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Practical Discrete Mathematics

You're reading from   Practical Discrete Mathematics Discover math principles that fuel algorithms for computer science and machine learning with Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781838983147
Length 330 pages
Edition 1st Edition
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Authors (2):
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Ryan T. White Ryan T. White
Author Profile Icon Ryan T. White
Ryan T. White
Archana Tikayat Ray Archana Tikayat Ray
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Archana Tikayat Ray
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Table of Contents (17) Chapters Close

Preface 1. Part I – Basic Concepts of Discrete Math
2. Chapter 1: Key Concepts, Notation, Set Theory, Relations, and Functions FREE CHAPTER 3. Chapter 2: Formal Logic and Constructing Mathematical Proofs 4. Chapter 3: Computing with Base-n Numbers 5. Chapter 4: Combinatorics Using SciPy 6. Chapter 5: Elements of Discrete Probability 7. Part II – Implementing Discrete Mathematics in Data and Computer Science
8. Chapter 6: Computational Algorithms in Linear Algebra 9. Chapter 7: Computational Requirements for Algorithms 10. Chapter 8: Storage and Feature Extraction of Graphs, Trees, and Networks 11. Chapter 9: Searching Data Structures and Finding Shortest Paths 12. Part III – Real-World Applications of Discrete Mathematics
13. Chapter 10: Regression Analysis with NumPy and Scikit-Learn 14. Chapter 11: Web Searches with PageRank 15. Chapter 12: Principal Component Analysis with Scikit-Learn 16. Other Books You May Enjoy

Python Implementation of Dijkstra's Algorithm

We have now learned how Dijkstra's algorithm works, but we will now implement it in Python.

The input to the algorithm will be a network and a source vertex. The simplest way we can represent a network is with a weight matrix like we introduced in Chapter 8, Storage and Feature Extraction of Graphs, Trees, and Networks. For the graph in Figure 9.7, we have the following weight matrix:

Figure 9.17 – A small network and its weight matrix

In the context of a shortest-distance problem, this weight matrix may be called a distance matrix, but we will refrain from using this terminology because, as we have seen in previous sections, these shortest path problems may or may not actually refer to distances.

The output from the algorithm will be a table like the one at the upper right of Figure 9.15, giving the shortest distance from the source vertex to each of the other vertices.

The table in Figure...

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