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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
+# $example on$
+from pyspark.mllib.feature import Normalizer
+from pyspark.mllib.util import MLUtils
+# $example off$
+
+if __name__ == "__main__":
+ sc = SparkContext(appName="NormalizerExample") # SparkContext
+
+ # $example on$
+ data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+ labels = data.map(lambda x: x.label)
+ features = data.map(lambda x: x.features)
+
+ normalizer1 = Normalizer()
+ normalizer2 = Normalizer(p=float("inf"))
+
+ # Each sample in data1 will be normalized using $L^2$ norm.
+ data1 = labels.zip(normalizer1.transform(features))
+
+ # Each sample in data2 will be normalized using $L^\infty$ norm.
+ data2 = labels.zip(normalizer2.transform(features))
+ # $example off$
+
+ print("data1:")
+ for each in data1.collect():
+ print(each)
+
+ print("data2:")
+ for each in data2.collect():
+ print(each)
+
+ sc.stop()