From da2012a0e152aa078bdd19a5c7f91786a2dd7016 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Tue, 8 Dec 2015 19:18:59 +0800 Subject: [SPARK-11551][DOC][EXAMPLE] Revert PR #10002 This reverts PR #10002, commit 78209b0ccaf3f22b5e2345dfb2b98edfdb746819. The original PR wasn't tested on Jenkins before being merged. Author: Cheng Lian Closes #10200 from liancheng/revert-pr-10002. --- .../src/main/python/ml/standard_scaler_example.py | 42 ---------------------- 1 file changed, 42 deletions(-) delete mode 100644 examples/src/main/python/ml/standard_scaler_example.py (limited to 'examples/src/main/python/ml/standard_scaler_example.py') diff --git a/examples/src/main/python/ml/standard_scaler_example.py b/examples/src/main/python/ml/standard_scaler_example.py deleted file mode 100644 index 139acecbfb..0000000000 --- a/examples/src/main/python/ml/standard_scaler_example.py +++ /dev/null @@ -1,42 +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 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) - # $example off$ - - sc.stop() -- cgit v1.2.3