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View Code? Open in Web Editor NEWbaseline correction using adaptive iteratively reweighted Penalized Least Squares
License: BSD 3-Clause "New" or "Revised" License
baseline correction using adaptive iteratively reweighted Penalized Least Squares
License: BSD 3-Clause "New" or "Revised" License
The R & Python code for computing the weight vector (e.g., https://github.com/zmzhang/airPLS/blob/a92b37a7bcc4d22eac02db2ebdb0a1b9ce1a1a55/airPLS.py#L81-L83C19) appear to differ from the equation given in the paper:
The code thus weights points with a lower value more highly, whereas the paper does the opposite. I don't know why the code uses a value for the first and last weights that raises e to a negative power.
1.what is the use of i on line 50?
2.How to adjust itermax?
The array edge value is determined by line 82 in the Python implementation.
It occurred to me that this expression is taking the maximum of negative values d[d<0].max()
which will likely be close to 0
in most cases... resulting in the exponential evaluating to 1
.
If this is not the intention, should the algorithm be updated to the max of the absolute of the negative values (i.e. d[d<0].abs().max()
)?
If this is the intention, can we just save the effort and set the value to 1
? The difference between 1
and very close to 1
doesn't seem worth the computation here...
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