I have ML ready samples. And each sample has a weight.
The weights distribute between
My problem arise because there are a lot of samples which are
And a lot of samples which are
I am going to sample data based on these weights. And samples with 0.99x will overshadow the other samples in the data set while 0.00x samples will have 0 significance.
The solution I am looking for, is some kind of function over those weights that will balance them a little bit / reduce those huge gaps (Therefor reducing the variance) AND still preserve their order
So if 0.997 turned into 0.88, 0.996 will turn into something < 0.88
[0.01, 0.1, 0.4, 0.6, 0.8, 0.997]
I would want something like:
[0.15, 0.2, 0.42, 0.6, 0.78, 0.9] (Just an example)