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Posted to commits@spark.apache.org by me...@apache.org on 2016/02/08 20:06:42 UTC
spark git commit: [SPARK-12986][DOC] Fix pydoc warnings in
mllib/regression.py
Repository: spark
Updated Branches:
refs/heads/master 140ddef37 -> edf4a0e62
[SPARK-12986][DOC] Fix pydoc warnings in mllib/regression.py
I have fixed the warnings by running "make html" under "python/docs/". They are caused by not having blank lines around indented paragraphs.
Author: Nam Pham <ph...@gmail.com>
Closes #11025 from nampham2/SPARK-12986.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/edf4a0e6
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/edf4a0e6
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/edf4a0e6
Branch: refs/heads/master
Commit: edf4a0e62e6fdb849cca4f23a7060da5ec782b07
Parents: 140ddef
Author: Nam Pham <ph...@gmail.com>
Authored: Mon Feb 8 11:06:41 2016 -0800
Committer: Xiangrui Meng <me...@databricks.com>
Committed: Mon Feb 8 11:06:41 2016 -0800
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python/pyspark/mllib/regression.py | 34 ++++++++++++++++++++-------------
1 file changed, 21 insertions(+), 13 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/edf4a0e6/python/pyspark/mllib/regression.py
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diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index 13b3397..4dd7083 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -219,8 +219,10 @@ class LinearRegressionWithSGD(object):
"""
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).
+
+ 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.
@@ -367,8 +369,10 @@ class LassoModel(LinearRegressionModelBase):
class LassoWithSGD(object):
"""
Train a regression model with L1-regularization using Stochastic Gradient Descent.
- This solves the l1-regularized least squares regression formulation
- f(weights) = 1/2n ||A weights-y||^2^ + regParam ||weights||_1
+ This solves the L1-regularized least squares regression formulation
+
+ 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.
@@ -505,8 +509,10 @@ class RidgeRegressionModel(LinearRegressionModelBase):
class RidgeRegressionWithSGD(object):
"""
Train a regression model with L2-regularization using Stochastic Gradient Descent.
- This solves the l2-regularized least squares regression formulation
- f(weights) = 1/2n ||A weights-y||^2^ + regParam/2 ||weights||^2^
+ This solves the L2-regularized least squares regression formulation
+
+ 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.
@@ -655,17 +661,19 @@ class IsotonicRegression(object):
Only univariate (single feature) algorithm supported.
Sequential PAV implementation based on:
- Tibshirani, Ryan J., Holger Hoefling, and Robert Tibshirani.
+
+ Tibshirani, Ryan J., Holger Hoefling, and Robert Tibshirani.
"Nearly-isotonic regression." Technometrics 53.1 (2011): 54-61.
- Available from [[http://www.stat.cmu.edu/~ryantibs/papers/neariso.pdf]]
+ Available from http://www.stat.cmu.edu/~ryantibs/papers/neariso.pdf
Sequential PAV parallelization based on:
- Kearsley, Anthony J., Richard A. Tapia, and Michael W. Trosset.
- "An approach to parallelizing isotonic regression."
- Applied Mathematics and Parallel Computing. Physica-Verlag HD, 1996. 141-147.
- Available from [[http://softlib.rice.edu/pub/CRPC-TRs/reports/CRPC-TR96640.pdf]]
- @see [[http://en.wikipedia.org/wiki/Isotonic_regression Isotonic regression (Wikipedia)]]
+ Kearsley, Anthony J., Richard A. Tapia, and Michael W. Trosset.
+ "An approach to parallelizing isotonic regression."
+ Applied Mathematics and Parallel Computing. Physica-Verlag HD, 1996. 141-147.
+ Available from http://softlib.rice.edu/pub/CRPC-TRs/reports/CRPC-TR96640.pdf
+
+ See `Isotonic regression (Wikipedia) <http://en.wikipedia.org/wiki/Isotonic_regression>`_.
.. versionadded:: 1.4.0
"""
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