Error about GPfit

[agopb]

The rBayesianOptimization sometimes generates an error during iteration as follows:

Error in GPfit::GP_fit(X = Par_Mat[Rounds_Unique, ], Y = Value_Vec[Rounds_Unique], : unused argument (verdose = TRUE)

Below is my code.

set.seed(1234) folds<-createFolds(c(1:nrow(t2)),k=5)

model1<-function(hidden1,learningrate1){ model_calib<-neuralnet(cor~.,data=z1,hidden=hidden1,learningrate=learningrate1, stepmax = 1e+08) model_valid<-predict(model_calib,z2)

list(Score=abs(cor(model_valid,z2$cor)), Pred=0) }

result<-as.matrix(1:nrow(t2),1) result<-as.matrix(result) max_par2<-vector() for(i in 1:5) { print(i)

n2<-folds[[i]]

z1<<-t2[-n2,c("cor","slope","altitude","tmp","soil","pre","evapo","NDVI")] z2<<-t2[n2,c("cor","slope","altitude","tmp","soil","pre","evapo","NDVI")]

Ba_op<-BayesianOptimization(model1, bounds=list(hidden1=c(1L,10L), learningrate1=c(0.0001,0.5)), init_grid_dt=data.frame(hidden1=c(1L,2L), learningrate1=c(0.0001,0.0002)), init_points=10,n_iter=40, kappa=2.576,eps=0.0, verdose=TRUE) max_par1<-cbind(i,Ba_op$Best_pat) max_par2<-rbind(max_par2,max_par1)

model_calib<-neuralnet(cor~.,data=z1,hidden=Ba_op$Best_Par["hidden1"],learningrate=Ba_op$Best_Par["learningrate1"], stepmax = 1e+06)

model_valid<-predict(model_calib,z2) model_valid<-as.matrix(model_valid)

result[n2,1]<-model_valid }