I want to find the maximum value for a that doesn't change the output from another function. (_find_mean simply finds the location of the maximum element in a matrix)
This is how I am currently doing it and was wondering if there was a better way.
def _find_max_a(matrix1, matrix2):
a = np.arange(0,10, 0.01)[::-1]
for val in a:
mat = matrix1 + val * matrix2
if _find_mean(matrix1) == _find_mean(mat):
return val
else:
pass
arange can build a descending vector. Building an ascending vector and reversing it seems wasteful. Consider
a = np.arange(10, 0, -0.01)
matrix1 is never modified, but its _find_mean is recomputed on each iteration. Yet another waste. Compute it once.
Instead of linear search, you may want to consider a bisection of the search interval.
else: pass achieves nothing, and can be safely omitted.
_find_mean is a kinda strange name for a function which
finds the location of the maximum element in a matrix
arange thing more efficiently. For larger range with finer accuracy, the amount of required memory is quite high (800kB for range 100, 0, -0.001 with dtype of np.float64, which is the default). Loop with manual decrement by 0.001 would introduce errors, whereas a scaled loop would require division.
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awhich doesn't change the output from the other function? \$\endgroup\$a, it loops on values ofa. So is your description correct? \$\endgroup\$