#
# 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()