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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 KernelDensity
# $example off$
if __name__ == "__main__":
sc = SparkContext(appName="KernelDensityEstimationExample") # SparkContext
# $example on$
# an RDD of sample data
data = sc.parallelize([1.0, 1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 6.0, 7.0, 8.0, 9.0, 9.0])
# Construct the density estimator with the sample data and a standard deviation for the Gaussian
# kernels
kd = KernelDensity()
kd.setSample(data)
kd.setBandwidth(3.0)
# Find density estimates for the given values
densities = kd.estimate([-1.0, 2.0, 5.0])
# $example off$
print(densities)
sc.stop()
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