You can use np.pad(): a = np.ones((4, 3, 2)) # npad is a tuple of (n_before, n_after) for each dimension npad = ((0, 0), (1, 2), (2, 1)) b = np.pad(a, pad_width=npad, mode=”constant”, constant_values=0) print(b.shape) # (4, 6, 5) print(b) # [[[ 0. 0. 0. 0. 0.] # [ 0. 0. 1. 1. 0.] # … Read more
Pass an explicit pointer to the first element with the array dimensions as separate parameters. For example, to handle arbitrarily sized 2-d arrays of int: void func_2d(int *p, size_t M, size_t N) { size_t i, j; … p[i*N+j] = …; } which would be called as … int arr1[10][20]; int arr2[5][80]; … func_2d(&arr1[0][0], 10, 20); … Read more
It’s only worth trying to do this in-place if you are under significant space constraints. If that’s the case, it is possible to speed up your code a little bit by iterating over a flattened view of the array. Since reshape returns a new view when possible, the data itself isn’t copied (unless the original … Read more
Should be private ArrayList<String[]> action = new ArrayList<String[]>(); action.add(new String[2]); … You can’t specify the size of the array within the generic parameter, only add arrays of specific size to the list later. This also means that the compiler can’t guarantee that all sub-arrays be of the same size, it must be ensured by you. … Read more
list.sort, sorted accept optional key parameter. key function is used to generate comparison key. >>> sorted(lst, key=lambda x: x[1], reverse=True) [[‘I’, 219], [‘A’, 22], [‘P’, 14], [‘V’, 13], [‘G’, 10], …] >>> sorted(lst, key=lambda x: -x[1]) [[‘I’, 219], [‘A’, 22], [‘P’, 14], [‘V’, 13], [‘G’, 10], …] >>> import operator >>> sorted(lst, key=operator.itemgetter(1), reverse=True) [[‘I’, … Read more
not is implemented through the __nonzero__ special method, which is required to return either True or False, so it can’t give the required result. Instead the ~ operator is used, which is implemented through the __not__ special method. For the same reason, & and | are used in place of and and or. PEP 335 … Read more
from_numpy() automatically inherits input array dtype. On the other hand, torch.Tensor is an alias for torch.FloatTensor. Therefore, if you pass int64 array to torch.Tensor, output tensor is float tensor and they wouldn’t share the storage. torch.from_numpy gives you torch.LongTensor as expected. a = np.arange(10) ft = torch.Tensor(a) # same as torch.FloatTensor it = torch.from_numpy(a) a.dtype … Read more
array_reduce($array, ‘array_merge’, array()) Example: $a = array(array(1, 2, 3), array(4, 5, 6)); $result = array_reduce($a, ‘array_merge’, array()); Result: array[1, 2, 3, 4, 5, 6];
A 2x faster approach would be to just use np.count_nonzero() but with the condition as needed. In [3]: arr Out[3]: array([[1, 2, 0, 3], [3, 9, 0, 4]]) In [4]: np.count_nonzero(arr==0) Out[4]: 2 In [5]:def func_cnt(): for arr in arrs: zero_els = np.count_nonzero(arr==0) # here, it counts the frequency of zeroes actually You can also … Read more