How to use Start method of regression Package

Best Keploy code snippet using regression.Start

regression_test.go

Source:regression_test.go Github

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...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 }...

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regression.go

Source:regression.go Github

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...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}...

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ilplang_listener.go

Source:ilplang_listener.go Github

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...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}...

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Start

Using AI Code Generation

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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}

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Start

Using AI Code Generation

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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

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Start

Using AI Code Generation

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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)

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Start

Using AI Code Generation

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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}

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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}

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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}

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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

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Start

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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}

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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}

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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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