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Added EigenValue and Vector Calculator
Added new calculator for Largest Eigen Value and Eigen Vector of a N x N Matrix using Power method. Added project to the index.html website and also in readme.md file.
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| <!DOCTYPE html> | ||
| <html lang="en"> | ||
| <head> | ||
| <meta charset="UTF-8" /> | ||
| <meta name="viewport" content="width=device-width, initial-scale=1.0" /> | ||
| <title>Eigenvalue and Eigenvector Calculator</title> | ||
| <link rel="stylesheet" href="style.css" /> | ||
| </head> | ||
| <body> | ||
| <div class="container"> | ||
| <h2>Largest Eigen Value and Eigen Vector Calculator</h2> | ||
| <form id="matrix-form"> | ||
| <label for="dimension">Enter matrix dimension (N x N):</label> | ||
| <input type="number" id="dimension" min="2" max="10" value="2" /> | ||
| <button type="button" onclick="generateMatrixInput()"> | ||
| Generate Matrix Input | ||
| </button> | ||
| </form> | ||
| <div id="matrix-input"></div> | ||
| <button id="calculate" style="display: none" onclick="calculateEigen()"> | ||
| Calculate | ||
| </button> | ||
| <div id="results"></div> | ||
| </div> | ||
| <script src="script.js"></script> | ||
| </body> | ||
| </html> |
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| function generateMatrixInput() { | ||
| const dimension = document.getElementById("dimension").value; | ||
| const matrixInputDiv = document.getElementById("matrix-input"); | ||
| matrixInputDiv.innerHTML = ""; // Clear previous inputs | ||
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| for (let i = 0; i < dimension; i++) { | ||
| const rowDiv = document.createElement("div"); | ||
| for (let j = 0; j < dimension; j++) { | ||
| const input = document.createElement("input"); | ||
| input.type = "number"; | ||
| input.id = `matrix-${i}-${j}`; | ||
| input.value = Math.floor(Math.random() * 10); // Default random values for fun | ||
| rowDiv.appendChild(input); | ||
| } | ||
| matrixInputDiv.appendChild(rowDiv); | ||
| } | ||
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| document.getElementById("calculate").style.display = "inline-block"; | ||
| } | ||
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| function calculateEigen() { | ||
| const dimension = document.getElementById("dimension").value; | ||
| const matrix = []; | ||
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| // Read matrix values from inputs | ||
| for (let i = 0; i < dimension; i++) { | ||
| const row = []; | ||
| for (let j = 0; j < dimension; j++) { | ||
| const value = parseFloat( | ||
| document.getElementById(`matrix-${i}-${j}`).value | ||
| ); | ||
| row.push(value); | ||
| } | ||
| matrix.push(row); | ||
| } | ||
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| // Perform power iteration to find largest eigenvalue and eigenvector | ||
| let eigenvalue = 0; | ||
| let eigenvector = new Array(dimension).fill(1); | ||
| const tolerance = 1e-6; | ||
| const maxIterations = 1000; | ||
|
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| for (let iter = 0; iter < maxIterations; iter++) { | ||
| let newEigenvector = multiplyMatrixVector(matrix, eigenvector); | ||
| let norm = Math.max(...newEigenvector.map(Math.abs)); | ||
| newEigenvector = newEigenvector.map((val) => val / norm); | ||
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| if (Math.abs(norm - eigenvalue) < tolerance) { | ||
| eigenvalue = norm; | ||
| eigenvector = newEigenvector; | ||
| break; | ||
| } | ||
|
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| eigenvalue = norm; | ||
| eigenvector = newEigenvector; | ||
| } | ||
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| displayResults(eigenvalue, eigenvector); | ||
| } | ||
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| function multiplyMatrixVector(matrix, vector) { | ||
| const result = []; | ||
| for (let i = 0; i < matrix.length; i++) { | ||
| let sum = 0; | ||
| for (let j = 0; j < vector.length; j++) { | ||
| sum += matrix[i][j] * vector[j]; | ||
| } | ||
| result.push(sum); | ||
| } | ||
| return result; | ||
| } | ||
|
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| function displayResults(eigenvalue, eigenvector) { | ||
| const resultsDiv = document.getElementById("results"); | ||
| resultsDiv.innerHTML = `<p><strong>Largest Eigenvalue:</strong> ${eigenvalue.toFixed(6)}</p>`; | ||
| resultsDiv.innerHTML += `<p class="value"><strong>Corresponding Eigenvector:</strong></br>[${eigenvector | ||
| .map((val) => val.toFixed(6)) | ||
| .join(", ")}]</p>`; | ||
| } |
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| body { | ||
| font-family: "Arial", sans-serif; | ||
| background: rgb(29,29,124); | ||
| background: linear-gradient(90deg, rgba(29,29,124,0.9641106442577031) 30%, rgba(0,23,28,1) 90%); | ||
| display: flex; | ||
| justify-content: center; | ||
| position: relative; | ||
| color: #473434; | ||
| } | ||
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| .container { | ||
| background-color: #ffffff; | ||
| padding: 30px; | ||
| border-radius: 15px; | ||
| box-shadow: 0 4px 10px rgba(0, 0, 0, 0.1); | ||
| width: 500px; | ||
| text-align: center; | ||
| position: absolute; | ||
| top: 85px; | ||
| } | ||
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| h1 { | ||
| margin-bottom: 20px; | ||
| font-size: 24px; | ||
| color: #0056b3; | ||
| } | ||
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| form { | ||
| margin-bottom: 20px; | ||
| } | ||
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| label { | ||
| font-size: 16px; | ||
| color: #333; | ||
| } | ||
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| input[type="number"] { | ||
| width: 60px; | ||
| padding: 5px; | ||
| margin: 10px 5px; | ||
| border: 1px solid #ccc; | ||
| border-radius: 5px; | ||
| font-size: 16px; | ||
| } | ||
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| button { | ||
| padding: 10px 15px; | ||
| margin-top: 20px; | ||
| font-size: 16px; | ||
| color: #fff; | ||
| background-color: #007bff; | ||
| border: none; | ||
| border-radius: 5px; | ||
| cursor: pointer; | ||
| transition: background-color 0.3s ease; | ||
| } | ||
| #matrix-form { | ||
| display: flex; | ||
| align-items: center; | ||
| flex-direction: column; | ||
| justify-content: center; | ||
| } | ||
| button:hover { | ||
| background-color: #0056b3; | ||
| } | ||
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| #matrix-input { | ||
| margin-top: 20px; | ||
| } | ||
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| #results { | ||
| margin-top: 30px; | ||
| background-color: #f9f9f9; | ||
| padding: 15px; | ||
| border-radius: 10px; | ||
| box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1); | ||
| } | ||
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| #results p { | ||
| font-size: 16px; | ||
| margin: 10px 0; | ||
| } | ||
| .value{ | ||
| display: flex; | ||
| justify-content: space-between; | ||
| flex-direction: column; | ||
| } | ||
| @media screen and (max-width: 530px) { | ||
| .container { | ||
| width: 70%; | ||
| } | ||
| } |
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