Hologram for AI // Assuming you have appropriate replacements for np.log, qiskit.optimize, DecisionTreeClassifier, pickle // Replace 'photonics' function with the actual implementation for photonic crystals
// Validate attitude matrices if (!isAttitudeMatrixValid(Cs)) { throw new Error("Cs is not a valid attitude matrix."); }
if (!isAttitudeMatrixValid(Cf)) { throw new Error("Cf is not a valid attitude matrix."); }
// Fit a cubic spline to the rotation vector const T = 3; const θ = linspace(0, T, 3); const rotationVector = t => logm(transpose(Cs) @ Cf); const θ_poly = cubicSplineFit(rotationVector, θ, zerosLike(θ), 100000, 'cubic');
// Compute angular velocity and acceleration const ω = diff(θ_poly) / θ; const ω̇ = diff(ω) / θ;
// Set the jerk at the endpoints to be equal ω̇[0] = ω̇[ω̇.length - 1];
// Solve for angular velocities const θ_inv = diag(1 / θ); const ω_solution = solve(addDiag(θ_inv, diag(ω̇)), subtract(ωs, ωf));
// Fit a cubic spline to the time matrix const t = linspace(0, T, 3); const t_poly = cubicSplineFit(t => exp(t), t, arange(len(t)), 100000, 'cubic');
// Interpolate attitude matrices with photonic crystals const C = [Cs]; for (let i = 0; i < t_poly.length - 1; i++) { const rotationMatrix = RY(2 * θ_poly[i]) @ CNOT(0, 1) @ RY(-2 * θ_poly[i]); const photonicCrystalMatrix = photonics(rotationMatrix); // Replace with actual photonic crystals logic C.push(multiplyMatrices(C[i], photonicCrystalMatrix)); }
// Generate data for the decision tree const data = []; const labels = []; for (let i = 0; i < C.length; i++) { const positions = getSubmatrix(C[i], 0, 0, 3, 3); const velocities = getSubmatrix(C[i], 0, 3, 3, 3); const photonicsData = photonics(positions, velocities);
data.push([positions, velocities, photonicsData]);
labels.push(i);
}
// Train the decision tree (replace with your machine learning library) const decisionTree = new DecisionTreeClassifier(); decisionTree.fit(data, labels);
// Save the decision tree to a file (replace with your file handling) // Note: In a real environment, consider using a backend server for file operations // or saving the model in a format compatible with your JavaScript runtime. // For example, with TensorFlow.js, you can save models in the browser. const decisionTreeSerialized = decisionTree.serialize(); // Replace with your library's serialization method saveToFile('decision_tree.json', decisionTreeSerialized);
// Plot positions and velocities (replace with your preferred plotting library) plotPositionsAndVelocities(positions);
// Function to check if an attitude matrix is valid function isAttitudeMatrixValid(matrix) { // Implement your validation logic // Example: Check if matrix is a valid 3x3 rotation matrix // ...
return true; // Modify based on your validation
}
// Replace these functions with actual implementations or use external libraries function linspace(start, end, steps) { // Implement linspace // ... }
function zerosLike(array) { // Implement zerosLike // ... }
function cubicSplineFit(func, x, y, maxfev, method) { // Implement cubicSplineFit // ... }
function diff(array) { // Implement diff // ... }
function solve(matrix, vector) { // Implement solve // ... }
function addDiag(matrix, diag) { // Implement addDiag // ... }
function subtract(array1, array2) { // Implement subtract // ... }
function multiplyMatrices(matrix1, matrix2) { // Implement matrix multiplication // ... }
function getSubmatrix(matrix, rowStart, colStart, numRows, numCols) { // Implement getSubmatrix // ... }
function arange(length) { // Implement arange // ... }
function saveToFile(filename, data) { // Implement saveToFile // ... }
function plotPositionsAndVelocities(positions