/*
* 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 solve an optimization problem using Limited-memory BFGS.
* Reference: http://en.wikipedia.org/wiki/Limited-memory_BFGS param: gradient
* Gradient function to be used. param: updater Updater to be used to update weights after every iteration.
* @class
* @memberof module:eclairjs/mllib/optimization
* @constructor
* @param {Gradient} gradient
* @param {Updater} updater
*/
function LBFGS() {
Utils.handleConstructor(this, arguments, gKernelP);
}
/**
* Run Limited-memory BFGS (L-BFGS) in parallel. Averaging the subgradients over different partitions is performed
* using one standard spark map-reduce in each iteration.
* @param {module:eclairjs/rdd.RDD} data - - Input data for L-BFGS. RDD of the set of data examples, each of the form (label, [feature values]).
* @param {Gradient} gradient - - Gradient object (used to compute the gradient of the loss function of one single data example)
* @param {Updater} updater - - Updater function to actually perform a gradient step in a given direction.
* @param {integer} numCorrections - - The number of corrections used in the L-BFGS update.
* @param {float} convergenceTol - - The convergence tolerance of iterations for L-BFGS which is must be nonnegative.
* Lower values are less tolerant and therefore generally cause more iterations to be run.
* @param {integer} maxNumIterations - - Maximal number of iterations that L-BFGS can be run.
* @param {float} regParam - - Regularization parameter
* @param {module:eclairjs/mllib/linalg.Vector} initialWeights - (undocumented)
* @returns {module:eclairjs.Tuple} A tuple containing two elements. The first element is a column matrix containing weights for every feature,
* and the second element is an array containing the loss computed for every iteration.
*/
LBFGS.runLBFGS = function (data,gradient,updater,numCorrections,convergenceTol,maxNumIterations,regParam,initialWeights) {
var args = {
target: LBFGS,
method: 'runLBFGS',
args: Utils.wrapArguments(arguments),
static: true,
kernelP: gKernelP,
stringify: true,
returnType: [Object]
};
return Utils.generate(args);
};
LBFGS.moduleLocation = '/mllib/optimization/LBFGS';
return LBFGS;
})();
};