stat

Type Members

1. class KernelDensity extends Serializable

   

   :: Experimental :: Kernel density estimation.

   :: 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()

2. class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with Serializable

   

   :: DeveloperApi :: MultivariateOnlineSummarizer implements MultivariateStatisticalSummary to compute the mean, variance, minimum, maximum, counts, and nonzero counts for samples in sparse or dense vector format in a online fashion.

   :: DeveloperApi :: MultivariateOnlineSummarizer implements MultivariateStatisticalSummary to compute the mean, variance, minimum, maximum, counts, and nonzero counts for samples in sparse or dense vector format in a online fashion.

   Two MultivariateOnlineSummarizer can be merged together to have a statistical summary of the corresponding joint dataset.

   A numerically stable algorithm is implemented to compute sample mean and variance: Reference: variance-wiki Zero elements (including explicit zero values) are skipped when calling add(), to have time complexity O(nnz) instead of O(n) for each column.

   Annotations

   @Since( "1.1.0" ) @DeveloperApi()

3. trait MultivariateStatisticalSummary extends AnyRef

   

   Trait for multivariate statistical summary of a data matrix.

   Trait for multivariate statistical summary of a data matrix.

   Annotations

   @Since( "1.0.0" )