ml-basic/dist/types/lib/matrix.d.ts

Lightweight, zero dependency, machine learning library

export default class Matrix {
    rows: number;
    columns: number;
    entries: Float64Array;
    constructor(matrix: Matrix);
    constructor(rows: number, columns: number, entries?: number[] | Float64Array);
    isEqualShape(matrix: Matrix): boolean;
    reshape(rows: number, columns: number): this;
    flat(): this;
    flip(): this;
    set(value: number): this;
    add(n: number): Matrix;
    add(matrix: Matrix): Matrix;
    sub(n: number): Matrix;
    sub(matrix: Matrix): Matrix;
    scale(n: number): Matrix;
    scale(matrix: Matrix): Matrix;
    apply(func: (value: number) => number): this;
    sum(): number;
    static mult(a: Matrix, b: Matrix): Matrix;
    mult(matrix: Matrix): this;
    static transpose(matrix: Matrix): Matrix;
    transpose(): this;
    private accumulate;
    static correlate(matrix: Matrix, kernel: Matrix, stride?: number, zeroPadding?: number): Matrix;
    correlate(kernel: Matrix, stride?: number, zeroPadding?: number): this;
    static reverseCorrelate(matrix: Matrix, kernel: Matrix, stride?: number): Matrix;
    static pool({ matrix, window, stride, zeroPadding, initial, pooler }: {
        matrix: Matrix;
        window: [number, number];
        stride?: number;
        zeroPadding?: number;
        initial?: number;
        pooler: (aggregate: number, value: number) => number;
    }): Matrix;
    clip(min: number, max: number): this;
    dialate(gap: number): this;
    static identity(n: number): Matrix;
    static random(rows: number, columns: number, min?: number, max?: number): Matrix;
    serialize(): {
        type: string;
        rows: number;
        columns: number;
        entries: number[];
    };
}