tracts.legacy.optimize_cob_fracs2#

optimize_cob_fracs2(p0, bins, Ls, data, nsamp, model_func, fracs, outofbounds_fun=None, cutoff=0, verbose=0, flush_delay=1, epsilon=0.001, gtol=1e-05, maxiter=None, full_output=True, func_args=None, fixed_params=None, ll_scale=1, reset_counter=True)#

Optimizes params to fit model to data using the cobyla method.

Parameters:

  • p0 – Initial parameters.

  • data – Spectrum with data.

  • model_function – Function to evaluate model spectrum. Should take arguments (params, pts).

  • out_of_bounds_fun (default None) – A function evaluating to True if the current parameters are in a forbidden region.

  • cutoff (default 0) – The number of bins to drop at the beginning of the array. This could be achieved with masks.

  • verbose (default 0) – If greater than zero, print optimization status every verbose steps.

  • flush_delay (default 0.5) – Standard output will be flushed once every flush_delay minutes. This is useful to avoid overloading I/O on clusters.

  • epsilon (default 1e-3) – Step-size to use for finite-difference derivatives.

  • gtol (default 1e-5) – Convergence criterion for optimization. For more info, see help(scipy.optimize.fmin_bfgs).

  • maxiter (default None) – Maximum iterations to run for.

  • full_output (default True) – If True, returns full outputs as described in help.(scipy.optimize.fmin_bfgs).

  • func_args (default None) – List of additional arguments to model_func. It is assumed that model_func’s first argument is an array of parameters to optimize.

  • fixed_params (default None) – (Not yet implemented). If not None, should be a list used to fix model parameters at particular values. For example, if the model parameters are (nu1,nu2,T,m), then fixed_params = [0.5,None,None,2] will hold nu1=0.5 and m=2. The optimizer will only change T and m. Note that the bounds lists must include all parameters. Optimization will fail if the fixed values lie outside their bounds. A full-length p0 should be passed in; values corresponding to fixed parameters are ignored.

  • ll_scale (default 1) – The bfgs algorithm may fail if your initial log-likelihood is too large. (This appears to be a flaw in the scipy implementation.) To overcome this, pass ll_scale > 1, which will simply reduce the magnitude of the log-likelihood. Once in a region of reasonable likelihood, you’ll probably want to re-optimize with ll_scale=1.

  • reset_counter (default True) – Resets the iteration counter to zero. Set to False to continue iteration count (e.g., if optimization continues from previous point).

Notes

This optimization method works well when we start reasonably close to the optimum. It is best at burrowing down a single minimum. It should also perform better when parameters range over scales.