KernelDensity¶

class pyspark.mllib.stat.KernelDensity[source]¶

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])

Methods

`estimate`(points)	Estimate the probability density at points
`setBandwidth`(bandwidth)	Set bandwidth of each sample.
`setSample`(sample)	Set sample points from the population.

Methods Documentation

estimate(points: Iterable[float]) → numpy.ndarray[source]¶: Estimate the probability density at points

setBandwidth(bandwidth: float) → None[source]¶: Set bandwidth of each sample. Defaults to 1.0

setSample(sample: pyspark.rdd.RDD[float]) → None[source]¶: Set sample points from the population. Should be a RDD

MultivariateGaussian

KolmogorovSmirnovTestResult