@jsmlt/jsmlt/distribution/kernel/base.js

JavaScript Machine Learning

"use strict";

Object.defineProperty(exports, "__esModule", {
  value: true
});
exports["default"] = void 0;

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

function _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }

/**
 * Base class for kernels, which calculate some distance metric between two data points
 */
var Kernel =
/*#__PURE__*/
function () {
  function Kernel() {
    _classCallCheck(this, Kernel);
  }

  _createClass(Kernel, [{
    key: "apply",

    /**
     * Evaluate the kernel on a pair of data points
     *
     * @param {Array.<number>} x - First input value
     * @param {Array.<number>} y - Second input value
     * @return {number} Kernel output
     */
    value: function apply(x, y) {
      throw new Error('Method must be implemented child class.');
    }
  }]);

  return Kernel;
}();

exports["default"] = Kernel;
module.exports = exports.default;