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

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