I have a symmetric matrix represented as a numpy array, like the following example:
[[ 1. 0.01735908 0.01628629 0.0183845 0.01678901 0.00990739 0.03326491 0.0167446 ] [ 0.01735908 1. 0.0213712 0.02364181 0.02603567 0.01807505 0.0130358 0.0107082 ] [ 0.01628629 0.0213712 1. 0.01293289 0.02041379 0.01791615 0.00991932 0.01632739] [ 0.0183845 0.02364181 0.01293289 1. 0.02429031 0.01190878 0.02007371 0.01399866] [ 0.01678901 0.02603567 0.02041379 0.02429031 1. 0.01496896 0.00924174 0.00698689] [ 0.00990739 0.01807505 0.01791615 0.01190878 0.01496896 1. 0.0110924 0.01514519] [ 0.03326491 0.0130358 0.00991932 0.02007371 0.00924174 0.0110924 1. 0.00808803] [ 0.0167446 0.0107082 0.01632739 0.01399866 0.00698689 0.01514519 0.00808803 1. ]]
And I need to find the indices (row and column) of the greatest value without considering the diagonal. Since is a symmetric matrix I just took the the upper triangle of the matrix.
ind = np.triu_indices(M_size, 1)
And then the index of the max value
max_ind = np.argmax(H[ind])
However max_ind is the index of the vector resulting after taking the upper triangle with triu_indices, how do I know which are the row and column of the value I've just found?
The matrix could be any size but it's always symmetric. Do you know a better method to achieve the same? Thank you
Couldn't you do this by using np.triu to return a copy of your matrix with all but the upper triangle zeroed, then just use np.argmax and np.unravel_index to get the row/column indices?
Example:
x = np.zeros((10,10))
x[3, 8] = 1
upper = np.triu(x, 1)
idx = np.argmax(upper)
row, col = np.unravel_index(idx, upper.shape)
The drawback of this method is that it creates a copy of the input matrix, but it should still be a lot quicker than looping over elements in Python. It also assumes that the maximum value in the upper triangle is > 0.