datastructures

datastructures

A collection of data structures in Ruby, made for my data structures challenge

Installation

gem install datastructures

Day 1: Queue

A queue is a simple container-based structure that mimics a real-life queue (e.g. waiting in line at the bank). It is FIFO (first-in-first-out), meaning that when you retrieve items from the queue, they are returned in the order in which they entered. Ruby Arrays provide methods that make Queue implementation trivially easy, but having them named appropriately and contained in a convenience class is worth it to see how they are implemented, and because other structures will inherit from this one. An alternate implementation could be done using a linked list.

Usage:

require 'datastructures'
queue = DataStructures::Queue.new
queue.enqueue('first')
queue.enqueue('second')
queue.size # => 2
queue.empty? # => false
queue.front # => 'first'
queue.back # => 'second'
queue.dequeue # => 'first'
queue.dequeue # => 'second'
queue.dequeue # => RuntimeError, "Queue underflow: nothing to dequeue"

Day 2: Stack

The stack is the sibling of the queue. It mimicks a real-life stack (e.g. of paper). It is FILO (first-in-last-out), so that when items are retrieved from the stack, they are returned in the reverse of the order in which they were added. Again, Ruby Arrays provide a perfect container. As with the Queue, it could also be implemented using a linked list.

Usage:

require 'datastructures'
stack = DataStructures::Stack.new
stack.push('first')
stack.push('second')
stack.size # => 2
stack.empty? # => false
stack.top # => 'second'
stack.bottom # => 'first'
stack.pop # => 'second'
stack.pop # => 'first'
stack.pop # => RuntimeError, "Stack underflow: nothing to pop"

Day 3: Tree

A tree is a directed graph where any two nodes are connected by only one edge, and there is a specified 'root' node, away from which all edges are directed. For any edge, the node that it points from is the parent, and the node it points to is the child. This implementation is currently very crude, and I hope to expand it by:

• separating the node and tree classes
• including search and shortest path algorithms
• include more traversal algorithms

Currently the implementation offers the ability to construct a tree and traverse it manually using family methods (parent, child, siblings, descendents). Nodes can have associated data.

require 'datastructures'

# start with a root TreeNode
tree = DataStructures::TreeNode.new('root node')
tree.is_root? # => true
tree.is_leaf? # => true

# children are also TreeNodes
child1 = DataStructures::TreeNode.new('child')
tree.add_child(child1)
tree.child_count # => 1
tree.add_child(DataStructures::TreeNode.new('another child'))
tree.child_count # => 2
tree.is_root? # => true
tree.is_leaf? # => false
child1.is_leaf? # => true

# we can traverse by querying the family
tree.siblings # => []
child1.siblings.map{ |sibling| sibling.data } # => ["another child"]

child1.parent == tree # => true
child1.parent == child.siblings.first.parent # => true

tree.descendents.map { |d| d.data } # => ["child", "another child"]

Day 4: Linked List

A linked list is a group of items which are ordered, and where the ordering is determined solely by the information each item contains about its neighbours.

This implementation is a doubly-linked list, which means that each item retains a reference to the next and previous items in the list.

The advantage of a linked list over a traditional array is that elements can be inserted and deleted efficiently.

ll = DataStructures::LinkedList.new('one')
ll.first.data # => 'one'
ll[0] # => 'one'
ll << 'two' # => ["one", "two"]
ll.size # => 2
ll.length # => 2
ll.last.data # => 'two'
ll[ll.size - 1] # => 'two'

# linked lists can act as stacks
ll.push 'three' # => ["one", "two", "three"]
ll.size # => 3
ll.pop # => "three"
ll.size # => 2
puts ll # => ["one", "two"]

# or as queues
ll.push 'three' # => ["one", "two", "three"]
ll.shift # => 'one'
ll.size # => 2
ll.first.data 'two'

# we can insert and delete
ll.insert(1, 'one point five') # => ["two", "one point five", "three"]
ll.delete(1)
ll.to_s # => ['two', 'three']

Day 5: Adjacency List

An adjacency list is a structure for representing a graph. Nodes are named; the name can be any hashable object. Data can be stored in nodes, but not edges. The graph can be traversed manually by querying the neighbours of a node.

The implementation is currently basic. I plan to improve it by:

• implementing depth- and breadth-first search
• implementing Dijkstra's algorithm for finding the shortest path between two nodes
• visualise the graph (maybe d3.js)

require 'datastructures'

al = DataStructures::AdjacencyList.new

al.add('one', 1, [2])
al.add('two', 2, [3])
al.add('three', 3, [1])
al.add('four', 4, [2])

al.get_node_value(1) # => 'one'
al.set_node_value(1, 'new value')

al.neighbours(1) # => [2]
al.adjacent?(2, 3) # => true
al.adjacent?(1, 4) # => false

al.add_edge(1, 4)
al.adjacent?(1, 4) # => true

al.delete_edge(2, 3)
al.adjacent?(2, 3) # => false

al.to_s
# 1 (new value) => [2, 4]
# 2 (two) => []
# 3 (three) => [1]
# 4 (four) => [2]