Packages

c

org.apache.spark.rdd

DoubleRDDFunctions

class DoubleRDDFunctions extends Logging with Serializable

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

Source
DoubleRDDFunctions.scala
Linear Supertypes
Serializable, Logging, AnyRef, Any
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  1. DoubleRDDFunctions
  2. Serializable
  3. Logging
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Visibility
  1. Public
  2. Protected

Instance Constructors

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

Type Members

  1. implicit class LogStringContext extends AnyRef
    Definition Classes
    Logging

Value Members

  1. 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.

  2. 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.

  3. def mean(): Double

    Compute the mean of this RDD's elements.

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

    Approximate operation to return the mean within a timeout.

  5. 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")
  6. 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")
  7. 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).

  8. 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).

  9. 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.

  10. def stdev(): Double

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

  11. def sum(): Double

    Add up the elements in this RDD.

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

    Approximate operation to return the sum within a timeout.

  13. def variance(): Double

    Compute the population variance of this RDD's elements.