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I have to find the maximum value of a numpy array ignoring the diagonal elements.
np.amax() provides ways to find it ignoring specific axes. How can I achieve the same ignoring all the diagonal elements?
321
Numpy diag() function is used to extract or construct a diagonal 2-d array. It contains two parameters: an input array and k , which decides the diagonal, i.e., k=0 for the main diagonal, k=1 for the above main diagonal, or k=-1 for the below diagonal.
NumPy arrays have a fixed size at creation, unlike Python lists (which can grow dynamically). Changing the size of an ndarray will create a new array and delete the original. The elements in a NumPy array are all required to be of the same data type, and thus will be the same size in memory.
This should work:
import numpy as np
import numpy.random
# create sample matrix
a = numpy.random.randint(10,size=(8,8))
a[0,0] = 100
which looks like
array([[100, 8, 6, 5, 5, 7, 4, 5],
[4, 6, 1, 7, 4, 5, 8, 5],
[0, 2, 0, 7, 4, 2, 7, 9],
[5, 7, 5, 9, 8, 3, 2, 8],
[2, 1, 3, 4, 0, 7, 8, 1],
[6, 6, 7, 6, 0, 6, 6, 8],
[6, 0, 1, 9, 7, 7, 9, 3],
[0, 5, 5, 5, 1, 5, 4, 4]])
# create mask
mask = np.ones((8,8))
mask = (mask - np.diag(np.ones(8))).astype(np.bool)
which looks like:
array([[False, True, True, True, True, True, True, True],
[ True, False, True, True, True, True, True, True],
[ True, True, False, True, True, True, True, True],
[ True, True, True, False, True, True, True, True],
[ True, True, True, True, False, True, True, True],
[ True, True, True, True, True, False, True, True],
[ True, True, True, True, True, True, False, True],
[ True, True, True, True, True, True, True, False]], dtype=bool)
Then
# calculate the maximum
out = np.amax(a[mask])
Output:
9
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