1 files changed, 35 insertions, 2 deletions
diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py
index da90554f41..cf5fdf2cf9 100644
--- a/python/pyspark/mllib/feature.py
+++ b/python/pyspark/mllib/feature.py
@@ -33,12 +33,13 @@ from py4j.protocol import Py4JJavaError
 from pyspark import SparkContext
 from pyspark.rdd import RDD, ignore_unicode_prefix
 from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper
-from pyspark.mllib.linalg import Vectors, DenseVector, SparseVector, _convert_to_vector
+from pyspark.mllib.linalg import (
+    Vector, Vectors, DenseVector, SparseVector, _convert_to_vector)
 from pyspark.mllib.regression import LabeledPoint
 
 __all__ = ['Normalizer', 'StandardScalerModel', 'StandardScaler',
            'HashingTF', 'IDFModel', 'IDF', 'Word2Vec', 'Word2VecModel',
-           'ChiSqSelector', 'ChiSqSelectorModel']
+           'ChiSqSelector', 'ChiSqSelectorModel', 'ElementwiseProduct']
 
 
 class VectorTransformer(object):
@@ -520,6 +521,38 @@ class Word2Vec(object):
         return Word2VecModel(jmodel)
 
 
+class ElementwiseProduct(VectorTransformer):
+    """
+    .. note:: Experimental
+
+    Scales each column of the vector, with the supplied weight vector.
+    i.e the elementwise product.
+
+    >>> weight = Vectors.dense([1.0, 2.0, 3.0])
+    >>> eprod = ElementwiseProduct(weight)
+    >>> a = Vectors.dense([2.0, 1.0, 3.0])
+    >>> eprod.transform(a)
+    DenseVector([2.0, 2.0, 9.0])
+    >>> b = Vectors.dense([9.0, 3.0, 4.0])
+    >>> rdd = sc.parallelize([a, b])
+    >>> eprod.transform(rdd).collect()
+    [DenseVector([2.0, 2.0, 9.0]), DenseVector([9.0, 6.0, 12.0])]
+    """
+    def __init__(self, scalingVector):
+        self.scalingVector = _convert_to_vector(scalingVector)
+
+    def transform(self, vector):
+        """
+        Computes the Hadamard product of the vector.
+        """
+        if isinstance(vector, RDD):
+            vector = vector.map(_convert_to_vector)
+
+        else:
+            vector = _convert_to_vector(vector)
+        return callMLlibFunc("elementwiseProductVector", self.scalingVector, vector)
+
+
 def _test():
     import doctest
     from pyspark import SparkContext