Calculating cumulative minimum with numpy arrays
For any 2-argument NumPy universal function, its accumulate
method is the cumulative version of that function. Thus, numpy.minimum.accumulate
is what you're looking for:
>>> numpy.minimum.accumulate([5,4,6,10,3])
array([5, 4, 4, 4, 3])
Create a matrix which lower triangle (np.tril
) is filled with values of your array nums
and your upper triangle (np.triu
, with second parameter 1, so the diagonal stays free) is filled with the maximum of the array. (EDIT: instead of the maximum, the first element of the array is the better way. -> comments)
nums = np.array([5.,3.,4.,2.,1.,1.,2.,0.])
oneSquare = np.ones((nums.size, nums.size))
A = nums * np.tril(oneSquare)
B = np.triu(oneSquare, 1) * nums[0]
A, B
Out:
(array([[ 5., 0., 0., 0., 0., 0., 0., 0.],
[ 5., 3., 0., 0., 0., 0., 0., 0.],
[ 5., 3., 4., 0., 0., 0., 0., 0.],
[ 5., 3., 4., 2., 0., 0., 0., 0.],
[ 5., 3., 4., 2., 1., 0., 0., 0.],
[ 5., 3., 4., 2., 1., 1., 0., 0.],
[ 5., 3., 4., 2., 1., 1., 2., 0.],
[ 5., 3., 4., 2., 1., 1., 2., 0.]]),
array([[ 0., 5., 5., 5., 5., 5., 5., 5.],
[ 0., 0., 5., 5., 5., 5., 5., 5.],
[ 0., 0., 0., 5., 5., 5., 5., 5.],
[ 0., 0., 0., 0., 5., 5., 5., 5.],
[ 0., 0., 0., 0., 0., 5., 5., 5.],
[ 0., 0., 0., 0., 0., 0., 5., 5.],
[ 0., 0., 0., 0., 0., 0., 0., 5.],
[ 0., 0., 0., 0., 0., 0., 0., 0.]]))
Now take the minimum of each row:
(A+B).min(axis=1)
Out:
array([ 5., 3., 3., 2., 1., 1., 1., 0.])