Hi,
I havent found exactly this problem before, and keen if anyone has any ideas. I have a 2d matrix x containing integer values. I want to count how many of each value per row and return a new 2d matrix num_of_value  that has the same number of rows, but the columns represent different values. So in num_of_value[0,1] is the number of occurences of 1 in row=0 and so on. My best solution so far is a loop, which might be inefficient for large number of unique values.
Any ideas?
#%%
x =  torch.tensor([[1, 2, 3, 4, 2, 5, 6, 7, 8, 9, 7, 0, 0, 6, 9, 0, 1, 0, 0],
        [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 1, 4, 1, 1, 2, 3, 1]])
# %%
# Loop through all values:

num_of_value = torch.zeros((len(x), x.max()+1), device=self.device)
for k in range(0,max_cluster_idx):
    num_k = (x == k).sum(dim=1)
    num_of_value[:,k] = num_k

print(num_of_value)
tensor([[ 5.,  2.,  2.,  1.,  1.,  1.,  2.,  2.,  1.,  2.],
        [ 0., 13.,  2.,  3.,  1.,  0.,  0.,  0.,  0.,  0.]])