How can I convert and reshape the following list to the 2D array with zeros?
# original list
[1, 0.96, 0.92, 0.88]
# 2D Array
[[1 0 0 0 ]
[0.96 1 0 0 ]
[0.92 0.96 1 0 ]
[0.88 0.92 0.96 1 ]]
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Would you please explain a bit? converting 1D to 2D is not a big deal but the output you want points something.Innat– Innat2020-10-26 08:45:02 +00:00Commented Oct 26, 2020 at 8:45
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Is the floating values in a 2D array need to follow some rules?Innat– Innat2020-10-26 08:45:46 +00:00Commented Oct 26, 2020 at 8:45
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I want to use the 2d array as a dot product with another array to measure decay. No rule, just that the floating values need to be upto 2 decimal points.Shantanu– Shantanu2020-10-26 08:46:14 +00:00Commented Oct 26, 2020 at 8:46
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@M.Innat The rule is quite obvious.. the 1D vector is repeated in each column with a shift by one down in every column cropping the last value. Have you tried with a for loop at least to show the process and then ask for a vectorized implementation?Mathieu– Mathieu2020-10-26 08:47:10 +00:00Commented Oct 26, 2020 at 8:47
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For loop will be slow, so checking if there is some funky way to do so in NumPy itself.Shantanu– Shantanu2020-10-26 08:50:17 +00:00Commented Oct 26, 2020 at 8:50
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3 Answers
Here's a funky vectorized way. We can leverage np.lib.stride_tricks.as_strided based scikit-image's view_as_windows to get sliding windowed views and solve it.
from skimage.util.shape import view_as_windows
# a is input array (convert to array with np.array() is input is list)
p = np.r_[a[::-1], np.zeros(len(a)-1, dtype=a.dtype)]
out = view_as_windows(p,len(a))[::-1]
Alternatively, keep it native to NumPy -
m = len(a)
n = p.strides[0]
out = np.lib.stride_tricks.as_strided(p[m-1:], shape=(m,m), strides=(-n,n))
1 Comment
Innat
Interesting, really funky ;)
The correct implementation with a for loop is:
import numpy as np
A = np.array([1, 0.96, 0.92, 0.88])
B = np.zeros((A.shape[0], A.shape[0]))
for i in range(A.shape[0]):
B[i:, i] = A[:A.shape[0]-i]
There should be a way to vectorize this and to get rid of the for loop for efficiency.. Anyone has an idea?
I found this SO post which is relatively similar and has a bunch of vectorized implementations: Sliding window of M-by-N shape numpy.ndarray
3 Comments
Innat
I found that
view_as_windows (1.42 s) is much slower than your solution, for loop (100 ms).
Divakar
@M.Innat That's not possible. Check again? Unless you are benchmarking on the 4 elements data?
Mathieu
@M.Innat That completely depends on the size of the data array. For a 4x4 array, a for loop is not that slow, but if you increase the size, the vectorized implementation will be faster.
Following works for you:
import numpy as np
arr = np.array([1, 0.96, 0.92, 0.88])
arr_cp = np.zeros((arr.shape[0], arr.shape[0]))
for i in range(arr.shape[0]):
arr_cp[i][:i+1] = np.flip(arr[:i+1])
print(arr_cp)
Output:
[[1. 0. 0. 0. ] [0.96 1. 0. 0. ] [0.92 0.96 1. 0. ] [0.88 0.92 0.96 1. ]]
