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+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import print_function
+
+from pyspark import SparkContext
+from pyspark.sql import SQLContext
+# $example on$
+from pyspark.ml.feature import ElementwiseProduct
+from pyspark.mllib.linalg import Vectors
+# $example off$
+
+if __name__ == "__main__":
+ sc = SparkContext(appName="ElementwiseProductExample")
+ sqlContext = SQLContext(sc)
+
+ # $example on$
+ data = [(Vectors.dense([1.0, 2.0, 3.0]),), (Vectors.dense([4.0, 5.0, 6.0]),)]
+ df = sqlContext.createDataFrame(data, ["vector"])
+ transformer = ElementwiseProduct(scalingVec=Vectors.dense([0.0, 1.0, 2.0]),
+ inputCol="vector", outputCol="transformedVector")
+ transformer.transform(df).show()
+ # $example off$
+
+ sc.stop()