R Under development (unstable) (2024-10-26 r87273 ucrt) -- "Unsuffered Consequences"
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> if(require(tree)) {
+ 
+ library( SplitSoftening )
+ 
+ is.even <- function( n ) { return( 0L == n%%2L ) }
+ 
+ # An alternative implementation of prediction with softsplits
+ predictSoftsplitsAlt <- function(fit, newdata)
+ {
+    datalen <- nrow(newdata)
+    tmp <- list()
+    for (ri in nrow(fit):1) { # go over all nodes from last to first
+       node <- fit[ri,]
+       if (0L==node$ncat) { # the node is a leaf
+          tmp[[ri]] <- matrix(node$yval, ncol=ncol(node$yval),
+             nrow=datalen, byrow=TRUE)
+       }
+       if (is.even(ri)) {
+          # compute values in the parent node
+          pni <- which(fit$childref==ri)[1] # index of the parent node
+          parent <- fit[pni,] # parent node record
+          # get newdata values of the variable appropriate for the parent's split
+          v <- newdata[[as.integer(factor(parent$var, levels=names(newdata)))]]
+          lower.sub <- as.integer(parent$ncat==1L)
+          lower <- ri+lower.sub
+          upper <- ri+1-lower.sub
+          if (parent$ncat<2L) {
+             # compute probability values
+             sval <- rep(NA, datalen) # init probability values
+             sval[v<=parent$lb] <- 1
+             sval[v>=parent$ub] <- 0
+             mv <- v>parent$lb & v<parent$splits
+             sval[mv] <- .5+(parent$splits-v[mv])/(2*(parent$splits-parent$lb))
+             mv <- v>=parent$splits & v<parent$ub
+             sval[mv] <- .5-(parent$splits-v[mv])/(2*(parent$splits-parent$ub))
+             # store values computed for the parent node
+             tmp[[pni]] <- sval*tmp[[lower]]+(1-sval)*tmp[[upper]]
+          } else {
+             # split on a categorial variable
+             stopifnot( typeof( v ) == "integer" )
+             # store values computed for the parent node
+             il <- !is.even(floor(parent$lb/2^(as.integer(v)-1)))
+             ir <- !is.even(floor(parent$ub/2^(as.integer(v)-1)))
+             im <- !il & !ir
+             tmp[[pni]] <- il * tmp[[lower]] + ir * tmp[[upper]] + im * matrix(parent$yval, ncol=ncol(parent$yval), nrow=datalen, byrow=TRUE)
+          }
+          # remove temporary values
+          tmp[[lower]] <- NULL
+          tmp[[upper]] <- NULL
+       }
+    }
+    return(tmp[[1]])
+ } 
+ 
+ create.data.gauss2 <- function() {
+ 	rbind( data.frame( matrix( -1 + rnorm( 1000 ), ncol=2 ), Y=factor( "neg" ) ), data.frame( matrix( 1 + rnorm( 1000 ), ncol=2 ), Y=factor( "pos" ) ) )
+ }
+ 
+ set.softening <- function(model, boundary.dist=1.0) {
+ 	index <- model$ncat %in% c(-1L, 1L)
+ 	model$lb[index] <- model$lb[index] - boundary.dist
+ 	model$ub[index] <- model$ub[index] + boundary.dist
+ 	return( model )
+ }
+ 
+ do.test <- function(desc, fn) {
+ 	print(desc)
+ 	stopifnot(fn())
+ }
+ 
+ group1 <- function () {
+ 	set.seed( 12345 )
+ 	d <- create.data.gauss2()
+ 	td <- d[names(d)!="Y"]
+ 	tr <- tree( Y~., d )
+ 	s <- softsplits( tr )
+ 
+ 	do.test( "predict train data with zero softening", function() {
+ 		res.tr <- predict( tr, d )
+ 		res.s <- predictSoftsplits( s, d )
+ 		return ( isTRUE(all.equal( c(res.tr), c(res.s) )) )
+ 	} )
+ 
+ 	do.test( "predict test data with zero softening", function() {
+ 		res.tr <- predict( tr, td )
+ 		res.s <- predictSoftsplits( s, td )
+ 		return ( isTRUE(all.equal( res.tr, res.s )) )
+ 	} )
+ 
+ 	do.test( "predict train data", function() {
+ 		ss <- set.softening(s, .75)
+ 		res.s <- predictSoftsplits( ss, d )
+ 		res.R <- predictSoftsplitsAlt( ss, d )
+ 		return ( isTRUE(all.equal( c(res.s), c(res.R) )) )
+ 	} )
+ 
+ 	do.test( "predict another data", function() {
+ 		ss <- set.softening(s, .75)
+ 		td <- create.data.gauss2()
+ 		td <- td[names(td)!="Y"]
+ 		res.s <- predictSoftsplits( ss, td )
+ 		res.R <- predictSoftsplitsAlt( ss, td )
+ 		return ( isTRUE(all.equal( c(res.s), c(res.R) )) )
+ 	} )
+ }
+ 
+ group1()
+ 
+ }
Loading required package: tree
Warning message:
In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
  there is no package called 'tree'
> 
> 
> proc.time()
   user  system elapsed 
   0.18    0.06    0.23