How can I quickly find the min or max sum of the elements of a row in an array?
For example:
1, 2
3, 4
5, 6
7, 8
The minimum sum would be row 0 (1 + 2), and the maximum sum would be row 3 (7 + 8)
print mat.shape
(8, 1, 2)
print mat
[[[-995.40045 -409.15112]]
[[-989.1511 3365.3267 ]]
[[-989.1511 3365.3267 ]]
[[1674.5447 3035.3523 ]]
[[ 0. 0. ]]
[[ 0. 3199. ]]
[[ 0. 3199. ]]
[[2367. 3199. ]]]
In native Python, min and max have key functions:
>>> LoT=[(1, 2), (3, 4), (5, 6), (7, 8)]
>>> min(LoT, key=sum)
(1, 2)
>>> max(LoT, key=sum)
(7, 8)
If you want the index of the first min or max in Python, you would do something like:
>>> min(((i, t) for i, t in enumerate(LoT)), key=lambda (i,x): sum(x))
(0, (1, 2))
And then peel that tuple apart to get what you want. You also could use that in numpy, but at unknown (to me) performance cost.
In numpy, you can do:
>>> a=np.array(LoT)
>>> a[a.sum(axis=1).argmin()]
array([1, 2])
>>> a[a.sum(axis=1).argmax()]
array([7, 8])
To get the index only:
>>> a.sum(axis=1).argmax()
3
You can do this using np.argmin and np.sum:
array_minimum_index = np.argmin([np.sum(x, axis=1) for x in mat])
array_maximum_index = np.argmax([np.sum(x, axis=1) for x in mat])
For your array, this results in array_minimum_index = 0 and array_maximum_index = 7, as your sums at those indices are -1404.55157 and 5566.0
To simply print out the values of the min and max sum, you can do this:
array_sum_min = min([np.sum(x,axis=1) for x in mat])
array_sum_max = max([np.sum(x,axis=1) for x in mat])
x = np.sum(x,axis=1)
min_x = x.min()
max_x = x.max()
presuming x is 4,2 array use np.sum to sum across the rows, then .min() returns the min value of your array and .max() returns the max value
You can use min and max and use sum as their key.
lst = [(1, 2), (3, 4), (5, 6), (7, 8)]
min(lst, key=sum) # (1, 2)
max(lst, key=sum) # (7, 8)
If you want the sum directly and you do not care about the tuple itself, then map can be of help.
min(map(sum, lst)) # 3
max(map(sum, lst)) # 15
