One of the data structures that I came across was a heap (as in heapsort if you've forgotten). A heap is a binary tree data structure that has the property that the parent is smaller for a min-heap or larger for a max-heap than its children. They can be used for priority queues as the minimum or maximum is always at the top of the heap or for sorting assuming that you a) take the top element and then b) recalculate the tree.
The code here follows Skiena's pretty closely excepting he starts his array at 1 and I use the more natural (for me anyway) 0 start. This code also implements only a min-heap, but you should take a bit of time to see if you can figure out how to make it either a min-heap or a max-heap as an initialization parameter. A couple of other things aren't that pretty (
extract_min!
in particular bugs me), but mostly it's not bad and is straight forward. So ... here's the code
class Heap
def initialize(a)
@q = []
a.each { |v| insert(v) } if a
end
def parent(n)
n == 0 ? -1 : (n-1) / 2
end
def young_child(n)
(2 * n) + 1
end
def insert(v)
@q << v
bubble_up(@q.size-1)
end
def bubble_up(n)
return if parent(n) == -1 # Root of heap, no parent
if @q[parent(n)] > @q[n]
swap(n, parent(n))
bubble_up(parent(n))
end
end
def swap(n, pn)
@q[n], @q[pn] = @q[pn], @q[n]
end
def min
@q.first
end
def extract_min!
m, @q[0] = @q.first, @q.last
@q.pop
bubble_down(0)
m
end
def bubble_down(n)
c = young_child(n)
min_index = n
0.upto(1) { |i| min_index = c+i if ((c+i) <= @q.size-1) &&(@q[min_index] > @q[c+i]) }
if (min_index != n)
swap(n, min_index)
bubble_down(min_index)
end
end
def sort!
a = []
while v = extract_min! do a << v end
a
end
end
h = Heap.new([12, 14, 6, 10, 8, 27, 1, 4, 9])
puts "extract_min! = #{h.extract_min!}"
puts "extract_min! = #{h.extract_min!}"
puts "extract_min! = #{h.extract_min!}"
puts "extract_min! = #{h.extract_min!}"
puts "min = #{h.min}"
puts "min = #{h.min}"
puts "sort! = #{h.sort!}"
Let me know if you have any questions or comments and if you make improvements, post those too.