Counts the item count times and returns the result as a new sketch.
Counts the item count times and returns the result as a new sketch.
Counts the item and returns the result as a new sketch.
Counts the item and returns the result as a new sketch.
Returns a new sketch that is the combination of this sketch and the other sketch.
Returns a new sketch that is the combination of this sketch and the other sketch.
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.
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.
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.
Returns the one-sided error bound on the error of each point query, i.
Returns the one-sided error bound on the error of each point query, i.e. frequency estimate.
The first frequency moment is the total number of elements in the stream.
The first frequency moment is the total number of elements in the stream.
The second frequency moment is \sum a_i^2, where a_i is the count of the i-th element.
The second frequency moment is \sum a_i^2, where a_i is the count of the i-th element.
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 probability p >= 1 - delta, it also holds that
estimatedFrequency <= trueFrequency + eps * totalCount.
Returns the set of heavy hitters.
Returns the set of heavy hitters.
The pluggable logic with which heavy hitters are being tracked.
The pluggable logic with which heavy hitters are being tracked.
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 that
estimatedInnerProduct <= actualInnerProduct + eps * thisTotalCount * otherTotalCount.
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.
An Option parameter about how many exact counts a sparse CMS wants to keep
An Option parameter about how many exact counts a sparse CMS wants to keep
Total number of elements counted (i.
Total number of elements counted (i.e. seen in the data stream) so far.
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.
Used for holding a single element, to avoid repeatedly adding elements from sparse counts tables.