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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.stat import Statistics
+# $example off$
+
+if __name__ == "__main__":
+ sc = SparkContext(appName="HypothesisTestingKolmogorovSmirnovTestExample")
+
+ # $example on$
+ parallelData = sc.parallelize([0.1, 0.15, 0.2, 0.3, 0.25])
+
+ # run a KS test for the sample versus a standard normal distribution
+ testResult = Statistics.kolmogorovSmirnovTest(parallelData, "norm", 0, 1)
+ # summary of the test including the p-value, test statistic, and null hypothesis
+ # if our p-value indicates significance, we can reject the null hypothesis
+ # Note that the Scala functionality of calling Statistics.kolmogorovSmirnovTest with
+ # a lambda to calculate the CDF is not made available in the Python API
+ print(testResult)
+ # $example off$
+
+ sc.stop()