[Live Now] Join the 1-day Testing & QA Summit featuring 15+ Expert Speakers.Join for FREE! Join TestMu Conference

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}

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.