# 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 provide the following interface:

```
# Let `h` be a heap, `v` be a value, and `n` be an integer size
length(h) # returns the number of elements
isempty(h) # returns whether the heap is empty
push!(h, v) # add a value to the heap
first(h) # return the first (top) value of a heap
pop!(h) # removes the first (top) value, and returns it
extract_all!(h) # removes all elements and returns sorted array
extract_all_rev!(h) # removes all elements and returns reverse sorted array
sizehint!(h, n) # reserve capacity for at least `n` elements
```

Mutable heaps (values can be changed after being pushed to a heap) are derived from `AbstractMutableHeap <: AbstractHeap`

, and additionally provides the following interface:

```
# Let `h` be a heap, `i` be a handle, and `v` be a value.
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)
delete!(h, i) # deletes the node with handle i from the heap
v, i = top_with_handle(h) # returns the top value of a heap and its handle
```

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 = BinaryMinHeap{Int}()
h = BinaryMaxHeap{Int}() # create an empty min/max binary heap of integers
h = BinaryMinHeap([1,4,3,2])
h = BinaryMaxHeap([1,4,3,2]) # create a min/max heap from a vector
h = MutableBinaryMinHeap{Int}()
h = MutableBinaryMaxHeap{Int}() # create an empty mutable min/max heap
h = MutableBinaryMinHeap([1,4,3,2])
h = MutableBinaryMaxHeap([1,4,3,2]) # create a mutable min/max heap from a vector
```

## Using alternate orderings

Heaps can also use alternate orderings apart from the default one defined by `Base.isless`

. This is accomplished by passing an instance of `Base.Ordering`

as the first argument to the constructor. The top of the heap will then be the element that comes first according to this ordering.

The following example uses 2-tuples to track the index of each element in the original array, but sorts only by the data value:

```
data = collect(enumerate(["foo", "bar", "baz"]))
h1 = BinaryHeap(data) # Standard lexicographic ordering for tuples
first(h1) # => (1, "foo")
h2 = BinaryHeap(Base.By(last), data) # Order by 2nd element only
first(h2) # => (2, "bar")
```

If the ordering type is a singleton it can be passed as a type parameter to the constructor instead:

```
BinaryHeap{T, O}() # => BinaryHeap{T}(O())
MutableBinaryHeap{T, O}() # => MutableBinaryHeap{T}(O())
```

## Min-max heaps

Min-max heaps maintain the minimum *and* the maximum of a set, allowing both to be retrieved in constant (`O(1)`

) time. The min-max heaps in this package are subtypes of `AbstractMinMaxHeap <: AbstractHeap`

and have the same interface as other heaps with the following additions:

```
# Let h be a min-max heap, k an integer
minimum(h) # return the smallest element
maximum(h) # return the largest element
popmin!(h) # remove and return the smallest element
popmin!(h, k) # remove and return the smallest k elements
popmax!(h) # remove and return the largest element
popmax!(h, k) # remove and return the largest k elements
popall!(h) # remove and return all the elements, sorted smallest to largest
popall!(h, o) # remove and return all the elements according to ordering o
```

The usual `first(h)`

and `pop!(h)`

are defined to be `minimum(h)`

and `popmin!(h)`

, respectively.

This package includes an implementation of a binary min-max heap (`BinaryMinMaxHeap`

).

Atkinson, M.D., Sack, J., Santoro, N., & Strothotte, T. (1986). Min-Max > Heaps and Generalized Priority Queues. Commun. ACM, 29, 996-1000. doi: 10.1145/6617.6621

Examples:

```
h = BinaryMinMaxHeap{Int}() # create an empty min-max heap with integer values
h = BinaryMinMaxHeap([1, 2, 3, 4]) # create a min-max heap from a vector
```

# Functions using heaps

Heaps can be used to extract the largest or smallest elements of an array without sorting the entire array first:

```
data = [0,21,-12,68,-25,14]
nlargest(3, data) # => [68,21,14]
nsmallest(3, data) # => [-25,-12,0]
```

Both methods also support the `by`

and `lt`

keywords to customize the sort order, as in `Base.sort`

:

```
nlargest(3, data, by=x -> x^2) # => [68,-25,21]
nsmallest(3, data, by=x -> x^2) # => [0,-12,14]
```

The lower-level `DataStructures.nextreme`

function takes a `Base.Ordering`

instance as the first argument and returns the first `n`

elements according to this ordering:

`DataStructures.nextreme(Base.Forward, n, a) # Equivalent to nsmallest(n, a)`

# Improving performance with Float data

One use case for custom orderings is to achieve faster performance with `Float`

elements with the risk of random ordering if any elements are `NaN`

. The provided `DataStructures.FasterForward`

and `DataStructures.FasterReverse`

orderings are optimized for this purpose and may achive a 2x performance boost:

```
h = BinaryHeap{Float64, DataStructures.FasterForward}() # faster min heap
h = BinaryHeap{Float64, DataStructures.FasterReverse}() # faster max heap
h = MutableBinaryHeap{Float64, DataStructures.FasterForward}() # faster mutable min heap
h = MutableBinaryHeap{Float64, DataStructures.FasterReverse}() # faster mutable max heap
DataStructures.nextreme(DataStructures.FasterReverse(), n, a) # faster nlargest(n, a)
DataStructures.nextreme(DataStructures.FasterForward(), n, a) # faster nsmallest(n, a)
```