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Which beta is being calculated by get_optimal_temperature on tools.py? #8
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This is the beta maximizing the conditional loglikelihood, i.e. the global one. It uses a root-finding method (brentq), to solve eq S39 of the paper (in the Supplementary Material). I will also upload an alternative method: instead of solving via root-finding method, one can use scipy.optimize.fmin_l_bfgs_b() to make python search for the optimal parameter value in order to maximize a function. This method allows to define custom cost functions, scipy.optimize does the rest. |
This is the beta maximizing the conditional loglikelihood, i.e. the global
one. It uses a root-finding method (brentq), to solve eq S39 of the paper
(in the Supplementary Material).
I will upload in the next few days a similar equivalent to calculate the
local one, i.e. an implementation for eq. S36.
I will also upload an alternative method: instead of solving via
root-finding method, one can use scipy.optimize.fmin_l_bfgs_b() to make
python search for the optimal parameter value in order to maximize a
function. This method allows to define custom cost functions,
scipy.optimize does the rest.
Il giorno lun 17 dic 2018 alle ore 13:04 edsf70 <notifications@github.com>
ha scritto:
… On *tools.py* which beta is calculated by *get_optimal_temperature*?
Global or local beta? Would you please be so kind providing functions to
calculate both? Thank you very much indeed!
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That would be great!!! Thank you very much indeed!! |
Would you please be so kind telling me if there is already an implementation for eq. S36 (local Beta)? |
@edsf70 I've implemented it based on the eqs39 implementation and it turned out like this:
Only thing is that it is not |
On tools.py which beta is calculated by get_optimal_temperature? Global or local beta? Would you please be so kind providing functions to calculate both? Thank you very much indeed!
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