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How to use addBody method of regression Package

Best Keploy code snippet using regression.addBody

addBody

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, 5},8			Y: []float64{2, 4, 6, 8, 10},9		},10	r.Train(11		regression.Data{12			X: []float64{6, 7, 8, 9, 10},13			Y: []float64{12, 14, 16, 18, 20},14		},15	r.Run()16	fmt.Printf("\nRegression Formula:\n%v\n", r.Formula)17}

addBody

Using AI Code Generation

1import (2func main() {3	r := new(regression.Regression)4	r.SetObserved("Y")5	r.SetVar(0, "X")6	r.AddDataPoint([]float64{1, 2}, []float64{2.5})7	r.AddDataPoint([]float64{2, 4}, []float64{4.5})8	r.AddDataPoint([]float64{3, 6}, []float64{6.5})9	r.AddDataPoint([]float64{4, 8}, []float64{8.5})10	r.Run()11	fmt.Printf("%+v\n", r.Coeffs)12	fmt.Printf("R2: %0.2f\n", r.R2)13}14import (15func main() {16	r := new(regression.Regression)17	r.SetObserved("Y")18	r.SetVar(0, "X")19	r.AddDataPoint([]float64{1, 2}, []float64{2.5})20	r.AddDataPoint([]float64{2, 4}, []float64{4.5})21	r.AddDataPoint([]float64{3, 6}, []float64{6.5})22	r.AddDataPoint([]float64{4, 8}, []float64{8.5})23	r.Run()24	fmt.Printf("%+v\n", r.Coeffs)25	fmt.Printf("R2: %0.2f\n", r.R2)26}27import (28func main() {29	r := new(regression.Regression)30	r.SetObserved("Y")31	r.SetVar(0, "X")32	r.AddDataPoint([]float64{1

addBody

Using AI Code Generation

1import (2func main() {3	r.SetObserved("Weight")4	r.SetVar(0, "Height")5	f, err := os.Open("data.csv")6	if err != nil {7		log.Fatal(err)8	}9	defer f.Close()10	records, err := regression.LoadRecords(f)11	if err != nil {12		log.Fatal(err)13	}14	if err := r.Train(records); err != nil {15		log.Fatal(err)16	}17	inputs = append(inputs, 70)18	fmt.Printf("Inputs: %v\n", inputs)19	fmt.Printf("Output: %0.2f\n", r.Predict(inputs))20}

addBody

Using AI Code Generation

1import java.io.File;2import java.io.FileNotFoundException;3import java.io.IOException;4import java.util.ArrayList;5import java.util.Scanner;6public class Main {7	public static void main(String[] args) throws FileNotFoundException, IOException {8		Regression reg = new Regression();9		File file = new File("data.txt");10		Scanner scan = new Scanner(file);11		while(scan.hasNextLine()) {12			String line = scan.nextLine();13			String[] entries = line.split(" ");14			double[] data = new double[entries.length];15			for(int i = 0; i < entries.length; i++) {16				data[i] = Double.parseDouble(entries[i]);17			}18			reg.addBody(data);19		}

addBody

Using AI Code Generation

1import (2type regression struct {3}4func (r *regression) init(n int) {5	r.weights = make([]float64, n)6}7func (r *regression) predict(input []float64) float64 {8	for i := 0; i < len(r.weights); i++ {9	}10}11func (r *regression) addBody(input []float64, output float64) {12	for i := 0; i < len(r.weights); i++ {13	}14}15func (r *regression) train(input [][]float64, output []float64, epochs int, learningRate float64) {16	for i := 0; i < epochs; i++ {17		for j := 0; j < len(input); j++ {18			prediction := r.predict(input[j])19			for k := 0; k < len(r.weights); k++ {20			}21		}22	}23}24func (r *regression) rmse(input [][]float64, output []float64) float64 {25	for i := 0; i < len(input); i++ {26		prediction := r.predict(input[i])27		sum += math.Pow(prediction-output[i], 2)28	}29	return math.Sqrt(sum / float64(len(input)))30}31func main() {32	rand.Seed(time.Now().UnixNano())33	r.init(3)34	input := [][]float64{35		{1, 2, 3},36		{4, 5, 6},37		{7, 8, 9},38		{10, 11, 12},39		{13, 14, 15},40	}41	output := []float64{1, 2, 3, 4, 5}42	r.train(input, output, 1000, 0.001)

addBody

Using AI Code Generation

1import java.io.*;2import java.util.*;3import java.lang.*;4import java.io.File;5import java.io.FileNotFoundException;6import java.io.IOException;7import java.util.Scanner;8import java.util.ArrayList;9import java.uti

addBody

Using AI Code Generation

1import "fmt"2func main() {3    fmt.Println("Hello, playground")4    r := Regression{}5    r.addBody(1,2)6    r.addBody(2,3)7    r.addBody(3,4)8    r.addBody(4,5)9    r.addBody(5,6)10    r.addBody(6,7)11    r.addBody(7,8)12    r.addBody(8,9)13    r.addBody(9,10)14    r.addBody(10,11)15    r.addBody(11,12)16    r.addBody(12,13)17    r.addBody(13,14)18    r.addBody(14,15)19    r.addBody(15,16)20    r.addBody(16,17)21    r.addBody(17,18)22    r.addBody(18,19)23    r.addBody(19,20)24    r.addBody(20,21)25    r.addBody(21,22)26    r.addBody(22,23)27    r.addBody(23,24)28    r.addBody(24,25)29    r.addBody(25,26)30    r.addBody(26,27)31    r.addBody(27,28)32    r.addBody(28,29)33    r.addBody(29,30)34    r.addBody(30,31)35    r.addBody(31,32)36    r.addBody(32,33)37    r.addBody(33,34)38    r.addBody(34,35)39    r.addBody(35,36)40    r.addBody(36,37)41    r.addBody(37,38)42    r.addBody(38,39)43    r.addBody(39,40)44    r.addBody(40,41)45    r.addBody(41,42)46    r.addBody(42,43)47    r.addBody(43,44)48    r.addBody(44,45)49    r.addBody(45,46)50    r.addBody(46,47)51    r.addBody(47,48)52    r.addBody(48,49)53    r.addBody(49,50)54    r.addBody(50,51)55    r.addBody(51,52)56    r.addBody(52,53)57    r.addBody(53

addBody

Using AI Code Generation

1import (2import "github.com/sajari/regression"3func main() {4	r.SetObserved("Body Fat")5	r.SetVar(0, "Weight")6	r.SetVar(1, "Height")7	r.SetVar(2, "Neck")8	r.SetVar(3, "Chest")9	r.SetVar(4, "Abdomen")10	r.SetVar(5, "Hip")11	r.SetVar(6, "Thigh")12	r.SetVar(7, "Knee")13	r.SetVar(8, "Ankle")14	r.SetVar(9, "Biceps")15	r.SetVar(10, "Forearm")16	r.SetVar(11, "Wrist")17	r.Train(regression.DataPoint(12.3, []float64{154, 69, 36, 92, 102, 90, 59, 37, 23, 32, 27, 17}))18	r.Train(regression.DataPoint(6.3, []float64{191, 71, 43, 101, 89, 104, 58, 38, 21, 33, 30, 18}))19	r.Train(regression.DataPoint(25.6, []float64{195, 70, 40, 108, 97, 93, 62, 39, 22, 37, 28, 20}))20	r.Train(regression.DataPoint(10.4, []float64{189, 68, 41, 110, 110, 104, 63, 40, 21, 34, 24, 18}))21	r.Train(regression.DataPoint(28.4, []float64{179, 68, 38, 97, 103, 101, 59, 37, 23,

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