# How to use Start method of regression Package

Best Keploy code snippet using regression.Start

regression_test.go

Source:regression_test.go

regression.go

Source:regression.go

ilplang_listener.go

Source:ilplang_listener.go

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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