From 040d6f2d13b132b3ef2a1e4f12f9f0e781c5a0b8 Mon Sep 17 00:00:00 2001 From: DB Tsai <dbtsai@alpinenow.com> Date: Mon, 29 Dec 2014 17:17:12 -0800 Subject: [PATCH] [SPARK-4972][MLlib] Updated the scala doc for lasso and ridge regression for the change of LeastSquaresGradient In #SPARK-4907, we added factor of 2 into the LeastSquaresGradient. We updated the scala doc for lasso and ridge regression here. Author: DB Tsai <dbtsai@alpinenow.com> Closes #3808 from dbtsai/doc and squashes the following commits: ec3c989 [DB Tsai] first commit --- .../main/scala/org/apache/spark/mllib/regression/Lasso.scala | 2 +- .../org/apache/spark/mllib/regression/RidgeRegression.scala | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala index f9791c6571..8ecd5c6ad9 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala @@ -45,7 +45,7 @@ class LassoModel ( /** * Train a regression model with L1-regularization using Stochastic Gradient Descent. * This solves the l1-regularized least squares regression formulation - * f(weights) = 1/n ||A weights-y||^2 + regParam ||weights||_1 + * f(weights) = 1/2n ||A weights-y||^2 + regParam ||weights||_1 * 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. diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala index c8cad773f5..076ba35051 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala @@ -45,7 +45,7 @@ class RidgeRegressionModel ( /** * Train a regression model with L2-regularization using Stochastic Gradient Descent. * This solves the l1-regularized least squares regression formulation - * f(weights) = 1/n ||A weights-y||^2 + regParam/2 ||weights||^2 + * f(weights) = 1/2n ||A weights-y||^2 + regParam/2 ||weights||^2 * 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. -- GitLab