2 files changed, 21 insertions, 21 deletions
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 03ff5a572e..19b90dfd6e 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -44,13 +44,13 @@ class LogisticRegressionModel(LinearModel):
 class LogisticRegressionWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0,
-              mini_batch_fraction=1.0, initial_weights=None):
+              miniBatchFraction=1.0, initialWeights=None):
         """Train a logistic regression model on the given data."""
         sc = data.context
         return _regression_train_wrapper(sc, lambda d, i:
                 sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(d._jrdd,
-                        iterations, step, mini_batch_fraction, i),
-                LogisticRegressionModel, data, initial_weights)
+                        iterations, step, miniBatchFraction, i),
+                LogisticRegressionModel, data, initialWeights)
 
 class SVMModel(LinearModel):
     """A support vector machine.
@@ -67,14 +67,14 @@ class SVMModel(LinearModel):
 
 class SVMWithSGD(object):
     @classmethod
-    def train(cls, data, iterations=100, step=1.0, reg_param=1.0,
-              mini_batch_fraction=1.0, initial_weights=None):
+    def train(cls, data, iterations=100, step=1.0, regParam=1.0,
+              miniBatchFraction=1.0, initialWeights=None):
         """Train a support vector machine on the given data."""
         sc = data.context
         return _regression_train_wrapper(sc, lambda d, i:
                 sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(d._jrdd,
-                        iterations, step, reg_param, mini_batch_fraction, i),
-                SVMModel, data, initial_weights)
+                        iterations, step, regParam, miniBatchFraction, i),
+                SVMModel, data, initialWeights)
 
 class NaiveBayesModel(object):
     """
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index e90b72893f..7656db07f6 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -47,57 +47,57 @@ class LinearRegressionModel(LinearRegressionModelBase):
     """A linear regression model derived from a least-squares fit.
 
     >>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
-    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initial_weights=array([1.0]))
+    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
     """
 
 class LinearRegressionWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0,
-              mini_batch_fraction=1.0, initial_weights=None):
+              miniBatchFraction=1.0, initialWeights=None):
         """Train a linear regression model on the given data."""
         sc = data.context
         return _regression_train_wrapper(sc, lambda d, i:
                 sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
-                        d._jrdd, iterations, step, mini_batch_fraction, i),
-                LinearRegressionModel, data, initial_weights)
+                        d._jrdd, iterations, step, miniBatchFraction, i),
+                LinearRegressionModel, data, initialWeights)
 
 class LassoModel(LinearRegressionModelBase):
     """A linear regression model derived from a least-squares fit with an
     l_1 penalty term.
 
     >>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
-    >>> lrm = LassoWithSGD.train(sc.parallelize(data), initial_weights=array([1.0]))
+    >>> lrm = LassoWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
     """
 
 class LassoWithSGD(object):
     @classmethod
-    def train(cls, data, iterations=100, step=1.0, reg_param=1.0,
-              mini_batch_fraction=1.0, initial_weights=None):
+    def train(cls, data, iterations=100, step=1.0, regParam=1.0,
+              miniBatchFraction=1.0, initialWeights=None):
         """Train a Lasso regression model on the given data."""
         sc = data.context
         return _regression_train_wrapper(sc, lambda d, i:
                 sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(d._jrdd,
-                        iterations, step, reg_param, mini_batch_fraction, i),
-                LassoModel, data, initial_weights)
+                        iterations, step, regParam, miniBatchFraction, i),
+                LassoModel, data, initialWeights)
 
 class RidgeRegressionModel(LinearRegressionModelBase):
     """A linear regression model derived from a least-squares fit with an
     l_2 penalty term.
 
     >>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
-    >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), initial_weights=array([1.0]))
+    >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
     """
 
 class RidgeRegressionWithSGD(object):
     @classmethod
-    def train(cls, data, iterations=100, step=1.0, reg_param=1.0,
-              mini_batch_fraction=1.0, initial_weights=None):
+    def train(cls, data, iterations=100, step=1.0, regParam=1.0,
+              miniBatchFraction=1.0, initialWeights=None):
         """Train a ridge regression model on the given data."""
         sc = data.context
         return _regression_train_wrapper(sc, lambda d, i:
                 sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(d._jrdd,
-                        iterations, step, reg_param, mini_batch_fraction, i),
-                RidgeRegressionModel, data, initial_weights)
+                        iterations, step, regParam, miniBatchFraction, i),
+                RidgeRegressionModel, data, initialWeights)
 
 def _test():
     import doctest