@astermind/astermind-pro/dist/elm/sparse-elm.d.ts

Astermind Pro - Premium ML Toolkit with Advanced RAG, Reranking, Summarization, and Information Flow Analysis

export interface SparseELMOptions {
    categories: string[];
    hiddenUnits?: number;
    maxLen?: number;
    useTokenizer?: boolean;
    activation?: 'relu' | 'tanh' | 'sigmoid' | 'linear';
    regularization: {
        type: 'l1' | 'l2' | 'elastic';
        lambda: number;
        alpha?: number;
    };
    sparsityTarget?: number;
    pruneThreshold?: number;
}
export interface SparseELMResult {
    label: string;
    prob: number;
}
/**
 * Sparse ELM with regularization and feature selection
 * Features:
 * - L1/L2/Elastic net regularization
 * - Weight pruning for sparsity
 * - Feature importance ranking
 * - Interpretable models
 */
export declare class SparseELM {
    private elm;
    private options;
    private trained;
    private weightMask;
    private featureImportance;
    constructor(options: SparseELMOptions);
    /**
     * Train sparse ELM with regularization
     */
    train(X: number[][], y: number[] | string[]): void;
    /**
     * Predict with sparse model
     */
    predict(X: number[] | number[][], topK?: number): SparseELMResult[];
    /**
     * Apply regularization to weights
     */
    private _applyRegularization;
    /**
     * Prune small weights for sparsity
     */
    private _pruneWeights;
    /**
     * Compute current sparsity ratio
     */
    private _computeSparsity;
    /**
     * Enforce target sparsity by pruning more weights
     */
    private _enforceSparsityTarget;
    /**
     * Compute feature importance based on weight magnitudes
     */
    private _computeFeatureImportance;
    /**
     * Get feature importance scores
     */
    getFeatureImportance(): number[];
    /**
     * Get sparsity statistics
     */
    getSparsityStats(): {
        sparsity: number;
        activeWeights: number;
        totalWeights: number;
    };
}
//# sourceMappingURL=sparse-elm.d.ts.map