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Changelog.md

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@@ -174,7 +174,7 @@ v0.3.9 / 2015-05-03
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v0.3.8 / 2015-04-18
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===================
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* Add special OrderedDict deprection for Numbers
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* Add special OrderedDict deprecation for Numbers
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* Fix warning about {A, B...}
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v0.3.7 / 2015-04-17

benchmark/README.md

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@@ -9,7 +9,7 @@ julia --project=benchmark -e '
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### To compare against baseline locally:
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Note, must have a `baseline` branch, which will be the refrence point against the currently active branch. A common use case is to point the baseline to the previous commit.
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Note, must have a `baseline` branch, which will be the reference point against the currently active branch. A common use case is to point the baseline to the previous commit.
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This can be accomplished with
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```
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### To compare against baseline locally (without re-running baseline):
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Running the above baseline comparison produces a `benchmark/result-baseline.json` file which is
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used as a refrence for new changes.
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used as a reference for new changes.
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If the baseline remains unchanged during development, then it is unnecessary to regenerate this file,
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and the following command may be used to save benchmarking time.
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```
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julia --project=benchmark -e '
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using Pkg; Pkg.instantiate();
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include("benchmark/incrementalrunjudge.jl");
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include("benchmark/pprintjudge.jl");'
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```
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```

docs/src/heaps.md

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@@ -157,7 +157,7 @@ DataStructures.nextreme(Base.Forward, n, a) # Equivalent to nsmallest(n, a)
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One use case for custom orderings is to achieve faster performance with `Float`
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elements with the risk of random ordering if any elements are `NaN`.
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The provided `DataStructures.FasterForward` and `DataStructures.FasterReverse`
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orderings are optimized for this purpose and may achive a 2x performance boost:
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orderings are optimized for this purpose and may achieve a 2x performance boost:
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```julia
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h = BinaryHeap{Float64, DataStructures.FasterForward}() # faster min heap

src/accumulator.jl

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@@ -75,7 +75,7 @@ Increments the count for `x` by `v` (defaulting to one)
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inc!(ct::Accumulator, x, v::Number) = (ct[x] += v)
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inc!(ct::Accumulator{T, V}, x) where {T, V} = inc!(ct, x, one(V))
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# inc! is preferred over push!, but we need to provide push! for the Bag interpreation
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# inc! is preferred over push!, but we need to provide push! for the Bag interpretation
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# which is used by classified_collections.jl
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Base.push!(ct::Accumulator, x) = inc!(ct, x)
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Base.push!(ct::Accumulator, x, a::Number) = inc!(ct, x, a)
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end
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Base.intersect(a::Accumulator, b::Accumulator, c::Accumulator...) = insersect(intersect(a,b), c...)
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Base.intersect(a::Accumulator, b::Accumulator, c::Accumulator...) = intersect(intersect(a,b), c...)
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Base.intersect(a::Accumulator, b::Accumulator) = intersect!(copy(a), b)
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function Base.intersect!(a::Accumulator, b::Accumulator)
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for k in union(keys(a), keys(b)) # union not interection as we want to check both multiplicities
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for k in union(keys(a), keys(b)) # union not intersection as we want to check both multiplicities
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va = a[k]
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vb = b[k]
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va >= 0 || throw(MultiplicityException(k, va))

src/avl_tree.jl

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Returns the rank of `key` present in the `tree`, if it present. A `KeyError` is thrown if `key`
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is not present.
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# Exemples
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# Examples
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```jldoctest
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julia> tree = AVLTree{Int}();

src/priorityqueue.jl

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# Construction inferring Key/Value types from input
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# e.g. PriorityQueue{}
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PriorityQueue(o1::Ordering, o2::Ordering) = throw(ArgumentError("PriorityQueue with two parameters must be called with an Ordering and an interable of pairs"))
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PriorityQueue(o1::Ordering, o2::Ordering) = throw(ArgumentError("PriorityQueue with two parameters must be called with an Ordering and an iterable of pairs"))
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PriorityQueue(kv, o::Ordering=Forward) = PriorityQueue(o, kv)
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function PriorityQueue(o::Ordering, kv)
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try

src/queue.jl

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@@ -9,7 +9,7 @@ Create a `Queue` object containing elements of type `T` for First In, First Out
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# Parameters
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- `T::Type` Queue element data type.
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- `blksize::Integer=1024` Unrolled linked-list bleck size (in bytes). Defualt = 1024.
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- `blksize::Integer=1024` Unrolled linked-list block size (in bytes). Default = 1024.
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# Examples
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```jldoctest

src/sorted_dict.jl

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## A SortedDict is a wrapper around balancedTree with methods similiar
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## A SortedDict is a wrapper around balancedTree with methods similar
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## to those of Julia container Dict.
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mutable struct SortedDict{K, D, Ord <: Ordering} <: AbstractDict{K,D}
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"""
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Base.push!(sd::SortedDict, p::Pair)
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Insert key-vaue pair `p`, i.e., a `k=>v` pair, into `sd`.
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Insert key-value pair `p`, i.e., a `k=>v` pair, into `sd`.
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If the key `k` is already present, this overwrites the old value.
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The key is also overwritten (not necessarily a no-op, since
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sort-order equivalence may differ from equality).

src/sorted_set.jl

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## A SortedSet is a wrapper around balancedTree with
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## methods similiar to those of the julia Set.
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## methods similar to those of the julia Set.
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mutable struct SortedSet{K, Ord <: Ordering} <: AbstractSet{K}
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bt::BalancedTree23{K,Nothing,Ord}

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