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Explore a world of data structures in Swift. Learn how to use data structures to organize, analyze, and manipulate data.

• iOS 8.0+ / Mac OS X 10.9+
• Xcode 7.2+

• If you need help, use Stack Overflow. (Tag 'algorithm')
• If you'd like to ask a general question, use Stack Overflow.
• If you found a bug, and can provide steps to reliably reproduce it, open an issue.
• If you have a feature request, open an issue.
• If you want to contribute, submit a pull request.

Embedded frameworks require a minimum deployment target of iOS 8 or OS X Mavericks (10.9).

• Download Algorithm

Visit the Installation page to learn how to install Algorithm using CocoaPods and Carthage.

Algorithm is a growing project and will encounter changes throughout its development. It is recommended that the Changelog be reviewed prior to updating versions.

• Probability
• DoublyLinkedList
• Stack
• Queue
• Deque
• RedBlackTree
• SortedSet
• SortedMultiSet
• SortedDictionary
• SortedMultiDictionary

Probability is a core feature. Your application may be completely catered to your users' habits and usage. To demonstrate this wonderful feature, let's look at some examples:

Determining the probability of rolling a 3 using a die of 6 numbers.

let die: Array<Int> = Array<Int>(arrayLiteral: 1, 2, 3, 4, 5, 6)
print(die.probabilityOf(3)) // Output: 0.166666666666667

The expected value of rolling a 3 or 6 with 100 trials using a die of 6 numbers.

let die: Array<Int> = Array<Int>(arrayLiteral: 1, 2, 3, 4, 5, 6)
print(die.expectedValueOf(100, elements: 3, 6)) // Output: 33.3333333333333

The above examples are quite simple. They use basic calculations to determine the probability of an outcome (X). What if you have a complicated condition? To solve this problem, it is possible to pass in blocks that return a boolean value. Each block may execute any operations it pleases, so long as it returns a "true" or "false". The "true" values contribute to the likelihood of an outcome, "false" results do not. Below is an example to demonstrate this feature.

let die: Array<Int> = Array<Int>(arrayLiteral: 1, 2, 3, 4, 5, 6)

let probabilityOfX: Double = die.probabilityOf { (number: Int) in
	if 5 < number || 0 == number % 3 {
		// Do more.
		return true
	}
	return false
}

if 0.33 < probabilityOfX {
	// Do something.
}

The DoublyLinkedList data structure is excellent for large growing collections of data. Below is an example of its usage.

let listA: DoublyLinkedList<Int> = DoublyLinkedList<Int>()
listA.insertAtFront(3)
listA.insertAtFront(2)
listA.insertAtFront(1)

let listB: DoublyLinkedList<Int> = DoublyLinkedList<Int>()
listB.insertAtBack(4)
listB.insertAtBack(5)
listB.insertAtBack(6)

let listC: DoublyLinkedList<Int> = listA + listB

listC.cursorToFront()
repeat {
	print(listC.cursor)
} while nil != listC.next
// Output:
// 1
// 2
// 3
// 4
// 5
// 6

The Stack data structure is a container of objects that are inserted and removed according to the last-in-first-out (LIFO) principle. Below is an example of its usage.

let stack: Stack<Int> = Stack<Int>()
stack.push(1)
stack.push(2)
stack.push(3)

while !stack.isEmpty {
	print(stack.pop())
}
// Output:
// 3
// 2
// 1

The Queue data structure is a container of objects that are inserted and removed according to the first-in-first-out (FIFO) principle. Below is an example of its usage.

let queue: Queue<Int> = Queue<Int>()
queue.enqueue(1)
queue.enqueue(2)
queue.enqueue(3)

while !queue.isEmpty {
	print(queue.dequeue())
}
// Output:
// 1
// 2
// 3

The Deque data structure is a container of objects that are inserted and removed according to the first-in-first-out (FIFO) and last-in-first-out (LIFO) principle. Essentially, a Deque is a Stack and Queue combined. Below are examples of its usage.

let dequeA: Deque<Int> = Deque<Int>()
dequeA.insertAtBack(1)
dequeA.insertAtBack(2)
dequeA.insertAtBack(3)

while !dequeA.isEmpty {
	print(dequeA.removeAtFront())
}
// Output:
// 1
// 2
// 3

let dequeB: Deque<Int> = Deque<Int>()
dequeB.insertAtBack(4)
dequeB.insertAtBack(5)
dequeB.insertAtBack(6)

while !dequeB.isEmpty {
	print(dequeB.removeAtBack())
}
// Output:
// 6
// 5
// 4

A RedBlackTree is a Balanced Binary Search Tree that maintains insert, remove, update, and search operations in a complexity of O(logn). The following implementation of a RedBlackTree also includes an order-statistic, which allows the data structure to be accessed using subscripts like an array or dictionary. RedBlackTrees may store unique keys or non-unique key values. Below is an example of its usage.

let rbA: RedBlackTree<Int, Int> = RedBlackTree<Int, Int>(uniqueKeys: true)

for var i: Int = 1000; 0 < i; --i {
	rbA.insert(1, value: 1)
	rbA.insert(2, value: 2)
	rbA.insert(3, value: 3)
}
print(rbA.count) // Output: 3

SortedSets are a powerful data structure for algorithm and analysis design. Elements within a SortedSet are unique and insert, remove, and search operations have a complexity of O(logn). The following implementation of a SortedSet also includes an order-statistic, which allows the data structure to be accessed using an index subscript like an array. Below are examples of its usage.

let setA: SortedSet<Int> = SortedSet<Int>(elements: 1, 2, 3) // Sorted: [1, 2, 3]
let setB: SortedSet<Int> = SortedSet<Int>(elements: 4, 3, 6) // Sorted: [3, 4, 6]

let setC: SortedSet<Int> = SortedSet<Int>(elements: 7, 1, 2) // Sorted: [1, 2, 7]
let setD: SortedSet<Int> = SortedSet<Int>(elements: 1, 7) // Sorted: [1, 7]

let setE: SortedSet<Int> = SortedSet<Int>(elements: 1, 6, 7) // Sorted: [1, 6, 7]

// Union.
print((setA + setB).count) // Output: 5
print(setA.union(setB).count) // Output: 5

// Intersect.
print(setC.intersect(setD).count) // Output: 2

// Subset.
print(setD < setC) // true
print(setD.isSubsetOf(setC)) // true

// Superset.
print(setD > setC) // false
print(setD.isSupersetOf(setC)) // false

// Contains.
print(setE.contains(setA.first!)) // true

// Probability.
print(setE.probabilityOf(setA.first!, setA.last!)) // 0.333333333333333

A SortedMultiSet is identical to a SortedSet, except that a SortedMultiSet allows non-unique elements. Look at SortedSet for examples of its usage.

A SortedDictionary is a powerful data structure that maintains a sorted set of keys with value pairs. Keys within a SortedDictionary are unique and insert, remove, update, and search operations have a complexity of O(logn).

A SortedMultiDictionary is identical to a SortedDictionary, except that a SortedMultiDictionary allows non-unique keys. Below is an example of its usage.

struct Student {
    var name: String
}

let dict: SortedMultiDictionary<String, Student> = SortedMultiDictionary<String, Student>()

// Do something with an alphabetically SortedMultiDictionary of Student structs.
for student in students {
	dict.insert(student.name, value: student)
}

Copyright (C) 2015 - 2016, Daniel Dahan and CosmicMind, Inc. http://cosmicmind.io. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

• Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

• Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

• Neither the name of Algorithm nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

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