MultivariateOnlineSummarizer

:: DeveloperApi :: MultivariateOnlineSummarizer implements MultivariateStatisticalSummary to compute the mean, variance, minimum, maximum, counts, and nonzero counts for instances 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 the mean and variance of instances: 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.

For weighted instances, the unbiased estimation of variance is defined by the reliability weights: https://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Reliability_weights.

Annotations: @Since( "1.1.0" ) @DeveloperApi()
Source: MultivariateOnlineSummarizer.scala

Linear Supertypes

Serializable, Serializable, MultivariateStatisticalSummary, AnyRef, Any

Instance Constructors

new MultivariateOnlineSummarizer()

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def add(sample: Vector): MultivariateOnlineSummarizer.this.type

Add a new sample to this summarizer, and update the statistical summary.
Add a new sample to this summarizer, and update the statistical summary.
sample
The sample in dense/sparse vector format to be added into this summarizer.
returns
This MultivariateOnlineSummarizer object.

Annotations
@Since( "1.1.0" )
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def count: Long

Sample size.
Sample size.

Definition Classes
MultivariateOnlineSummarizer → MultivariateStatisticalSummary
Annotations
@Since( "1.1.0" )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def max: Vector

Maximum value of each dimension.
Maximum value of each dimension.

Definition Classes
MultivariateOnlineSummarizer → MultivariateStatisticalSummary
Annotations
@Since( "1.1.0" )
def mean: Vector

Sample mean of each dimension.
Sample mean of each dimension.

Definition Classes
MultivariateOnlineSummarizer → MultivariateStatisticalSummary
Annotations
@Since( "1.1.0" )
def merge(other: MultivariateOnlineSummarizer): MultivariateOnlineSummarizer.this.type

Merge another MultivariateOnlineSummarizer, and update the statistical summary.
Merge another MultivariateOnlineSummarizer, and update the statistical summary. (Note that it's in place merging; as a result, this object will be modified.)
other
The other MultivariateOnlineSummarizer to be merged.
returns
This MultivariateOnlineSummarizer object.

Annotations
@Since( "1.1.0" )
def min: Vector

Minimum value of each dimension.
Minimum value of each dimension.

Definition Classes
MultivariateOnlineSummarizer → MultivariateStatisticalSummary
Annotations
@Since( "1.1.0" )
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def normL1: Vector

L1 norm of each dimension.
L1 norm of each dimension.

Definition Classes
MultivariateOnlineSummarizer → MultivariateStatisticalSummary
Annotations
@Since( "1.2.0" )
def normL2: Vector

L2 (Euclidian) norm of each dimension.
L2 (Euclidian) norm of each dimension.

Definition Classes
MultivariateOnlineSummarizer → MultivariateStatisticalSummary
Annotations
@Since( "1.2.0" )
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def numNonzeros: Vector

Number of nonzero elements in each dimension.
Number of nonzero elements in each dimension.

Definition Classes
MultivariateOnlineSummarizer → MultivariateStatisticalSummary
Annotations
@Since( "1.1.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def variance: Vector

Unbiased estimate of sample variance of each dimension.
Unbiased estimate of sample variance of each dimension.

Definition Classes
MultivariateOnlineSummarizer → MultivariateStatisticalSummary
Annotations
@Since( "1.1.0" )
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Doc: package stat

class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with Serializable

Instance Constructors

new MultivariateOnlineSummarizer()

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def add(sample: Vector): MultivariateOnlineSummarizer.this.type

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def count: Long

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def max: Vector

def mean: Vector

def merge(other: MultivariateOnlineSummarizer): MultivariateOnlineSummarizer.this.type

def min: Vector

final def ne(arg0: AnyRef): Boolean

def normL1: Vector

def normL2: Vector

final def notify(): Unit

final def notifyAll(): Unit

def numNonzeros: Vector

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

def toString(): String

def variance: Vector

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from MultivariateStatisticalSummary

Inherited from AnyRef

Inherited from Any

Ungrouped