returns | Make your functions return something | Functional Programming library

There is much debate about unsigned. Without going too much into subjective territory, consider the following: What matters is not whether the value returned from rand() cannot be negative. What matters is that rand() returns a value of a certain type and that type determines what you can do with that value. rand() never returns a negative value, but does it make sense to apply operations on the value that make the value negative? Certainly yes. For example you might want to do:

You'd have to use np.repeat twice here.

I ran into the same issue today and came up with the following solution based on this very helpful article by Andrew Luca

In PrivateRoute.js:

This function were removed in TensorFlow version 2.6. According to the keras in rstudio reference

update to

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.

This is a good question... and I would have hoped there would be a practical guide somewhere. One could question if SO would be a good place to ask this question, but regardless, here's my attempt to summarize the various scale_color_*() and scale_fill_*() functions built into ggplot2. Here, we'll describe the range of functions using scale_color_*(); however, the same general rules will apply for scale_fill_*() functions.

There are 22 functions in all, but happily we can group them intelligently based on practical usage scenarios. There are three key criteria that can be used to define practically how to use each of the scale_color_*() functions:

1. Nature of the mapping data. Is the data mapped to the color aesthetic discrete or continuous? CONTINUOUS data is something that can be explained via real numbers: time, temperature, lengths - these are all continuous because even if your observations are 1 and 2, there can exist something that would have a theoretical value of 1.5. DISCRETE data is just the opposite: you cannot express this data via real numbers. Take, for example, if your observations were: "Model A" and "Model B". There is no obvious way to express something in-between those two. As such, you can only represent these as single colors or numbers.

2. The Colorspace. The color palette used to draw onto the plot. By default, ggplot2 uses (I believe) a color palette based on evenly-spaced hue values. There are other functions built into the library that use either Brewer palettes or Viridis colorspaces.

3. The level of Specification. Generally, once you have defined if the scale function is continuous and in what colorspace, you have variation on the level of control or specification the user will need or can specify. A good example of this is the functions: *_continuous(), *_gradient(), *_gradient2(), and *_gradientn().

We can start off with continuous scales. These functions are all used when applied to observations that are continuous variables (see above). The functions here can further be defined if they are either binned or not binned. "Binning" is just a way of grouping ranges of a continuous variable to all be assigned to a particular color. You'll notice the effect of "binning" is to change the legend keys from a "colorbar" to a "steps" legend.

The continuous example (colorbar legend):

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.

Note: When referring to documentation and source code, I provide links to an unofficial GitHub mirror of R's official Subversion repository. The links are bound to commit 97b6424 in the GitHub repo, which maps to revision 81461 in the Subversion repo (the latest at the time of this edit).

substitute is a "special" whose arguments are not evaluated (doc).

This is a specialty of Math.min, as specified:

21.3.2.25 Math.min ( ...args )

[...]

1. For each element number of coerced, do

a. If number is NaN, return NaN.

b. If number is -0𝔽 and lowest is +0𝔽, set lowest to -0𝔽.

c. If number < lowest, set lowest to number.

1. Return lowest.

Note that in most cases, +0 and -0 are treated equally, also in the ToString conversion, thus (-0).toString() evaluates to "0". That you can observe the difference in the browser console is an implementation detail of the browser.