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- KRR_logit_predict <- function(test_data, train_data, alphas_pred, sigma, dist_method = "Euclidean", progress = TRUE){
- # example: KRR_logit_predict(test_dat, train_dat, theSol, sigma)
- pred_yhat <- matrix(nrow = length(test_data), ncol = length(train_data))
- if(isTRUE(progress)){
- total_iter <- length(test_data) * length(train_data)
- pb <- txtProgressBar(min = 0, max = total_iter, style = 3)
- }
- iter <- 0
- for(j in 1:length(test_data)){
- for(i in 1:length(train_data)){
- g_i <- get_k(train_data[[i]],
- test_data[[j]], sigma, dist_method = dist_method)
- k_i <- round(mean(g_i),3)
- pred_yhat[j,i] <- k_i
- if(isTRUE(progress)){setTxtProgressBar(pb, iter)}
- iter <- iter + 1
- }
- }
- if(isTRUE(progress)){close(pb)}
- pred <- 1 / (1 + exp(-as.vector(pred_yhat %*% alphas_pred)))
- return(pred)
- }
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