clustering-tfjs/dist/clustering/agglomerative.d.ts

High-performance TypeScript clustering algorithms (K-Means, Spectral, Agglomerative) with TensorFlow.js acceleration and scikit-learn compatibility

import type { DataMatrix, LabelVector, AgglomerativeClusteringParams, BaseClustering } from './types';
/**
 * Agglomerative (hierarchical) clustering estimator skeleton.
 *
 * Only the constructor, parameter validation and public property definitions
 * are implemented as part of this initial task. The actual clustering logic
 * will be added in subsequent tasks.
 */
export declare class AgglomerativeClustering implements BaseClustering<AgglomerativeClusteringParams> {
    /**
     * Hyper-parameters describing the behaviour of this instance.
     */
    readonly params: AgglomerativeClusteringParams;
    /**
     * Cluster labels produced by `fit` / `fitPredict`.
     *
     * Populated after calling `fit`.
     */
    labels_: LabelVector | null;
    /**
     * Children of each non-leaf node in the hierarchical clustering tree.
     * Shape: `(nSamples-1, 2)` where each row gives the indices of the merged
     * clusters. Lazily populated by future implementation.
     */
    children_: number[][] | null;
    /**
     * Number of leaves in the hierarchical clustering tree (equals `nSamples`).
     */
    nLeaves_: number | null;
    /**
     * Allowed linkage strategies.
     */
    private static readonly VALID_LINKAGES;
    /**
     * Allowed distance metrics.
     */
    private static readonly VALID_METRICS;
    constructor(params: AgglomerativeClusteringParams);
    /**
     * Fits the estimator to the provided data matrix.
     *
     * Note: The actual algorithm is not implemented yet. The stub only exists so
     * the public interface is complete and unit tests can assert that the method
     * is callable.
     */
    fit(_X: DataMatrix): Promise<void>;
    fitPredict(_X: DataMatrix): Promise<LabelVector>;
    private static validateParams;
}
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