I have an input array X.
[[68 72 2 0 15 74 34 20 36 3]
[20 2 79 20 80 45 15 20 11 45]
[42 13 80 35 3 3 38 70 74 75]
[80 20 78 5 34 13 80 11 20 72]
[20 13 15 20 13 75 81 20 75 13]
[20 32 15 20 29 2 75 3 45 80]
[72 74 80 20 64 45 79 74 20 1]
[37 20 6 5 15 20 80 45 29 20]
[15 20 13 75 80 65 15 35 20 60]
[20 75 2 13 78 20 15 45 20 72]]
Can someone help me understand the below code -
y = np.zeros_like(x)
y[:, :-1], y[:, -1] = x[:, 1:], x[:, 0]
First:
y = np.zeros_like(x)
This creates an array full of zeros with the same size as x and stores it in y.
Then y[:, :-1], y[:, -1] <- all but the last column, and the last column
is set = to:
x[:, 1:], x[:, 0] <- all but the first column, and the first column.
It's a very inefficient way to roll the first column to the last.
A much better way to do this is
y = np.roll(x, -1, axis = 1)
Shorthand for:
y = np.zeros_like(x)
y[:, :-1] = x[:, 1:]
y[:, -1] = x[:, 0]
Which translates to:
Make y an array of 0s of the same dimensions as x.
Set the part of y which includes all the rows and all columns except the last one equal to the part of x which includes all the rows and all columns except the first one.
Set the last column of y equal to the first column of x.
Basically, y will look like x except with the first column removed and tacked on at the end.
