org.apache.spark.mllib.stat

KernelDensity

class KernelDensity extends Serializable

:: Experimental :: Kernel density estimation. Given a sample from a population, estimate its probability density function at each of the given evaluation points using kernels. Only Gaussian kernel is supported.

Scala example:

val sample = sc.parallelize(Seq(0.0, 1.0, 4.0, 4.0))
val kd = new KernelDensity()
  .setSample(sample)
  .setBandwidth(3.0)
val densities = kd.estimate(Array(-1.0, 2.0, 5.0))
Annotations
@Since( "1.4.0" ) @Experimental()
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  1. new KernelDensity()

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  10. def estimate(points: Array[Double]): Array[Double]

    Estimates probability density function at the given array of points.

    Estimates probability density function at the given array of points.

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    @Since( "1.4.0" )

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  18. def setBandwidth(bandwidth: Double): KernelDensity.this.type

    Sets the bandwidth (standard deviation) of the Gaussian kernel (default: 1.0).

    Sets the bandwidth (standard deviation) of the Gaussian kernel (default: 1.0).

    Annotations
    @Since( "1.4.0" )

  19. def setSample(sample: JavaRDD[Double]): KernelDensity.this.type

    Sets the sample to use for density estimation (for Java users).

    Sets the sample to use for density estimation (for Java users).

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    @Since( "1.4.0" )

  20. def setSample(sample: RDD[Double]): KernelDensity.this.type

    Sets the sample to use for density estimation.

    Sets the sample to use for density estimation.

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    @Since( "1.4.0" )

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