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40 Algorithms Every Programmer Should Know
40 Algorithms Every Programmer Should Know

40 Algorithms Every Programmer Should Know: Hone your problem-solving skills by learning different algorithms and their implementation in Python

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40 Algorithms Every Programmer Should Know

Overview of Algorithms

This book covers the information needed to understand, classify, select, and implement important algorithms. In addition to explaining their logic, this book also discusses data structures, development environments, and production environments that are suitable for different classes of algorithms. We focus on modern machine learning algorithms that are becoming more and more important. Along with the logic, practical examples of the use of algorithms to solve actual everyday problems are also presented.

This chapter provides an insight into the fundamentals of algorithms. It starts with a section on the basic concepts needed to understand the workings of different algorithms. This section summarizes how people started using algorithms to mathematically formulate a certain class of problems. It also mentions the limitations of different algorithms. The next section explains the various ways to specify the logic of an algorithm. As Python is used in this book to write the algorithms, how to set up the environment to run the examples is explained. Then, the various ways that an algorithm's performance can be quantified and compared against other algorithms are discussed. Finally, this chapter discusses various ways a particular implementation of an algorithm can be validated.

To sum up, this chapter covers the following main points:

  • What is an algorithm?

  • Specifying the logic of an algorithm

  • Introducing Python packages

  • Algorithm design techniques

  • Performance analysis

  • Validating an algorithm

What is an algorithm?

In the simplest terms, an algorithm is a set of rules for carrying out some calculations to solve a problem. It is designed to yield results for any valid input according to precisely defined instructions. If you look up the word algorithm in an English language dictionary (such as American Heritage), it defines the concept as follows:

"An algorithm is a finite set of unambiguous instructions that, given some set of initial conditions, can be performed in a prescribed sequence to achieve a certain goal and that has a recognizable set of end conditions."

Designing an algorithm is an effort to create a mathematical recipe in the most efficient way that can effectively be used to solve a real-world problem. This recipe may be used as the basis for developing a more reusable and generic mathematical solution that can be applied to a wider set of similar problems.

The phases of an algorithm

The different phases of developing, deploying, and finally using an algorithm are illustrated in the following diagram:

As we can see, the process starts with understanding the requirements from the problem statement that detail what needs to be done. Once the problem is clearly stated, it leads us to the development phase.

The development phase consists of two phases:

  • The design phase: In the design phase, the architecture, logic, and implementation details of the algorithm are envisioned and documented. While designing an algorithm, we keep both accuracy and performance in mind. While searching for the solution to a given problem, in many cases we will end up having more than one alternative algorithm. The design phase of an algorithm is an iterative process that involves comparing different candidate algorithms. Some algorithms may provide simple and fast solutions but may compromise on accuracy. Other algorithms may be very accurate but may take considerable time to run due to their complexity. Some of these complex algorithms may be more efficient than others. Before making a choice, all the inherent tradeoffs of the candidate algorithms should be carefully studied. Particularly for a complex problem, designing an efficient algorithm is really important. A correctly designed algorithm will result in an efficient solution that will be capable of providing both satisfactory performance and reasonable accuracy at the same time.

  • The coding phase: In the coding phase, the designed algorithm is converted into a computer program. It is important that the actual program implements all the logic and architecture suggested in the design phase.

The designing and coding phases of an algorithm are iterative in nature. Coming up with a design that meets both functional and non-functional requirements may take lots of time and effort. Functional requirements are those requirements that dictate what the right output for a given set of input data is. Non-functional requirements of an algorithm are mostly about the performance for a given size of data. Validation and performance analysis of an algorithm are discussed later in this chapter. Validating an algorithm is about verifying that an algorithm meets its functional requirements. Performance analysis of an algorithm is about verifying that it meets its main non-functional requirement: performance.

Once designed and implemented in a programming language of your choice, the code of the algorithm is ready to be deployed. Deploying an algorithm involves the design of the actual production environment where the code will run. The production environment needs to be designed according to the data and processing needs of the algorithm. For example, for parallelizable algorithms, a cluster with an appropriate number of computer nodes will be needed for the efficient execution of the algorithm. For data-intensive algorithms, a data ingress pipeline and the strategy to cache and store data may need to be designed. Designing a production environment is discussed in more detail in Chapter 13, Large Scale Algorithms, and Chapter 14, Practical Considerations. Once the production environment is designed and implemented, the algorithm is deployed, which takes the input data, processes it, and generates the output as per the requirements.

Specifying the logic of an algorithm

When designing an algorithm, it is important to find different ways to specify its details. The ability to capture both its logic and architecture is required. Generally, just like building a home, it is important to specify the structure of an algorithm before actually implementing it. For more complex distributed algorithms, pre-planning the way their logic will be distributed across the cluster at running time is important for the iterative efficient design process. Through pseudocode and execution plans, both these needs are fulfilled and are discussed in the next section.

Understanding pseudocode

The simplest way to specify the logic for an algorithm is to write the higher-level description of an algorithm in a semi-structured way, called pseudocode. Before writing the logic in pseudocode, it is helpful to first describe its main flow by writing the main steps in plain English. Then, this English description is converted into pseudocode, which is a structured way of writing this English description that closely represents the logic and flow for the algorithm. Well-written algorithm pseudocode should describe the high-level steps of the algorithm in reasonable detail, even if the detailed code is not relevant to the main flow and structure of the algorithm. The following figure shows the flow of steps:

Note that once the pseudocode is written (as we will see in the next section), we are ready to code the algorithm using the programming language of our choice.

A practical example of pseudocode

Figure 1.3 shows the pseudocode of a resource allocation algorithm called SRPMP. In cluster computing, there are many situations where there are parallel tasks that need to be run on a set of available resources, collectively called a resource pool. This algorithm assigns tasks to a resource and creates a mapping set, called Ω. Note that the presented pseudocode captures the logic and flow of the algorithm, which is further explained in the following section:

1: BEGIN Mapping_Phase
2: Ω = { }
3: k = 1
4: FOREACH Ti∈T
5: ωi = RA(Δk,Ti)
6: add {ωi,Ti} to Ω
7: state_changeTi [STATE 0: Idle/Unmapped] → [STATE 1: Idle/Mapped]
8: k=k+1
9: IF (k>q)
10: k=1
11: ENDIF
12: END FOREACH
13: END Mapping_Phase

Let's parse this algorithm line by line:

  1. We start the mapping by executing the algorithm. The Ω mapping set is empty.

  2. The first partition is selected as the resource pool for the T1 task (see line 3 of the preceding code). Television Rating Point (TRPS) iteratively calls the Rheumatoid Arthritis (RA) algorithm for each Ti task with one of the partitions chosen as the resource pool.

  3. The RA algorithm returns the set of resources chosen for the Ti task, represented by ωi (see line 5 of the preceding code).

  4. Ti and ωi are added to the mapping set (see line 6 of the preceding code).

  5. The state of Ti is changed from STATE 0:Idle/Mapping to STATE 1:Idle/Mapped (see line 7 of the preceding code).

  6. Note that for the first iteration, k=1 and the first partition is selected. For each subsequent iteration, the value of k is increased until k>q.

  7. If k becomes greater than q, it is reset to 1 again (see lines 9 and 10 of the preceding code).

  8. This process is repeated until a mapping between all tasks and the set of resources they will use is determined and stored in a mapping set called Ω.

  9. Once each of the tasks is mapped to a set of the resources in the mapping phase, it is executed.

Using snippets

With the popularity of simple but powerful coding language such as Python, an alternative approach is becoming popular, which is to represent the logic of the algorithm directly in the programming language in a somewhat simplified version. Like pseudocode, this selected code captures the important logic and structure of the proposed algorithm, avoiding detailed code. This selected code is sometimes called a snippet. In this book, snippets are used instead of pseudocode wherever possible as they save one additional step. For example, let's look at a simple snippet that is about a Python function that can be used to swap two variables:

define swap(x, y)
buffer = x
x = y
y = buffer
Note that snippets cannot always replace pseudocode. In pseudocode, sometimes we abstract many lines of code as one line of pseudocode, expressing the logic of the algorithm without becoming distracted by unnecessary coding details.

Creating an execution plan

Pseudocode and snippets are not always enough to specify all the logic related to more complex distributed algorithms. For example, distributed algorithms usually need to be divided into different coding phases at runtime that have a precedence order. The right strategy to divide the larger problem into an optimal number of phases with the right precedence constraints is crucial for the efficient execution of an algorithm.

We need to find a way to represent this strategy as well to completely represent the logic and structure of an algorithm. An execution plan is one of the ways of detailing how the algorithm will be subdivided into a bunch of tasks. A task can be mappers or reducers that can be grouped together in blocks called stages. The following diagram shows an execution plan that is generated by an Apache Spark runtime before executing an algorithm. It details the runtime tasks that the job created for executing our algorithm will be divided into:

Note that the preceding diagram has five tasks that have been divided into two different stages: Stage 11 and Stage 12.

Introducing Python packages

Once designed, algorithms need to be implemented in a programming language as per the design. For this book, I chose the programming language Python. I chose it because Python is a flexible and open source programming language. Python is also the language of choice for increasingly important cloud computing infrastructures, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

The official Python home page is available at https://www.python.org/, which also has instructions for installation and a useful beginner's guide.

If you have not used Python before, it is a good idea to browse through this beginner's guide to self-study. A basic understanding of Python will help you to better understand the concepts presented in this book.

For this book, I expect you to use the recent version of Python 3. At the time of writing, the most recent version is 3.7.3, which is what we will use to run the exercises in this book.

Python packages

Python is a general-purpose language. It is designed in a way that comes with bare minimum functionality. Based on the use case that you intend to use Python for, additional packages need to be installed. The easiest way to install additional packages is through the pip installer program. This pip command can be used to install the additional packages:

pip install a_package

The packages that have already been installed need to be periodically updated to get the latest functionality. This is achieved by using the upgrade flag:

pip install a_package --upgrade

Another Python distribution for scientific computing is Anaconda, which can be downloaded from http://continuum.io/downloads.

In addition to using the pip command to install new packages, for Anaconda distribution, we also have the option of using the following command to install new packages:

conda install a_package

To update the existing packages, the Anaconda distribution gives us the option to use the following command:

conda update a_package

There are all sorts of Python packages that are available. Some of the important packages that are relevant for algorithms are described in the following section.

The SciPy ecosystem

Scientific Python (SciPy)—pronounced sigh pie—is a group of Python packages created for the scientific community. It contains many functions, including a wide range of random number generators, linear algebra routines, and optimizers. SciPy is a comprehensive package and, over time, people have developed many extensions to customize and extend the package according to their needs.

The following are the main packages that are part of this ecosystem:

  • NumPy: For algorithms, the ability to create multi-dimensional data structures, such as arrays and matrices, is really important. NumPy offers a set of array and matrix data types that are important for statistics and data analysis. Details about NumPy can be found at http://www.numpy.org/.

  • scikit-learn: This machine learning extension is one of the most popular extensions of SciPy. Scikit-learn provides a wide range of important machine learning algorithms, including classification, regression, clustering, and model validation. You can find more details about scikit-learn at http://scikit-learn.org/.

  • pandas: pandas is an open source software library. It contains the tabular complex data structure that is used widely to input, output, and process tabular data in various algorithms. The pandas library contains many useful functions and it also offers highly optimized performance. More details about pandas can be found at http://pandas.pydata.org/.

  • Matplotlib: Matplotlib provides tools to create powerful visualizations. Data can be presented as line plots, scatter plots, bar charts, histograms, pie charts, and so on. More information can be found at https://matplotlib.org/.

  • Seaborn: Seaborn can be thought of as similar to the popular ggplot2 library in R. It is based on Matplotlib and offers an advanced interface for drawing brilliant statistical graphics. Further details can be found at https://seaborn.pydata.org/.

  • iPython: iPython is an enhanced interactive console that is designed to facilitate the writing, testing, and debugging of Python code.

  • Running Python programs: An interactive mode of programming is useful for learning and experimenting with code. Python programs can be saved in a text file with the .py extension and that file can be run from the console.

Implementing Python via the Jupyter Notebook

Another way to run Python programs is through the Jupyter Notebook. The Jupyter Notebook provides a browser-based user interface to develop code. The Jupyter Notebook is used to present the code examples in this book. The ability to annotate and describe the code with texts and graphics makes it the perfect tool for presenting and explaining an algorithm and a great tool for learning.

To start the notebook, you need to start the Juypter-notebook process and then open your favorite browser and navigate to http://localhost:8888:

Note that a Jupyter Notebook consists of different blocks called cells.

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Key benefits

  • Learn the techniques you need to know to design algorithms for solving complex problems
  • Become familiar with neural networks and deep learning techniques
  • Explore different types of algorithms and choose the right data structures for their optimal implementation

Description

Algorithms have always played an important role in both the science and practice of computing. Beyond traditional computing, the ability to use algorithms to solve real-world problems is an important skill that any developer or programmer must have. This book will help you not only to develop the skills to select and use an algorithm to solve real-world problems but also to understand how it works. You’ll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, such as searching and sorting, with the help of practical examples. As you advance to a more complex set of algorithms, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them. Further on, case studies such as weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. Finally, you’ll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.

Who is this book for?

This book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most commonly used algorithms in a clear and concise way or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Although Python programming experience is a must, knowledge of data science will be helpful but not necessary.

What you will learn

  • Explore existing data structures and algorithms found in Python libraries
  • Implement graph algorithms for fraud detection using network analysis
  • Work with machine learning algorithms to cluster similar tweets and process Twitter data in real time
  • Predict the weather using supervised learning algorithms
  • Use neural networks for object detection
  • Create a recommendation engine that suggests relevant movies to subscribers
  • Implement foolproof security using symmetric and asymmetric encryption on Google Cloud Platform (GCP)

Product Details

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Publication date : Jun 12, 2020
Length: 382 pages
Edition : 1st
Language : English
ISBN-13 : 9781789801217
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Table of Contents

18 Chapters
Section 1: Fundamentals and Core Algorithms Chevron down icon Chevron up icon
Overview of Algorithms Chevron down icon Chevron up icon
Data Structures Used in Algorithms Chevron down icon Chevron up icon
Sorting and Searching Algorithms Chevron down icon Chevron up icon
Designing Algorithms Chevron down icon Chevron up icon
Graph Algorithms Chevron down icon Chevron up icon
Section 2: Machine Learning Algorithms Chevron down icon Chevron up icon
Unsupervised Machine Learning Algorithms Chevron down icon Chevron up icon
Traditional Supervised Learning Algorithms Chevron down icon Chevron up icon
Neural Network Algorithms Chevron down icon Chevron up icon
Algorithms for Natural Language Processing Chevron down icon Chevron up icon
Recommendation Engines Chevron down icon Chevron up icon
Section 3: Advanced Topics Chevron down icon Chevron up icon
Data Algorithms Chevron down icon Chevron up icon
Cryptography Chevron down icon Chevron up icon
Large-Scale Algorithms Chevron down icon Chevron up icon
Practical Considerations Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.8
(33 Ratings)
5 star 54.5%
4 star 15.2%
3 star 6.1%
2 star 9.1%
1 star 15.2%
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Vitaliy Nov 21, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
You don't know how to teach... adpost.news founder Whytell Don.
Subscriber review Packt
NHJFromSH Feb 23, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
For analytical professionals, this is a good reference book to use which aligns the use cases in different business areas and proper algorithms.
Amazon Verified review Amazon
Ivan and Zorro Mar 22, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Easy enough for beginning programmers to use and digest. This book is well and clearly written. There is some traditional algorithm coverage, but the main focus is on writing machine learning algorithms. Starts at the beginning and assumes no knowledge of Python, although familiarity with a programming language will help it doesn’t have to be Python. I guess if you are new to Python though you will quickly fall in love. While there is something to be said of language agnostic algorithm, it is nice to have discussion and implantation in the same book and in a modern language.
Amazon Verified review Amazon
Kindle Customer May 17, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is true to its name and has so many algorithms and presents them well. It beats any data science text in presenting them as well (even though the theory of data science is not presented in whole in this book). I would call this book both practical and meaningful for anyone wishing to use python for any purpose. Excellent book, well-written, well-presented, and easy to learn from.
Amazon Verified review Amazon
RT Jul 30, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Who the book is aimed at: if you self-identify as a data scientist, serious algorithms specialist, or even the quant type, then you won’t be disappointed! If you’re just starting in the field, the author has done the hard work of selecting some of the commonly used techniques & algorithms in the field today.The content’s breadth and depth is masterfully selected, it won’t disappoint!Style & Language: easy to read plain english; the book does have a strong Python flavour (v3.7+), but even R-focused practitioners will likely find some of the content rewarding.Examples: the diagrams/figures convey the basic ideas very well, and the author has also setup a live git repo, and it seems he does plan to keep it updated.PS. Having just started this book review, it’s is a work-in-progress, I’ll update as I go through more chapters.
Amazon Verified review Amazon
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