Unconstrained least-squares parameter and initial condition optimizer
for n-dimensional DS trajectories. Fits N-dimensional parameter
spaces.
Uses MINPACK Levenberg-Marquardt algorithm wrapper from
SciPy.minimize.
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run(self,
parDict=None,
extra_pars=None,
verbose=False)
Begin parameter estimation run. |
source code
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_make_res_float(self,
pars)
Returns a function that converts residual vector to its norm (a
single floating point total residual). |
source code
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gradient_total_residual(self,
x,
eps=None,
pars=None,
use_ridder=False)
Compute gradient of total residual (norm of the residual function) at
x as a function of parameter names specified (defaults to all free
parameters). |
source code
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Hessian_total_residual(self,
x,
eps_inner=None,
eps_outer=None,
pars=None,
use_ridder_inner=False,
use_ridder_outer=False)
Compute Hessian of total residual (norm of the residual function) at
x as a function of parameter names specified (defaults to all free
parameters), USING FINITE DIFFERENCES. |
source code
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Inherited from ParamEst :
__init__ ,
evaluate ,
find_logs ,
iterate ,
key_logged_residual ,
par_sensitivity ,
pars_array_to_dict ,
pars_dict_to_array ,
pars_to_ixs ,
resetParArgs ,
reset_log ,
setFn ,
show_log_record ,
weighted_par_sensitivity
Inherited from object :
__delattr__ ,
__getattribute__ ,
__hash__ ,
__new__ ,
__reduce__ ,
__reduce_ex__ ,
__repr__ ,
__setattr__ ,
__str__
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