final case class Distribution(count: Long = 0L, mean: Double = 0.0, sumOfSquaredDeviation: Double = 0.0, range: Option[Range] = _root_.scala.None, bucketOptions: Option[BucketOptions] = _root_.scala.None, bucketCounts: Seq[Long] = _root_.scala.Seq.empty, exemplars: Seq[Exemplar] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = ...) extends GeneratedMessage with Updatable[Distribution] with Product with Serializable

Distribution contains summary statistics for a population of values. It optionally contains a histogram representing the distribution of those values across a set of buckets.

The summary statistics are the count, mean, sum of the squared deviation from the mean, the minimum, and the maximum of the set of population of values. The histogram is based on a sequence of buckets and gives a count of values that fall into each bucket. The boundaries of the buckets are given either explicitly or by formulas for buckets of fixed or exponentially increasing widths.

Although it is not forbidden, it is generally a bad idea to include non-finite values (infinities or NaNs) in the population of values, as this will render the mean and sum_of_squared_deviation fields meaningless.

count

The number of values in the population. Must be non-negative. This value must equal the sum of the values in bucket_counts if a histogram is provided.

mean

The arithmetic mean of the values in the population. If count is zero then this field must be zero.

sumOfSquaredDeviation

The sum of squared deviations from the mean of the values in the population. For values x_i this is: Sum[i=1..n]((x_i - mean)^2) Knuth, "The Art of Computer Programming", Vol. 2, page 323, 3rd edition describes Welford's method for accumulating this sum in one pass. If count is zero then this field must be zero.

range

If specified, contains the range of the population values. The field must not be present if the count is zero.

bucketOptions

Defines the histogram bucket boundaries. If the distribution does not contain a histogram, then omit this field.

bucketCounts

The number of values in each bucket of the histogram, as described in bucket_options. If the distribution does not have a histogram, then omit this field. If there is a histogram, then the sum of the values in bucket_counts must equal the value in the count field of the distribution. If present, bucket_counts should contain N values, where N is the number of buckets specified in bucket_options. If you supply fewer than N values, the remaining values are assumed to be 0. The order of the values in bucket_counts follows the bucket numbering schemes described for the three bucket types. The first value must be the count for the underflow bucket (number 0). The next N-2 values are the counts for the finite buckets (number 1 through N-2). The N'th value in bucket_counts is the count for the overflow bucket (number N-1).

exemplars

Must be in increasing order of value field.

Annotations
@SerialVersionUID()
Linear Supertypes
Product, Equals, Updatable[Distribution], GeneratedMessage, Serializable, Serializable, AnyRef, Any
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Inherited
  1. Distribution
  2. Product
  3. Equals
  4. Updatable
  5. GeneratedMessage
  6. Serializable
  7. Serializable
  8. AnyRef
  9. Any
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Visibility
  1. Public
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Instance Constructors

  1. new Distribution(count: Long = 0L, mean: Double = 0.0, sumOfSquaredDeviation: Double = 0.0, range: Option[Range] = _root_.scala.None, bucketOptions: Option[BucketOptions] = _root_.scala.None, bucketCounts: Seq[Long] = _root_.scala.Seq.empty, exemplars: Seq[Exemplar] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = ...)

    count

    The number of values in the population. Must be non-negative. This value must equal the sum of the values in bucket_counts if a histogram is provided.

    mean

    The arithmetic mean of the values in the population. If count is zero then this field must be zero.

    sumOfSquaredDeviation

    The sum of squared deviations from the mean of the values in the population. For values x_i this is: Sum[i=1..n]((x_i - mean)^2) Knuth, "The Art of Computer Programming", Vol. 2, page 323, 3rd edition describes Welford's method for accumulating this sum in one pass. If count is zero then this field must be zero.

    range

    If specified, contains the range of the population values. The field must not be present if the count is zero.

    bucketOptions

    Defines the histogram bucket boundaries. If the distribution does not contain a histogram, then omit this field.

    bucketCounts

    The number of values in each bucket of the histogram, as described in bucket_options. If the distribution does not have a histogram, then omit this field. If there is a histogram, then the sum of the values in bucket_counts must equal the value in the count field of the distribution. If present, bucket_counts should contain N values, where N is the number of buckets specified in bucket_options. If you supply fewer than N values, the remaining values are assumed to be 0. The order of the values in bucket_counts follows the bucket numbering schemes described for the three bucket types. The first value must be the count for the underflow bucket (number 0). The next N-2 values are the counts for the finite buckets (number 1 through N-2). The N'th value in bucket_counts is the count for the overflow bucket (number N-1).

    exemplars

    Must be in increasing order of value field.

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. def addAllBucketCounts(__vs: Iterable[Long]): Distribution
  5. def addAllExemplars(__vs: Iterable[Exemplar]): Distribution
  6. def addBucketCounts(__vs: Long*): Distribution
  7. def addExemplars(__vs: Exemplar*): Distribution
  8. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  9. val bucketCounts: Seq[Long]
  10. val bucketOptions: Option[BucketOptions]
  11. def clearBucketCounts: Distribution
  12. def clearBucketOptions: Distribution
  13. def clearExemplars: Distribution
  14. def clearRange: Distribution
  15. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  16. def companion: Distribution.type
    Definition Classes
    Distribution → GeneratedMessage
  17. val count: Long
  18. def discardUnknownFields: Distribution
  19. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  20. val exemplars: Seq[Exemplar]
  21. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  22. def getBucketOptions: BucketOptions
  23. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  24. def getField(__field: FieldDescriptor): PValue
    Definition Classes
    Distribution → GeneratedMessage
  25. def getFieldByNumber(__fieldNumber: Int): Any
    Definition Classes
    Distribution → GeneratedMessage
  26. def getRange: Range
  27. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  28. val mean: Double
  29. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  30. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  31. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  32. val range: Option[Range]
  33. def serializedSize: Int
    Definition Classes
    Distribution → GeneratedMessage
  34. val sumOfSquaredDeviation: Double
  35. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  36. final def toByteArray: Array[Byte]
    Definition Classes
    GeneratedMessage
  37. final def toByteString: ByteString
    Definition Classes
    GeneratedMessage
  38. final def toPMessage: PMessage
    Definition Classes
    GeneratedMessage
  39. def toProtoString: String
    Definition Classes
    Distribution → GeneratedMessage
  40. val unknownFields: UnknownFieldSet
  41. def update(ms: (Lens[Distribution, Distribution]) ⇒ Mutation[Distribution]*): Distribution
    Definition Classes
    Updatable
  42. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  43. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  44. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  45. def withBucketCounts(__v: Seq[Long]): Distribution
  46. def withBucketOptions(__v: BucketOptions): Distribution
  47. def withCount(__v: Long): Distribution
  48. def withExemplars(__v: Seq[Exemplar]): Distribution
  49. def withMean(__v: Double): Distribution
  50. def withRange(__v: Range): Distribution
  51. def withSumOfSquaredDeviation(__v: Double): Distribution
  52. def withUnknownFields(__v: UnknownFieldSet): Distribution
  53. final def writeDelimitedTo(output: OutputStream): Unit
    Definition Classes
    GeneratedMessage
  54. def writeTo(_output__: CodedOutputStream): Unit
    Definition Classes
    Distribution → GeneratedMessage
  55. final def writeTo(output: OutputStream): Unit
    Definition Classes
    GeneratedMessage

Inherited from Product

Inherited from Equals

Inherited from Updatable[Distribution]

Inherited from GeneratedMessage

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped