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diff --git a/examples/src/main/python/ml/elementwise_product_example.py b/examples/src/main/python/ml/elementwise_product_example.py
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--- a/examples/src/main/python/ml/elementwise_product_example.py
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@@ -1,39 +0,0 @@
-#
-# 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()