@obliczeniowo/elementary/perceptron/classes/perceptron.d.ts

Library made in Angular version 19

/**
 * Single perceptron model
 */
export declare class Perceptron {
    protected paramsLength: number;
    protected eta: number;
    protected weights: number[];
    protected error: number;
    protected errors: number[];
    constructor(paramsLength: number, eta: number);
    protected getFactor(parameters: number[]): number;
    getError(): number;
    getErrors(): number[];
    setParamsLength(paramsLength: number): void;
    setEta(eta: number): void;
    /**
     * Output perceptron response
     * @param parameters input parameters
     * @returns -1 / 1 value
     */
    y(parameters: number[]): 1 | -1;
    /**
     * Add training data single record
     * @param parameters array of this.parameters length
     * @param y expected value -1 or 1
     */
    train(parameters: number[], y: number): void;
    /**
     * Trening perceptron over the epoch times clear previously created table
     * @param x input parameters training table (size must be equal parameter field of this class)
     * @param y output expected value
     * @param epoch how many time repeat training session
     */
    fit(x: number[][], y: number[], epoch: number): void;
    /** */
    getWeights(): number[];
    /** */
    setWeights(weights: number[]): void;
    /**
     * Calc linear function factors for m dimension that is equal to number of input parameters
     * and weights inside perceptron
     *
     * @param n - determine index for f(xn) function of m dimension, n is integer from range 0
     * to m - 1
     * @returns table of factors for f(xn) function, to calculate xn of m dimension you need to
     * calc:
     *
     * xn = suma od k = 0 do k < n { f[k] * x[k] } + f[n]
     */
    fXnFactors(n: number): number[];
}