# How to use End method of regression Package

Best Keploy code snippet using regression.End

linear_regression_series.go

Source:linear_regression_series.go

`...56 return lrs.InnerSeries.Len()57 }58 return lrs.Limit59}60// GetEndIndex returns the effective limit end.61func (lrs LinearRegressionSeries) GetEndIndex() int {62 windowEnd := lrs.GetOffset() + lrs.GetLimit()63 innerSeriesLastIndex := lrs.InnerSeries.Len() - 164 return MinInt(windowEnd, innerSeriesLastIndex)65}66// GetOffset returns the data offset.67func (lrs LinearRegressionSeries) GetOffset() int {68 if lrs.Offset == 0 {69 return 070 }71 return lrs.Offset72}73// GetValues gets a value at a given index.74func (lrs *LinearRegressionSeries) GetValues(index int) (x, y float64) {75 if lrs.InnerSeries == nil || lrs.InnerSeries.Len() == 0 {76 return77 }78 if lrs.IsZero() {79 lrs.computeCoefficients()80 }81 offset := lrs.GetOffset()82 effectiveIndex := MinInt(index+offset, lrs.InnerSeries.Len())83 x, y = lrs.InnerSeries.GetValues(effectiveIndex)84 y = (lrs.m * lrs.normalize(x)) + lrs.b85 return86}87// GetFirstValues computes the first linear regression value.88func (lrs *LinearRegressionSeries) GetFirstValues() (x, y float64) {89 if lrs.InnerSeries == nil || lrs.InnerSeries.Len() == 0 {90 return91 }92 if lrs.IsZero() {93 lrs.computeCoefficients()94 }95 x, y = lrs.InnerSeries.GetValues(0)96 y = (lrs.m * lrs.normalize(x)) + lrs.b97 return98}99// GetLastValues computes the last linear regression value.100func (lrs *LinearRegressionSeries) GetLastValues() (x, y float64) {101 if lrs.InnerSeries == nil || lrs.InnerSeries.Len() == 0 {102 return103 }104 if lrs.IsZero() {105 lrs.computeCoefficients()106 }107 endIndex := lrs.GetEndIndex()108 x, y = lrs.InnerSeries.GetValues(endIndex)109 y = (lrs.m * lrs.normalize(x)) + lrs.b110 return111}112// Render renders the series.113func (lrs *LinearRegressionSeries) Render(r Renderer, canvasBox Box, xrange, yrange Range, defaults Style) {114 style := lrs.Style.InheritFrom(defaults)115 Draw.LineSeries(r, canvasBox, xrange, yrange, style, lrs)116}117// Validate validates the series.118func (lrs *LinearRegressionSeries) Validate() error {119 if lrs.InnerSeries == nil {120 return fmt.Errorf("linear regression series requires InnerSeries to be set")121 }122 return nil123}124// IsZero returns if we've computed the coefficients or not.125func (lrs *LinearRegressionSeries) IsZero() bool {126 return lrs.m == 0 && lrs.b == 0127}128//129// internal helpers130//131func (lrs *LinearRegressionSeries) normalize(xvalue float64) float64 {132 return (xvalue - lrs.avgx) / lrs.stddevx133}134// computeCoefficients computes the `m` and `b` terms in the linear formula given by `y = mx+b`.135func (lrs *LinearRegressionSeries) computeCoefficients() {136 startIndex := lrs.GetOffset()137 endIndex := lrs.GetEndIndex()138 p := float64(endIndex - startIndex)139 xvalues := NewValueBufferWithCapacity(lrs.Len())140 for index := startIndex; index < endIndex; index++ {141 x, _ := lrs.InnerSeries.GetValues(index)142 xvalues.Enqueue(x)143 }144 lrs.avgx = Seq{xvalues}.Average()145 lrs.stddevx = Seq{xvalues}.StdDev()146 var sumx, sumy, sumxx, sumxy float64147 for index := startIndex; index < endIndex; index++ {148 x, y := lrs.InnerSeries.GetValues(index)149 x = lrs.normalize(x)150 sumx += x151 sumy += y...`

polynomial_regression_series.go

Source:polynomial_regression_series.go

`...44 return prs.InnerSeries.Len()45 }46 return prs.Limit47}48// GetEndIndex returns the effective limit end.49func (prs PolynomialRegressionSeries) GetEndIndex() int {50 windowEnd := prs.GetOffset() + prs.GetLimit()51 innerSeriesLastIndex := prs.InnerSeries.Len() - 152 return MinInt(windowEnd, innerSeriesLastIndex)53}54// GetOffset returns the data offset.55func (prs PolynomialRegressionSeries) GetOffset() int {56 if prs.Offset == 0 {57 return 058 }59 return prs.Offset60}61// Validate validates the series.62func (prs *PolynomialRegressionSeries) Validate() error {63 if prs.InnerSeries == nil {64 return fmt.Errorf("linear regression series requires InnerSeries to be set")65 }66 endIndex := prs.GetEndIndex()67 if endIndex >= prs.InnerSeries.Len() {68 return fmt.Errorf("invalid window; inner series has length %d but end index is %d", prs.InnerSeries.Len(), endIndex)69 }70 return nil71}72// GetValues returns the series value for a given index.73func (prs *PolynomialRegressionSeries) GetValues(index int) (x, y float64) {74 if prs.InnerSeries == nil || prs.InnerSeries.Len() == 0 {75 return76 }77 if prs.coeffs == nil {78 coeffs, err := prs.computeCoefficients()79 if err != nil {80 panic(err)81 }82 prs.coeffs = coeffs83 }84 offset := prs.GetOffset()85 effectiveIndex := MinInt(index+offset, prs.InnerSeries.Len())86 x, y = prs.InnerSeries.GetValues(effectiveIndex)87 y = prs.apply(x)88 return89}90// GetFirstValues computes the first poly regression value.91func (prs *PolynomialRegressionSeries) GetFirstValues() (x, y float64) {92 if prs.InnerSeries == nil || prs.InnerSeries.Len() == 0 {93 return94 }95 if prs.coeffs == nil {96 coeffs, err := prs.computeCoefficients()97 if err != nil {98 panic(err)99 }100 prs.coeffs = coeffs101 }102 x, y = prs.InnerSeries.GetValues(0)103 y = prs.apply(x)104 return105}106// GetLastValues computes the last poly regression value.107func (prs *PolynomialRegressionSeries) GetLastValues() (x, y float64) {108 if prs.InnerSeries == nil || prs.InnerSeries.Len() == 0 {109 return110 }111 if prs.coeffs == nil {112 coeffs, err := prs.computeCoefficients()113 if err != nil {114 panic(err)115 }116 prs.coeffs = coeffs117 }118 endIndex := prs.GetEndIndex()119 x, y = prs.InnerSeries.GetValues(endIndex)120 y = prs.apply(x)121 return122}123func (prs *PolynomialRegressionSeries) apply(v float64) (out float64) {124 for index, coeff := range prs.coeffs {125 out = out + (coeff * math.Pow(v, float64(index)))126 }127 return128}129func (prs *PolynomialRegressionSeries) computeCoefficients() ([]float64, error) {130 xvalues, yvalues := prs.values()131 return matrix.Poly(xvalues, yvalues, prs.Degree)132}133func (prs *PolynomialRegressionSeries) values() (xvalues, yvalues []float64) {134 startIndex := prs.GetOffset()135 endIndex := prs.GetEndIndex()136 xvalues = make([]float64, endIndex-startIndex)137 yvalues = make([]float64, endIndex-startIndex)138 for index := startIndex; index < endIndex; index++ {139 x, y := prs.InnerSeries.GetValues(index)140 xvalues[index-startIndex] = x141 yvalues[index-startIndex] = y142 }143 return144}145// Render renders the series.146func (prs *PolynomialRegressionSeries) Render(r Renderer, canvasBox Box, xrange, yrange Range, defaults Style) {147 style := prs.Style.InheritFrom(defaults)148 Draw.LineSeries(r, canvasBox, xrange, yrange, style, prs)149}...`

regression_test.go

Source:regression_test.go

`...28 {1, 1},29 },30 regression: &Regression{31 Start: 0,32 End: 1,33 Intercept: 0,34 Gradient: 1,35 Width: 0,36 },37 },38 "two negative samples": {39 input: []Sample{40 {-1, -1},41 {0, 0},42 },43 regression: &Regression{44 Start: -1,45 End: 0,46 Intercept: 0,47 Gradient: 1,48 Width: 0,49 },50 },51 "lower gradient line": {52 input: []Sample{53 {1, 1},54 {3, 2},55 {5, 3},56 },57 regression: &Regression{58 Start: 1,59 End: 5,60 Intercept: 0.5,61 Gradient: 0.5,62 Width: 0,63 },64 },65 "non-zero width line": {66 input: []Sample{67 {1, 2},68 {2, 1},69 {2, 3},70 {3, 2},71 {3, 4},72 {4, 3},73 },74 regression: &Regression{75 Start: 1,76 End: 4,77 Intercept: 1.363,78 Gradient: 0.454,79 Width: 1.142,80 },81 },82 }83 for name, tc := range tests {84 t.Run(name, func(t *testing.T) {85 regressionBuffer := RegressionBuffer{}86 for _, s := range tc.input {87 regressionBuffer.Add(s)88 }89 regression, err := regressionBuffer.Regression()90 if tc.err != "" && err == nil {91 t.Fatalf("expected error: %v, got no error", tc.err)92 }93 if tc.err != "" && tc.err != err.Error() {94 t.Fatalf("expected error: %v, got: %v", tc.err, err.Error())95 }96 if tc.regression != nil {97 if tc.regression.Start != regression.Start {98 t.Fatalf("expected Start: %v, got: %v", tc.regression.Start, regression.Start)99 }100 if tc.regression.End != regression.End {101 t.Fatalf("expected End: %v, got: %v", tc.regression.End, regression.End)102 }103 if !float64Equals(tc.regression.Intercept, regression.Intercept) {104 t.Fatalf("expected Intercept: %v, got: %v", tc.regression.Intercept, regression.Intercept)105 }106 if !float64Equals(tc.regression.Gradient, regression.Gradient) {107 t.Fatalf("expected Gradient: %v, got: %v", tc.regression.Gradient, regression.Gradient)108 }109 if !float64Equals(tc.regression.Width, regression.Width) {110 t.Fatalf("expected Width: %v, got: %v", tc.regression.Width, regression.Width)111 }112 }113 })114 }115}...`

End

Using AI Code Generation

`1import (2func main() {3 r := new(regression.Regression)4 r.Train(regression.Data{5 X: []float64{1, 2, 3},6 Y: []float64{1, 2, 3},7 })8 r.End()9 fmt.Println(r.Coeff(0), r.Coeff(1))10}`

End

Using AI Code Generation

`1import (2func main() {3 x := []float64{0.5, 1, 1.5, 2, 2.5}4 y := []float64{0.5, 1, 1.5, 2, 2.5}5 r := stat.LinearRegression(x, y, nil, false)6 fmt.Println(r.End())7}`

End

Using AI Code Generation

`1import (2func main() {3 r.SetObserved("Y")4 r.SetVar(0, "X")5 r.Train(regression.DataPoint(1, []float64{1}, []float64{1}))6 r.Train(regression.DataPoint(2, []float64{2}, []float64{2}))7 r.Train(regression.DataPoint(3, []float64{3}, []float64{3}))8 r.Train(regression.DataPoint(4, []float64{4}, []float64{4}))9 r.Train(regression.DataPoint(5, []float64{5}, []float64{5}))10 r.Run()11 fmt.Printf("\nRegression Formula:\n%v\n", r.Formula)12 fmt.Printf("\nR²: %v\n", r.R2)13 fmt.Printf("\nX=1, Y=%v\n", r.Predict([]float64{1}))14 fmt.Printf("\nX=2, Y=%v\n", r.Predict([]float64{2}))15 fmt.Printf("\nX=3, Y=%v\n", r.Predict([]float64{3}))16 fmt.Printf("\nX=4, Y=%v\n", r.Predict([]float64{4}))17 fmt.Printf("\nX=5, Y=%v\n", r.Predict([]float64{5}))18}`

End

Using AI Code Generation

`1import (2func main() {3 r.SetObserved("Y")4 r.SetVar(0, "X")5 rand.Seed(time.Now().UnixNano())6 for x := 0.0; x < 10.0; x++ {7 y := 5.0*x + 10.0 + rand.Float64()*5.08 r.Train(regression.DataPoint(y, []float64{x}))9 }10 r.Run()11 fmt.Printf("\nformula for Y:\n%v\n", r.Formula)12}`

End

Using AI Code Generation

`1import (2func main() {3 r := new(regression.Regression)4 r.SetObserved("Y")5 r.SetVar(0, "X")6 r.AddDataPoint(0, 0, 0.0)7 r.AddDataPoint(1, 1, 1.0)8 r.AddDataPoint(2, 2, 2.0)9 r.AddDataPoint(3, 3, 3.0)10 r.AddDataPoint(4, 4, 4.0)11 r.AddDataPoint(5, 5, 5.0)12 r.AddDataPoint(6, 6, 6.0)13 r.AddDataPoint(7, 7, 7.0)14 r.AddDataPoint(8, 8, 8.0)15 r.AddDataPoint(9, 9, 9.0)16 r.Run()`

End

Using AI Code Generation

`1import (2func main() {3 g4 := gsd.ParseToGraph("testgraph.gsd")4 fmt.Println("Graph:")5 fmt.Println(g4)6 fmt.Println()7 reg := regression.Regression{}8 reg.SetGraph(g4)9 reg.SetSource("A")10 reg.SetTarget("D")11 reg.SetWeight("weight")12 fmt.Println("Regression:")13 fmt.Println(reg)14 fmt.Println()15 fmt.Println("Regression Path:")16 fmt.Println(reg.End())17 fmt.Println()18 reg.SetTarget("E")19 fmt.Println("Regression Path:")20 fmt.Println(reg.End())21 fmt.Println()22 reg.SetTarget("F")23 fmt.Println("Regression Path:")24 fmt.Println(reg.End())25 fmt.Println()26 reg.SetTarget("G")27 fmt.Println("Regression Path:")28 fmt.Println(reg.End())29 fmt.Println()30 reg.SetTarget("H")31 fmt.Println("Regression Path:")32 fmt.Println(reg.End())33 fmt.Println()34 reg.SetTarget("I")35 fmt.Println("Regression Path:")36 fmt.Println(reg.End())37 fmt.Println()38 reg.SetTarget("J")39 fmt.Println("Regression Path:")40 fmt.Println(reg.End())41 fmt.Println()42 reg.SetTarget("K")43 fmt.Println("Regression Path:")44 fmt.Println(reg.End())45 fmt.Println()46 reg.SetTarget("L")47 fmt.Println("Regression Path:")48 fmt.Println(reg.End())49 fmt.Println()50}`

End

Using AI Code Generation

`1import (2func main() {3 rand.Seed(time.Now().UnixNano())4 r.SetObserved("Y")5 r.SetVar(0, "X")6 for x := 0.0; x < 10; x++ {7 y := x + rand.Float64()*28 r.Train(regression.DataPoint(y, []float64{x}))9 }10 r.Run()11 fmt.Printf("\nRegression Formula:\n%v\n", r.Formula)12 fmt.Printf("\nR2: %v\n", r.R2)13}`

End

Using AI Code Generation

`1import (2func main() {3 x := []float64{1, 2, 3, 4, 5}4 y := []float64{1, 2, 3, 4, 5}5 reg := stat.LinearRegression{}6 reg.Fit(x, y, nil, false)7 fmt.Printf("intercept: %f slope: %f\n", reg.Intercept, reg.Slope)8 fmt.Println(reg.Predict([]float64{6}))9}`

End

Using AI Code Generation

`1import (2func main() {3 r := new(regression.Regression)4 r.Train(regression.Data{5 {X: []float64{1}, Y: 2},6 {X: []float64{2}, Y: 4},7 {X: []float64{3}, Y: 6},8 {X: []float64{4}, Y: 8},9 })10 r.Run()11 fmt.Printf("Regression formula:\n%v\n", r.Formula)12 fmt.Printf("Predicted value for X=5: %v\n", r.Predict([]float64{5}))13}`

End

Using AI Code Generation

`1import (2func main() {3 r.SetObserved("Y")4 r.SetVar(0, "X")5 r.Train(regression.DataPoint(1, []float64{1}, []string{"X"}))6 r.Train(regression.DataPoint(2, []float64{2}, []string{"X"}))7 r.Train(regression.DataPoint(3, []float64{3}, []string{"X"}))8 r.Train(regression.DataPoint(4, []float64{4}, []string{"X"}))9 r.Train(regression.DataPoint(5, []float64{5}, []string{"X"}))10 r.Run()11 for x := 1.0; x <= 5.0; x++ {12 fmt.Printf("x = %.2f, predicted y = %2.f\n", x, r.End([]float64{x}))13 }14}`

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