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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

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