in-place

问题 What does the &= operator mean in Python, and can you give me a working example? I am trying to understand the __iand__ operator. I just don't know what &= means and have looked online but couldn't find it. 回答1: Explanation Understandable that you can't find much reference on it. I find it hard to get references on this too, but they exist. The i in iand means in-place , so it's the in-place operator for & . &= calls the __iand__ operator, if it is implemented. If not implemented, it is the

问题 What does the &= operator mean in Python, and can you give me a working example? I am trying to understand the __iand__ operator. I just don't know what &= means and have looked online but couldn't find it. 回答1: Explanation Understandable that you can't find much reference on it. I find it hard to get references on this too, but they exist. The i in iand means in-place , so it's the in-place operator for & . &= calls the __iand__ operator, if it is implemented. If not implemented, it is the

问题 In a pytorch model training process I get this error: RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.LongTensor [128, 1]] is at version 8; expected version 7 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck! with stack trace sys:1: RuntimeWarning: Traceback of forward call that caused the error:

问题 I have a list and I want to shuffle a portion of it in-place . I am aware of random.shuffle() and that it works in-place, but if I slice the list, it shuffles the sliced copy of the original input, leaving the original list untouched: import random l = list(range(20)) print(l) # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19] random.shuffle(l[:10]) # I wish it was shuffling the first half print(l) # but does nothing to `l` # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13

问题 I have the following code which is attempting to normalize the values of an m x n array (It will be used as input to a neural network, where m is the number of training examples and n is the number of features). However, when I inspect the array in the interpreter after the script runs, I see that the values are not normalized; that is, they still have the original values. I guess this is because the assignment to the array variable inside the function is only seen within the function. How

问题 I have the following code which is attempting to normalize the values of an m x n array (It will be used as input to a neural network, where m is the number of training examples and n is the number of features). However, when I inspect the array in the interpreter after the script runs, I see that the values are not normalized; that is, they still have the original values. I guess this is because the assignment to the array variable inside the function is only seen within the function. How

问题 I have a question related to one asked some years ago on Intel Developer Forum about the in-place reshaping of an array. In short, the answer was that an array of a certain rank can be allocated, and a pointer created that refers to the same memory location (i.e. in-place), but with a different rank, e.g.: use, intrinsic :: ISO_C_BINDING integer, allocatable, target :: rank1_array(:) integer, pointer :: rank3_array(:,:,:) integer :: i ! Allocate rank1_array allocate(rank1_array(24)) ! Created

问题 I am preparing for a software job interview, and I have trouble with in-place array modifications. For example, in the out-shuffle problem you interleave two halves of an array, so that 1 2 3 4 5 6 7 8 would become 1 5 2 6 3 7 4 8. This question asks for a constant-memory solution (and linear-time, although I'm not sure that's even possible). First I thought a linear algorithm is trivial, but then I couldn't work it out. Then I did find a simple O(n^2) algorithm but it took me a long time.

问题 The implementation below is stable as it used <= instead of < at line marked XXX. This also makes it more efficient. Is there any reason to use < and not <= at this line? /** class for In place MergeSort **/ class MergeSortAlgorithm extends SortAlgorithm { void sort(int a[], int lo0, int hi0) throws Exception { int lo = lo0; int hi = hi0; pause(lo, hi); if (lo >= hi) { return; } int mid = (lo + hi) / 2; /* * Partition the list into two lists and sort them recursively */ sort(a, lo, mid); sort

问题 I realize that dropping NaN s from a dataframe is as easy as df.dropna but for some reason that isn't working on mine and I'm not sure why. Here is my original dataframe: fish_frame1: 0 1 2 3 4 5 6 7 0 #0915-8 NaN NaN NaN NaN NaN NaN NaN 1 NaN NaN NaN LIVE WGT NaN AMOUNT NaN TOTAL 2 GBW COD NaN NaN 2,280 NaN $0.60 NaN $1,368.00 3 POLLOCK NaN NaN 1,611 NaN $0.01 NaN $16.11 4 WHAKE NaN NaN 441 NaN $0.70 NaN $308.70 5 GBE HADDOCK NaN NaN 2,788 NaN $0.01 NaN $27.88 6 GBW HADDOCK NaN NaN 16,667