@jsmlt/jsmlt/distribution/kernel/sigmoid.js

JavaScript Machine Learning

"use strict";

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

var _base = _interopRequireDefault(require("./base"));

var Arrays = _interopRequireWildcard(require("../arrays"));

function _getRequireWildcardCache() { if (typeof WeakMap !== "function") return null; var cache = new WeakMap(); _getRequireWildcardCache = function _getRequireWildcardCache() { return cache; }; return cache; }

function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } var cache = _getRequireWildcardCache(); if (cache && cache.has(obj)) { return cache.get(obj); } var newObj = {}; if (obj != null) { var hasPropertyDescriptor = Object.defineProperty && Object.getOwnPropertyDescriptor; for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) { var desc = hasPropertyDescriptor ? Object.getOwnPropertyDescriptor(obj, key) : null; if (desc && (desc.get || desc.set)) { Object.defineProperty(newObj, key, desc); } else { newObj[key] = obj[key]; } } } } newObj["default"] = obj; if (cache) { cache.set(obj, newObj); } return newObj; }

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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; }

function _possibleConstructorReturn(self, call) { if (call && (_typeof(call) === "object" || typeof call === "function")) { return call; } return _assertThisInitialized(self); }

function _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return self; }

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function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }

function _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }

/**
 * The Sigmoid kernel. The formula of this kernel is as follows:
 *   tanh(gamma * <x, y> + coef0)
 */
var SigmoidKernel =
/*#__PURE__*/
function (_Kernel) {
  _inherits(SigmoidKernel, _Kernel);

  /**
   * Initialize the Sigmoid 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)
   */
  function SigmoidKernel() {
    var _this;

    var _ref = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {},
        _ref$gamma = _ref.gamma,
        gamma = _ref$gamma === void 0 ? 0.01 : _ref$gamma,
        _ref$coef = _ref.coef0,
        coef0 = _ref$coef === void 0 ? 0 : _ref$coef;

    _classCallCheck(this, SigmoidKernel);

    _this = _possibleConstructorReturn(this, _getPrototypeOf(SigmoidKernel).call(this));
    /**
     * Normalization parameter of basic dot product
     * @type {number}
     */

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

    _this.coef0 = coef0;
    return _this;
  }
  /**
   * @see {@link Kernel#apply}
   */


  _createClass(SigmoidKernel, [{
    key: "apply",
    value: function apply(x, y) {
      return Math.tanh(this.gamma * Arrays.dot(x, y) + this.coef0);
    }
  }]);

  return SigmoidKernel;
}(_base["default"]);

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