Source code for pyspark.mllib.stat.KernelDensity

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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)