From 91dc609703bc012c32b636595b86e60eb56424a4 Mon Sep 17 00:00:00 2001 From: Bryan Cutler Date: Fri, 12 Feb 2016 10:00:37 -0800 Subject: [PATCH] [SPARK-12630] Fixed 'Allowed values' formatting of reg param to get rid of Sphinx warnings --- python/pyspark/mllib/classification.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index 64c016213f1df..b24592c3798e6 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -294,11 +294,11 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, (default: 0.01) :param regType: The type of regularizer used for training our model. - :Allowed values: + Allowed values: + - "l1" for using L1 regularization - - "l2" for using L2 regularization + - "l2" for using L2 regularization (default) - None for no regularization - (default: "l2") :param intercept: Boolean parameter which indicates the use or not of the augmented representation for training data (i.e., whether bias @@ -344,11 +344,11 @@ def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType (default: 0.01) :param regType: The type of regularizer used for training our model. - :Allowed values: + Allowed values: + - "l1" for using L1 regularization - - "l2" for using L2 regularization + - "l2" for using L2 regularization (default) - None for no regularization - (default: "l2") :param intercept: Boolean parameter which indicates the use or not of the augmented representation for training data (i.e., whether bias @@ -524,11 +524,11 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01, (default: None) :param regType: The type of regularizer used for training our model. - :Allowed values: + Allowed values: + - "l1" for using L1 regularization - - "l2" for using L2 regularization + - "l2" for using L2 regularization (default) - None for no regularization - (default: "l2") :param intercept: Boolean parameter which indicates the use or not of the augmented representation for training data (i.e. whether bias