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  • package apache
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  • package spark
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    apache
  • package mllib

    RDD-based machine learning APIs (in maintenance mode).

    RDD-based machine learning APIs (in maintenance mode).

    The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,

    • no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
    • bug fixes in the RDD-based APIs will still be accepted.

    The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.

    Definition Classes
    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package stat
    Definition Classes
    mllib
  • package distribution
    Definition Classes
    stat
  • package test
    Definition Classes
    stat
  • KernelDensity
  • MultivariateOnlineSummarizer
  • MultivariateStatisticalSummary
  • Statistics
t

org.apache.spark.mllib.stat

MultivariateStatisticalSummary

trait MultivariateStatisticalSummary extends AnyRef

Trait for multivariate statistical summary of a data matrix.

Annotations
@Since("1.0.0")
Linear Supertypes
AnyRef, Any
Known Subclasses
MultivariateOnlineSummarizer
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Abstract Value Members

  1. abstract def count: Long

    Sample size.

    Sample size.

    Annotations
    @Since("1.0.0")
  2. abstract def max: Vector

    Maximum value of each column.

    Maximum value of each column.

    Annotations
    @Since("1.0.0")
  3. abstract def mean: Vector

    Sample mean vector.

    Sample mean vector.

    Annotations
    @Since("1.0.0")
  4. abstract def min: Vector

    Minimum value of each column.

    Minimum value of each column.

    Annotations
    @Since("1.0.0")
  5. abstract def normL1: Vector

    L1 norm of each column

    L1 norm of each column

    Annotations
    @Since("1.2.0")
  6. abstract def normL2: Vector

    Euclidean magnitude of each column

    Euclidean magnitude of each column

    Annotations
    @Since("1.2.0")
  7. abstract 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.

    Annotations
    @Since("1.0.0")
  8. abstract def variance: Vector

    Sample variance vector.

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

    Annotations
    @Since("1.0.0")
  9. abstract def weightSum: Double

    Sum of weights.

    Sum of weights.

    Annotations
    @Since("3.0.0")

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
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    AnyRef → Any
  2. final def ##: Int
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  3. final def ==(arg0: Any): Boolean
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  5. def clone(): AnyRef
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  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: AnyRef): Boolean
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  8. def finalize(): Unit
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  10. def hashCode(): Int
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  12. final def ne(arg0: AnyRef): Boolean
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  14. final def notifyAll(): Unit
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  17. final def wait(): Unit
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  18. final def wait(arg0: Long, arg1: Int): Unit
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    @throws(classOf[java.lang.InterruptedException])
  19. final def wait(arg0: Long): Unit
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    @throws(classOf[java.lang.InterruptedException]) @native()

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