Comments (8)
We might just want to give the option of passing **kwargs
to FitBase.fit
.
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If set to ‘jac’, the scale is iteratively updated using the inverse norms of the columns of the Jacobian matrix (as described in [JJMore]).
That's more or less what I was referring to – would this work for us? If we can avoid having to manually set this in 99.999% of cases, this seems like a huge win.
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That might do the trick. Would it hurt to expose **kwargs though?
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Probably not – at least if it's a named dictionary (curve_fit_args
? fit_args
? algorithm_options
?), where it doesn't make the FitBase
API more convoluted. I'd just like to avoid making the common case more complex.
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I've modified FitBase
to use x_scale='jac'
. However, **kwargs
should still be exposed.
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Will do that later.
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We might need update scipy
in our artiq-env
for that to work, by the way.
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Un-assigning myself, as FitBase
seems like a lost cause (or, to be more precise, requires serious amounts of work to be generally useful for my experiments, like proper support for multi-dimensional data), and I'm thus unlikely to do much work on it.
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Related Issues (20)
- Gradient descent based fitting for non-gaussian distributions HOT 6
- oitg.fitting.sinusoid unit tests fail HOT 5
- oitg/fitting/dipole_bsb_car_rsb should use relative imports HOT 1
- Fitting with no parameter bounds raises exception HOT 1
- Calculateing Binomial Data-Point Confidence Interval HOT 2
- Global Fit Optimisation Routine HOT 2
- Timeresolved Readout Routines HOT 1
- Account for spontaneous shelving and deshelving in threshold.py
- upstream the results code HOT 4
- documentation: document fitting.sinusoid parameters properly
- fitting: depreciate sin/cos in favor of fitting.sinusoid HOT 1
- Line fitting fails for two data points
- Error for Loading results HOT 3
- exponential_decay: `t_1_e` error propagation
- `fitting.parabola` does not calculate extremum position error
- v_function: improve heuristics HOT 2
- FitBase should pass covariance matrix to the derived_parameter_function
- FitBase: pass constants and user initialised parameters to parameter_initialiser HOT 1
- Robust sinc^2-like frequency scan fit for arbitrary pulse areas HOT 4
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