From 26558da3edd18417b9d3d287d9142fa199ef04b5 Mon Sep 17 00:00:00 2001 From: tanyinyan Date: Wed, 18 Mar 2015 14:38:37 +0800 Subject: [PATCH] Update SVM.scala --- .../scala/org/apache/spark/mllib/classification/SVM.scala | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala index e4d5c808609ac..0c89d0fa1902b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala @@ -168,7 +168,8 @@ object SVMWithSGD { * @param miniBatchFraction Fraction of data to be used per iteration. * @param initialWeights Initial set of weights to be used. Array should be equal in size to * the number of features in the data. - * @param useFeatureScaling Set if the algorithm should use feature scaling to improve the convergence during optimization. + * @param useFeatureScaling Set if the algorithm should use feature scaling to improve the + * convergence during optimization. */ def train( input: RDD[LabeledPoint], @@ -178,7 +179,8 @@ object SVMWithSGD { miniBatchFraction: Double, initialWeights: Vector, useFeatureScaling: Boolean): SVMModel = { - new SVMWithSGD(stepSize, numIterations, regParam, miniBatchFraction).setFeatureScaling(useFeatureScaling) + new SVMWithSGD(stepSize, numIterations, regParam, miniBatchFraction) + .setFeatureScaling(useFeatureScaling) .run(input, initialWeights) }