# ends.index.finder: Identifies numerical indices of the end nodes of a 'rktree'... In forestRK: Implements the Forest-R.K. Algorithm for Classification Problems

## Description

Identifies numerical indices of the end nodes of a `rktree` by closely examining the structure of the `rktree` flag (obtained via `construct.treeRK()\$flag`); the precise algorithm used is the following:

if m-th string in the list of `rktree` flag is a substring of one or more of (m + 1),...,n-th strings in the list of flag, then the node represented by the m-th string of the flag is not an end node; otherwise, the node represented by the m-th string of the flag is the end node.

## Usage

 `1` ``` ends.index.finder(tr.flag = matrix()) ```

## Arguments

 `tr.flag` a `construct.treeRK()\$flag` object or a similar flag matrix.

## Value

A vector that lists the indices of the end nodes of a given `rktree` (indices that are consistent to the indices in `x.node.list`, `y.new.node.list`, and `flag` that are returned by the `construct.treeRK` function).

## Author(s)

Hyunjin Cho, h56cho@uwaterloo.ca Rebecca Su, y57su@uwaterloo.ca

## See Also

`construct.treeRK`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ``` ## example: iris dataset ## load the forestRK package library(forestRK) # covariates of training data set x.train <- x.organizer(iris[,1:4], encoding = "num")[c(1:25,51:75,101:125),] y.train <- y.organizer(iris[c(1:25,51:75,101:125),5])\$y.new # Construct a tree # min.num.obs.end.node.tree is set to 5 by default; # entropy is set to TRUE by default tree.entropy <- construct.treeRK(x.train, y.train) # Find indices of end nodes of tree.entropy end.node.index <- ends.index.finder(tree.entropy\$flag) ```

forestRK documentation built on July 19, 2019, 5:04 p.m.