Source: mllib/regression/LinearRegressionWithSGD.js

/*
 * Copyright 2015 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.
 */

var Utils = require('../../utils.js');

var LinearRegressionModel = require('./LinearRegressionModel.js');

var gKernelP;

/**
 * Construct a LinearRegression object with default parameters: {stepSize: 1.0, numIterations: 100, miniBatchFraction: 1.0}.
 * @constructor
 * @memberof module:eclairjs/mllib/regression
 * @classdesc Train a linear regression model with no regularization using Stochastic Gradient Descent.
 * This solves the least squares regression formulation f(weights) = 1/n ||A weights-y||^2^ (which is the mean squared error).
 * Here the data matrix has n rows, and the input RDD holds the set of rows of A, each with its corresponding right hand side label y.
 * See also the documentation for the precise formulation.
 */
function LinearRegressionWithSGD() {
}

LinearRegressionWithSGD.DEFAULT_NUM_ITERATIONS = 100;

/**
 * Train a Linear Regression model given an RDD of (label, features) pairs.
 * We run a fixed number of iterations of gradient descent using the specified step size.
 * Each iteration uses miniBatchFraction fraction of the data to calculate a stochastic gradient.
 * The weights used in gradient descent are initialized using the initial weights provided.
 *
 * @param {module:eclairjs/rdd.RDD} rdd of LabeledPoints1
 * @param {integer} numIterations
 * @param {float} [stepSize] - step size to be used for each iteration of gradient descent, defaults to 1.0
 * @param {floar} [miniBatchFraction] - fraction of data to be used per iteration, defaults to 1.0
 * @param {Vactor} [initialWeights] - initial set of weights to be used. Array should be equal in size to the number of features in the data.
 * @returns {module:eclairjs/mllib/regression.LinearRegressionModel}
 */
LinearRegressionWithSGD.train = function(rdd, numIterations, miniBatchFraction, initialWeights) {
  var args = {
    target: LinearRegressionWithSGD,
    method: 'train',
    args: Utils.wrapArguments(arguments),
    static: true,
    kernelP: gKernelP,
    returnType: LinearRegressionModel
  };

  return Utils.generate(args);
};

LinearRegressionWithSGD.moduleLocation = '/mllib/regression/LinearRegressionWithSGD';

module.exports = function(kP) {
  if (kP) gKernelP = kP;
  return LinearRegressionWithSGD;
};