www.gusucode.com > stats 源码程序 matlab案例代码 > stats/PredictEvalutionTimeOfObjectiveInAnOptimizedModelExample.m
%% Predict Evalution Time of Objective In an Optimized Model % This example shows how to estimate the objective function evalution time % in an optimized Bayesian model of SVM classification. %% % Create an optimized SVM model. For details of this model, see % <docid:stats_ug.bvan2wn-1>. rng default grnpop = mvnrnd([1,0],eye(2),10); redpop = mvnrnd([0,1],eye(2),10); redpts = zeros(100,2); grnpts = redpts; for i = 1:100 grnpts(i,:) = mvnrnd(grnpop(randi(10),:),eye(2)*0.02); redpts(i,:) = mvnrnd(redpop(randi(10),:),eye(2)*0.02); end cdata = [grnpts;redpts]; grp = ones(200,1); grp(101:200) = -1; c = cvpartition(200,'KFold',10); sigma = optimizableVariable('sigma',[1e-5,1e5],'Transform','log'); box = optimizableVariable('box',[1e-5,1e5],'Transform','log'); minfn = @(z)kfoldLoss(fitcsvm(cdata,grp,'CVPartition',c,... 'KernelFunction','rbf','BoxConstraint',z.box,... 'KernelScale',z.sigma)); results = bayesopt(minfn,[sigma,box],'IsObjectiveDeterministic',true,... 'AcquisitionFunctionName','expected-improvement-plus','Verbose',0); %% % Predict the evaluation time for various points. sigma = logspace(-5,5,11)'; box = 1e5*ones(size(sigma)); XTable = table(sigma,box); time = predictObjectiveEvaluationTime(results,XTable); [XTable,table(time)]