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.
Let X be a CMS, and let count_X[j, k] denote the value in X's 2-dimensional count table at row j and column k.
Let X be a CMS, and let count_X[j, k] denote the value in X's 2-dimensional count table at row j and column k. Then the Count-Min sketch estimate of the inner product between A and B is the minimum inner product between their rows: estimatedInnerProduct = min_j (\sum_k count_A[j, k] * count_B[j, k]|)
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.
The general Count-Min sketch structure, used for holding any number of elements.