Results | Common Result Classes to improve code reusability

The same thing occurred to me yesterday when I used Colab. A possible reason may be that the version of opencv-python(4.1.2.30) does not match opencv-python-headless(4.5.5.62). Or the latest version 4.5.5 may have something wrong...

I uninstalled opencv-python-headless==4.5.5.62 and installed 4.1.2.30 and it fixed.

It all makes sense and has a simple pattern besides () -> null being a Callable I think. The Runnable is clearly different from the Supplier/Callable as it has no input and output values. The difference between Callable and Supplier is that with the Callable you have to handle exceptions.

The reason that () -> null is a Callable without an exception is the return type of your definition Callable. It requires you to return the reference to some object. The only possible reference to return for Void is null. This means that the lambda () -> null is exactly what your definition demands. It would also work for your Supplier example if you would remove the Callable definition. However, it uses Callable over Supplier as the Callable has the exact type.

Callable is chosen over Supplier as it is more specific (as a comment already suggested). The Java Docs state that it chooses the most specific type if possible:

Type inference is a Java compiler's ability to look at each method invocation and corresponding declaration to determine the type argument (or arguments) that make the invocation applicable. The inference algorithm determines the types of the arguments and, if available, the type that the result is being assigned, or returned. Finally, the inference algorithm tries to find the most specific type that works with all of the arguments.

You can try to locate the installed version by running:

Code readability, short-circuiting and it is not guaranteed that Ord will always outperform a || operand. Computer systems are more complicated than expected, even though they are man-made.

There was a case where a for loop with a much more complicated condition ran faster on an IBM. The CPU didn't cool and thus instructions were executed faster, that was a possible reason. What I am trying to say, focus on other areas to improve code than fighting small-cases which will differ depending on the CPU and the boolean evaluation (compiler optimizations).

Since C++17 the order of evaluation is specified such that the operands of = are evaluated right-to-left and those of << are evaluated left-to-right, matching the associativity of these operators. (But this doesn't apply to all operators, e.g. + and other arithmetic operators.)

So in

NA is a statistical or data integrity concept: the idea of a "missing value". Eg if your data comes from people filling in forms, a bad entry or missing entry would be treated as NA.

NaN is a numerical or computational concept: something that is "not a number". Eg 0/0 is NAN, because the result of this computation is undefined (but note that 1/0 is Inf, or infinity, and similarly -1/0 is -Inf).

The way that R handles these concepts internally isn't something that you should ever be concerned about.

Your setuptools version is likely to be yanked:

https://pypi.org/project/setuptools/60.3.0/

Not sure how to fix that without a working pip though.

An answer from a core developer:

Unintended consequence of Mark Shannon's change that refactors fast opcode dispatching: https://github.com/python/cpython/commit/4958f5d69dd2bf86866c43491caf72f774ddec97 -- the INPLACE_ADD opcode no longer uses the "slow" dispatch path that checks for interrupts and such.

The problem you've encountered here is due to recycling (not the eco-friendly kind). When applying an operation to two vectors that requires them to be the same length, R often automatically recycles, or repeats, the shorter one, until it is long enough to match the longer one. Your unexpected results are due to the fact that R recycles the vector c("p", "o") to be length 4 (length of the larger vector) and essentially converts it to c("p", "o", "p", "o"). If we compare c("p", "o", "p", "o") and c("p", "o", "l", "o") we can see we get the unexpected results of above: