<![CDATA[Linear_Best_Fit_using_Plotly]]>
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slope * xi + intercept);
const bestFitLine = {
x: scatterData.x,
y: bestFitY,
mode: 'lines',
type: 'scatter',
name: 'Best Fit Line',
line: { color: 'red' }
};
const layout = {
title: 'Linear Best Fit Line',
margin: { t: 30 },
xaxis: { title: 'X-axis' },
yaxis: { title: 'Y-axis' }
};
// Plot the scatter data and the best-fit line
Plotly.newPlot("plot", [scatterData, bestFitLine], layout);
//readCSVFromPath()
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a + b, 0);
const sumY = y.reduce((a, b) => a + b, 0);
const sumXY = x.reduce((sum, xi, i) => sum + xi * y[i], 0);
const sumXX = x.reduce((sum, xi) => sum + xi * xi, 0);
const slope = (n * sumXY - sumX * sumY) / (n * sumXX - sumX * sumX);
const intercept = (sumY - slope * sumX) / n;
return { slope, intercept };
}
function readCSVFromPath() {
//fetch('https://webejs.iwant2study.org:8000/static/sessions/317ea4eb-99af-4d88-a13a-06f65fd35ac8/source/data.csv') // Replace 'yourfile.csv' with the actual file name
fetch('./data.csv') // Replace 'yourfile.csv' with the actual file name
.then(response => {
if (!response.ok) {
throw new Error("Failed to fetch the CSV file");
}
return response.text(); // Return the CSV content as plain text
})
.then(data => {
const parsedData = parseCSV(data); // Parse the CSV data
console.log("Parsed CSV Data:", parsedData); // Log the parsed data for debugging
// You can now use the parsedData for further processing in your simulation
})
.catch(error => {
console.error("Error reading the CSV file:", error);
});
}
// Function to parse the CSV data
function parseCSV(csvText) {
const rows = csvText.split("\n").filter(row => row.trim() !== ""); // Split by new lines and filter empty rows
const result = rows.map(row => row.split(",")); // Split each row by commas
return result;
}
// Call the function to read the CSV file
function polynomialRegression(x, y, degree) {
const X = x.map(xi => Array.from({length: degree + 1}, (_, i) => Math.pow(xi, i)));
const Xt = math.transpose(X);
const XtX = math.multiply(Xt, X);
const XtY = math.multiply(Xt, y);
const coefficients = math.lusolve(XtX, XtY).flat();
return coefficients;
}
// Function to generate a line of best fit
function generateBestFitLine(xData, yData) {
// Dynamically determine the degree of polynomial (start with quadratic)
const degree = 2; // Adjust based on data complexity
const coefficients = polynomialRegression(xData, yData, degree);
// Generate y-values for the best fit line
const bestFitY = xData.map(xi =>
coefficients.reduce((acc, coef, i) => acc + coef * Math.pow(xi, i), 0)
);
return bestFitY;
}
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