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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}
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