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kandi X-RAY | loop Summary
kandi X-RAY | loop Summary
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of loop
loop Key Features
loop Examples and Code Snippets
def while_loop(cond,
body,
loop_vars,
shape_invariants=None,
parallel_iterations=10,
back_prop=True,
swap_memory=False,
name=None,
def while_loop_v2(cond,
body,
loop_vars,
shape_invariants=None,
parallel_iterations=10,
back_prop=True,
swap_memory=False,
m
def experimental_tpu_fit_loop(model,
dataset,
epochs=100,
verbose=1,
callbacks=None,
initial_epoch=0
Community Discussions
Trending Discussions on loop
QUESTION
I have been trying to learn about functional programming, but I still struggle with thinking like a functional programmer. One such hangup is how one would implement index-heavy operations which rely strongly on loops/order-of-execution.
For example, consider the following Java code:
...ANSWER
Answered 2022-Mar-07 at 21:17This is not an index-heavy operation, in fact you can do this with a one-liner with scanl1 :: (a -> a -> a) -> [a] -> [a]
:
QUESTION
I am getting an error in Android Studio to do with my Cursor.
I have the following line in my code
...ANSWER
Answered 2021-Nov-14 at 15:06I had an error like this.
My solution : change method getColumnIndex
into getColumnIndexOrThrow
.
QUESTION
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 sum(np.arange(N))
and 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):
updated script:
...ANSWER
Answered 2021-Oct-16 at 17:42From 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:
QUESTION
Is the time complexity of nested for, while, and if statements the same? Suppose a
is given as an array of length n
.
ANSWER
Answered 2022-Mar-02 at 14:43Am I right?
Yes!
The double loop:
QUESTION
I have a single even-sized vector that I want to transform into a vector of pairs where each pair contains always two elements. I know that I can do this using simple loops but I was wondering if there is a nice standard-library tool for this? It can be assumed that the original vector always contains an even amount of elements.
Example:
...ANSWER
Answered 2022-Feb-14 at 14:26There's a quick-and-dirty approach, which will kinda-hopefully-maybe do what you asked for, and will not even copy the data at all... but the downside is that you can't be certain it will work. It relies on undefined behavior, and can thus not be recommended. I'm describing it because I believe it's what one imagines, intuitively, that we might be able to do.
So, it's about using std::span
with re-interpretation of the vector data:
QUESTION
I have read several answers on SO already, and gathered these use-cases:
- When a function
panic!
s - When a function has an infinite loop in it
But it is still unclear to me why we need to define the function like this:
...ANSWER
Answered 2022-Jan-22 at 13:23The main difference between these signatures boils down to the fact that !
can coerce into any other type, and thus is compatible with any other type (since this code path is never taken, we can assume it to be of any type we need). It's important when we have multiple possible code paths, such as if-else
or match
.
For example, consider the following (probably contrived, but hopefully clear enough) code:
QUESTION
I am trying to efficiently compute a summation of a summation in Python:
WolframAlpha is able to compute it too a high n value: sum of sum.
I have two approaches: a for loop method and an np.sum method. I thought the np.sum approach would be faster. However, they are the same until a large n, after which the np.sum has overflow errors and gives the wrong result.
I am trying to find the fastest way to compute this sum.
...ANSWER
Answered 2022-Jan-16 at 12:49(fastest methods, 3 and 4, are at the end)
In a fast NumPy method you need to specify dtype=np.object
so that NumPy does not convert Python int
to its own dtypes (np.int64
or others). It will now give you correct results (checked it up to N=100000).
QUESTION
These two loops are equivalent in C++ and Rust:
...ANSWER
Answered 2022-Jan-12 at 10:20Overflow in the iterator state.
The C++ version will loop forever when given a large enough input:
QUESTION
I have a matrix with many rows and columns, of the nature
...ANSWER
Answered 2022-Jan-02 at 17:02How about this?
QUESTION
I'm studying for the final exam for my introduction to C++ class. Our professor gave us this problem for practice:
...Explain why the code produces the following output:
120 200 16 0
ANSWER
Answered 2021-Dec-13 at 20:55It does not default to zero. The sample answer is wrong. Undefined behaviour is undefined; the value may be 0, it may be 100. Accessing it may cause a seg fault, or cause your computer to be formatted.
As to why it's not an error, it's because C++ is not required to do bounds checking on arrays. You could use a vector and use the at
function, which throws exceptions if you go outside the bounds, but arrays do not.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
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