e.Attr("href")57		log.Println("Visiting item: ", productLink)58		detailCollector.Visit(productLink)59	})60	detailCollector.OnHTML("div.ui-pdp-container__row.ui-pdp-component-list.pr-16.pl-16", func(e *colly.HTMLElement) {61		log.Println("Extracting product details")62		title := e.ChildText(".ui-pdp-title")63		price := e.ChildText(".price-tag-fraction")64		user := e.ChildText(".ui-pdp-color--BLUE")65		//amount := e.ChildText(".ui-pdp-color--BLACK.ui-pdp-size--XSMALL.ui-pdp-family--REGULAR.ui-pdp-seller__header__subtitle")66		amount := e.ChildText(".ui-pdp-buybox__quantity__available")67		amount = strings.ReplaceAll(amount, "(", "")68		//ui-pdp-buybox__quantity__available69		new := e.ChildText(".ui-pdp-subtitle")70		item := Item{}71		item.Title = title72		priceF, _ := strconv.ParseFloat(price, 32)73		item.Price = float64(priceF) / float64(100)74		item.User = user75		item.Amount, _ = strconv.Atoi(strings.Split(amount, " ")[0])76		itens = append(itens, item)77		item.New = strings.Contains(new, "Novo")78		log.Println(item)79	})80	detailCollector.OnHTML("h1.ui-pdp-title", func(e *colly.HTMLElement) {81		// el := e.Request.Visit(e.Attr("ol"))82		// log.Println(el)83		log.Println("Visiting product", e)84		product := Item{}85		product.Title = e.Text //ui-pdp-title86	})87	c.OnRequest(func(r *colly.Request) {88		fmt.Println("Visiting", r.URL)89	})90	term := flag.String("term", "item", "term to be used during web-scraping")91	flag.Parse()92	fmt.Println(*term)93	site := "https://lista.mercadolivre.com.br/"94	displayMode := "_DisplayType_LF"95	err := c.Visit(site + *(term) + displayMode)96	if err != nil {97		fmt.Println(err)98	}99	results, _ := json.MarshalIndent(itens, "", " ")100	_ = ioutil.WriteFile("results.json", results, 0644)101	data, err := ioutil.ReadFile("results.json")102	if err != nil {103		log.Println(err)104	}105	json.Unmarshal(data, &itens)106	finalData := removeOutliers(&itens)107	perceptions, _ := json.MarshalIndent(finalData, "", " ")108	_ = ioutil.WriteFile("perceptions.json", perceptions, 0644)109}110func findSimilarities(items []Item) {111	for i := 0; i < len(items)/2; i++ {112		for j := 1; j < len(items)/2; j++ {113			similarity := strutil.Similarity(items[i].Title, items[j].Title, metrics.NewHamming())114			log.Println("Similarity ", items[i].Title, items[j].Title, similarity)115		}116	}117}118func extractPrices(items *[]Item) []float64 {119	data := []float64{}120	for _, v := range *items {121		data = append(data, v.Price)122	}123	return data124}125func findQuartile(items *[]Item) (stats.Outliers, error) {126	prices := extractPrices(items)127	q, err := stats.QuartileOutliers(prices)128	if err != nil {129		return stats.Outliers{}, err130	}131	return q, nil132}133func removeOutliers(items *[]Item) []Item {134	data := []float64{}135	for _, v := range *items {136		data = append(data, v.Price)137	}138	std, _ := stats.StandardDeviation(data)139	mean, _ := stats.Mean(data)140	log.Println(std)141	q, _ := stats.Quartile(data)142	log.Println(q)143	cleanedData := []Item{}144	for _, v := range *items {145		if math.Abs(v.Price) > math.Abs(mean-threshold*std) && math.Abs(v.Price) < math.Abs(mean+threshold*std) {146			cleanedData = append(cleanedData, v)147		}148	}149	log.Println(len(cleanedData))150	for _, v := range cleanedData {151		log.Println(v.Price)152	}153	return cleanedData154}...

sample.go

Source:sample.go

1package main2import (3	"fmt"4	"io"5	"regexp"6	"sort"7	"bandr.me/p/gbenchdiff/internal/stats"8)9const alpha = 0.0510type Metric struct {11	Name     string12	TimeUnit string13	RealTime Sample14	CPUTime  Sample15}16type Sample struct {17	Values  []float6418	RValues []float64 // without outliers19	Min     float6420	Mean    float6421	Max     float6422}23func (s *Sample) removeOutliers() {24	q1 := Percentile(s.Values, 0.25)25	q3 := Percentile(s.Values, 0.75)26	lo := q1 - 1.5*(q3-q1)27	hi := q3 + 1.5*(q3-q1)28	for _, value := range s.Values {29		if value >= lo && value <= hi {30			s.RValues = append(s.RValues, value)31		}32	}33}34func (s *Sample) ComputeStats() {35	s.removeOutliers()36	s.Min, s.Max = Bounds(s.RValues)37	s.Mean = Mean(s.RValues)38}39func (o Sample) Print(w io.Writer, n Sample, tu string) {40	u, err := stats.MannWhitneyUTest(o.RValues, n.RValues, stats.LocationDiffers)41	pval := u.P42	delta := "~"43	note := ""44	if err == stats.ErrZeroVariance {45		note = "(zero variance)"46	} else if err == stats.ErrSampleSize {47		note = "(too few samples)"48	} else if err == stats.ErrSamplesEqual {49		note = "(all equal)"50	} else if err != nil {51		note = fmt.Sprintf("(%s)", err)52	} else if pval < alpha {53		if n.Mean == o.Mean {54			delta = "0.00%"55		} else {56			pct := ((n.Mean - o.Mean) / o.Mean) * 100.057			delta = fmt.Sprintf("%+.2f%%", pct)58		}59	}60	if note == "" && pval != -1 {61		note = fmt.Sprintf("(p=%0.2f n=%d+%d)", pval, len(o.RValues), len(n.RValues))62	}63	fmt.Fprintf(w, "\t%s\t%s", delta, note)64	fmt.Fprintf(w, "\t%.2f%s\t%.2f%s", o.Mean, tu, n.Mean, tu)65}66func findMetric(m []Metric, name string) int {67	for i := range m {68		if m[i].Name == name {69			return i70		}71	}72	return -173}74func GetMetrics(benchmarks []Benchmark, filterRe *regexp.Regexp) []Metric {75	var metrics []Metric76	for _, b := range benchmarks {77		if filterRe != nil && !filterRe.MatchString(b.Name) {78			continue79		}80		if b.RunType != "iteration" {81			continue82		}83		i := findMetric(metrics, b.Name)84		if i == -1 {85			metrics = append(metrics, Metric{86				Name:     b.Name,87				TimeUnit: b.TimeUnit,88			})89			i = len(metrics) - 190		}91		metrics[i].RealTime.Values = append(metrics[i].RealTime.Values, b.RealTime)92		metrics[i].CPUTime.Values = append(metrics[i].CPUTime.Values, b.CPUTime)93	}94	for i := range metrics {95		r := metrics[i].RealTime.Values96		sort.Float64s(r)97		metrics[i].RealTime.Values = r98		metrics[i].RealTime.ComputeStats()99		c := metrics[i].CPUTime.Values100		sort.Float64s(c)101		metrics[i].CPUTime.Values = c102		metrics[i].CPUTime.ComputeStats()103	}104	return metrics105}...

CoreFunctionality.go

Source:CoreFunctionality.go

...10	var musicForLightModeVal, lightForTempModeVal float6411	thisCondVals = GetDataCurCond(currCond)12	stats := Stats()13	support := SupportFuncs()14	tempArr:=stats.RemoveOutliers(support.GetWeightedFieldArray(thisCondVals, "TempIn"))15	lightArr := stats.RemoveOutliers(support.GetWeightedFieldArray(thisCondVals, "LightIn"))16	musicArr := stats.RemoveOutliers(support.GetWeightedFieldArray(thisCondVals, "MusicIn"))17	tempMode, _ := stats.GetMode(tempArr)18	lightModeOA, lightModeCnt := stats.GetMode(lightArr)19	musicModeOA, musicModeCnt := stats.GetMode(musicArr)20	controlledValues := thisCondVals.CtrledVals21	for _,j := range controlledValues{22		if(j.TempIn == tempMode[0]){23			lightForTemp = append(lightForTemp,j.LightIn )//* float64(j.HomesCount))24		}25	}26	if(len(lightForTemp)!=0) {27		lightForTempMode, tempLightModeCnt1 := stats.GetMode(lightForTemp)28		lightForTempModeVal = lightForTempMode[0]29		tempLightModeCnt = tempLightModeCnt130	}...

RemoveOutliers

Using AI Code Generation

1import "fmt"2import "stats"3func main() {4    data := []float64{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}5    fmt.Println("Original data:", data)6    data = stats.RemoveOutliers(data, 1.5)7    fmt.Println("Modified data:", data)8}

RemoveOutliers

Using AI Code Generation

1import (2func main() {3	rand.Seed(time.Now().UnixNano())4	for i := 0; i < 10; i++ {5		x = append(x, rand.Float64()*100)6	}7	fmt.Println("Before removing outliers", x)8	x = stats.RemoveOutliers(x)9	fmt.Println("After removing outliers", x)10}11func RemoveOutliers(x []float64) []float64 {12	mean := Mean(x)13	sd := StdDev(x)14	for _, v := range x {15		if v > mean-3*sd && v < mean+3*sd {16			r = append(r, v)17		}18	}19}20func Mean(x []float64) float64 {21	for _, v := range x {22	}23	return sum / float64(len(x))24}

RemoveOutliers

Using AI Code Generation

1import (2func main() {3    floatSlice := []float64{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0}4    cleanSlice, _ := stats.RemoveOutliers(floatSlice, nil)5    fmt.Println(cleanSlice)6}7import (8func main() {9    floatSlice := []float64{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0}10    outlierMode := stats.OutlierMode{11    }12    cleanSlice, _ := stats.RemoveOutliers(floatSlice, outlierMode)13    fmt.Println(cleanSlice)14}15import (16func main() {17    intSlice := []int{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}

RemoveOutliers

Using AI Code Generation

1import (2func main() {3	mySlice := []float64{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}4	newSlice, _ := stats.RemoveOutliers(mySlice, 2, 1.0)5	fmt.Println("New Slice:", newSlice)6}

RemoveOutliers

Using AI Code Generation

1import (2func main() {3    var arr = []int{}4    rand.Seed(time.Now().UnixNano())5    for i := 0; i < 10; i++ {6        arr = append(arr, rand.Intn(100))7    }8    fmt.Println(arr)9    fmt.Println(RemoveOutliers(arr))10}11import (12func main() {13    var arr = []int{}14    rand.Seed(time.Now().UnixNano())15    for i := 0; i < 10; i++ {16        arr = append(arr, rand.Intn(100))17    }18    fmt.Println(arr)19    fmt.Println(RemoveOutliers(arr))20}21import (22func main() {23    var arr = []int{}24    rand.Seed(time.Now().UnixNano())25    for i := 0; i < 10; i++ {26        arr = append(arr, rand.Intn(100))27    }28    fmt.Println(arr)29    fmt.Println(RemoveOutliers(arr))30}31import (32func main() {33    var arr = []int{}34    rand.Seed(time.Now().UnixNano())35    for i := 0; i < 10; i++ {36        arr = append(arr, rand.Intn(100))37    }38    fmt.Println(arr)39    fmt.Println(RemoveOutliers(arr))40}41import (42func main() {43    var arr = []int{}44    rand.Seed(time.Now().UnixNano())45    for i := 0; i < 10; i++ {46        arr = append(arr, rand

RemoveOutliers

Using AI Code Generation

1import (2func main() {3    canvas := svg.New(os.Stdout)4    canvas.Start(width, height)5    canvas.Rect(0, 0, width, height, "fill:white")6    canvas.Gstyle("fill:none;stroke:black;stroke-width:1")7    s := stats.New()8    s.Add(1, 1)9    s.Add(2, 2)10    s.Add(3, 3)11    s.Add(4, 4)12    s.Add(5, 5)13    s.Add(6, 6)14    s.Add(7, 7)15    s.Add(8, 8)16    s.Add(9, 9)17    s.Add(10, 10)18    s.Add(11, 11)19    s.Add(12, 12)20    s.Add(13, 13)21    s.Add(14, 14)22    s.Add(15, 15)23    s.Add(16, 16)24    s.Add(17, 17)25    s.Add(18, 18)26    s.Add(19, 19)27    s.Add(20, 20)28    s.Add(21, 21)29    s.Add(22, 22)30    s.Add(23, 23)31    s.Add(24, 24)32    s.Add(25, 25)33    s.Add(26, 26)34    s.Add(27, 27)35    s.Add(28, 28)36    s.Add(29, 29)37    s.Add(30, 30)38    s.Add(31, 31)39    s.Add(32, 32)40    s.Add(33, 33)41    s.Add(34, 34)42    s.Add(35, 35)43    s.Add(36, 36)44    s.Add(37, 37)45    s.Add(38, 38)46    s.Add(39, 39)47    s.Add(40, 40)48    s.Add(41, 41)49    s.Add(

RemoveOutliers

Using AI Code Generation

1import (2func main() {3	s := make([]float64, 1000)4	for i := 0; i < 1000; i++ {5		s[i] = math.Floor(rand.NormFloat64()*stdDev + mean)6	}7	fmt.Printf("Mean before: %.2f8", stats.Mean(s))9	fmt.Printf("Standard deviation before: %.2f10", stats.StdDev(s))11	stats.RemoveOutliers(&s, 1.0)12	fmt.Printf("Mean after: %.2f13", stats.Mean(s))14	fmt.Printf("Standard deviation after: %.2f15", stats.StdDev(s))16}

RemoveOutliers

Using AI Code Generation

1import (2func main() {3	rand.Seed(time.Now().UnixNano())4	randomNumbers := make([]float64, 100)5	for i := 0; i < 100; i++ {6		randomNumbers[i] = rand.Float64() * 1007	}8	s := stats.New(randomNumbers)9	s.RemoveOutliers(10)10	fmt.Printf("The summary of the data set is:11	fmt.Printf("Mean: %f12", s.Mean())13	fmt.Printf("Median: %f14", s.Median())15	fmt.Printf("Mode: %f16", s.Mode())17	fmt.Printf("Standard Deviation: %f18", s.StandardDeviation())19	fmt.Printf("Variance: %f20", s.Variance())21}22Akshay Babloo is a software engineer at Microsoft. He is a Microsoft MVP for Visual Studio and Development Technologies. He has been developing software for more than 10 years. He has worked on a variety of projects ranging from Windows Phone to the Azure cloud platform. He has also worked on many different technologies including C#, ASP.NET, ASP.NET MVC, WPF, Windows Phone, Windows Azure, SQL Server, and many more. He has been a speaker at many conferences and user groups. He has also written many technical articles on his blog. He is a member of the .NET Foundation. He is also a Microsoft Certified Trainer (MCT). He is a Microsoft Certified Professional (MCP). He is also a Microsoft Certified Solution Developer (MCSD). He is a Microsoft Certified Technology

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.