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PyAlgDat 1.0.2

ABOUT PYALGDAT
FEATURES
REQUIREMENTS
INSTALLATION
EXAMPLES
DOCUMENTATION
LICENSE
AUTHOR
CHANGELOG

ABOUT PYALGDAT
PyAlgDat is a collection of data structures and algorithms written in Python.
The purpose of the code is to show how many of the abstract data types (ADTs) and
algorithms being thought in Computer Science courses can be realised in Python.
My primary focus has been to write a library which presents a clear
implementation of the various data structures and algorithms and how they can
be used. This means that I have made a conscious tradeoff where clarity of the
code outweighs subtle and exotic implementation constructs.
The library has mostly been implemented as a recreational project and should
as such not be used in production code, since most of the data structures and
algorithms are already available in the standard Python library. However,
writing software that is robust, performs well, and is easy to maintain requires
knowledge of data structures and algorithms. Therefore, implementing and
experimenting with these provides valuable knowledge about the inner workings
and implementation details found in such standard libraries.

FEATURES
Data structures included in the library

Dynamic array
Stack
Queue
BinaryHeap

MinHeap
MaxHeap

LinkedList

Singly linked list
Doubly linked list

Partition/Union-Find

Graph

Directed
Undirected
Directed weighted
Undirected weighted

Additionally, the library contains the most common algorithms and operations
needed when working with these data structures.

REQUIREMENTS
The library is selfcontained and does not have any external dependencies.
PyAlgDat should run on any platform with Python 2.7 or above.

INSTALLATION
The package can be installed using pip
$ pip install PyAlgDat

EXAMPLES
PyAlgDat has a collection of functional test examples which shows how the
library can be used from a client’s perspective.

Shortest path using Dijkstra’s algorithm
Below is a simple example showing howto create a directed weighted graph
using PyAlgDat and how the shortest path in this graph can be found using
Dijkstra’s algorithm.
#!/usr/bin/env python

"""
Test of Dijkstra's algorithm for a Directed Weighted Graph.
"""

def create_graph():
"""
Creates a Directed Weighted Graph
"""
# Create an empty directed weighted graph
graph = DirectedWeightedGraph(7)

# Create vertices
vertex0 = UnWeightedGraphVertex(graph, "A")
vertex1 = UnWeightedGraphVertex(graph, "B")
vertex2 = UnWeightedGraphVertex(graph, "C")
vertex3 = UnWeightedGraphVertex(graph, "D")
vertex4 = UnWeightedGraphVertex(graph, "E")
vertex5 = UnWeightedGraphVertex(graph, "F")
vertex6 = UnWeightedGraphVertex(graph, "G")

# Add vertices
graph.add_vertex(vertex0)
graph.add_vertex(vertex1)
graph.add_vertex(vertex2)
graph.add_vertex(vertex3)
graph.add_vertex(vertex4)
graph.add_vertex(vertex5)
graph.add_vertex(vertex6)

# Add edges
graph.add_edge(vertex0, vertex1, 7) # ( A <- B, 7 )
graph.add_edge(vertex1, vertex2, 2) # ( B <- C, 2 )
graph.add_edge(vertex1, vertex6, 3) # ( B -> G, 3 )
graph.add_edge(vertex2, vertex3, 2) # ( C -> D, 2 )
graph.add_edge(vertex2, vertex6, 4) # ( C -> G, 4 )
graph.add_edge(vertex3, vertex4, 5) # ( D -> E, 5 )
graph.add_edge(vertex3, vertex6, 1) # ( D -> G, 1 )
graph.add_edge(vertex4, vertex5, 6) # ( E -> F, 6 )
graph.add_edge(vertex5, vertex0, 1) # ( F <- A, 1 )
graph.add_edge(vertex5, vertex6, 4) # ( F <- G, 4 )
graph.add_edge(vertex6, vertex0, 7) # ( G -> A, 7 )
graph.add_edge(vertex6, vertex4, 1) # ( G -> E, 1 )

# B--<--7--<--A
# / \ / \
# / \ / \
# 2 3 7 1
# / \ / \
# / \ / \
# C-->--4-->--G--<--4--<--F
# \ / \ /
# \ / \ /
# 2 1 1 6
# \ / \ /
# \ / \ /
# D-->--5-->--E

return graph

if __name__ == "__main__":

# Make it possible to use py_alg_dat without performing
# an installation. This is needed in order to be able
# to run: python dijkstra_test.py, without having
# performed an installation of the package. The is
# neccessary due to Python's handling of relative
# imports.
if __package__ is None:
import sys
from os import path
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
from py_alg_dat.graph import DirectedWeightedGraph
from py_alg_dat.graph_vertex import UnWeightedGraphVertex
from py_alg_dat.graph_algorithms import GraphAlgorithms
else:
from ..py_alg_dat.graph import DirectedWeightedGraph
from ..py_alg_dat.graph_vertex import UnWeightedGraphVertex
from ..py_alg_dat.graph_algorithms import GraphAlgorithms

# Create the graph
GRAPH = create_graph()
# Run Dijkstra starting at vertex "A"
TABLE = GraphAlgorithms.dijkstras_algorithm(GRAPH, GRAPH[0])
# Find the edges in the Spanning Tree and its total weight
SPANNING_TREE_EDGES = set()
SPANNING_TREE_WEIGHT = 0
for i in xrange(len(TABLE)):
entry = TABLE[i]
if entry.predecessor != None:
edge = entry.edge
SPANNING_TREE_EDGES.add(edge)
SPANNING_TREE_WEIGHT += edge.get_weight()
print "Edges in Spanning Tree: " + str(SPANNING_TREE_EDGES)
print "Weight of Spanning Tree: " + str(SPANNING_TREE_WEIGHT)

Minimum spanning tree using Kruskal’s algorithm
Below is a simple example showing howto create an un-directed weighted graph
using PyAlgDat and how the minimum spanning tree of this graph can be found
using Kruskal’s algorithm.
#!/usr/bin/env python

"""
Test of Kruskal's algorithm for a UnDirected Weighted Graph.
"""

def create_graph():
"""
Creates an UnDirected Weighted Graph
"""
# Create an empty undirected weighted graph
graph = UnDirectedWeightedGraph(7)

# Create vertices
vertex1 = UnWeightedGraphVertex(graph, "A")
vertex2 = UnWeightedGraphVertex(graph, "B")
vertex3 = UnWeightedGraphVertex(graph, "C")
vertex4 = UnWeightedGraphVertex(graph, "D")
vertex5 = UnWeightedGraphVertex(graph, "E")
vertex6 = UnWeightedGraphVertex(graph, "F")
vertex7 = UnWeightedGraphVertex(graph, "G")

# Add vertices
graph.add_vertex(vertex1)
graph.add_vertex(vertex2)
graph.add_vertex(vertex3)
graph.add_vertex(vertex4)
graph.add_vertex(vertex5)
graph.add_vertex(vertex6)
graph.add_vertex(vertex7)

# Add edges
graph.add_edge(vertex1, vertex2, 7) # (A - B, 7)
graph.add_edge(vertex1, vertex4, 5) # (A - D, 5)
graph.add_edge(vertex2, vertex3, 8) # (B - C, 8)
graph.add_edge(vertex2, vertex4, 9) # (B - D, 9)
graph.add_edge(vertex2, vertex5, 7) # (B - E, 7)
graph.add_edge(vertex3, vertex5, 5) # (C - E, 5)
graph.add_edge(vertex4, vertex5, 15) # (D - E, 1)
graph.add_edge(vertex4, vertex6, 6) # (D - F, 6)
graph.add_edge(vertex5, vertex6, 8) # (E - F, 8)
graph.add_edge(vertex5, vertex7, 9) # (E - G, 9)
graph.add_edge(vertex6, vertex7, 11) # (F - G, 11)
return graph

if __name__ == "__main__":
# Make it possible to use py_alg_dat without performing
# an installation. This is needed in order to be able
# to run: python kruskal_test.py, without having
# performed an installation of the package. The is
# neccessary due to Python's handling of relative
# imports.
if __package__ is None:
import sys
from os import path
sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
from py_alg_dat.graph import UnDirectedWeightedGraph
from py_alg_dat.graph_vertex import UnWeightedGraphVertex
from py_alg_dat.graph_algorithms import GraphAlgorithms
else:
from ..py_alg_dat.graph import UnDirectedWeightedGraph
from ..py_alg_dat.graph_vertex import UnWeightedGraphVertex
from ..py_alg_dat.graph_algorithms import GraphAlgorithms

# Create the graph
GRAPH = create_graph()
# Run Kruskal's algorithm
MST = GraphAlgorithms.kruskals_algorithm(GRAPH)
print MST
The above examples -and others can be found in the ‘examples’ folder in
the PyAlgDat directory.

DOCUMENTATION
The PyAlgDat API contains Docstrings for all classes and methods. Additional
documentation about the library can be found in the ‘docs’ folder in the
PyAlgDat directory.
The full documentation is at http://pyalgdat.readthedocs.org/en/latest/.

LICENSE
PyAlgDat is published under the MIT License. The copyright and license are
specified in the file “LICENSE.txt” in the PyAlgDat directory and shown
below.

AUTHOR
Brian Horn, trycatchhorn@gmail.com

CHANGELOG

1.0.2 (2016-09-01)

Added Bellman-Ford graph algorithm
Made unit tests part of the distribution

1.0.1 (2015-07-19)

First release on PyPi
Fixed a problem in singly linked list when inserting an element at a specific index, causing the reference to the tail element to be wrong.
Fixed a problem in singly linked list when removing the last element.
Implemented functionality to remove a vertex from a graph.
Implemented functionality to remove an edge from a graph.
Implemented shallow copy functionality in array_list.py.
Implemented support for slicing in array_list.py.
Implemented equal -and not equal operators in class UnDirectedGraph.
Implemented equal -and not equal operators in singly_linked_list.py.
Implemented equal -and not equal operators in doubly_linked_list.py.
Implemented shallow copy functionality in singly_linked_list.py.
Implemented shallow copy functionality in doubly_linked_list.py.
Library has now changed name to PyAlgDat.
Removed attribute ‘numberOfVertices’ in graph.py.
Removed attribute ‘numberOfEdges’ in graph.py.
Renamed attribute ‘vertexName’ in graph_vertex.py to ‘vertex_name’
Renamed attribute ‘vertexNumber’ in graph_vertex.py to ‘vertex_number’
Renamed attribute ‘vertexWeight’ in graph_vertex.py to ‘vertex_weight’
PyAlgDat is licensed under The MIT License (MIT)
Made PyAlgDat pylint compliant
Added content to README.rst
Added examples folder containing test programs showing API usage

1.0.0 (2014-02-09)

First public release