Class

org.apache.spark.ml.odkl

ExtendedMultivariateOnlineSummarizer

Related Doc: package odkl

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class ExtendedMultivariateOnlineSummarizer extends MultivariateOnlineSummarizer with Serializable with Logging

Created by dmitriybugaichenko on 30.12.15.

Utility used for estimating extended stat for the set of vectors. In addition to mean, deviation and count estimates percentiles

Linear Supertypes
Logging, MultivariateOnlineSummarizer, Serializable, Serializable, MultivariateStatisticalSummary, AnyRef, Any
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Inherited
  1. ExtendedMultivariateOnlineSummarizer
  2. Logging
  3. MultivariateOnlineSummarizer
  4. Serializable
  5. Serializable
  6. MultivariateStatisticalSummary
  7. AnyRef
  8. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new ExtendedMultivariateOnlineSummarizer(dimension: Int, compression: Double)

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    dimension

    Expected dimension of vectors to aggregate

    compression

    How should accuracy be traded for size? A value of N here will give quantile errors almost always less than 3/N with considerably smaller errors expected for extreme quantiles. Conversely, you should expect to track about 5 N centroids for this accuracy.

Value Members

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

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

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

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

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    Definition Classes
    ExtendedMultivariateOnlineSummarizer → MultivariateOnlineSummarizer
  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  7. val compression: Double

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    How should accuracy be traded for size? A value of N here will give quantile errors almost always less than 3/N with considerably smaller errors expected for extreme quantiles.

    How should accuracy be traded for size? A value of N here will give quantile errors almost always less than 3/N with considerably smaller errors expected for extreme quantiles. Conversely, you should expect to track about 5 N centroids for this accuracy.

  8. def count: Long

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    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )
  9. val dimension: Int

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    Expected dimension of vectors to aggregate

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

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

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    Definition Classes
    AnyRef → Any
  12. def finalize(): Unit

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

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    Definition Classes
    AnyRef → Any
  14. def hashCode(): Int

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

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    Definition Classes
    Any
  16. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  17. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  18. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  19. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  20. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  21. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  22. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  23. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  24. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging
  25. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  26. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  27. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  28. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  29. def max: Vector

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

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

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    Definition Classes
    ExtendedMultivariateOnlineSummarizer → MultivariateOnlineSummarizer
  32. def min: Vector

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

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    Definition Classes
    AnyRef
  34. def normL1: Vector

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

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

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    Definition Classes
    AnyRef
  37. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  38. def numNonzeros: Vector

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    Definition Classes
    MultivariateOnlineSummarizer → MultivariateStatisticalSummary
    Annotations
    @Since( "1.1.0" )
  39. def percentile(p: Double): Vector

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  40. val percentileAggregators: Array[SeriallizableAvlTreeDigest]

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

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    Definition Classes
    AnyRef
  42. def toString(): String

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    Definition Classes
    AnyRef → Any
  43. def variance: Vector

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

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Logging

Inherited from MultivariateOnlineSummarizer

Inherited from Serializable

Inherited from Serializable

Inherited from MultivariateStatisticalSummary

Inherited from AnyRef

Inherited from Any

Ungrouped