 MultivariateOnlineSummarizer

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.

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.

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1. MultivariateOnlineSummarizer
2. Serializable
3. Serializable
4. MultivariateStatisticalSummary
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1. final def !=(arg0: AnyRef): Boolean

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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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.

7. final def asInstanceOf[T0]: T0

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8. def clone(): AnyRef

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protected[java.lang]
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@throws( ... )

Sample size.

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

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11. def equals(arg0: Any): Boolean

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12. def finalize(): Unit

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

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14. def hashCode(): Int

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15. final def isInstanceOf[T0]: Boolean

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16. def max: Vector

Maximum value of each column.

Maximum value of each column.

Definition Classes
MultivariateOnlineSummarizerMultivariateStatisticalSummary
17. def mean: Vector

Sample mean vector.

Sample mean vector.

Definition Classes
MultivariateOnlineSummarizerMultivariateStatisticalSummary
18. 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.

19. def min: Vector

Minimum value of each column.

Minimum value of each column.

Definition Classes
MultivariateOnlineSummarizerMultivariateStatisticalSummary
20. final def ne(arg0: AnyRef): Boolean

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AnyRef
21. def normL1: Vector

L1 norm of each column

L1 norm of each column

Definition Classes
MultivariateOnlineSummarizerMultivariateStatisticalSummary
22. def normL2: Vector

Euclidean magnitude of each column

Euclidean magnitude of each column

Definition Classes
MultivariateOnlineSummarizerMultivariateStatisticalSummary
23. final def notify(): Unit

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24. final def notifyAll(): Unit

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25. def numNonzeros: Vector

Number of nonzero elements (including explicitly presented zero values) in each column.

Number of nonzero elements (including explicitly presented zero values) in each column.

Definition Classes
MultivariateOnlineSummarizerMultivariateStatisticalSummary
26. final def synchronized[T0](arg0: ⇒ T0): T0

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27. def toString(): String

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28. def variance: Vector

Sample variance vector.

Sample variance vector. Should return a zero vector if the sample size is 1.

Definition Classes
MultivariateOnlineSummarizerMultivariateStatisticalSummary
29. final def wait(): Unit

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

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

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