@astermind/astermind-elm/dist/core/ELMChain.d.ts

JavaScript Extreme Learning Machine (ELM) library for browser and Node.js.

export interface EncoderLike {
    /** Return hidden/embedding for a batch of vectors: (N x Din) -> (N x Dout) */
    getEmbedding(X: number[][]): number[][];
    /** Optional name for debugging / summary */
    name?: string;
}
export interface ChainOptions {
    /** L2-normalize rows after each encoder (default: false) */
    normalizeEach?: boolean;
    /** L2-normalize rows at the end (default: false) */
    normalizeFinal?: boolean;
    /** Check that each stage receives non-empty batch and consistent dims (default: true) */
    validate?: boolean;
    /** Throw if any encoder throws (default: true). If false, bubbles partial outputs. */
    strict?: boolean;
    /** Optional chain name used in logs & summary */
    name?: string;
}
export declare class ELMChain {
    private encoders;
    private opts;
    private lastDims;
    constructor(encoders?: EncoderLike[], opts?: ChainOptions);
    /** Add encoder at end */
    add(encoder: EncoderLike): void;
    /** Insert encoder at position (0..length) */
    insertAt(index: number, encoder: EncoderLike): void;
    /** Remove encoder at index; returns removed or undefined */
    removeAt(index: number): EncoderLike | undefined;
    /** Remove all encoders */
    clear(): void;
    /** Number of stages */
    length(): number;
    /** Human-friendly overview (dims are filled after the first successful run) */
    summary(): string;
    /**
     * Compute embeddings.
     * Overloads allow a single vector or a batch.
     */
    getEmbedding(input: number[]): number[];
    getEmbedding(input: number[][]): number[][];
    /**
     * Run once to collect per-stage timings (ms) and final dims.
     * Returns { timings, dims } where dims[i] is input dim to stage i,
     * dims[i+1] is that stage’s output dim.
     */
    profile(input: number[] | number[][]): {
        timings: number[];
        dims: number[];
    };
}