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from typing import Iterable, Optional
import numpy as np
from numpy import ndarray
from pyspark.mllib.common import callMLlibFunc
from pyspark.rdd import RDD
[docs]class KernelDensity:
"""
Estimate probability density at required points given an RDD of samples
from the population.
Examples
--------
>>> 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) -> None:
self._bandwidth: float = 1.0
self._sample: Optional[RDD[float]] = None
[docs] def setBandwidth(self, bandwidth: float) -> None:
"""Set bandwidth of each sample. Defaults to 1.0"""
self._bandwidth = bandwidth
[docs] def setSample(self, sample: RDD[float]) -> None:
"""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: Iterable[float]) -> ndarray:
"""Estimate the probability density at points"""
points = list(points)
densities = callMLlibFunc("estimateKernelDensity", self._sample, self._bandwidth, points)
return np.asarray(densities)