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c

org.apache.spark.rdd

DoubleRDDFunctions

class DoubleRDDFunctions extends Logging with Serializable

Extra functions available on RDDs of Doubles through an implicit conversion.

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  1. DoubleRDDFunctions
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Instance Constructors

  1. new DoubleRDDFunctions(self: RDD[Double])

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  11. def histogram(buckets: Array[Double], evenBuckets: Boolean = false): Array[Long]

    Compute a histogram using the provided buckets.

    Compute a histogram using the provided buckets. The buckets are all open to the right except for the last which is closed. e.g. for the array [1, 10, 20, 50] the buckets are [1, 10) [10, 20) [20, 50] e.g <=x<10, 10<=x<20, 20<=x<=50 And on the input of 1 and 50 we would have a histogram of 1, 0, 1

    Note

    If your histogram is evenly spaced (e.g. [0, 10, 20, 30]) this can be switched from an O(log n) insertion to O(1) per element. (where n = # buckets) if you set evenBuckets to true. buckets must be sorted and not contain any duplicates. buckets array must be at least two elements All NaN entries are treated the same. If you have a NaN bucket it must be the maximum value of the last position and all NaN entries will be counted in that bucket.

  12. def histogram(bucketCount: Int): (Array[Double], Array[Long])

    Compute a histogram of the data using bucketCount number of buckets evenly spaced between the minimum and maximum of the RDD.

    Compute a histogram of the data using bucketCount number of buckets evenly spaced between the minimum and maximum of the RDD. For example if the min value is 0 and the max is 100 and there are two buckets the resulting buckets will be [0, 50) [50, 100]. bucketCount must be at least 1 If the RDD contains infinity, NaN throws an exception If the elements in RDD do not vary (max == min) always returns a single bucket.

  13. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  14. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  15. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  16. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  17. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  18. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  19. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  20. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  21. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  22. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  23. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  24. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  25. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  26. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  27. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  28. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  29. def mean(): Double

    Compute the mean of this RDD's elements.

  30. def meanApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble]

    Approximate operation to return the mean within a timeout.

  31. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  32. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  33. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  34. def popStdev(): Double

    Compute the population standard deviation of this RDD's elements.

    Compute the population standard deviation of this RDD's elements.

    Annotations
    @Since( "2.1.0" )
  35. def popVariance(): Double

    Compute the population variance of this RDD's elements.

    Compute the population variance of this RDD's elements.

    Annotations
    @Since( "2.1.0" )
  36. def sampleStdev(): Double

    Compute the sample standard deviation of this RDD's elements (which corrects for bias in estimating the standard deviation by dividing by N-1 instead of N).

  37. def sampleVariance(): Double

    Compute the sample variance of this RDD's elements (which corrects for bias in estimating the variance by dividing by N-1 instead of N).

  38. def stats(): StatCounter

    Return a org.apache.spark.util.StatCounter object that captures the mean, variance and count of the RDD's elements in one operation.

  39. def stdev(): Double

    Compute the population standard deviation of this RDD's elements.

  40. def sum(): Double

    Add up the elements in this RDD.

  41. def sumApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble]

    Approximate operation to return the sum within a timeout.

  42. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  43. def toString(): String
    Definition Classes
    AnyRef → Any
  44. def variance(): Double

    Compute the population variance of this RDD's elements.

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

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

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