Inputing images into an array in Javascript and then displaying them in a table
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , How about using appendChildfor(var i = 1; i < 6; i++) {
document.getElementById('d' + i).appendChild(diceB[i]);

Tag : python , By : user109127
Date : March 29 2020, 07:55 AM
Does that help Pretty simple really with view_as_windows from scikitimage, to get those sliding windowed views as a 6D array with the fourth axis being singleton. Then, use advancedindexing to select the ones we want based off the y and x indices for indexing into the second and third axes of the windowed array to get our B. Hence, the implementation would be  from skimage.util.shape import view_as_windows
BSZ = 16, 16 # Blocksize
A6D = view_as_windows(A,(1,BSZ[0],BSZ[1]))
B_out = A6D[np.arange(N),y,x,0]
In [78]: A
Out[78]:
array([[[ 5, 5, 3, 5, 3, 8],
[ 5, *2, 6, 2, 2, 4],
[ 4, 3, 4, 9, 3, 8],
[ 6, 3, 3, 10, 4, 5],
[10, 2, 5, 7, 6, 7],
[ 5, 4, 2, 5, 2, 10]],
[[ 4, 9, 8, 4, 9, 8],
[ 7, 10, 8, 2, 10, 9],
[10, *9, 3, 2, 4, 7],
[ 5, 10, 8, 3, 5, 4],
[ 6, 8, 2, 4, 10, 4],
[ 2, 8, 6, 2, 7, 5]],
[[ *4, 8, 7, 2, 9, 9],
[ 2, 10, 2, 3, 8, 8],
[10, 7, 5, 8, 2, 10],
[ 7, 4, 10, 9, 6, 9],
[ 3, 4, 9, 9, 10, 3],
[ 6, 4, 10, 2, 6, 3]]])
In [79]: y
Out[79]: array([1, 2, 0])
In [80]: x
Out[80]: array([1, 1, 0])
In [81]: B
Out[81]:
array([[[ 2, 6],
[ 3, 4]],
[[ 9, 3],
[10, 8]],
[[ 4, 8],
[ 2, 10]]])

Pytorch tensor to numpy array
Tag : python , By : user165781
Date : March 29 2020, 07:55 AM
I wish this helpful for you I have a pytorch Tensor of size torch.Size([4, 3, 966, 1296]) , There are 4 dimensions of the tensor you want to convert. [:, ::1, :, :]

Inputing 2d column array into 2d numpy array
Date : March 29 2020, 07:55 AM
around this issue what is the "pythonic" way of inputting 2d column vector to a 2d numpy array (actual 2d array)? example problem below: , found it: sigma_points[:, [0]] = mu

pytorch compute pairwise difference: Incorrect result in NumPy vs PyTorch and different PyTorch versions
Tag : python , By : snapshooter
Date : March 29 2020, 07:55 AM
To fix the issue you can do The issue arises because of using PyTorch 0.1. If using PyTorch 1.0.1, the same operation of NumPy generalize to PyTorch without any modifications and issues. Here is a snapshot of the run in Colab. >>> t1 = torch.from_numpy(a)
>>> t2 = torch.from_numpy(b)
>>> t1[np.newaxis, ...]  t2[:, np.newaxis, ...]
(0 ,.,.) =
2 2 2
1 2 4
[torch.LongTensor of size 1x2x3]
>>> torch.__version__
'0.1.12_1'
>>> t1[:1, ]  t2
>>> tensor([[2, 2, 2], # t1_r1
[4, 1, 1]]) # t1_r2
>>> t1[1:, ]  t2
>>> tensor([[ 1, 1, 1], # t2_r1
[1, 2, 4]]) # t2_r2

