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Editor to share with you how to achieve spark mllib random gradient descent method, I hope you will learn something after reading this article, let's discuss it together!

The running code is as follows: package spark.regressionAnalysis/** * random gradient descent (stochastic gradient descent,SGD) * SGD is a variation of the steepest gradient descent method. * using the steepest gradient descent method, N iterations will be carried out until the objective function converges or reaches a certain convergence limit. * m samples will be calculated in each iteration, resulting in a large amount of calculation. * to simplify the calculation, SGD calculates the gradient for only one sample per iteration until it converges. * Random gradient descent, i.e. (the fastest way down from the top of Zijinshan Mountain) * * Created by eric on 16-7-10. * / import scala.collection.mutable.HashMapobject SGD {val data = HashMap [Int,Int] () / create dataset def getData (): HashMap [Int Int] = {/ / generate the content of the dataset for (I (16roomi)) / / write the formula yroom16x} data / / return the dataset} var θ: Double = 0 / / the first step assumes that θ is 0 var α: Double = 0.1 / / set the step coefficient The magnitude of each drop def sgd (XGV double) = {/ / set iterative formula θ = θ-α * ((θ * x)-y) / / iterative formula} def main (args: Array [String]) {val dataSource = getData () / / get the dataset dataSource.foreach (myMap = > {/ / start iterative sgd (myMap._1) MyMap._2) / / input data}) println ("final result θ value" + θ) / / display result}} result as shown in the figure

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