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Author: Leon Starr Publisher: Apress ISBN: 1484222172 Category : Computers Languages : en Pages : 318
Book Description
Learn how to translate an executable model of your application into running code. This is not a book about theory, good intentions or possible future developments. You’ll benefit from translation technology and solid software engineering principles that are demonstrated with concrete examples using an open source tool chain. Models don’t deliver enough value if they are not on a direct path to code production. But to waste time building models that are merely pictures of your code doesn’t add much value either. In this book, you’ll translate detailed, yet platform-independent models that solve real application problems. Using a pragmatic approach, Models to Code quickly dives into two case studies of Executable UML models. The models and code are extensively annotated and illustrate key principles that are emphasized throughout the book. You’ll work with code production using "C" as the implementation language and targeting microcomputer class processors. This might not be your particular target language or platform, but you can use you can use what you learn here to engineer or re-evaluate your own code translation system to dramatically increase the value of both your modeling and code generation solution. Written by three leading experts, Models to Code is an exceptional resource for producing software by model translation— add it to your library today. What You'll Learn See how detailed models resolve ambiguity and contradiction common in requirements. Examine how a model can be detailed enough to be executable and testable while remaining platform independent Produce code from a model, leaving the model intact so it can be redeployed on new platforms or adapted to changing software and hardware technology. Implement platform independent model execution rules in platform specific run-time code Who This Book Is For Modelers and systems engineers on active MBSE projects (using Executable UML or not), projects using Simulink, Matlab, Dymola, MatrixX and other math modelling tools. Any developers with current or past model experience, professors, students, systems engineers, embedded systems developers, or anyone interested in learning more about software modelling.
Author: Leon Starr Publisher: Apress ISBN: 1484222172 Category : Computers Languages : en Pages : 318
Book Description
Learn how to translate an executable model of your application into running code. This is not a book about theory, good intentions or possible future developments. You’ll benefit from translation technology and solid software engineering principles that are demonstrated with concrete examples using an open source tool chain. Models don’t deliver enough value if they are not on a direct path to code production. But to waste time building models that are merely pictures of your code doesn’t add much value either. In this book, you’ll translate detailed, yet platform-independent models that solve real application problems. Using a pragmatic approach, Models to Code quickly dives into two case studies of Executable UML models. The models and code are extensively annotated and illustrate key principles that are emphasized throughout the book. You’ll work with code production using "C" as the implementation language and targeting microcomputer class processors. This might not be your particular target language or platform, but you can use you can use what you learn here to engineer or re-evaluate your own code translation system to dramatically increase the value of both your modeling and code generation solution. Written by three leading experts, Models to Code is an exceptional resource for producing software by model translation— add it to your library today. What You'll Learn See how detailed models resolve ambiguity and contradiction common in requirements. Examine how a model can be detailed enough to be executable and testable while remaining platform independent Produce code from a model, leaving the model intact so it can be redeployed on new platforms or adapted to changing software and hardware technology. Implement platform independent model execution rules in platform specific run-time code Who This Book Is For Modelers and systems engineers on active MBSE projects (using Executable UML or not), projects using Simulink, Matlab, Dymola, MatrixX and other math modelling tools. Any developers with current or past model experience, professors, students, systems engineers, embedded systems developers, or anyone interested in learning more about software modelling.
Author: Leon Starr Publisher: Apress ISBN: 9781484222164 Category : Computers Languages : en Pages :
Book Description
This book teaches you how to translate an executable model of your application to efficient, running code on an embedded microcomputer platform without any mysterious gaps or hidden proprietary tools. There are many benefits to a model-oriented approach to software engineering but the path from models to code is not always clear. Using a pragmatic approach, Models to Code uses annotated model and code examples to illustrate the key principles. You will start off with a brief overview of model based engineering concepts, then quickly dive into two case study Executable UML models that expose the key model elements. You will also understand the future of code translation and approaches you can take with other platforms and languages, as well as open source enhancements and alternative strategies for developers to try. Although the techniques are shown using C and a microcomputer, they are not specific to that language or platform. The code generation strategy you learn can easily be adapted to your own implementation technology. Written by three industry experts, Models to Code is your number one resource for software modelling – add it to your library today. What You Will Learn The purpose and benefits of model driven code generation The specific differences between application and implementation details What details are required in an Executable UML model prior to implementation How to specify an implementation without modifying the application models How to specify an Executable UML model in the pycca scripting language How to specify implementation choices in pycca How a model can be repackaged as an efficient implementation automatically Diverse strategies for converting various model elements into code elements Who This Book Is For This book is for modelers and systems engineers on active MBSE projects (using Executable UML or not), projects using Simulink, Matlab, Dymola, MatrixX and other math modelling tools. Any developers with current or past model experience, professors and students, systems engineers, embedded systems developers, or anyone interested in learning more about software modelling.
Author: Scott Wlaschin Publisher: Pragmatic Bookshelf ISBN: 1680505491 Category : Computers Languages : en Pages : 426
Book Description
You want increased customer satisfaction, faster development cycles, and less wasted work. Domain-driven design (DDD) combined with functional programming is the innovative combo that will get you there. In this pragmatic, down-to-earth guide, you'll see how applying the core principles of functional programming can result in software designs that model real-world requirements both elegantly and concisely - often more so than an object-oriented approach. Practical examples in the open-source F# functional language, and examples from familiar business domains, show you how to apply these techniques to build software that is business-focused, flexible, and high quality. Domain-driven design is a well-established approach to designing software that ensures that domain experts and developers work together effectively to create high-quality software. This book is the first to combine DDD with techniques from statically typed functional programming. This book is perfect for newcomers to DDD or functional programming - all the techniques you need will be introduced and explained. Model a complex domain accurately using the F# type system, creating compilable code that is also readable documentation---ensuring that the code and design never get out of sync. Encode business rules in the design so that you have "compile-time unit tests," and eliminate many potential bugs by making illegal states unrepresentable. Assemble a series of small, testable functions into a complete use case, and compose these individual scenarios into a large-scale design. Discover why the combination of functional programming and DDD leads naturally to service-oriented and hexagonal architectures. Finally, create a functional domain model that works with traditional databases, NoSQL, and event stores, and safely expose your domain via a website or API. Solve real problems by focusing on real-world requirements for your software. What You Need: The code in this book is designed to be run interactively on Windows, Mac and Linux.You will need a recent version of F# (4.0 or greater), and the appropriate .NET runtime for your platform.Full installation instructions for all platforms at fsharp.org.
Author: Frances Buontempo Publisher: Pragmatic Bookshelf ISBN: 1680506587 Category : Computers Languages : en Pages : 307
Book Description
Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.
Author: Allen B. Downey Publisher: No Starch Press ISBN: 1718502176 Category : Computers Languages : en Pages : 277
Book Description
Modeling and Simulation in Python teaches readers how to analyze real-world scenarios using the Python programming language, requiring no more than a background in high school math. Modeling and Simulation in Python is a thorough but easy-to-follow introduction to physical modeling—that is, the art of describing and simulating real-world systems. Readers are guided through modeling things like world population growth, infectious disease, bungee jumping, baseball flight trajectories, celestial mechanics, and more while simultaneously developing a strong understanding of fundamental programming concepts like loops, vectors, and functions. Clear and concise, with a focus on learning by doing, the author spares the reader abstract, theoretical complexities and gets right to hands-on examples that show how to produce useful models and simulations.
Author: Jeremy Howard Publisher: O'Reilly Media ISBN: 1492045497 Category : Computers Languages : en Pages : 624
Book Description
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Author: Julia Lerman Publisher: "O'Reilly Media, Inc." ISBN: 1449312942 Category : Computers Languages : en Pages : 193
Book Description
Annotation Code first is an additional means of building a model to be used with the Entity Framework and is creating a lot of excitement in the .NET development community. The reader will begin with an overview of what code first is, why it was created, how it fits into the Entity Framework and when to use it over the alternatives.
Author: Steven Kelly Publisher: John Wiley & Sons ISBN: 0470249250 Category : Computers Languages : en Pages : 445
Book Description
"[The authors] are pioneers. . . . Few in our industry have their breadth of knowledge and experience." —From the Foreword by Dave Thomas, Bedarra Labs Domain-Specific Modeling (DSM) is the latest approach to software development, promising to greatly increase the speed and ease of software creation. Early adopters of DSM have been enjoying productivity increases of 500–1000% in production for over a decade. This book introduces DSM and offers examples from various fields to illustrate to experienced developers how DSM can improve software development in their teams. Two authorities in the field explain what DSM is, why it works, and how to successfully create and use a DSM solution to improve productivity and quality. Divided into four parts, the book covers: background and motivation; fundamentals; in-depth examples; and creating DSM solutions. There is an emphasis throughout the book on practical guidelines for implementing DSM, including how to identify the necessary language constructs, how to generate full code from models, and how to provide tool support for a new DSM language. The example cases described in the book are available the book's Website, www.dsmbook.com, along with, an evaluation copy of the MetaEdit+ tool (for Windows, Mac OS X, and Linux), which allows readers to examine and try out the modeling languages and code generators. Domain-Specific Modeling is an essential reference for lead developers, software engineers, architects, methodologists, and technical managers who want to learn how to create a DSM solution and successfully put it into practice.
Author: Hadley Wickham Publisher: "O'Reilly Media, Inc." ISBN: 1491910364 Category : Computers Languages : en Pages : 521
Book Description
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Author: fib Fédération internationale du béton Publisher: fib Fédération internationale du béton ISBN: 2883941106 Category : Technology & Engineering Languages : en Pages : 202
Book Description
fib Model Code 2010 represents the state-of-the-art of code-type models for structural behaviour of concrete. It comprises constitutive relations and material models together with the most important explanatory notes. However the underlying normative work, i.e. the fundamental data as well as the considerations and discussions behind the formulas could not be given within the Model Code text. Based on various experiences gained after the publication of Model Code 1990 this lacking background information will lead in the following to numerous questions arising from Model Code users. Consequently the present bulletin claims to conquer this general weakness of codes in a way to guard against any future misunderstandings of the Model Code 2010 related to its chapter 5.1 (Concrete). It discusses the given formulas in connection with experimental data and the most important international literature. The constitutive relations or material models, being included in MC1990 and forming the basis and point of origin of the Task Group’s work, were critically evaluated, if necessary and possible adjusted, or replaced by completely new approaches. Major criteria have been the physical and thermodynamical soundness as well as practical considerations like simplicity and operationality. This state-of-the-art report is intended for practicizing engineers as well as for researchers and represents a comprehensible summary of the relevant knowledge available to the members of the fib Task Group 8.7 at the time of its drafting. Besides the fact that the bulletin is a background document for Chapter 5.1 of MC2010, it will provide an important foundation for the development of future generations of code-type models related to the characteristics and the behaviour of structural concrete. Further it will offer insights into the complexity of the normative work related to concrete modelling, leading to a better understanding and adequate appreciation of MC2010.