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During development for #1197, we found a potential mathematical inconsistency in the formula for the "Log" unscaling function.
FOQUS/foqus_lib/framework/graph/nodeVars.py
Lines 727 to 728 in fb0777c
For #1197, we found that the following alternative seems to work:
def unscale_log(array_in, lo, hi): result = lo * np.power(hi / lo, array_in) return result
For reference, a plot of the scaling formulas can be found here: https://www.desmos.com/calculator/dxyj2nmpln
The text was updated successfully, but these errors were encountered:
@franflame thank you for opening this issue. You are correct and I agree with your suggested form. The original scaled form is
$scaled = 10 \frac{\log unscaled - \log min}{\log max - \log min}$
Solving for $unscaled$ yields
$\log \frac{unscaled}{min} = \frac{scaled}{10} \log \frac{max}{min}$
$unscaled = min (10 \log \frac{max}{min})^\frac{scaled}{10} = min (\frac{max}{min})^\frac{scaled}{10}$
which matches your alternative formula.
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I concur with @bpaul4, the math checks out.
sotorrio1
bpaul4
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During development for #1197, we found a potential mathematical inconsistency in the formula for the "Log" unscaling function.
FOQUS/foqus_lib/framework/graph/nodeVars.py
Lines 727 to 728 in fb0777c
For #1197, we found that the following alternative seems to work:
For reference, a plot of the scaling formulas can be found here: https://www.desmos.com/calculator/dxyj2nmpln
The text was updated successfully, but these errors were encountered: