Function for predicting Logistic Kernel Ridge Regression. Arguments: test_data = testing data set, train_data = data used to train model, alphas_pred = alpha parameters resulting from KRR_logit_optim() or KRR_logit(), sigma = sigma parameter of Gaussian RBF kernel, dist_method = distance method from `proxy` package, progress = T/F progress bar

Logistic_Kernel_Ridge_Regression_Prediction.R

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)
}