Class

org.apache.spark.mllib.stat

KernelDensity

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class KernelDensity extends Serializable

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" )
Source
KernelDensity.scala
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Instance Constructors

  1. new KernelDensity()

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Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  6. final def eq(arg0: AnyRef): Boolean

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  7. def equals(arg0: Any): Boolean

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

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    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" )

  9. def finalize(): Unit

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  10. final def getClass(): Class[_]

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  11. def hashCode(): Int

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  13. final def ne(arg0: AnyRef): Boolean

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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

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

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

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    @Since( "1.4.0" )

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

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    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" )

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

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    Sets the sample to use for density estimation.

    Sets the sample to use for density estimation.

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    @Since( "1.4.0" )

  19. final def synchronized[T0](arg0: ⇒ T0): T0

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  20. def toString(): String

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  23. final def wait(arg0: Long): Unit

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