How to use Render method of regression Package

Best Keploy code snippet using regression.Render

regression.go

Source:regression.go

...47		Updated: now,48		Status:  stat,49	})50	if err != nil {51		render.Render(w, r, ErrInvalidRequest(err))52		return53	}54	render.Status(r, http.StatusOK)55}56func (rg *regression) Start(w http.ResponseWriter, r *http.Request) {57	t := r.URL.Query().Get("total")58	total, err := strconv.Atoi(t)59	if err != nil {60		render.Render(w, r, ErrInvalidRequest(err))61		return62	}63	app := rg.getMeta(w, r, true)64	if app == "" {65		return66	}67	id := uuid.New().String()68	now := time.Now().Unix()69	// user := "default"70	err = rg.run.Put(r.Context(), run.TestRun{71		ID:      id,72		Created: now,73		Updated: now,74		Status:  run.TestRunStatusRunning,75		CID:     graph.DEFAULT_COMPANY,76		App:     app,77		User:    graph.DEFAULT_USER,78		Total:   total,79	})80	if err != nil {81		render.Render(w, r, ErrInvalidRequest(err))82		return83	}84	render.Status(r, http.StatusOK)85	render.JSON(w, r, map[string]string{86		"id": id,87	})88}89func (rg *regression) GetTC(w http.ResponseWriter, r *http.Request) {90	id := chi.URLParam(r, "id")91	app := rg.getMeta(w, r, false)92	tcs, err := rg.svc.Get(r.Context(), graph.DEFAULT_COMPANY, app, id)93	if err != nil {94		render.Render(w, r, ErrInvalidRequest(err))95		return96	}97	render.Status(r, http.StatusOK)98	render.JSON(w, r, tcs)99}100func (rg *regression) getMeta(w http.ResponseWriter, r *http.Request, appRequired bool) string {101	app := r.URL.Query().Get("app")102	if app == "" && appRequired {103		rg.logger.Error("request for fetching testcases should include app id")104		render.Render(w, r, ErrInvalidRequest(errors.New("missing app id")))105		return ""106	}107	return app108}109func (rg *regression) GetTCS(w http.ResponseWriter, r *http.Request) {110	app := rg.getMeta(w, r, true)111	if app == "" {112		return113	}114	offsetStr := r.URL.Query().Get("offset")115	limitStr := r.URL.Query().Get("limit")116	var (117		offset int118		limit  int119		err    error120	)121	if offsetStr != "" {122		offset, err = strconv.Atoi(offsetStr)123		if err != nil {124			rg.logger.Error("request for fetching testcases in converting offset to integer")125		}126	}127	if limitStr != "" {128		limit, err = strconv.Atoi(limitStr)129		if err != nil {130			rg.logger.Error("request for fetching testcases in converting limit to integer")131		}132	}133	tcs, err := rg.svc.GetAll(r.Context(), graph.DEFAULT_COMPANY, app, &offset, &limit)134	if err != nil {135		render.Render(w, r, ErrInvalidRequest(err))136		return137	}138	render.Status(r, http.StatusOK)139	render.JSON(w, r, tcs)140}141func (rg *regression) PostTC(w http.ResponseWriter, r *http.Request) {142	// key := r.Header.Get("key")143	// if key == "" {144	// 	rg.logger.Error("missing api key")145	// 	render.Render(w, r, ErrInvalidRequest(errors.New("missing api key")))146	// 	return147	// }148	data := &TestCaseReq{}149	if err := render.Bind(r, data); err != nil {150		rg.logger.Error("error parsing request", zap.Error(err))151		render.Render(w, r, ErrInvalidRequest(err))152		return153	}154	// rg.logger.Debug("testcase posted",zap.Any("testcase request",data))155	now := time.Now().UTC().Unix()156	inserted, err := rg.svc.Put(r.Context(), graph.DEFAULT_COMPANY, []models.TestCase{{157		ID:       uuid.New().String(),158		Created:  now,159		Updated:  now,160		Captured: data.Captured,161		URI:      data.URI,162		AppID:    data.AppID,163		HttpReq:  data.HttpReq,164		HttpResp: data.HttpResp,165		Deps:     data.Deps,166	}})167	if err != nil {168		rg.logger.Error("error putting testcase", zap.Error(err))169		render.Render(w, r, ErrInvalidRequest(err))170		return171	}172	// rg.logger.Debug("testcase inserted",zap.Any("testcase ids",inserted))173	if len(inserted) == 0 {174		rg.logger.Error("unknown failure while inserting testcase")175		render.Render(w, r, ErrInvalidRequest(err))176		return177	}178	render.Status(r, http.StatusOK)179	render.JSON(w, r, map[string]string{"id": inserted[0]})180}181func (rg *regression) DeNoise(w http.ResponseWriter, r *http.Request) {182	// key := r.Header.Get("key")183	// if key == "" {184	// 	rg.logger.Error("missing api key")185	// 	render.Render(w, r, ErrInvalidRequest(errors.New("missing api key")))186	// 	return187	// }188	data := &TestReq{}189	if err := render.Bind(r, data); err != nil {190		rg.logger.Error("error parsing request", zap.Error(err))191		render.Render(w, r, ErrInvalidRequest(err))192		return193	}194	err := rg.svc.DeNoise(r.Context(), graph.DEFAULT_COMPANY, data.ID, data.AppID, data.Resp.Body, data.Resp.Header)195	if err != nil {196		rg.logger.Error("error putting testcase", zap.Error(err))197		render.Render(w, r, ErrInvalidRequest(err))198		return199	}200	render.Status(r, http.StatusOK)201}202func (rg *regression) Test(w http.ResponseWriter, r *http.Request) {203	data := &TestReq{}204	if err := render.Bind(r, data); err != nil {205		rg.logger.Error("error parsing request", zap.Error(err))206		render.Render(w, r, ErrInvalidRequest(err))207		return208	}209	pass, err := rg.svc.Test(r.Context(), graph.DEFAULT_COMPANY, data.AppID, data.RunID, data.ID, data.Resp)210	if err != nil {211		rg.logger.Error("error putting testcase", zap.Error(err))212		render.Render(w, r, ErrInvalidRequest(err))213		return214	}215	render.Status(r, http.StatusOK)216	render.JSON(w, r, map[string]bool{"pass": pass})217}...

polynomial_regression_series.go

Source:polynomial_regression_series.go

...143		yvalues[index-startIndex] = y144	}145	return146}147// Render renders the series.148func (prs *PolynomialRegressionSeries) Render(r render.Renderer, canvasBox render.Box, xrange, yrange sequence.Range, defaults render.Style) {149	style := prs.Style.InheritFrom(defaults)150	drawLineSeries(r, canvasBox, xrange, yrange, style, prs)151}...

function.go

Source:function.go

1package linearRegression2import (3	"fmt"4	"github.com/go-graphite/carbonapi/expr/helper"5	"github.com/go-graphite/carbonapi/expr/interfaces"6	"github.com/go-graphite/carbonapi/expr/types"7	"github.com/go-graphite/carbonapi/pkg/parser"8	"github.com/gonum/matrix/mat64"9)10type linearRegression struct {11	interfaces.FunctionBase12}13func GetOrder() interfaces.Order {14	return interfaces.Any15}16func New(configFile string) []interfaces.FunctionMetadata {17	res := make([]interfaces.FunctionMetadata, 0)18	f := &linearRegression{}19	functions := []string{"linearRegression"}20	for _, n := range functions {21		res = append(res, interfaces.FunctionMetadata{Name: n, F: f})22	}23	return res24}25// linearRegression(seriesList, startSourceAt=None, endSourceAt=None)26func (f *linearRegression) Do(e parser.Expr, from, until int32, values map[parser.MetricRequest][]*types.MetricData) ([]*types.MetricData, error) {27	arg, err := helper.GetSeriesArg(e.Args()[0], from, until, values)28	if err != nil {29		return nil, err30	}31	degree := 132	var results []*types.MetricData33	for _, a := range arg {34		r := *a35		if len(e.Args()) > 2 {36			r.Name = fmt.Sprintf("linearRegression(%s,'%s','%s')", a.GetName(), e.Args()[1].StringValue(), e.Args()[2].StringValue())37		} else if len(e.Args()) > 1 {38			r.Name = fmt.Sprintf("linearRegression(%s,'%s')", a.GetName(), e.Args()[2].StringValue())39		} else {40			r.Name = fmt.Sprintf("linearRegression(%s)", a.GetName())41		}42		r.Values = make([]float64, len(a.Values))43		r.IsAbsent = make([]bool, len(r.Values))44		r.StopTime = a.GetStopTime()45		// Removing absent values from original dataset46		nonNulls := make([]float64, 0)47		for i := range a.Values {48			if !a.IsAbsent[i] {49				nonNulls = append(nonNulls, a.Values[i])50			}51		}52		if len(nonNulls) < 2 {53			for i := range r.IsAbsent {54				r.IsAbsent[i] = true55			}56			results = append(results, &r)57			continue58		}59		// STEP 1: Creating Vandermonde (X)60		v := helper.Vandermonde(a.IsAbsent, degree)61		// STEP 2: Creating (X^T * X)**-162		var t mat64.Dense63		t.Mul(v.T(), v)64		var i mat64.Dense65		err := i.Inverse(&t)66		if err != nil {67			continue68		}69		// STEP 3: Creating I * X^T * y70		var c mat64.Dense71		c.Product(&i, v.T(), mat64.NewDense(len(nonNulls), 1, nonNulls))72		// END OF STEPS73		for i := range r.Values {74			r.Values[i] = helper.Poly(float64(i), c.RawMatrix().Data...)75		}76		results = append(results, &r)77	}78	return results, nil79}80// Description is auto-generated description, based on output of https://github.com/graphite-project/graphite-web81func (f *linearRegression) Description() map[string]types.FunctionDescription {82	return map[string]types.FunctionDescription{83		"linearRegression": {84			Description: "Graphs the liner regression function by least squares method.\n\nTakes one metric or a wildcard seriesList, followed by a quoted string with the\ntime to start the line and another quoted string with the time to end the line.\nThe start and end times are inclusive (default range is from to until). See\n``from / until`` in the render\\_api_ for examples of time formats. Datapoints\nin the range is used to regression.\n\nExample:\n\n.. code-block:: none\n\n  &target=linearRegression(Server.instance01.threads.busy, '-1d')\n  &target=linearRegression(Server.instance*.threads.busy, \"00:00 20140101\",\"11:59 20140630\")",85			Function:    "linearRegression(seriesList, startSourceAt=None, endSourceAt=None)",86			Group:       "Calculate",87			Module:      "graphite.render.functions",88			Name:        "linearRegression",89			Params: []types.FunctionParam{90				{91					Name:     "seriesList",92					Required: true,93					Type:     types.SeriesList,94				},95				{96					Name: "startSourceAt",97					Type: types.Date,98				},99				{100					Name: "endSourceAt",101					Type: types.Date,102				},103			},104		},105	}106}...

Render

Using AI Code Generation

1import (2func main() {3	r.SetObserved("Y")4	r.SetVar(0, "X")5	for x := 0.0; x < 10; x += 0.1 {6		y := 5.0*x + 10.0 + 5.0*rand.NormFloat64()7		r.Train(regression.DataPoint(y, []float64{x}))8	}9	r.Run()10	r.Render()11}12import (13func main() {14	r.SetObserved("Y")15	r.SetVar(0, "X")16	for x := 0.0; x < 10; x += 0.1 {17		y := 5.0*x + 10.0 + 5.0*rand.NormFloat64()18		r.Train(regression.DataPoint(y, []float64{x}))19	}20	r.Run()21	fmt.Printf("\nRegression Formula:\n%v\n\n", r.Formula)

Render

Using AI Code Generation

1import (2func main() {3	f, err := os.Open("data.csv")4	if err != nil {5		log.Fatal(err)6	}7	defer f.Close()8	r := new(regression.Regression)9	r.SetObserved("y")10	r.SetVar(0, "x")11	if err := r.ReadCSV(f); err != nil {12		log.Fatal(err)13	}14	if err := r.Run(); err != nil {15		log.Fatal(err)16	}17	fmt.Printf("%v18}19import (20func main() {21	f, err := os.Open("data.csv")22	if err != nil {23		log.Fatal(err)24	}25	defer f.Close()26	r := new(regression.Regression)27	r.SetObserved("y")28	r.SetVar(0, "x")29	if err := r.ReadCSV(f); err != nil {30		log.Fatal(err)31	}32	if err := r.Run(); err != nil {33		log.Fatal(err)34	}35	for x := 0.0; x <= 10.0; x += 1.0 {36		pred, err := r.Predict([]float64{x})37		if err != nil {38			log.Fatal(err)39		}40		obs = append(obs, pred)41	}42	fmt.Printf("%v43}44import (

Render

Using AI Code Generation

1import (2func main() {3    irisFile, err := os.Open(filepath.Join("data", "iris.csv"))4    if err != nil {5        log.Fatal(err)6    }7    defer irisFile.Close()8    r, err := csv.Regression(irisFile)9    if err != nil {10        log.Fatal(err)11    }12    irisDF := dataframe.ReadCSV(irisFile)13    petalWidth := irisDF.Col("PetalWidth").Float()14    sepalWidth := irisDF.Col("SepalWidth").Float()15    sepalLength := irisDF.Col("SepalLength").Float()16    petalLength := irisDF.Col("PetalLength").Float()17    species := irisDF.Col("Species").Records()18    species := irisDF.Col("Species").Records()19    for i := 0; i < irisDF.Nrow(); i++ {20        r.Train(regression.DataPoint(petalWidth[i], []float64{21        }), species[i])22    }23    r.Run()24    r, err := csv.Regression(irisFile)25    if err != nil {26        log.Fatal(err)27    }28    irisDF := dataframe.ReadCSV(irisFile)

Render

Using AI Code Generation

1import (2type regression struct {3}4func (r *regression) Render(x []float64, y []float64) {5	for i := 0; i < len(x); i++ {6	}7	r.m = (float64(len(x))*sumXY - sumX*sumY) / (float64(len(x))*sumX2 - sumX*sumX)8	r.b = (sumY - r.m*sumX) / float64(len(x))9}10func (r *regression) Predict(x float64) float64 {11}12func (r *regression) R2(x []float64, y []float64) float64 {13	for i := 0; i < len(x); i++ {14	}15	meanX := sumX / float64(len(x))16	meanY := sumY / float64(len(x))17	numerator := float64(len(x))*sumXY - sumX*sumY18	denominator := math.Sqrt((float64(len(x))*sumX2-sumX*sumX)*(float64(len(x))*sumY2-sumY*sumY))19	return (numerator * numerator) / (denominator * denominator)20}21func main() {22	x = make([]float64, 0)23	y = make([]float64, 0)24	file, err := os.Open("data.txt")25	if err != nil {26		log.Fatal(err

Render

Using AI Code Generation

1import (2func main() {3	rand.Seed(time.Now().UnixNano())4	r.SetObserved("y")5	r.SetVar(0, "x")6	for i := 0; i < 100; i++ {7		x := rand.Float64() * 2.0 * 3.141598		r.Train(regression.DataPoint(y(x), []float64{x}))9	}10	r.Run()11	r.Render()12}13import (14func main() {15	rand.Seed(time.Now().UnixNano())16	r.SetObserved("y")17	r.SetVar(0, "x")18	for i := 0; i < 100; i++ {19		x := rand.Float64() * 2.0 * 3.1415920		r.Train(regression.DataPoint(y(x), []float64{x}))21	}22	r.Run()23	fmt.Printf("Predicted value for X=1.0 is %0.2f24", r.Predict([]float64{1.0}))25}26import (27func main() {28	rand.Seed(time.Now().UnixNano())29	r.SetObserved("y")30	r.SetVar(0, "x")31	for i := 0; i < 100; i++ {32		x := rand.Float64() * 2.0 * 3.1415933		r.Train(regression.DataPoint(y(x), []float64{x}))34	}35	r.Run()36	fmt.Printf("Predicted value for X=1.0 is %0.2f37", r.Predict([]float64{1.0}))38}

Render

Using AI Code Generation

1import (2func main() {3    for x := 0.0; x < 10.0; x++ {4        y := 2.0*x + 1.0 + math.Exp(x)5        points = append(points, regression.Point{X: x, Y: y})6    }7    r := new(regression.Regression)8    r.SetObserved(points)9    r.SetVar(0, "x")10    r.SetVar(1, "y")11    r.Train()12    fmt.Printf("Predicted value for %0.2f: %0.2f13", 5.0, r.Predict([]float64{5.0}))14}

Render

Using AI Code Generation

1import (2func main() {3	rand.Seed(time.Now().UTC().UnixNano())4	x := make([]float64, 50)5	y := make([]float64, 50)6	for i := 0; i < 50; i++ {7		x[i] = rand.Float64()8		y[i] = 2*x[i] + 1 + rand.Float64()9	}10	scatter, err := plotter.NewScatter(plotter.XYs{11		XYs: make(plotter.XYs, 50),12	})13	if err != nil {14		panic(err)15	}16	scatter.GlyphStyle.Color = color.RGBA{R: 255, B: 128, A: 255}17	for i := range scatter.XYs {18	}19	p, err := plot.New()20	if err != nil {21		panic(err)22	}23	p.Add(scatter)24	if err := p.Save(4*vg.Inch, 4*vg.Inch, "points.png"); err != nil {25		panic(err)26	}27	reg := stat.LinearRegression(mat.NewVecDense(50, x), mat.NewVecDense(50, y), nil, false)28	l, err := plotter.NewLine(reg.Predict(mat.NewVecDense(50, x)))29	if err != nil {30		panic(err)31	}32	l.LineStyle.Width = vg.Points(1)

Render

Using AI Code Generation

1import (2func main() {3	var (4	f, err := os.Create("output.svg")5	if err != nil {6		fmt.Println(err)7	}8	canvas := svg.New(f)9	canvas.Start(width, height)10	canvas.Title("Regression Line")11	canvas.Rect(0, 0, width, height, "fill:rgb(255,255,255)")12	r := regression.NewRegression()13	r.AddData(1, 1)14	r.AddData(2, 2)15	r.AddData(3, 3)16	r.AddData(4, 4)17	r.AddData(5, 5)18	r.Render(canvas, width, height, 0, 0, width, height)19	canvas.End()20}

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.