RBST

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The RBST reference class

The RBST reference class implements the data structure binary search tree (BST).

Keywords
RBST
RBST
Details

A BST is a particular type of container storing items or elements (nodes) by following a binary tree structure. The BST has one root on top, which is the first node of the tree, and each node in the BST has at most two sub-nodes (left sub-node and right sub-node) which can be the roots of their sub-trees.

The BST should be equipped with the "<" and "=" operators such that any two nodes in the tree can be compared. And therefore, the ">" is defined. The BST structure follows strictly the rules that, for a certain node in the tree, any nodes in its left sub-tree must be strictly smaller than it, any nodes in its right sub-tree must be strictly larger than it, and any two nodes in the tree must not be equal.

Therefore, the BST is a special set or dictionary equipped with "<", ">" operators.

When you create a new RBST instance, you have to input two functions which defines the bodies of the two private methods lessthan and equal. The RBST instance then will use them to make comparison and decide where to put new nodes (build the BST).

Each time a new node is inserted, the BST algorithm finds its location on the tree. Then you can imagine, the BST is efficient in maintaining (inserting and deleting), searching and traversing the tree. An average O(log n) time complexity can be achieved by applying the BST algorithm.

A very important fact is that, the RBST only compares the nodes by using the function equal. So it will regard any two nodes identical if equal returns TRUE, even though they are different.

We see that the BST can also be regarded as a dictionary, as the key of the dictionary is actually the value input into insert, delete and search_for.

The traversals of the BST (in-order, pre-order, and post-order) are implemented as well. A callback function can be input into the traverse function to specify how to treat the traversed nodes. By default (if you do not input anything here) the traverse function prints the traversed nodes. But of course you can, for example, store them by changing the callback function, see the examples below.

The elements in the BST are not necessarily to be of the same type, and they can even contain functions.

Format

An object of class R6ClassGenerator of length 24.

Class Method

The class method belongs to the class.

new(lessthan, equal, ..., collapse=NULL)

The new method creates a new instance of the RBST class containing the values in ... and collapse as its nodes.

The argument lessthan takes a function defining the "<" operation, and the argument equal takes a function defining the "=" operation. Both of the functions takes two values of the nodes in the tree and return a boolean.

lessthan can take, for example, the form

lessthan <- function(x, y) return(x$num < y$num)

where x and y are values of two nodes in the tree with the attribute num.

equal can take, for example, the form

equal <- function(x, y) return(x$num == y$num)

where x and y are values of two nodes in the tree with the attribute num.

Immutable Methods

The immutable methods do not change the nodes of the instance.

traverse(mode="in", callback=function(item){print(item)}, ...)

The traverse method takes at least two arguments which are mode and callback.

The mode takes a value in one of the three strings "in", "pre", and "post" which indicate traverse-in-order, traverse-pre-order, and traverse-post-order, respectively.

The callback takes a function input specifying how to handle the value of each node in the tree. By default, callback prints the nodes by using the print function.

The method also takes ... as the additional arguments for the callback function if any.

search_for(val)

The method search_for uses the equal function to compare val with the nodes in BST. It returns the value of the node if the node is equal to the given value, and NULL otherwise.

As the tree has been structured strictly by following the rules introduced above, there is no need to search the whole tree in most cases, and the maintaining and searching are efficient.

min

The active method min returns the smallest node in the tree, and NULL if the tree is empty.

max

The active method min returns the largest node in the tree, and NULL if the tree is empty.

Mutable Methods

The mutable methods changes the nodes of the instance.

insert(val)

The method insert inserts a new node and returns TRUE showing that the inserting is successful, but if there is one node in BST that is equal to the node, the insert will do nothing and return a FALSE showing that the inserting fails.

delete(val)

The method delete removes the node which is equal to val. If the node is found, then it will be removed and the function returns a TRUE, and if the node is not found, then it will do nothing and returns a FALSE,

References

For the details about the BST data structure, see BST at Wikipedia.

R6DS for the introduction of the reference class and some common methods

• RBST
Examples
# NOT RUN {
### create a new instance

# you have to define two functions for "<" and "="
lessthan <- function(x, y) return(x$key < y$key)
equal <- function(x, y) return(x$key == y$key)
# remember that the nodes in the BST have the "key" variable
# and it is numeric

# to create a new instance of the class
bst <- RBST$new(lessthan=lessthan, equal=equal) # of course you can start to push elements when creating the instance # the previous BST instance will be removed by doing so # and the memory allocated for that one will be cleared, # as now bst has been pointed to another instance of the class. bst <- RBST$new(lessthan=lessthan, equal=equal,
list(key=5, val="5"), collapse=list(list(key=3,val="3"), list(key=9,val="9")))
# the following sentence is equivalent to the above
bst <- RBST$new(lessthan=lessthan, equal=equal, list(key=5, val="5"), list(key=3,val="3"), list(key=9,val="9")) # where the three lists are inserted into the BST ### maintaining bst$insert(list(key=5, val="6"))
# FALSE though their val are different
bst$insert(list(key=6, val="5")) # TRUE bst$delete(list(key=7, val="7"))
# FALSE
bst$delete(list(key=6, val="7")) # TRUE and delete list(key=6, val="5") # though val are different ### searching bst$search_for(list(key=0, val="0"))
# NULL
bst$search_for(list(key=5, val="0")) # the BST has a node whose key is 5 ### min and max # min and max are two active functions # so the parenthesis is not needed bst$min
bst$max ### traversing # by default, the callback function prints the nodes # but you can re-define the callback function queue <- RQueue$new()
callback <- function(item)queue$enqueue(item) # remember that RQueue is a reference class # so the new callback will store the traversed nodes bst$traverse(callback=callback)
tmp = queue$dequeue(); print(tmp) while(!is.null(tmp)) {tmp = queue$dequeue(); print(tmp)}
bst$traverse(mode = "in", callback=callback) tmp = queue$dequeue(); print(tmp)
while(!is.null(tmp)) {tmp = queue$dequeue(); print(tmp)} # pre-order traversing bst$traverse(mode = "pre", callback=callback)
tmp = queue$dequeue(); print(tmp) while(!is.null(tmp)) {tmp = queue$dequeue(); print(tmp)}

# post-order traversing
bst$traverse(mode = "post", callback=callback) tmp = queue$dequeue(); print(tmp)
while(!is.null(tmp)) {tmp = queue\$dequeue(); print(tmp)}

# }
Documentation reproduced from package R6DS, version 1.1.0, License: GPL-3

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