Randomize p0 for decomposition #109
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Closes #100. Now we can choose between either a CT-like to BSFG-like initiallization (in addition to providing p0's directly). By default, we choose a BSFG-like initial value. This is because we often find a solution that looks CT-like; so by starting with he competitor model, we are unlikely to have received the CT-like result just due to a local minium. In addition, the input values are by default randomized for each run (currently an initial value betwen 1/5 and 5* the proposed value of the model(s)).
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As for now there is a problem with the choice of the boundaries for the differential evolution, now that we have randomized the input conditions. They should be chosen depending on how the nld / gsf look like before normalization. I get one case now that deviates quite a bit from the others, and with the current automatically set boundaries is lies at ~1/1000 of the absolute values of the rest. |
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Rerunning the notebook might already have solved the problem... |
- Extending the bounds was necessary after chaning the initial conditions - Added "parabola" as a 3rd option in extractor
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Closes #100. Now we can choose between either a CT-like to BSFG-like initiallization (in addition to providing p0's directly). By default, we choose a BSFG-like initial value. This is because we often find a solution that looks CT-like; so by starting with he competitor model, we are unlikely to have received the CT-like result just due to a local minium.
In addition, the input values are by default randomized for each run (currently an initial value betwen 1/5 and 5* the proposed value of the model(s)).