ml-regression/src/regression/potential-regression.js

Regression algorithms

import { BaseRegression, maybeToPrecision } from "ml-regression-base";
import { PolynomialRegression } from "ml-regression-polynomial";

/*
 * Function that calculate the potential fit in the form f(x) = A*x^M
 * with a given M and return de A coefficient.
 *
 * @param {Vector} X - Vector of the x positions of the points.
 * @param {Vector} Y - Vector of the x positions of the points.
 * @param {Number} M - The exponent of the potential fit.
 * @return {Number} A - The A coefficient of the potential fit.
 */
export class PotentialRegression extends BaseRegression {
  /**
   * @class
   * @param x - Independent variable
   * @param y - Dependent variable
   * @param M
   */
  constructor(x, y, M) {
    super();
    if (x === true) {
      // reloading model
      this.A = y.A;
      this.M = y.M;
    } else {
      let n = x.length;
      if (n !== y.length) {
        throw new RangeError("input and output array have a different length");
      }

      let linear = new PolynomialRegression(x, y, [M]);
      this.A = linear.coefficients[0];
      this.M = M;
    }
  }

  _predict(x) {
    return this.A * x ** this.M;
  }

  toJSON() {
    return {
      name: "potentialRegression",
      A: this.A,
      M: this.M,
    };
  }

  toString(precision) {
    return `f(x) = ${maybeToPrecision(this.A, precision)} * x^${this.M}`;
  }

  toLaTeX(precision) {
    if (this.M >= 0) {
      return `f(x) = ${maybeToPrecision(this.A, precision)}x^{${this.M}}`;
    } else {
      return `f(x) = \\frac{${maybeToPrecision(this.A, precision)}}{x^{${-this
        .M}}}`;
    }
  }

  static load(json) {
    if (json.name !== "potentialRegression") {
      throw new TypeError("not a potential regression model");
    }
    return new PotentialRegression(true, json);
  }
}