aoptimality_criterion¶
-
pyapprox.optimal_experimental_design.
aoptimality_criterion
(homog_outer_prods, design_factors, design_prob_measure, return_grad=True, noise_multiplier=None, regression_type='lstsq')[source]¶ Evaluate the A-optimality criterion for a given design probability measure for the linear model
y(x)=F(x)θ+η(x)ϵ.The criteria is
trace[C(μ)]where
C(μ)=M−11M0M−1- Parameters
- homog_outer_prodsnp.ndarray (num_design_factors,num_design_factors,
num_design_pts)
The hessian M_1 of the error for each design point
- design_factorsnp.ndarray (num_design_pts,num_design_factors)
The design factors evaluated at each of the design points
- design_prob_measurenp.ndarray (num_design_pts)
The prob measure μ on the design points
- return_gradboolean
True - return the value and gradient of the criterion False - return only the value of the criterion
- noise_multipliernp.ndarray (num_design_pts)
The design dependent noise function η(x)
- regression_typestring
The method used to compute the coefficients of the linear model. Currently supported options are
lstsq
andquantile
.
- Returns
- valuefloat
The value of the objective function
- gradnp.ndarray (num_design_pts)
The gradient of the objective function. Only if return_grad is True.