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From m...@apache.org
Subject spark git commit: [SPARK-12986][DOC] Fix pydoc warnings in mllib/regression.py
Date Mon, 08 Feb 2016 19:06:42 GMT
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 <phamducnam@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 <phamducnam@gmail.com>
Authored: Mon Feb 8 11:06:41 2016 -0800
Committer: Xiangrui Meng <meng@databricks.com>
Committed: Mon Feb 8 11:06:41 2016 -0800

----------------------------------------------------------------------
 python/pyspark/mllib/regression.py | 34 ++++++++++++++++++++-------------
 1 file changed, 21 insertions(+), 13 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/spark/blob/edf4a0e6/python/pyspark/mllib/regression.py
----------------------------------------------------------------------
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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