KaneAI - World’s First E2E Software Testing Agent.Book a Demo Join TestMu Conference

How to use fillCache method of regression Package

Best Keploy code snippet using regression.fillCache

regression.go

Source:regression.go

...391		fmt.Println("found invalid value in json", j, x.Kind())392	}393	return o394}395func (r *Regression) fillCache(ctx context.Context, t *models.TestCase) (string, error) {396	index := fmt.Sprintf("%s-%s-%s", t.CID, t.AppID, t.URI)397	_, ok1 := r.noisyFields[index]398	_, ok2 := r.fieldCounts[index]399	if ok1 && ok2 {400		return index, nil401	}402	r.mu.Lock()403	defer r.mu.Unlock()404	// check again after the lock405	_, ok1 = r.noisyFields[index]406	_, ok2 = r.fieldCounts[index]407	if !ok1 || !ok2 {408		var anchors []map[string][]string409		fieldCounts, noisyFields := map[string]map[string]int{}, map[string]bool{}410		tcs, err := r.tdb.GetKeys(ctx, t.CID, t.AppID, t.URI)411		if err != nil {412			return "", err413		}414		for _, v := range tcs {415			//var appAnchors map[string][]string416			//for _, a := range v.Anchors {417			//	appAnchors[a] = v.AllKeys[a]418			//}419			anchors = append(anchors, v.Anchors)420			for k, v1 := range v.AllKeys {421				if fieldCounts[k] == nil {422					fieldCounts[k] = map[string]int{}423				}424				for _, v2 := range v1 {425					fieldCounts[k][v2] = fieldCounts[k][v2] + 1426				}427				if !isAnchor(fieldCounts[k]) {428					noisyFields[k] = true429				}430			}431		}432		r.fieldCounts[index], r.noisyFields[index], r.anchors[index] = fieldCounts, noisyFields, anchors433	}434	return index, nil435}436func (r *Regression) isDup(ctx context.Context, t *models.TestCase) (bool, error) {437	reqKeys := map[string][]string{}438	filterKeys := map[string][]string{}439	index, err := r.fillCache(ctx, t)440	if err != nil {441		return false, err442	}443	// add headers444	for k, v := range t.HttpReq.Header {445		reqKeys["header."+k] = []string{strings.Join(v, "")}446	}447	// add url params448	for k, v := range t.HttpReq.URLParams {449		reqKeys["url_params."+k] = []string{v}450	}451	// add body if it is a valid json452	if json.Valid([]byte(t.HttpReq.Body)) {453		var result interface{}...

fillCache

Using AI Code Generation

1import (2func main() {3	var (4	f, err := os.Open("data.txt")5	if err != nil {6		log.Fatal(err)7	}8	defer f.Close()9	scanner := bufio.NewScanner(f)10	for scanner.Scan() {11		line := strings.Split(scanner.Text(), " ")12		x, err := strconv.ParseFloat(line[0], 64)13		if err != nil {14			log.Fatal(err)15		}16		y, err := strconv.ParseFloat(line[1], 64)17		if err != nil {18			log.Fatal(err)19		}20		xAxis = append(xAxis, x)21		yAxis = append(yAxis, y)22	}23	r := NewRegression(xAxis, yAxis)24	r.FillCache()25	slope, intercept := r.GetSlopeIntercept()26	start := time.Now()27	predictedValue := r.Predict(5.5)28	end := time.Now()29	elapsed := end.Sub(start)30	fmt.Println("Predicted value for 5.5 is ", predictedValue)31	fmt.Println("Time taken to predict the value is ", elapsed)32	graph := chart.Chart{33		Series: []chart.Series{34			chart.ContinuousSeries{35				Style: chart.Style{36					StrokeColor: chart.GetDefaultColor(0).WithAlpha(64),37					FillColor:   chart.GetDefaultColor(0).WithAlpha(64),38				},

fillCache

Using AI Code Generation

1import (2type Regression struct {3}4func NewRegression(iterations int, learningRate float64) *Regression {5	return &Regression{6	}7}8func (r *Regression) Fit(features, labels []float64) {9	r.featureMean = make([]float64, len(features))10	r.featureStd = make([]float64, len(features))11	for i := 0; i < len(features); i++ {12		r.featureMean[i] = mean(features)13		r.featureStd[i] = standardDeviation(features)14		for j := 0; j < len(features); j++ {15			features[j] = (features[j] - r.featureMean[i]) / r.featureStd[i]16		}17	}18	r.labelMean = mean(labels)19	r.labelStd = standardDeviation(labels)20	for i := 0; i < len(labels); i++ {21		labels[i] = (labels[i] - r.labelMean) / r.labelStd22	}23	r.coefficients = make([]float64, len(features))24	r.intercept = rand.Float64()25	r.gradientDescent(features, labels)26}27func (r *Regression) Predict(features []float64) []float64 {28	for i := 0; i < len(features); i++ {29		features[i] = (features[i] - r.featureMean[i]) / r.featureStd[i]30	}31	predicted := make([]float64, len(features))

fillCache

Using AI Code Generation

1import (2func main() {3	reg := regression.New()4	rand.Seed(int64(time.Now().Nanosecond()))5	x := mat64.NewVector(100, nil)6	y := mat64.NewVector(100, nil)7	for i := 0; i < 100; i++ {8		x.SetVec(i, rand.Float64())9		y.SetVec(i, rand.Float64())10	}11	reg.FillCache(x, y)12	prediction := reg.Predict(0.5)13	fmt.Println(prediction)14}

fillCache

Using AI Code Generation

1import (2func main() {3	r.FillCache("data.csv")4	fmt.Println(r.Cache)5	os.Exit(0)6}7import (8func main() {9	r.FillCache("data.csv")10	fmt.Println(r.Cache)11	fmt.Println(r.Regression())12	os.Exit(0)13}14import (15func main() {16	r.FillCache("data.csv")17	fmt.Println(r.Cache)18	fmt.Println(r.Regression())19	fmt.Println(r.Predict(1.0))20	os.Exit(0)21}22import (23func main() {24	r.FillCache("data.csv")25	fmt.Println(r.Cache)26	fmt.Println(r.Regression())27	fmt.Println(r.Predict(1.0))28	fmt.Println(r.Predict(2.0))29	os.Exit(0)30}31import (32func main() {33	r.FillCache("data.csv")34	fmt.Println(r.Cache)35	fmt.Println(r.Regression())36	fmt.Println(r.Predict(1.0))37	fmt.Println(r.Predict(2.0))

fillCache

Using AI Code Generation

1import (2func main() {3	reg := regression.NewRegression()4	reg.FillCache("data.txt")5	fmt.Println(reg.Cache)6}7import (8func main() {9	reg := regression.NewRegression()10	reg.FillCache("data.txt")11	fmt.Println(reg.Cache)12	fmt.Println(reg.Predict(1.5))13}14import (15func main() {16	reg := regression.NewRegression()17	reg.FillCache("data.txt")18	fmt.Println(reg.Cache)19	fmt.Println(reg.Predict(1.5))20	reg.Cache = append(reg.Cache, regression.DataPoint{X: 2.0, Y: 2.0})21	fmt.Println(reg.Cache)22	fmt.Println(reg.Predict(1.5))23}24import (25func main() {26	reg := regression.NewRegression()27	reg.FillCache("data.txt")28	fmt.Println(reg.Cache)29	fmt.Println(reg.Predict(1.5))30	reg.Cache = append(reg.Cache, regression.DataPoint{X: 2.0, Y: 2.0})31	fmt.Println(reg.Cache)32	fmt.Println(reg.Predict(1.5))33	reg.Cache = append(reg.Cache, regression.DataPoint{X: 3.0, Y:

fillCache

Using AI Code Generation

1import (2func main() {3	fmt.Println("Hello World")4	reg.FillCache()5}6import (7func main() {8	fmt.Println("Hello World")9	reg.FillCache()10}11import (12func main() {13	fmt.Println("Hello World")14	reg.FillCache()15}16import (17func main() {18	fmt.Println("Hello World")19	reg.FillCache()20}21import (22func main() {23	fmt.Println("Hello World")24	reg.FillCache()25}26import (27func main() {28	fmt.Println("Hello World")29	reg.FillCache()30}31import (32func main() {33	fmt.Println("Hello World")34	reg.FillCache()35}36import (37func main() {38	fmt.Println("Hello World")39	reg.FillCache()40}41import (42func main() {43	fmt.Println("Hello World")44	reg.FillCache()45}

fillCache

Using AI Code Generation

1import (2func main() {3	reg := NewRegression()4	x := make([]float64, 100)5	y := make([]float64, 100)6	for i := 0; i < 100; i++ {7		x[i] = rand.Float64() * 1008		y[i] = math.Sin(x[i]) + rand.Float64()9	}10	reg.FillCache(x, y)11	fmt.Println(reg)12}13import (14func main() {15	reg := NewRegression()16	x := make([]float64, 100)17	y := make([]float64, 100)18	for i := 0; i < 100; i++ {19		x[i] = rand.Float64() * 10020		y[i] = math.Sin(x[i]) + rand.Float64()21	}22	reg.FillCache(x, y)23	fmt.Println(reg.Predict(50))24}25import (26func main() {27	reg := NewRegression()28	x := make([]float64, 100)29	y := make([]float64, 100)30	for i := 0; i < 100; i++ {31		x[i] = rand.Float64() * 10032		y[i] = math.Sin(x[i]) + rand.Float64()33	}34	reg.FillCache(x, y)35	fmt.Println(reg.Predict(50))36}37import (

fillCache

Using AI Code Generation

1import (2func main() {3	r := new(regression.Regression)4	r.SetObserved("Y")5	r.SetVar(0, "X")6	r.Train(regression.Data{7		{X: []float64{1}, Y: 1},8		{X: []float64{2}, Y: 2},9		{X: []float64{3}, Y: 3},10		{X: []float64{4}, Y: 4},11		{X: []float64{5}, Y: 5},12		{X: []float64{6}, Y: 6},13		{X: []float64{7}, Y: 7},14		{X: []float64{8}, Y: 8},15		{X: []float64{9}, Y: 9},16		{X: []float64{10}, Y: 10},17	})18	fmt.Printf("formula:\n%v\n\n", r.Formula)19}20import (21func main() {22	r := new(regression.Regression)23	r.SetObserved("Y")24	r.SetVar(0, "X")25	r.Train(regression.Data{26		{X: []float64{1}, Y: 1},27		{X: []float64{2}, Y: 2},28		{X: []float64{3}, Y: 3},29		{X: []float64{4}, Y: 4

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.