@jsmlt/jsmlt/distribution/kernel/polynomial.js

JavaScript Machine Learning

'use strict';

Object.defineProperty(exports, "__esModule", {
  value: true
});

var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();

var _base = require('./base');

var _base2 = _interopRequireDefault(_base);

var _linalg = require('../math/linalg');

var LinAlg = _interopRequireWildcard(_linalg);

function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }

function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }

function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }

function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; }

function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } // Internal dependencies


/**
 * The Polynomial kernel. The formula of this kernel is as follows:
 *   (gamma * <x, y> + coef0)^degree
 */
var PolynomialKernel = function (_Kernel) {
  _inherits(PolynomialKernel, _Kernel);

  /**
   * Initialize the Gaussian kernel with user-specified parameters
   *
   * @param {Object} [options] - User-defined options
   * @param {number} [options.gamma = 1] - Normalization parameter of basic dot product
   * @param {number} [options.coef0 = 1] - Bias coefficient (not part of the dot product)
   * @param {number} [options.degree = 2] - Degree of the polynomial
   */
  function PolynomialKernel() {
    var _ref = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {},
        _ref$gamma = _ref.gamma,
        gamma = _ref$gamma === undefined ? 1 : _ref$gamma,
        _ref$coef = _ref.coef0,
        coef0 = _ref$coef === undefined ? 1 : _ref$coef,
        _ref$degree = _ref.degree,
        degree = _ref$degree === undefined ? 2 : _ref$degree;

    _classCallCheck(this, PolynomialKernel);

    /**
     * Normalization parameter of basic dot product
     * @type {number}
     */
    var _this = _possibleConstructorReturn(this, (PolynomialKernel.__proto__ || Object.getPrototypeOf(PolynomialKernel)).call(this));

    _this.gamma = gamma;

    /**
     * Bias coefficient (not part of the dot product)
     * @type {number}
     */
    _this.coef0 = coef0;

    /**
     * Degree of the polynomial
     * @type {number}
     */
    _this.degree = degree;
    return _this;
  }

  /**
   * @see {@link Kernel#apply}
   */


  _createClass(PolynomialKernel, [{
    key: 'apply',
    value: function apply(x, y) {
      return (this.gamma * LinAlg.dot(x, y) + this.coef0) ** this.degree;
    }
  }]);

  return PolynomialKernel;
}(_base2.default);

exports.default = PolynomialKernel;
module.exports = exports['default'];