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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