AlgorithmADTs 0.1.4

General purpose Abstract Data Types for Algorithmics

Algorithm Abstract Data Types

Finlay's package for Abstract Data Types written for Algorithmics class

Installation

Run the following command in your terminal: pip install AlgorithmADTs

AlgorithmADTs can now be imported into your python scripts!

I recommend from AlgorithmADTs import * to include all functionality, but you can also import from AlgorithmADTs.AbstractDataTypes or AlgorithmADTs.GraphAlgorithms

ADTS:

Array
    create: Integer -> Array
    set: Array x Integer x Element -> Array
    get: Array x Integer -> Element

List 
    create: None -> List
    is_empty: Array -> Boolean
    set: Array x Integer x Element -> List
    get: Array x Integer -> Element
    append: Array x Element -> List

Stack
    create: None -> Stack
    push: Stack x Element -> Stack
    pop: Stack -> Stack
    is_empty: Stack -> Boolean
    head: Stack -> Element

Queue
    create: None -> Queue
    enqueue: Queue x Element -> Queue
    dequeue: Queue -> Queue
    is_empty: Queue -> Boolean
    head: Queue -> Element

PriorityQueue
    create: None -> Priority Queue
    enqueue: Priority Queue x Element x Integer -> Priority Queue
    dequeue: Priority Queue -> Priority Queue
    is_empty: Priority Queue -> Boolean
    head: Priority Queue -> Element

Dictionary
    create: None -> Dictionary 
    get: Dictionary x Element -> Element
    set: Dictionary x Element x Element -> Dictionary 
    add: Dictionary x Element x Element -> Dictionary
    remove: Dictionary x Element -> Dictionary 
    has_key: Dictionary x Element -> Boolean
    is_empty: Dictionary -> Boolean

Graph
    create: None -> Graph
    add_node: Graph x Element -> Graph
    add_edge: Graph x Element x Element -> Graph
    adjacent: Graph x Element x Element -> Boolean
    neighbours: Graph x Element -> List

Multiple nodes and edges can now be added at one time with add_nodes and add_edges, using an iterable

WeightedGraph (inherits from Graph)
    create: None -> Graph
    add_node: Graph x Element -> Graph
    add_edge: Graph x Element x Element -> Graph
    adjacent: Graph x Element x Element -> Boolean
    neighbours: Graph x Element -> List
    get_weight: Graph x Element x Element -> integer

Note that there is no restriction in these classes that elements be hashable, unlike some Python data types e.g. a Python dict requires keys to be hashable.

It also defines a variable infinity, set equal to float('inf')

The following magic methods are supported:

• __getitem__ and __setitem__ for classes with a 'get' and 'set' function. This allows you to call instance[key] and instance[key] = value.
• __iter__ for Array and List, which operates as expected. Dictionary iter returns an iterable of keys. This enables iterating through a class like for elem in instance
• __str__ and __repr__ are defined for all classes except graphs and allow for classes to be easily viewed through printing Note that only the head element is visible for a stack or queue, so it is the only information that can be returned by these methods
• Numerical magic methods (e.g. __add__) are defined for matrices

Graph Algorithms

Currently, the following graph algorithms are defined:

• Prim's algorithm for computing the Minimal Spanning Tree of a weighted, undirected graph
• Dijkstra's algorithm for finding the single source shortest path in a weighted graph
• The Bellman-Ford algorithm which extends the functionality of Dijkstra's algorithm to allow for negative weights
• The two variants of the Floyd-Warshall algorithm to calculate shortest path between all nodes and transitive closure of an unweighted graph
• The PageRank algorithm for determining the relative importance of nodes in an unweighted graph

Version things

To implement:

• Optional hashing for graphs?
• Search methods like DPS BFS