Algorithm alternatives and similar libraries
Based on the "Data Structures / Algorithms" category.
Alternatively, view Algorithm alternatives based on common mentions on social networks and blogs.

KeyPathKit
KeyPathKit is a library that provides the standard functions to manipulate data along with a callsyntax that relies on typed keypaths to make the call sites as short and clean as possible. 
BinaryKit
💾🔍🧮 BinaryKit helps you to break down binary data into bits and bytes, easily access specific parts and write data to binary. 
RandMyMod
RandMyMod base on your own struct or class create one or a set of instance, which the variable's value in the instance is automatic randomized. 
OneWaySynchronizer
The simplest abstraction to synchronize local data with remote source. For iOS, wirtten in swift.
InfluxDB  Power RealTime Data Analytics at Scale
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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README
Welcome to Algorithm
Algorithm is a library of tools that is used to create intelligent applications.
Features
 [x] Probability Tools
 [x] Expected Value
 [x] Programmable Probability Blocks
 [x] Array Extensions
 [x] Set Extensions
Data Structures
 [x] DoublyLinkedList
 [x] Stack
 [x] Queue
 [x] Deque
 [x] RedBlackTree
 [x] SortedSet
 [x] SortedMultiSet
 [x] SortedDictionary
 [x] SortedMultiDictionary
Requirements
 iOS 8.0+ / Mac OS X 10.9+
 Xcode 8.0+
Communication
 If you need help, use Stack Overflow. (Tag 'cosmicmind')
 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.
Installation
Embedded frameworks require a minimum deployment target of iOS 8.
CocoaPods
CocoaPods is a dependency manager for Cocoa projects. You can install it with the following command:
$ gem install cocoapods
To integrate Algorithm's core features into your Xcode project using CocoaPods, specify it in your Podfile
:
source 'https://github.com/CocoaPods/Specs.git'
platform :ios, '8.0'
use_frameworks!
pod 'Algorithm', '~> 3.1.0'
Then, run the following command:
$ pod install
Carthage
Carthage is a decentralized dependency manager that builds your dependencies and provides you with binary frameworks.
You can install Carthage with Homebrew using the following command:
$ brew update
$ brew install carthage
To integrate Algorithm into your Xcode project using Carthage, specify it in your Cartfile:
github "CosmicMind/Algorithm"
Run carthage update
to build the framework and drag the built Algorithm.framework
into your Xcode project.
Changelog
Algorithm is a growing project and will encounter changes throughout its development. It is recommended that the Changelog be reviewed prior to updating versions.
Samples
The following are samples to see how Algorithm may be used within your applications.
Visit the Samples repo to see example projects using Algorithm.
Probability
Each data structure within Algorithm is equipped with probability tools.
Basic Probability
For example, determining the probability of rolling a 3 using a die of 6 numbers.
let die = [Int](arrayLiteral: 1, 2, 3, 4, 5, 6)
if 0.1 < die.probability(of: 3)
// Do something ...
}
Conditional Probability
For conditional probabilities that require a more complex calculation, use block statements.
let die = [Int](arrayLiteral: 1, 2, 3, 4, 5, 6)
let pOfX = die.probability { (number) in
return 5 < number  0 == number % 3
}
if 0.33 < pOfX {
// Do something ...
}
Expected Value
The expected value of rolling a 3 or 6 with 100 trials using a die of 6 numbers.
let die = [Int](arrayLiteral: 1, 2, 3, 4, 5, 6)
if 20 < die.expectedValue(trials: 100, for: 3, 6) {
// Do something ...
}
DoublyLinkedList
The DoublyLinkedList data structure is excellent for large growing collections of data. Below is an example of its usage.
var listA = DoublyLinkedList<Int>()
listA.insert(atFront: 3)
listA.insert(atFront: 2)
listA.insert(atFront: 1)
var listB = DoublyLinkedList<Int>()
listB.insert(atBack: 4)
listB.insert(atBack: 5)
listB.insert(atBack: 6)
var listC = listA + listB
listC.cursorToFront()
var value = listC.cursor
while nil != value {
// Do something ...
value = listC.next()
}
Stack
The Stack data structure is a container of objects that are inserted and removed according to the lastinfirstout (LIFO) principle. Below is an example of its usage.
var stack = Stack<Int>()
stack.push(1)
stack.push(2)
stack.push(3)
while !stack.isEmpty {
let value = stack.pop()
// Do something ...
}
Queue
The Queue data structure is a container of objects that are inserted and removed according to the firstinfirstout (FIFO) principle. Below is an example of its usage.
var queue = Queue<Int>()
queue.enqueue(1)
queue.enqueue(2)
queue.enqueue(3)
while !queue.isEmpty {
let value = queue.dequeue()
// Do something ...
}
Deque
The Deque data structure is a container of objects that are inserted and removed according to the firstinfirstout (FIFO) and lastinfirstout (LIFO) principle. Essentially, a Deque is a Stack and Queue combined. Below are examples of its usage.
var dequeA = Deque<Int>()
dequeA.insert(atBack: 1)
dequeA.insert(atBack: 2)
dequeA.insert(atBack: 3)
while !dequeA.isEmpty {
let value = dequeA.removeAtFront()
// Do something ...
}
var dequeB = Deque<Int>()
dequeB.insert(atBack: 4)
dequeB.insert(atBack: 5)
dequeB.insert(atBack: 6)
while !dequeB.isEmpty {
let value = dequeB.removeAtFront()
// Do something ...
}
RedBlackTree
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 orderstatistic, which allows the data structure to be accessed using subscripts like an array or dictionary. RedBlackTrees may store unique keys or nonunique key values. Below is an example of its usage.
var ages = RedBlackTree<String, Int>(uniqueKeys: true)
ages.insert(value: 16, for: "Sarah")
ages.insert(value: 12, for: "Peter")
ages.insert(value: 23, for: "Alex")
let node = ages[1]
if "Peter" == node.key {
// Do something ...
}
SortedSet
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 orderstatistic, 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>(elements: 1, 2, 3)
let setB = SortedSet<Int>(elements: 4, 3, 6)
let setC = SortedSet<Int>(elements: 7, 1, 2)
let setD = SortedSet<Int>(elements: 1, 7)
let setE = SortedSet<Int>(elements: 1, 6, 7)
// Union.
setA + setB
setA.union(setB)
// Intersection.
setC.intersection(setD)
// Subset.
setD < setC
setD.isSubset(of: setC)
// Superset.
setD > setC
setD.isSuperset(of: setC)
// Contains.
setE.contains(setA.first!)
// Probability.
setE.probability(of: setA.first!, setA.last!)
SortedMultiSet
A SortedMultiSet is identical to a SortedSet, except that a SortedMultiSet allows nonunique elements. Look at SortedSet for examples of its usage.
SortedDictionary
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).
SortedMultiDictionary
A SortedMultiDictionary is identical to a SortedDictionary, except that a SortedMultiDictionary allows nonunique keys. Below is an example of its usage.
struct Student {
var name: String
}
let sarah = Student(name: "Sarah")
let peter = Student(name: "Peter")
let alex = Student(name: "Alex")
var students = SortedMultiDictionary<String, Student>()
students.insert(value: sarah, for: sarah.name)
students.insert(value: peter, for: peter.name)
students.insert(value: alex, for: alex.name)
for student in students {
// Do something ...
}
License
The MIT License (MIT)
Copyright (C) 2019, CosmicMind, Inc. http://cosmicmind.com. All rights reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*Note that all licence references and agreements mentioned in the Algorithm README section above
are relevant to that project's source code only.