/*
* Copyright 2016 IBM Corp.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
module.exports = function(kernelP) {
return (function() {
var Utils = require('../../utils.js');
var gKernelP = kernelP;
/**
* Class used to perform steps (weight update) using Gradient Descent methods.
* For general minimization problems, or for regularized problems of the form min L(w) + regParam * R(w),
* the compute function performs the actual update step, when given some (e.g. stochastic) gradient direction
* for the loss L(w), and a desired step-size (learning rate).The updater is responsible to also perform the
* update coming from the regularization term R(w) (if any regularization is used).
* @class
* @memberof module:eclairjs/mllib/optimization
* @constructor
* @extends Updater
*/
function SquaredL2Updater() {
Utils.handleConstructor(this, arguments, gKernelP);
}
SquaredL2Updater.moduleLocation = '/mllib/optimization/SquaredL2Updater';
return SquaredL2Updater;
})();
};