@astermind/astermind-premium/dist/elm/sparse-kernel-elm.d.ts

Astermind Premium - Premium ML Toolkit

export interface SparseKernelELMOptions {
    categories: string[];
    kernelType?: 'rbf' | 'polynomial' | 'linear' | 'sigmoid';
    numLandmarks?: number;
    landmarkSelection?: 'random' | 'kmeans' | 'diverse';
    gamma?: number;
    degree?: number;
    coef0?: number;
    activation?: 'relu' | 'tanh' | 'sigmoid' | 'linear';
    maxLen?: number;
    useTokenizer?: boolean;
}
export interface SparseKernelELMResult {
    label: string;
    prob: number;
}
/**
 * Sparse Kernel ELM with landmark-based approximation
 * Features:
 * - Sparse kernel matrix approximation
 * - Landmark selection strategies
 * - Reduced computational complexity
 * - Scalable to large datasets
 */
export declare class SparseKernelELM {
    private kelm;
    private categories;
    private options;
    private landmarks;
    private trained;
    constructor(options: SparseKernelELMOptions);
    /**
     * Train with sparse kernel approximation
     */
    train(X: number[][], y: number[] | string[]): void;
    /**
     * Select landmark points
     */
    private _selectLandmarks;
    /**
     * K-means landmark selection
     */
    private _kmeansLandmarks;
    /**
     * Diverse landmark selection
     */
    private _diverseLandmarks;
    /**
     * Get labels for landmarks
     */
    private _getLandmarkLabels;
    private _euclideanDistance;
    /**
     * Predict using sparse kernel
     */
    predict(X: number[] | number[][], topK?: number): SparseKernelELMResult[];
    /**
     * Get selected landmarks
     */
    getLandmarks(): number[][];
}
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