Added tensor-based method to calculate Koopman generator called tgEDMD

[lueckem]

This pull requests adds a tensor-based method for calculating the Koopman generator called tgEDMD. The main functions for this method are in "scikit_tt/data_driven/tgedmd.py". I also added tests in "tests/tgedmd.py" and an example in "examples/lemon_slice_tgedmd.py".

Because tgEDMD needs to access derivatives of basis functions, I replaced the functions that provide the basis functions in "scikit_tt/data_driven/transform.py" with classes. Previous usage is not impacted by this change.

[codecov-commenter]

Codecov Report

Merging #15 into master will decrease coverage by 83.04%. The diff coverage is 1.26%.

@@            Coverage Diff             @@
##           master     #15       +/-   ##
==========================================
- Coverage   89.01%   5.97%   -83.05%     
==========================================
  Files          13      14        +1     
  Lines        1784    2378      +594     
==========================================
- Hits         1588     142     -1446     
- Misses        196    2236     +2040     

Impacted Files	Coverage Δ
scikit_tt/data_driven/regression.py	4.00% <0.00%> (-94.99%)	:arrow_down:
scikit_tt/data_driven/tedmd.py	0.00% <0.00%> (ø)
scikit_tt/data_driven/transform.py	1.27% <0.38%> (-97.88%)	:arrow_down:
scikit_tt/data_driven/tgedmd.py	1.00% <1.00%> (ø)
scikit_tt/tensor_train.py	7.54% <5.88%> (-82.01%)	:arrow_down:
scikit_tt/slim.py	6.06% <0.00%> (-93.94%)	:arrow_down:
scikit_tt/models.py	6.11% <0.00%> (-93.89%)	:arrow_down:
scikit_tt/solvers/sle.py	10.20% <0.00%> (-89.80%)	:arrow_down:
scikit_tt/solvers/evp.py	9.33% <0.00%> (-84.00%)	:arrow_down:
... and 7 more

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[PGelss]

Thank you very much for your efforts. That's great work.