Added x_transform for unevenly spaced data

[vhu43]

My proposed solution for #98

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 98.23%. Comparing base (ea2f90f) to head (dc7fc65).

❗ Current head dc7fc65 differs from pull request most recent head 492d89a. Consider uploading reports for the commit 492d89a to get more accurate results

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #142      +/-   ##
==========================================
+ Coverage   98.20%   98.23%   +0.02%     
==========================================
  Files           5        5              
  Lines         223      226       +3     
==========================================
+ Hits          219      222       +3     
  Misses          4        4              

Flag	Coverage Δ
unittests	98.23% <100.00%> (+0.02%)	⬆️

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[vhu43]

Sorry, accidentally did some wonky stuff with data_generator.py. Fixed this (to the best of my knowledge) and fixed a bug in the transform_x I wrote. I think this should be working

[arvkevi]

Thanks for the PR @vhu43, I'll be able to spend more time with it later this week. I'm concerned because there is a syntax error on line 245 (missing parenthesis) and my CI didn't pick it up, so I'm looking into that issue. In the meantime, do you want to push a fix for the syntax error and get tests passing? When I ran them locally, a few failed.

[vhu43]

Sure thing, I fixed the error specified in it. I am working on some other analysis for my graduate degree, but will be available to fix implementation issues if needed.

[arvkevi]

I think we need to update the threshold logic.

Do you want to try updating this code block, to be:

                knee = self.x[threshold_index]
                norm_knee = self.x_normalized[threshold_index]

Then, to get tests to pass you'd have to make these changes:

Change this line in test sine to this:

    expected_knees = [4.5, 4.9, 7.7, 8.1]

and change this line in test logistic
to this:

        online=False,