For example, I have a stream of array with numbers ranging from 0.0
to 10.0
inclusive.
I want to assign the numbers in arr
to 5
bins of equal length quickly.
By equal length I mean the bin intervals are [0.0, 2.0), [2.0, 4.0), [4.0, 6.0), [6.0, 8.0), [8.0, 10.0]
.
The problem is that the last interval is not same as the other intervals.
Test:
import numpy as np
# Things we know and can pre-calculate
n_bins = 5
minimal = 0.0
maximal = 10.0
reciprocal_bin_length = n_bins / (maximal - minimal)
# Let's say the stream gives 1001 numbers every time.
data = np.arange(1001)/100
norm_data = (data - minimal) * reciprocal_bin_length
norm_data = norm_data.astype(int)
print(norm_data.max())
print(norm_data.min())
Result:
5
0
The bin index should be 0, 1, 2, 3, or 4, but not 5.
A "poor man's solution" could be to calculate the minimum between your array norm_data
and nbins-1
:
norm_data = np.minimum(norm_data,nbins-1)
So all 5
s (and above) will be converted into 4
s. Mind that of course here you will not do a proper range check (120.0
will also end up in bin 4).