AlphabetOptimalDesign

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

Bases: object

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.

Methods Summary

bayesian_objective_jacobian_components(…)
get_objective_and_jacobian(design_factors, …)
minimax_nonlinear_constraints(…)
solve([options, init_design, return_full])
solve_nonlinear_bayesian(samples, design_samples)
solve_nonlinear_minimax(parameter_samples, …)

Methods Documentation

bayesian_objective_jacobian_components(parameter_samples, design_samples)

get_objective_and_jacobian(design_factors, homog_outer_prods, noise_multiplier, opts)

minimax_nonlinear_constraints(parameter_samples, design_samples)

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

solve_nonlinear_bayesian(samples, design_samples, sample_weights=None, options=None, return_full=False, x0=None)

solve_nonlinear_minimax(parameter_samples, design_samples, options=None, return_full=False, x0=None)