AlphabetOptimalDesign

class pyapprox.expdesign.AlphabetOptimalDesign(criteria, design_factors, noise_multiplier=None, opts=None, regression_type='lstsq')

Bases: object

Construct optimal experimental designs using functions of the fisher information matrix

Notes

Even though scipy.optimize.minimize may print the warning “UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations” the Hessian is not zero so do not make this change

Methods Summary

constraint(xx)
objective(xx, **kwargs)
setup_objective(criteria, homog_outer_prods, ...)
solve([options, init_design, return_full])

Methods Documentation

constraint(xx)

objective(xx, **kwargs)

setup_objective(criteria, homog_outer_prods, design_factors, noise_multiplier, opts)

solve(options=None, init_design=None, return_full=False)