I want to test the performance of some code using an exponentially increasing value. So that as an extra digit is added to the numbers_size the increment is multiplied by 10. This is how I'm doing it so far but it looks a bit hacky. Suggestions for improvements without introducing non-standard libraries?
numbers_size = 100
increment = 100
numbers_range = 1000000000
while numbers_size < numbers_range:
t = time.time()
test( numbers_size )
taken_t = time.time() - t
print numbers_size, test, taken_t
increment = 10 ** (len(str(numbers_size))-1)
numbers_size += increment
If you consider numpy as one of the standards ;), you may use numpy.logspace since that is what it is supposed to do.... (note: 100=10^2, 1000000000=10^9)
for n in numpy.logspace(2,9,num=9-2, endpoint=False):
test(n)
example 2 (note: 100=10^2, 1000000000=10^9, want to go at a step 10x, it is 9-2+1 points...):
In[14]: np.logspace(2,9,num=9-2+1,base=10,dtype='int')
Out[14]:
array([ 100, 1000, 10000, 100000, 1000000,
10000000, 100000000, 1000000000])
example 3:
In[10]: np.logspace(2,9,dtype='int')
Out[10]:
array([ 100, 138, 193, 268, 372,
517, 719, 1000, 1389, 1930,
2682, 3727, 5179, 7196, 10000,
13894, 19306, 26826, 37275, 51794,
71968, 100000, 138949, 193069, 268269,
372759, 517947, 719685, 1000000, 1389495,
1930697, 2682695, 3727593, 5179474, 7196856,
10000000, 13894954, 19306977, 26826957, 37275937,
51794746, 71968567, 100000000, 138949549, 193069772,
268269579, 372759372, 517947467, 719685673, 1000000000])
on your case, we use endpoint=False
since you want not to include the endpoint... (e.g. np.logspace(2,9,num=9-2, endpoint=False)
)
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