DataStructures.jl

This package implements a variety of data structures, including

• Deque (based on block-list)
• Stack
• Queue
• Disjoint Sets
• Binary Heap
• Mutable Binary Heap
• Ordered Dicts and Sets
• Dictionaries with Defaults

Deque

The Deque type implements a double-ended queue using a list of blocks. This data structure supports constant-time insertion/removal of elements at both ends of a sequence.

Usage:

a = Deque{Int}()
isempty(a)          # test whether the dequeue is empty
length(a)           # get the number of elements
push!(a, 10)        # add an element to the back
pop!(a)             # remove an element from the back
unshift!(a, 20)     # add an element to the front
shift!(a)           # remove an element from the front
front(a)            # get the element at the front
back(a)             # get the element at the back

Note: Julia's Vector type also provides this interface, and thus can be used as a deque. However, the Deque type in this package is implemented as a list of contiguous blocks (default size = 2K). As a deque grows, new blocks may be created and linked to existing blocks. This way avoids the copying when growing a vector.

Benchmark shows that the performance of Deque is comparable to Vector on push!, and is noticeably faster on unshift! (by about 30% to 40%).

Stack and Queue

The Stack and Queue types are a light-weight wrapper of a deque type, which respectively provide interfaces for FILO and FIFO access.

Usage of Stack:

s = Stack(Int)
push!(s, x)
x = top(s)
x = pop!(s)

Usage of Queue:

q = Queue(Int)
enqueue!(q, x)
x = front(q)
x = back(q)
x = dequeue!(q)

Accumulators and Counters

A accumulator, as defined below, is a data structure that maintains an accumulated number for each key. This is a counter when the accumulated values reflect the counts.

type Accumulator{K, V<:Number}
    map::Dict{K, V}
end

There are different ways to construct an accumulator/counter:

a = accumulator(K, V)    # construct an accumulator with key-type K and 
                         # accumulated value type V

a = accumulator(dict)    # construct an accumulator from a dictionary

a = counter(K)           # construct a counter, i.e. an accumulator with
                         # key type K and value type Int


a = counter(dict)        # construct a counter from a dictionary

a = counter(seq)         # construct a counter by counting keys in a sequence

Usage of an accumulator/counter:

# let a and a2 be accumulators/counters

a[x]             # get the current value/count for x. 
                 # if x was not added to a, it returns zero(V)

add!(a, x)       # add the value/count for x by 1
add!(a, x, v)    # add the value/count for x by v
add!(a, a2)      # add all counts from a2 to a1

pop!(a, x)       # remove a key x from a, and returns its current value

merge(a, a2)     # return a new accumulator/counter that combines the
                 # values/counts in both a and a2

Disjoint Sets

Some algorithms, such as finding connected components in undirected graph and Kruskal's method of finding minimum spanning tree, require a data structure that can efficiently represent a collection of disjoint subsets. A widely used data structure for this purpose is the Disjoint set forest.

Usage:

a = IntDisjointSet(10)      # creates a forest comprised of 10 singletons
union!(a, 3, 5)             # merges the sets that contain 3 and 5 into one
in_same_set(a, x, y)        # determines whether x and y are in the same set

One may also use other element types

a = DisjointSet{String}(["a", "b", "c", "d"])
union!(a, "a", "b")
in_same_set(a, "c", "d")

Note that the internal implementation of IntDisjointSet is based on vectors, and is very efficient. DisjointSet{T} is a wrapper of IntDisjointSet, which uses a dictionary to map input elements to an internal index.

Heaps

Heaps are data structures that efficiently maintain the minimum (or maximum) for a set of data that may dynamically change.

All heaps in this package are derived from AbstractHeap, and provides the following interface:

let h be a heap, i be a handle, and v be a value.

- length(h)         # returns the number of elements

- isempty(h)        # returns whether the heap is empty

- push!(h, v)       # add a value to the heap

- top(h)            # return the top value of a heap

- pop!(h)           # removes the top value, and returns it

Mutable heaps (values can be changed after being pushed to a heap) are derived from AbstractMutableHeap <: AbstractHeap, and additionally provides the following interface:

- i = push!(h, v)       # adds a value to the heap and and returns a handle to v
                    
- update!(h, i, v)      # updates the value of an element (referred to by the handle i)

Currently, both min/max versions of binary heap (type BinaryHeap) and mutable binary heap (type MutableBinaryHeap) have been implemented.

Examples of constructing a heap:

h = binary_minheap(Int)            
h = binary_maxheap(Int)            # create an empty min/max binary heap of integers

h = binary_minheap([1,4,3,2])      
h = binary_maxheap([1,4,3,2])      # create a min/max heap from a vector

h = mutable_binary_minheap(Int)    
h = mutable_binary_maxheap(Int)    # create an empty mutable min/max heap

h = mutable_binary_minheap([1,4,3,2])    
h = mutable_binary_maxheap([1,4,3,2])    # create a mutable min/max heap from a vector

OrderedDicts and OrderedSets

OrderedDicts are simply dictionaries whose entries have a particular order. For OrderedDicts (and OrderedSets), order refers to insertion order, which allows deterministic iteration over the dictionary or set.

d = OrderedDict(Char,Int)
for c in 'a':'e'
    d[c] = c-'a'+1
end
collect(d) # => [('a',1),('b',2),('c',3),('d',4),('e',5)]

s = OrderedSet(π,e,γ,catalan,φ)
collect(s) # => [π = 3.1415926535897...,
           #     e = 2.7182818284590...,
           #     γ = 0.5772156649015...,
		   #     catalan = 0.9159655941772...,
		   #	 φ = 1.6180339887498...]

All standard Associative and Dict functions are available for OrderedDicts, and all Set operations are available for OrderedSets.

Note that to create an OrderedSet of a particular type, you must specify the type in curly-braces:

# create an OrderedSet of Strings
strs = OrderedSet{String}()

DefaultDict and DefaultOrderedDict

A DefaultDict allows specification of a default value to return when a requested key is not in a dictionary.

While the implementation is slightly different, a DefaultDict can be thought to provide a normal Dict with a default value. A DefaultOrderedDict does the same for an OrderedDict.

Constructors:

DefaultDict(default, kv)                        # create a DefaultDict with a default value or function,
                                                # optionally wrapping an existing dictionary
										        # or array of key-value pairs

DefaultDict(KeyType, ValueType, default)        # create a DefaultDict with Dict type (KeyType,ValueType)

DefaultOrderedDict(default, kv)                 # create a DefaultOrderedDict with a default value or function,
                                                # optionally wrapping an existing dictionary
							  	                # or array of key-value pairs

DefaultOrderedDict(KeyType, ValueType, default) # create a DefaultOrderedDict with Dict type (KeyType,ValueType)

Examples using DefaultDict:

dd = DefaultDict(1)               # create an (Any=>Any) DefaultDict with a default value of 1
dd = DefaultDict(String, Int, 0)  # create a (String=>Int) DefaultDict with a default value of 0

d = ['a'=>1, 'b'=>2]
dd = DefaultDict(0, d)            # provide a default value to an existing dictionary
dd['c'] == 0                      # true
#d['c'] == 0                      # false

dd = DefaultOrderedDict(time)     # call time() to provide the default value for an OrderedDict
dd = DefaultDict(Dict)            # Create a dictionary of dictionaries
                                  # Dict() is called to provide the default value
dd = DefaultDict(()->myfunc())    # call function myfunc to provide the default value

# create a Dictionary of type String=>DefaultDict{String, Int}, where the default of the
# inner set of DefaultDicts is zero
dd = DefaultDict(String, DefaultDict, ()->DefaultDict(String,Int,0))

Note that in the last example, we need to use a function to create each new DefaultDict. If we forget, we will end up using the same DefaultDict for all default values:

julia> dd = DefaultDict(String, DefaultDict, DefaultDict(String,Int,0));

julia> dd["a"]
DefaultDict{String,Int64,Int64,Dict{K,V}}()

julia> dd["b"]["a"] = 1
1

julia> dd["a"]
["a"=>1]

##Trie

An implementation of the Trie data structure. This is an associative structure, with String keys.

t=Trie{Int}()
t["Rob"]=42
t["Roger"]=24
t.haskey("Rob") #true
t.get("Rob") #42