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Machine Learning with R

You're reading from   Machine Learning with R Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781801071321
Length 762 pages
Edition 4th Edition
Languages
Tools
Concepts
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Author (1):
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Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
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Toc

Table of Contents (19) Chapters Close

Preface 1. Thinking Computationally 2. Abstraction in Detail FREE CHAPTER 3. Algorithmic Thinking and Complexity 4. Understanding the Machine 5. Data Structures 6. Reusing Your Code and Modularity 7. Outlining the Challenge 8. Building a Simple Command-Line Interface 9. Reading Data from Different Formats 10. Finding Information in Text 11. Clustering Data 12. Reflecting on What We Have Built 13. The Problems of Scale 14. Dealing with GPUs and Specialized Hardware 15. Profiling Your Code 16. Unlock Your Exclusive Benefits 17. Other Books You May Enjoy 18. Index

Writing some simple tests

Our first task is to set up CMake for adding tests. This includes finding the Google Test library and the CMake support functions by adding the following lines to the root CMakeLists.txt:

find_package(GTest CONFIG REQUIRED)
include(GoogleTest)

Now we can link the GTest::gtest_main library to our test executables and the gtest_discover_tests function to register the tests contained therein to the CTest harness. (This makes actually running all the tests in a project very easy.) The next task is to create a new executable target in the readers/CMakeLists.txt file and register the tests. The code is as follows:

add_executable(test_ducky_readers
    test_csv_reader.cpp
    test_json_reader.cpp
    )
target_link_libraries(test_ducky_readers PRIVATE
  ducky_readers
  spdlog::spdlog
  GTest::gtest_main
  )
gtest_discover_tests(test_ducky_readers)

We’ve already added the two test source files, though we have yet to create them. As the names...

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