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How to use Start method of regression Package

Best Keploy code snippet using regression.Start

regression_test.go

Source:regression_test.go

...27				{0, 0},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			}...

regression.go

Source:regression.go

...7	X float648	Y float649}10type Regression struct {11	Start float6412	End   float6413	Intercept float6414	Gradient  float6415	Width float6416}17type RegressionBuffer struct {18	start float6419	end   float6420	n float6421	sx  float6422	sy  float6423	sxy float6424	sx2 float6425	sy2 float6426	width float6427}28func (r *RegressionBuffer) Add(s Sample) {29	if r.n == 0 {30		r.start = s.X31	}32	r.end = s.X33	r.n++34	r.sx = r.sx + s.X35	r.sy = r.sy + s.Y36	r.sxy = r.sxy + s.X*s.Y37	r.sx2 = r.sx2 + s.X*s.X38	r.sy2 = r.sy2 + s.Y*s.Y39	if r.n >= 2 {40		gradient, err := r.Gradient()41		if err != nil {42			panic(err)43		}44		intercept, err := r.Intercept()45		if err != nil {46			panic(err)47		}48		d := math.Abs(s.Y - (gradient*s.X + intercept))49		r.width = math.Max(r.width, d)50	}51}52func (r *RegressionBuffer) Intercept() (float64, error) {53	if r.n < 2 {54		return 0, errors.New("intercept requires at least two samples")55	}56	intercept := (r.sy*r.sx2 - r.sx*r.sxy) / (r.n*r.sx2 - r.sx*r.sx)57	return intercept, nil58}59func (r *RegressionBuffer) Gradient() (float64, error) {60	if r.n < 2 {61		return 0, errors.New("gradient requires at least two samples")62	}63	gradient := (r.n*r.sxy - r.sx*r.sy) / (r.n*r.sx2 - r.sx*r.sx)64	return gradient, nil65}66func (r *RegressionBuffer) Regression() (*Regression, error) {67	if r.n < 2 {68		return nil, errors.New("regression requires at least two samples")69	}70	intercept, err := r.Intercept()71	if err != nil {72		return nil, err73	}74	gradient, err := r.Gradient()75	if err != nil {76		return nil, err77	}78	regression := Regression{79		Start: r.start,80		End:   r.end,81		Intercept: intercept,82		Gradient:  gradient,83		Width: r.width,84	}85	return &regression, nil86}...

ilplang_listener.go

Source:ilplang_listener.go

...3import "github.com/antlr/antlr4/runtime/Go/antlr"4// ILPLangListener is a complete listener for a parse tree produced by ILPLangParser.5type ILPLangListener interface {6	antlr.ParseTreeListener7	// EnterStart is called when entering the start production.8	EnterStart(c *StartContext)9	// EnterClassification_task is called when entering the classification_task production.10	EnterClassification_task(c *Classification_taskContext)11	// EnterRegression_task is called when entering the regression_task production.12	EnterRegression_task(c *Regression_taskContext)13	// EnterTerm is called when entering the term production.14	EnterTerm(c *TermContext)15	// EnterRegression_term is called when entering the regression_term production.16	EnterRegression_term(c *Regression_termContext)17	// ExitStart is called when exiting the start production.18	ExitStart(c *StartContext)19	// ExitClassification_task is called when exiting the classification_task production.20	ExitClassification_task(c *Classification_taskContext)21	// ExitRegression_task is called when exiting the regression_task production.22	ExitRegression_task(c *Regression_taskContext)23	// ExitTerm is called when exiting the term production.24	ExitTerm(c *TermContext)25	// ExitRegression_term is called when exiting the regression_term production.26	ExitRegression_term(c *Regression_termContext)27}...

Start

Using AI Code Generation

1import (2func main() {3	r := new(regression.Regression)4	r.SetObserved("Y")5	r.SetVar(0, "X")6	r.Train(7		regression.DataPoint(2.71, []float64{1.0}),8		regression.DataPoint(3.14, []float64{2.0}),9		regression.DataPoint(1.41, []float64{3.0}),10		regression.DataPoint(1.62, []float64{4.0}),11	r.Run()12	fmt.Printf("\nRegression formula:\n%v\n", r.Formula)13}

Start

Using AI Code Generation

1import (2func main() {3	r.SetObserved("y")4	r.SetVar(0, "x")5	r.Train(6		regression.DataPoint(2.71, []float64{1}),7		regression.DataPoint(4.62, []float64{2}),8		regression.DataPoint(9.26, []float64{3}),9		regression.DataPoint(12.85, []float64{4}),10		regression.DataPoint(19.31, []float64{5}),11	r.Run()12	fmt.Printf("\nRegression Formula:\n%v\n", r.Formula)13	fmt.Printf("\nR2: %v\n", r.R2)14}15import (16func main() {17	r.SetObserved("y")18	r.SetVar(0, "x")19	r.Train(20		regression.DataPoint(2.71, []float64{1}),21		regression.DataPoint(4.62, []float64{2}),22		regression.DataPoint(9.26, []float64{3}),23		regression.DataPoint(12.85, []float64{4}),24		regression.DataPoint(19.31, []float64{5}),25	r.Fit()26	fmt.Printf("\nRegression Formula:\n%v\n", r.Formula)27	fmt.Printf("\nR2: %v\n", r.R2)28}29import (30func main() {31	r.SetObserved("y")32	r.SetVar(0, "x")33	r.Train(34		regression.DataPoint(2.71, []float64{1

Start

Using AI Code Generation

1import (2func main() {3	r.SetObserved("Y")4	r.SetVar(0, "X")5	r.Train(6		regression.Data{7			X: []float64{1, 2, 3, 4},8			Y: []float64{2, 4, 6, 8},9		},10	r.Run()11	fmt.Printf("\nRegression Formula:\n%v\n", r.Formula)12	fmt.Printf("\nRegression:\n%v\n", r)13	fmt.Printf("\nRegression Coefficients:\n%v\n", r.Coeffs)14	fmt.Printf("\nR2:\n%v\n", r.R2)15	fmt.Printf("\nSigma:\n%v\n", r.Sigma)16	fmt.Printf("\nF:\n%v\n", r.F)17	fmt.Printf("\nMSE:\n%v\n", r.MSE)18	fmt.Printf("\nAdj. R2:\n%v\n", r.AdjR2)19	fmt.Printf("\nStd. Error:\n%v\n", r.StdErr)20	fmt.Printf("\nT Stat:\n%v\n", r.TStat)21	fmt.Printf("\nSSR:\n%v\n", r.SSR)22	fmt.Printf("\nSSE:\n%v\n", r.SSE)23	fmt.Printf("\nSST:\n%v\n", r.SST)24	fmt.Printf("\nDFR:\n%v\n", r.DFR)25	fmt.Printf("\nDFE:\n%v\n", r.DFE)26	fmt.Printf("\nDFT:\n%v\n", r.DFT)27	fmt.Printf("\nF Stat:\n%v\n", r.FStat)28	fmt.Printf("\nProb (F Stat):\n%v\n", r.ProbFStat)29	fmt.Printf("\nProb (T Stat):\n%v\n", r.ProbTStat)30	fmt.Printf("\nAIC:\n%v\n", r.AIC)31	fmt.Printf("\nBIC:\n%v\n", r.BIC)32	fmt.Printf("\nLog-Likelihood:\n%v\n", r.LogLikelihood)33	fmt.Printf("\nDurbin-Watson:\n%v\n", r.DurbinWatson)

Start

Using AI Code Generation

1import (2func main() {3	r.SetObserved("Temperature")4	r.SetVar(0, "Humidity")5	r.Train(6		regression.DataPoint(22.1, []float64{71.1}),7		regression.DataPoint(19.4, []float64{69.8}),8		regression.DataPoint(18.1, []float64{68.0}),9		regression.DataPoint(17.3, []float64{66.2}),10		regression.DataPoint(15.5, []float64{65.4}),11		regression.DataPoint(15.1, []float64{64.7}),12		regression.DataPoint(14.4, []float64{63.0}),13		regression.DataPoint(13.5, []float64{61.3}),14		regression.DataPoint(12.5, []float64{59.0}),15		regression.DataPoint(11.9, []float64{57.5}),16		regression.DataPoint(11.0, []float64{55.6}),17	r.Run()18	fmt.Printf("\nRegression Formula:\n%v\n", r.Formula)19	fmt.Printf("\nR2: %v\n", r.R2)20}

Start

Using AI Code Generation

1import (2func main() {3	r.SetObserved("Y")4	r.SetVar(0, "X")5	r.Train(regression.Data{6		{X: []float64{0}, Y: 0},7		{X: []float64{1}, Y: 1},8		{X: []float64{2}, Y: 2},9	})10	r.Run()11	fmt.Printf("output: %s\n", r.Formula)12}

Start

Using AI Code Generation

1import (2func main() {3	r.SetObserved("y")4	r.SetVar(0, "x")5	rand.Seed(time.Now().UnixNano())6	for i := 0; i < 100; i++ {7		x := rand.Float64()8		y := 2*x + 1 + rand.Float64()9		r.Train(regression.DataPoint(y, []float64{x}))10	}11	r.Run()12	fmt.Printf("\nRegression Formula:\n")13	fmt.Printf("%v\n\n", r.Formula)14	fmt.Printf("Predicted values:\n")15	for x := 0.0; x <= 1.0; x += 0.1 {16		y, _ := r.Predict([]float64{x})17		fmt.Printf("x: %v, y: %0.2f\n", x, y)18	}19}

Start

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"}, 1))6	r.Train(regression.DataPoint(1, []float64{2}, []string{"X"}, 2))7	r.Train(regression.DataPoint(1, []float64{3}, []string{"X"}, 3))8	r.Train(regression.DataPoint(1, []float64{4}, []string{"X"}, 4))9	r.Train(regression.DataPoint(1, []float64{5}, []string{"X"}, 5))10	r.Train(regression.DataPoint(2, []float64{1}, []string{"X"}, 2))11	r.Train(regression.DataPoint(2, []float64{2}, []string{"X"}, 4))12	r.Train(regression.DataPoint(2, []float64{3}, []string{"X"}, 6))13	r.Train(regression.DataPoint(2, []float64{4}, []string{"X"}, 8))14	r.Train(regression.DataPoint(2, []float64{5}, []string{"X"}, 10))15	r.Train(regression.DataPoint(3, []float64{1}, []string{"X"}, 3))16	r.Train(regression.DataPoint(3, []float64{2}, []string{"X"}, 6))17	r.Train(regression.DataPoint(3, []float64{3}, []string{"X"}, 9))18	r.Train(regression.DataPoint(3, []float64{4}, []string{"X"}, 12))19	r.Train(regression.DataPoint(3, []float64{5}, []string{"X"}, 15))20	r.Train(regression.DataPoint(4, []float64{1}, []string{"X"}, 4))21	r.Train(regression.DataPoint(4, []float64{2}, []string

Start

Using AI Code Generation

1import (2func main() {3	r.SetObserved("Y")4	r.SetVar(0, "X")5	r.Train(regression.Data{6		{X: []float64{1}, Y: 1},7		{X: []float64{2}, Y: 2},8		{X: []float64{3}, Y: 3},9		{X: []float64{4}, Y: 4},10		{X: []float64{5}, Y: 5},11		{X: []float64{6}, Y: 6},12		{X: []float64{7}, Y: 7},13		{X: []float64{8}, Y: 8},14		{X: []float64{9}, Y: 9},15		{X: []float64{10}, Y: 10},16		{X: []float64{11}, Y: 11},17		{X: []float64{12}, Y: 12},18		{X: []float64{13}, Y: 13},19		{X: []float64{14}, Y: 14},20		{X: []float64{15}, Y: 15},21		{X: []float64{16}, Y: 16},22		{X: []float64{17}, Y: 17},23		{X: []float64{18}, Y: 18},24		{X: []float64{19}, Y: 19},25		{X: []float64{20}, Y: 20},26	})27	r.Run()28	fmt.Printf("\nRegression Formula:\n%v\n", r.Formula)29	fmt.Printf("\nR2:\n%v\n", r.R2)30	fmt.Printf("\nMAE:\n%v\n", r.MAE)31	fmt.Printf("\nRMSE:\n%v\n", math.Sqrt(r.MSE))32}

Start

Using AI Code Generation

1import (2func main() {3	r := new(regression.Regression)4	r.SetObserved("Y")5	r.SetVar(0, "X")6	r.Train(regression.DataPoint(1, 2))7	r.Train(regression.DataPoint(2, 3))8	r.Train(regression.DataPoint(3, 4))9	r.Train(regression.DataPoint(4, 5))10	r.Train(regression.DataPoint(5, 6))11	r.Train(regression.DataPoint(6, 7))12	r.Train(regression.DataPoint(7, 8))13	r.Train(regression.DataPoint(8, 9))14	r.Train(regression.DataPoint(9, 10))15	r.Train(regression.DataPoint(10, 11))16	r.Run()17	r.Summary()18	fmt.Printf("\nRegression formula:\n%v\n", r.Formula)19	fmt.Printf("\nR^2: %v\n", r.R2)20}

Start

Using AI Code Generation

1import java.util.*;2import java.io.*;3{4	public static void main(String[] args) {5		Regression r=new Regression();6		r.Start();7	}8}9import java.util.*;10import java.io.*;11{12	public void Start()13	{14		Scanner sc=new Scanner(System.in);15		System.out.println("Enter the degree of the polynomial");16		int degree=sc.nextInt();17		System.out.println("Enter the number of data pairs");18		int n=sc.nextInt();19		double[][] data=new double[n][2];20		System.out.println("Enter the data pairs");21		for(int i=0;i<n;i++)22		{23			for(int j=0;j<2;j++)24			{25				data[i][j]=sc.nextDouble();26			}27		}28		Polynomial p=new Polynomial(degree);29		p.CalculateCoefficients(data);30		System.out.println("The coefficients of the polynomial are");31		for(int i=0;i<=degree;i++)32		{33			System.out.print(p.coefficients[i]+" ");34		}35		System.out.println();36		System.out.println("Enter the value of x");37		double x=sc.nextDouble();38		System.out.println("The value of the polynomial at x is "+p.Evaluate(x));39	}40}41import java.util.*;42import java.io.*;43{44	int degree;45	double[] coefficients;46	Polynomial(int degree)47	{48		this.degree=degree;49		coefficients=new double[degree+1];50	}51	public void CalculateCoefficients(double[][] data)52	{53		int n=data.length;54		double[][] augmentedMatrix=new double[n][degree+2];55		for(int i=0;i<n;i++)56		{57			for(int j=0;j<=degree;j++)58			{59				augmentedMatrix[i][j]=Math.pow(data[i][0],j);60			}61			augmentedMatrix[i][degree+1]=data[i][1];62		}63		for(int i=0;i<n;i++)64		{65			double divisor=augmentedMatrix[i][i];66			for(int j=0;j<=degree+1;j++)67			{

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