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Programming Leftovers

Submitted by Roy Schestowitz on Monday 15th of February 2021 03:04:05 AM Filed under
Development
  • AST Matchmaking made easy

    C++ has multiple features which are designed to be simple expressions which the compiler expands to something less-convenient to write. Range-based for loops are a good example as they are a metaphor for an explicit loop with calls to begin and end among other things. Lambdas are another good example as they are a metaphor for a callable object. C++20 adds several more, including rewriting use of operator!=(...) to use !operator==(...) and operator<(...) to use the spaceship operator.

    [I admit that in writing this blog post I searched for a metaphor for “a device which aids understanding by replacing the thing it describes with something more familiar” before realizing the recursion. I haven’t heard these features described as metaphorical before though…]

    All of these metaphorical replacements can be explored in the Clang AST or on CPP Insights.

    Matching these internal representations is confusing and can cause incorrect transformations. None of these internal representations are matchable in the new IgnoreUnlessSpelledInSource mode.

    In the default matching mode, the CallExprs for begin and end are matched, as are the CXXRecordDecl implicit in the lambda and hidden comparisons within rewritten binary operators such as spaceship (causing bugs in clang-tidy checks).

  • Steinar H. Gunderson: Idle language musings

    PHP makes the easy things easy, and in the process makes the wrong things easy and the right things hard.

    C makes the easy things hard, the hard things possible, and in the process makes the wrong things just as easy as the right things.

    Rust makes everything hard, but the wrong things even harder.

  • What is Decision Trees

    For classification we use Information gain where as for Decision Tree Regression, we use Mean Squared Error. Check out this link for Decision Tree Regression hyper parameters tuning in Python.

    We calculate for each parent and child, the mean squared error.

    We choose a node which has least mean squared error. That becomes the root node.

»

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