text: #
  # 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.
  #
  
  import numpy as np
  
  from pyspark.mllib.common import callMLlibFunc
  from pyspark.rdd import RDD
  
  
  class KernelDensity(object):
      """
      Estimate probability density at required points given an RDD of samples
      from the population.
  
      >>> kd = KernelDensity()
      >>> sample = sc.parallelize([0.0, 1.0])
      >>> kd.setSample(sample)
      >>> kd.estimate([0.0, 1.0])
      array([ 0.12938758,  0.12938758])
      """
      def __init__(self):
          self._bandwidth = 1.0
          self._sample = None
  
      def setBandwidth(self, bandwidth):
          """Set bandwidth of each sample. Defaults to 1.0"""
          self._bandwidth = bandwidth
  
      def setSample(self, sample):
          """Set sample points from the population. Should be a RDD"""
          if not isinstance(sample, RDD):
              raise TypeError("samples should be a RDD, received %s" % type(sample))
          self._sample = sample
  
      def estimate(self, points):
          """Estimate the probability density at points"""
          points = list(points)
          densities = callMLlibFunc(
              "estimateKernelDensity", self._sample, self._bandwidth, points)
          return np.asarray(densities)
  