package stat
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class
KernelDensity extends Serializable
Kernel density estimation.
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" )

class
MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with Serializable
MultivariateOnlineSummarizer implements MultivariateStatisticalSummary to compute the mean, variance, minimum, maximum, counts, and nonzero counts for instances in sparse or dense vector format in an online fashion.
MultivariateOnlineSummarizer implements MultivariateStatisticalSummary to compute the mean, variance, minimum, maximum, counts, and nonzero counts for instances in sparse or dense vector format in an 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 the mean and variance of instances: Reference: variancewiki Zero elements (including explicit zero values) are skipped when calling add(), to have time complexity O(nnz) instead of O(n) for each column.
For weighted instances, the unbiased estimation of variance is defined by the reliability weights: see Reliability weights (Wikipedia).
 Annotations
 @Since( "1.1.0" )

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" )
Value Members

object
Statistics
API for statistical functions in MLlib.
API for statistical functions in MLlib.
 Annotations
 @Since( "1.1.0" )