Nothing
#######################################
# summary.gsom - GrowingSOM
# Alex Hunziker - 2017
#######################################
# This function gives a (text) summary of a gsom_object
summary.gsom <- function(object, ...){
# GSOM Maps
if(!is.null(object$training)){
# Information about of Training Dataset
observations <- sum(object$nodes$freq)
dimenstions <- ncol(object$nodes$codes)
if(!is.null(object$nodes$predict)) predict = paste("and", ncol(object$nodes$predict), "predicted variable(s)")
else predict = ""
# Total number of nodes
nodes <- nrow(object$nodes$position)
# Average Distance, No of Iterations
iterations <- nrow(object$training)
distance <- object$training$meandist[iterations]
if(is.null(object[["data"]])) sorstat = "is not"
else sorstat = "is"
# Print
cat("Growing SOM map with", nodes, "nodes.\n")
cat("Training data used:", observations, "Observations with", dimenstions, "Dimensions", predict, "\n")
cat("Training data", sorstat, "stored in the model.\n")
cat("Mean Distance to the closest unit in the map is:", distance, "(after", iterations, "iterations)\n")
# Observations that were mapped onto an existing GSOM map
} else {
if(!is.null(object$nodes$predict)){
distance <- mean(object$prediction$dist)
observations <- length(object$prediction$dist)
} else {
distance <- mean(object$mapped$dist)
observations <- length(object$mapped$dist)
}
nodes <- nrow(object$nodes$position)
cat("Growing SOM map with", nodes, "nodes.\n")
cat(observations, "have been mapped onto a trained gsom map.\n")
if(!is.null(object$nodes$predict)){
depvarno <- ncol(object$nodes$predict)
cat("Predictions for", depvarno, "variables are stored.\n")
}
if(is.null(object[["data"]])) sorstat = "is not"
else sorstat = "is"
cat("Training data", sorstat, "stored in the model.\n")
cat("Mean Distance to the closest unit in the map is:", distance, "(for the mapped observations)\n")
}
}
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