**Yet Another Blog in Statistical Computing » S+/R**, and kindly contributed to R-bloggers)

Similar to the back propagation neural network, the general regression neural network (GRNN) is also a good tool for the function approximation in the modeling toolbox. Proposed by Specht in 1991, GRNN has advantages of instant training and easy tuning. A GRNN would be formed instantly with just a 1-pass training with the development data. In the network development phase, the only hurdle is to tune the hyper-parameter, which is known as **sigma**, governing the smoothness of a GRNN.

The grnn package (http://flow.chasset.net/r-grnn/) is the implementation of GRNN in R and was just published on CRAN last month. Although the grnn package is still in the early phase, e.g. version 0.1, it is very easy to use and has a great potential for future improvements. For instance, the guess() function to predict new cases is only able to take 1 record at a time. Therefore, the user needs to write his / her own function to generate predicted values from a data frame. In addition, there is no automatic scheme to find the optimal value of the smooth parameter **sigma**. The user has to come up with his / her own solution.

Below is my test drive of grnn package over the weekend. By leveraging the power of foreach package, I wrote a simple function to let the guess() function able to score a whole matrix instead of a single row. Additionally, I used a hold-out sample to search for the optimal value of **sigma**, which turns out to work out pretty well and identifies the lowest SSE for the hold-out sample with **sigma** = 0.55.

pkgs <- c('MASS', 'doParallel', 'foreach', 'grnn') lapply(pkgs, require, character.only = T) registerDoParallel(cores = 8) data(Boston) # PRE-PROCESSING DATA X <- Boston[-14] st.X <- scale(X) Y <- Boston[14] boston <- data.frame(st.X, Y) # SPLIT DATA SAMPLES set.seed(2013) rows <- sample(1:nrow(boston), nrow(boston) - 200) set1 <- boston[rows, ] set2 <- boston[-rows, ] # DEFINE A FUNCTION TO SCORE GRNN pred_grnn <- function(x, nn){ xlst <- split(x, 1:nrow(x)) pred <- foreach(i = xlst, .combine = rbind) %dopar% { data.frame(pred = guess(nn, as.matrix(i)), i, row.names = NULL) } } # SEARCH FOR THE OPTIMAL VALUE OF SIGMA BY THE VALIDATION SAMPLE cv <- foreach(s = seq(0.2, 1, 0.05), .combine = rbind) %dopar% { grnn <- smooth(learn(set1, variable.column = ncol(set1)), sigma = s) pred <- pred_grnn(set2[, -ncol(set2)], grnn) test.sse <- sum((set2[, ncol(set2)] - pred$pred)^2) data.frame(s, sse = test.sse) } cat("\n### SSE FROM VALIDATIONS ###\n") print(cv) jpeg('grnn_cv.jpeg', width = 800, height = 400, quality = 100) with(cv, plot(s, sse, type = 'b')) cat("\n### BEST SIGMA WITH THE LOWEST SSE ###\n") print(best.s <- cv[cv$sse == min(cv$sse), 1]) # SCORE THE WHOLE DATASET WITH GRNN final_grnn <- smooth(learn(set1, variable.column = ncol(set1)), sigma = best.s) pred_all <- pred_grnn(boston[, -ncol(set2)], final_grnn) jpeg('grnn_fit.jpeg', width = 800, height = 400, quality = 100) plot(pred_all$pred, boston$medv) dev.off()

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