# Source code for pyspark.mllib.stat.KernelDensity

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import sys
if sys.version > '3':
xrange = range
import numpy as np
from pyspark.mllib.common import callMLlibFunc
from pyspark.rdd import RDD
[docs]class KernelDensity(object):
"""
.. note:: Experimental
Estimate probability density at required points given a 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
[docs] def setBandwidth(self, bandwidth):
"""Set bandwidth of each sample. Defaults to 1.0"""
self._bandwidth = bandwidth
[docs] 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
[docs] 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)