#
# 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 StandardScaler
# $example off$

if __name__ == "__main__":
    sc = SparkContext(appName="StandardScalerExample")
    sqlContext = SQLContext(sc)

    # $example on$
    dataFrame = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
    scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures",
                            withStd=True, withMean=False)

    # Compute summary statistics by fitting the StandardScaler
    scalerModel = scaler.fit(dataFrame)

    # Normalize each feature to have unit standard deviation.
    scaledData = scalerModel.transform(dataFrame)
    scaledData.show()
    # $example off$

    sc.stop()