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implimented scatterplot, saves to another file (IEEE-VIT#51)
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@Harsh-Avinash
Harsh-Avinash authored Oct 19, 2023
1 parent 3062b65 commit 5e69e63
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2 changes: 2 additions & 0 deletions algorithms/Linear Regression/.gitignore
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@@ -0,0 +1,2 @@
go.mod
go.sum
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241 changes: 138 additions & 103 deletions algorithms/Linear Regression/linear_regression.go
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Expand Up @@ -2,12 +2,12 @@ package main

// import whatever packages you will require here
import (
"encoding/csv"
"strconv"
"os"
"log"
"encoding/csv"
"log"
"os"
"strconv"

"gonum.org/v1/plot"
"gonum.org/v1/plot"
"gonum.org/v1/plot/plotter"
"gonum.org/v1/plot/vg"
)
Expand All @@ -30,90 +30,89 @@ import (

// function to read a csv and return the csv reader object
func read_csv(filename string) ([][]float32, error) {
// Read the file into a string
// if error occurs, return an empty slice and the error
f, err := os.Open(filename)
if err != nil {
return nil, err
}
defer f.Close()

// Create a new reader to evaluate the file as a csv
// if error occurs, return an empty slice and the error
lines, err := csv.NewReader(f).ReadAll()
if err != nil {
return nil, err
}

//convert the string array to float array
var lines_converted [][]float32
for i:=0; i<len(lines); i++ {
temp1, err := strconv.ParseFloat(lines[i][0], 32)
if err != nil {
log.Fatal(err)
}
temp2, err := strconv.ParseFloat(lines[i][1], 32)
if err != nil {
log.Fatal(err)
}
lines_converted = append(lines_converted, []float32{float32(temp1), float32(temp2)})
}
return lines_converted, nil
// Read the file into a string
// if error occurs, return an empty slice and the error
f, err := os.Open(filename)
if err != nil {
return nil, err
}
defer f.Close()

// Create a new reader to evaluate the file as a csv
// if error occurs, return an empty slice and the error
lines, err := csv.NewReader(f).ReadAll()
if err != nil {
return nil, err
}

//convert the string array to float array
var lines_converted [][]float32
for i := 0; i < len(lines); i++ {
temp1, err := strconv.ParseFloat(lines[i][0], 32)
if err != nil {
log.Fatal(err)
}
temp2, err := strconv.ParseFloat(lines[i][1], 32)
if err != nil {
log.Fatal(err)
}
lines_converted = append(lines_converted, []float32{float32(temp1), float32(temp2)})
}
return lines_converted, nil
}

// function to calculate the mean of a specified column
func mean_of_col(arr [][]float32, col_number int) (float32) {
// calculate the sum of the array
var calculated_mean_value float32
calculated_mean_value = 0
for i:=0; i<len(arr); i++ {
calculated_mean_value += arr[i][col_number]
}
calculated_mean_value = calculated_mean_value / float32(len(arr))
return calculated_mean_value
func mean_of_col(arr [][]float32, col_number int) float32 {
// calculate the sum of the array
var calculated_mean_value float32
calculated_mean_value = 0
for i := 0; i < len(arr); i++ {
calculated_mean_value += arr[i][col_number]
}
calculated_mean_value = calculated_mean_value / float32(len(arr))
return calculated_mean_value
}


// function to calculate slope m and intercept c using the required formula

// // FORMULA
// // m = (Σ(x - x')(y - y'))/(Σ(x-x')^2)
// // where x' and y' are the means of x and y respectively

func calculate_slope_and_intercept(csv_object [][]float32) (float32, float32) {
var numerator, denominator float32
numerator = 0
denominator = 0

// calculate the mean of x and y
var x_mean, y_mean float32
x_mean = mean_of_col(csv_object, 0)
y_mean = mean_of_col(csv_object, 1)

// calculate the numerator and denominator
for i:=0; i<len(csv_object); i++ {
numerator += (csv_object[i][0] - x_mean) * (csv_object[i][1] - y_mean)
denominator += (csv_object[i][0] - x_mean) * (csv_object[i][0] - x_mean)
}

// calculate the slope and intercept
var slope, intercept float32
slope = numerator / denominator
intercept = y_mean - slope * x_mean

return slope, intercept
var numerator, denominator float32
numerator = 0
denominator = 0

// calculate the mean of x and y
var x_mean, y_mean float32
x_mean = mean_of_col(csv_object, 0)
y_mean = mean_of_col(csv_object, 1)

// calculate the numerator and denominator
for i := 0; i < len(csv_object); i++ {
numerator += (csv_object[i][0] - x_mean) * (csv_object[i][1] - y_mean)
denominator += (csv_object[i][0] - x_mean) * (csv_object[i][0] - x_mean)
}

// calculate the slope and intercept
var slope, intercept float32
slope = numerator / denominator
intercept = y_mean - slope*x_mean

return slope, intercept
}

func predict_y(x float32, slope float32, intercept float32) float32 {
var y_pred = slope * x + intercept
return y_pred
var y_pred = slope*x + intercept
return y_pred
}

// function to plot regression line

func plot_regression_line(slope float32, intercept float32) {
// insert code here
p := plot.New()
// insert code here
p := plot.New()

plotter.DefaultLineStyle.Width = vg.Points(1)
plotter.DefaultGlyphStyle.Radius = vg.Points(3)
Expand Down Expand Up @@ -144,8 +143,41 @@ func plot_regression_line(slope float32, intercept float32) {

// function to plot the data points

func plot_data_points() {
// insert code here
// plot_data_points creates a scatter plot of the data and saves it as a PNG file.
// csv_object: The data points to be plotted.
// plot_name: The name of the PNG file to save the plot.
func plot_data_points(csv_object [][]float32, plot_name string) {
// Create a new plot
p := plot.New()

// Customize the appearance
plotter.DefaultGlyphStyle.Radius = vg.Points(3)
p.Title.Text = "Data Points Scatter Plot"
p.X.Label.Text = "X"
p.Y.Label.Text = "Y"

// Prepare the data points to be plotted
pts := make(plotter.XYs, len(csv_object))
for i := range csv_object {
pts[i].X = float64(csv_object[i][0])
pts[i].Y = float64(csv_object[i][1])
}

// Create a scatter plotter and add the points to it
scatter, err := plotter.NewScatter(pts)
if err != nil {
log.Panic(err)
}

p.Add(scatter)

// Save the plot to the specified PNG file
err = p.Save(1500, 900, plot_name)
if err != nil {
log.Panic(err)
}

log.Println("Data points plotted successfully and saved as ", plot_name)
}

// function to calculate R-squared value to measure accuracy of model
Expand All @@ -155,43 +187,46 @@ func plot_data_points() {
// where y_pred is the predicted value for y, y' is the mean

func calculate_r_squared(csv_object [][]float32, slope float32, intercept float32) float32 {
var y_mean = mean_of_col(csv_object, 1)
var numerator, denominator float32
var y_mean = mean_of_col(csv_object, 1)
var numerator, denominator float32

for i:=0; i<len(csv_object); i++ {
var y_pred = predict_y(csv_object[i][0], slope, intercept)
numerator += (y_pred - y_mean) * (y_pred - y_mean)
denominator += (csv_object[i][1] - y_mean) * (csv_object[i][1] - y_mean)
}
for i := 0; i < len(csv_object); i++ {
var y_pred = predict_y(csv_object[i][0], slope, intercept)
numerator += (y_pred - y_mean) * (y_pred - y_mean)
denominator += (csv_object[i][1] - y_mean) * (csv_object[i][1] - y_mean)
}

var r_squared = numerator / denominator
return r_squared
var r_squared = numerator / denominator
return r_squared
}

// MAIN FUNCTION

func main() {
// define the csv file path
var filepath string
filepath = "sample_data.csv"

// read the csv file into a file reader object
var csv_object, err = read_csv(filepath)

// if error occurs, print the error and exit
if err != nil {
log.Fatal(err)
}

// calculate the slope and intercept
var slope, intercept = calculate_slope_and_intercept(csv_object)

// calculate r^2 value
var r_squared = calculate_r_squared(csv_object, slope, intercept)

plot_regression_line(slope, intercept)
// print the slope, intercept and r^2 value
log.Println("slope: ", slope)
log.Println("intercept: ", intercept)
log.Println("r^2: ", r_squared)
// define the csv file path
var filepath string
filepath = "sample_data.csv"

// read the csv file into a file reader object
var csv_object, err = read_csv(filepath)

// if error occurs, print the error and exit
if err != nil {
log.Fatal(err)
}

// calculate the slope and intercept
var slope, intercept = calculate_slope_and_intercept(csv_object)

// calculate r^2 value
var r_squared = calculate_r_squared(csv_object, slope, intercept)

plot_regression_line(slope, intercept)
// print the slope, intercept and r^2 value
log.Println("slope: ", slope)
log.Println("intercept: ", intercept)
log.Println("r^2: ", r_squared)

// plot the data points
plot_data_points(csv_object, "DataPoints.png")
}

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