trait CMSCounting[K, C[_]] extends AnyRef
A trait for CMS implementations that can count elements in a data stream and that can answer point queries (i.e. frequency estimates) for these elements.
- K
The type used to identify the elements to be counted.
- C
The type of the actual CMS that implements this trait.
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abstract
def
+(item: K, count: Long): C[K]
Counts the item
count
times and returns the result as a new sketch. -
abstract
def
++(other: C[K]): C[K]
Returns a new sketch that is the combination of this sketch and the other sketch.
-
abstract
def
delta: Double
Returns the bound on the probability that a query estimate does NOT lie within some small interval (an interval that depends on
eps
) around the truth. -
abstract
def
eps: Double
Returns the one-sided error bound on the error of each point query, i.e.
Returns the one-sided error bound on the error of each point query, i.e. frequency estimate.
-
abstract
def
f2: Approximate[Long]
The second frequency moment is
\sum a_i^2
, where
a_iis the count of the i-th element.
-
abstract
def
frequency(item: K): Approximate[Long]
Returns an estimate of the total number of times this item has been seen in the stream so far.
Returns an estimate of the total number of times this item has been seen in the stream so far. This estimate is an upper bound.
It is always true that
estimatedFrequency >= trueFrequency
. With probabilityp >= 1 - delta
, it also holds thatestimatedFrequency <= trueFrequency + eps * totalCount
. -
abstract
def
innerProduct(other: C[K]): Approximate[Long]
Returns an estimate of the inner product against another data stream.
Returns an estimate of the inner product against another data stream.
In other words, let a_i denote the number of times element i has been seen in the data stream summarized by this CMS, and let b_i denote the same for the other CMS. Then this returns an estimate of
<a, b> = \sum a_i b_i
.Note: This can also be viewed as the join size between two relations.
It is always true that actualInnerProduct <= estimatedInnerProduct. With probability
p >= 1 - delta
, it also holds thatestimatedInnerProduct <= actualInnerProduct + eps * thisTotalCount * otherTotalCount
. -
abstract
def
maxExactCountOpt: Option[Int]
An Option parameter about how many exact counts a sparse CMS wants to keep
-
abstract
def
totalCount: Long
Total number of elements counted (i.e.
Total number of elements counted (i.e. seen in the data stream) so far.
Concrete Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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def
+(item: K): C[K]
Counts the item and returns the result as a new sketch.
-
final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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def
depth: Int
Number of hash functions (also: number of rows in the counting table).
Number of hash functions (also: number of rows in the counting table). This number is derived from
delta
. -
final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
f1: Long
The first frequency moment is the total number of elements in the stream.
-
def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
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-
def
maxExactCount: Int
Number of exact counts a sparse CMS wants to keep.
Number of exact counts a sparse CMS wants to keep. This number is derived from
maxExactCountOpt
. -
final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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def
width: Int
Number of counters per hash function (also: number of columns in the counting table).
Number of counters per hash function (also: number of columns in the counting table). This number is derived from
eps
.