clustering-tfjs
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High-performance TypeScript clustering algorithms (K-Means, Spectral, Agglomerative) with TensorFlow.js acceleration and scikit-learn compatibility
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JavaScript
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exports.createConstantEigenvector = createConstantEigenvector;
const tf = __importStar(require("../tf-adapter"));
/**
* Creates the constant eigenvector for connected graphs in spectral clustering.
*
* For a connected graph, the smallest eigenvalue of the normalized Laplacian is 0,
* and its corresponding eigenvector should be constant. However, numerical computation
* can introduce small variations. sklearn replaces this with the theoretical constant
* eigenvector to improve clustering stability.
*
* sklearn uses a simple constant vector 1/sqrt(n) for all entries, not the
* degree-weighted version. This is then scaled by the spectral embedding normalization.
*
* @param affinity The affinity matrix
* @returns The constant eigenvector as a column vector (n x 1)
*/
function createConstantEigenvector(affinity) {
return tf.tidy(() => {
const n = affinity.shape[0];
// Create simple constant vector: all entries are 1/sqrt(n)
// This gives a unit-norm vector with all equal entries
const value = 1.0 / Math.sqrt(n);
const constantVec = tf.fill([n, 1], value);
return constantVec;
});
}