returns | Make your functions return something | Functional Programming library
kandi X-RAY | returns Summary
kandi X-RAY | returns Summary
Make your functions return something meaningful, typed, and safe!.
Top functions reviewed by kandi - BETA
- Creates a decorator factory
- Create a release function to release the given value
- Creates a function that uses the given acquire function
- Return a wrapped function
- Interpret args and kwargs
- Wrapper for eager_curry_curry
- Check all laws of a given container type
- Build a search strategy from a given container type
- Collect elements from iterable
- Collect the elements from the iterable
- Collects all the elements from the iterable
- Collects all elements from iterable
- Applies the result to the given container
- Return the result of the given expression
- Return the type of the function
- Create a function that applies the given function to each instance
- Create a search strategy from a legal container type
- Implements the expression expr
- Wrap a function into a Future
- Return a new instance type
- Maps the value of the given container to a new value
- Analyze a function call
- Evaluate the expression
- Marks a function as kinded
- Binds a function to an async context
- Analyze member access
- Creates a ContextualFutureResult
returns Key Features
returns Examples and Code Snippets
def _ragged_getitem(rt_input, key_list): """Helper for indexing and slicing ragged tensors with __getitem__(). Extracts the specified piece of the `rt_input`. See `RaggedTensor.__getitem__` for examples and restrictions. Args: rt_input
def get_replicated_var_handle(self, name: Text, handle_id: Text, vars_: Union[List[core_types.Tensor], List[v
def implicit_val_and_grad(f): """Returns a function which differentiates f with respect to variables. The wrapped function returns the value and the gradient of f when called with the same arguments. The gradient is with respect to all trainab
def played_from_start(entry): entry = str(entry) # Without this, np.nan is a float. if entry == 'nan' or entry == '': return 0 if entry.startswith('Bench'): return 0 if entry == 'Starting': return 9
y = np.array([1,2,1,3])
array([True, False, True, False])
x = np.array([[1,2],[3,4],[5,6],[7,8]]) x Out: array([[1, 2], [3, 4], [5, 6], [7, 8]]) <
data["age"].append(int(split) <- here
rect = pygame.Rect(block.x+scx, block.y+scy, 10, 10) rect.inflate_ip(zoom, zoom) pygame.draw.rect(screen, block.color, rect)
df.loc[df.jpgs.apply(lambda x: "123.jpg" not in x)]
from starlette.concurrency import iterate_in_threadpool @app.middleware("http") async def some_middleware(request: Request, call_next): response = await call_next(request) response_body = [chunk async for chunk in response.body_it
>>> from itertools import product >>> a = [''.join(p) for p in product('01', repeat=3)] >>> a ['000', '001', '010', '011', '100', '101', '110', '111']
Trending Discussions on returns
I saw a video about speed of loops in python, where it was explained that doing
sum(range(N)) is much faster than manually looping through
range and adding the variables together, since the former runs in C due to built-in functions being used, while in the latter the summation is done in (slow) python. I was curious what happens when adding
numpy to the mix. As I expected
np.sum(np.arange(N)) is the fastest, but
np.sum(range(N)) are even slower than doing the naive for loop.
Why is this?
Here's the script I used to test, some comments about the supposed cause of slowing done where I know (taken mostly from the video) and the results I got on my machine (python 3.10.0, numpy 1.21.2):
ANSWERAnswered 2021-Oct-16 at 17:42
From the cpython source code for
sum sum initially seems to attempt a fast path that assumes all inputs are the same type. If that fails it will just iterate:
Returns a pseudo-random integral value between 0 and RAND_MAX (0 and RAND_MAX included).
Since it is guaranteed that a non-negative integer will be returned, why the return type is signed?
I am not talking about if we should use it here. Is it a historical issue or some bad design?...
ANSWERAnswered 2022-Mar-02 at 02:12
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:
Say I have this array:...
ANSWERAnswered 2022-Jan-09 at 16:18
You'd have to use
np.repeat twice here.
I'm using React Router v6 and am creating private routes for my application.
In file PrivateRoute.js, I've the code...
ANSWERAnswered 2021-Nov-12 at 21:20
I ran into the same issue today and came up with the following solution based on this very helpful article by Andrew Luca
Im attempting to find model performance metrics (F1 score, accuracy, recall) following this guide https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/
This exact code was working a few months ago but now returning all sorts of errors, very confusing since i havent changed one character of this code. Maybe a package update has changed things?
I fit the sequential model with model.fit, then used model.evaluate to find test accuracy. Now i am attempting to use model.predict_classes to make class predictions (model is a multi-class classifier). Code shown below:...
ANSWERAnswered 2021-Aug-19 at 03:49
This function were removed in TensorFlow version 2.6. According to the keras in rstudio reference
First off, I have no idea how to decently phrase the question, so this is up for suggestions.
Lets say we have following overloaded methods:...
ANSWERAnswered 2022-Mar-17 at 08:29
It all makes sense and has a simple pattern besides
() -> null being a
Callable I think. The
Runnable is clearly different from the
Callable as it has no input and output values. The difference between
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
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
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.
There are so many ways to define colour scales within
ggplot2. After just loading
ggplot2 I count
22 functions beginging with
scale_colour_*) and same number beginging with
scale_fill_*. Is it possible to briefly name the purpose of the functions below? Particularly I struggle with the differences of some of the functions and when to use them.
What I tried
I've tried to make some research on the web but the more I read the more I get onfused. To drop some random example: "The default scale for continuous fill scales is
scale_fill_continuous() which in turn defaults to
scale_fill_gradient()". I do not get what the difference of both functions is. Again, this is just an example. Same is true for
scale_color_discrete() where I can not name the difference. And in case of
scale_color_datetime() the destription says "
scale_*_gradient creates a two colour gradient (low-high),
scale_*_gradient2 creates a diverging colour gradient (low-mid-high),
scale_*_gradientn creates a n-colour gradient." which is nice to know but how is this related to
scale_color_datetime()? Looking for those functions on the web does not give me very informative sources either. Reading on this topic gets also chaotic because there are tons of color palettes in different packages which are sequential/ diverging/ qualitative plus one can set same color in different ways, i.e. by color name, rgb, number, hex code or palette name. In part this is not directly related to the question about the
2*22 functions but in some cases it is because providing a "wrong" palette results in an error (e.g. the error
"Continuous value supplied to discrete scale).
Why I ask this
I need to do many plots for my work and I am supposed to provide some function that returns all kind of plots. The plots are supposed to have similiar layout so that they fit well together. One aspect I need to consider here is that the colour scales of the plots go well together. See here for example, where so many different kind of plots have same colour scale. I was hoping I could use some general function which provides a colour palette to any data, regardless of whether the data is continuous or categorical, whether it is a fill or col easthetic. But since this is not how colour scales are defined in
ggplot2 I need to understand what all those functions are good for.
ANSWERAnswered 2022-Feb-01 at 18:14
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_fill_*() functions built into
ggplot2. Here, we'll describe the range of functions using
scale_color_*(); however, the same general rules will apply for
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
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
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 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.
The Colorspace. The color palette used to draw onto the plot. By default,
ggplot2uses (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.
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:
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):
When I use
NaN, it returns,
ANSWERAnswered 2021-Dec-24 at 10:45
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
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
The way that R handles these concepts internally isn't something that you should ever be concerned about.
I'm having trouble understanding how/why parentheses work where they otherwise should not work®....
ANSWERAnswered 2022-Jan-09 at 16:14
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).
I know (-0 === 0) comes out to be true. I am curious to know why -0 < 0 happens?
When I run this code in stackoverflow execution context, it returns
ANSWERAnswered 2021-Dec-22 at 14:17
This is a specialty of
Math.min, as specified:
18.104.22.168 Math.min ( ...args )
- 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.
- 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.
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