Description
Question 1 (26 points):
In lib280-asn6 you are provided with a fully functional 2-3 tree class called TwoThreeTree280. It
implements the KeyedBasicDict280 interface and therefore supports the operations we saw in class.
It does not, however, implement KeyedDict280 which adds additional operations including all of the
methods in KeyedLinearIterator280 which, in turn, includes all of the public operations on a cursor.
Note that KeyedDict280 is the same interface that is implemented by KeyedChainedHashTable280 so
you should be somewhat familiar with it from the previous assignment.
The task for this question is to extend the TwoThreeTree280 to a class called IterableTwoThreeTree280
which allows linear iteration over the key-element pairs stored in the two-three tree in ascending keyorder. We will achieve this by adding additional references to leaf nodes so that the leaf nodes form
a bi-linked list. Note that adding this feature to a 2-3 tree results in exactly a B+ tree of order 3 (see
textbook Section 14.1). We aren’t going to call it a B+ tree class though, because we will be specifically
a B+ tree of order 3, and higher-order B+ trees will not be supported. Figure 1 in the Appendix shows
the differences between a 2-3 tree and a B+ tree of order 3 containing the same elements. The algorithms for insertion and deletion are the same in both kinds of tree, except that in the case of the B+
tree, references to/from the predecessor and successor leaf nodes in key-order have to be adjusted to
maintain the bi-linked list of leaf nodes.
The full class hierarchy of IterableTwoThreeTree280 is shown in Figure 2 of the Appendix. The
hierarchy of tree node classes is shown in Figure 3 of the Appendix.
To implement the IterableTwoThreeTree280, the following tasks must be carried out:
1. Make an extension of LeafTwoThreeNode280 that adds references to its predecessor and successor
leaf nodes. This has been done for you in the class LinkedLeafTwoThreeNode280.
2. Override the TwoThreeTree280::createNewLeafNode() method by adding a new protected method
in IterableTwoThreeTree280 that it returns a new LinkedLeafTwoThreeNode280 object instead
of a TwoThreeNode280 object. This has already been done for you.
3. (10 points) In IterableTwoThreeTree280, override the insert and delete methods of TwoThreeTree280
with modified versions that correctly maintain the additional predecessor and successor references in the LinkedLeafTwoThreeNode280. Each leaf node should always point to the the leaf
node immediately to the left of it (the predecessor) and to the right of it (the successor) even if
they are not siblings. Of course, the leaf node with the smallest key has no predecessor and the
leaf node with the largest key has no successor.
In IterableTwoThreeTree280, the insert and delete methods from TwoThreeTree280 already
have been copied, and TODO comments have been inserted indicating where you need to add
additional code to maintain the additional leaf node references. The comments also provide a
few hints. You should not have to modify any of the existing code for insert or delete, just
add new code to deal with the linking and unlinking of leaf nodes from their successors and
predecessors. Maintaining these links is very similar to inserting and removing nodes from the
middle of a doubly-linked list.
4. (12 points) Implement the additional methods required by KeyedDict280 (and, by extension,
KeyedLinearIterator280). Some of these have been done for you, others have not. TODO
comments in IterableTwoThreeTree280 indicate which methods you need to implement and
maybe even a hint or two. In this class, the linear iterator allows positioning of the cursor along
the leaf-level of the tree.
5. (4 points) In the main() function, write a regression test to test the methods required by KeyedDict280
(and, by extension, KeyedLinearIterator280). You to not need to explicitly test the insertion
and deletion methods since testing of the methods from KeyedLinearIterator280 will reveal
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any problems with the new leaf node linkages, but you will need to insert and delete items to
create test cases.
You must test all of the methods listed in the interfaces that are coloured blue in Figure 2 of
the Appendix.
Use instances of the local class called Loot, which has been defined in the main method, as the
data items to insert into the tree for testing. This class implements the type of item depicted in
Figure 1 in the Appendix consisting of the name of a magic item from a fantasy game, and its
value in gold pieces. The item keys are the item names (strings).
Hint: The toStringByLevel() method you’ve been given prints not only the 2-3 tree’s structure, but also
displays current linear ordering of the nodes that results from following the successor links in the leaf nodes,
beginning with the leftmost leaf node. This may be helpful for the debugging of step 2.
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Question 2 (46 points):
In Question 2 you will be implementing a k-D tree. We begin with introducing some algorithms that
you will need. Then we will present what you must do.
Helper Algorithms for Implementing k-dimensional Trees
As we saw in class, in order to build a k-D tree we need to be able to find the median of a set of
elements efficiently. The “j-th smallest element” algorithm will do this for us. If we have an array of n
elements, then finding the n/2-smallest element is the same as finding the median.
Below is a version of the j-th smallest element algorithm that operates on a subarray of an array
specified by offsets le f t and right (inclusive). It places at offset j (le f t ≤ j ≤ right) the element that
belongs at offset j if the subarray were sorted. Moreover, all of the elements in the subarray smaller
than that belonging at offset j are placed between offsets le f t and j − 1 and all of the elements in the
subarray larger than that element are placed between offsets j + 1 and right (but there is no guarantee
on the ordering of any of these elements!). Thus, if we want to find the median element of a subarray
bounded by le f t and right, we can call
jSmallest(list, left, right, (left+right)/2)
The offset (le f t + right)/2 (integer division!) is always the element in the middle of the subarray
between offsets le f t and right because the average of two numbers is always equal to the number
halfway in between them.
Algorithm jSmallest ( list , left , right , j )
list – array of comparable elements
left – offset of start of subarray for which we want the median element
right – offset of end of subarray for which we want the median element
j – we want to find the element that belongs at array index j
To find the median of the subarray between array indices ’ left ’ and ’ right ’ ,
pass in j = ( right + left )/2.
Precondition : left <= j <= right
Precondition : all elements in ’ list ’ are unique ( things get messy otherwise !)
Postcondition : the element x that belongs at offset j , if the subarray were
sorted , is at offset j . Elements in the subarray
smaller than x are to the left of offset j and the
elements in the subarray larger than x are to the right
of offset j .
if( right > left )
// Partition the subarray using the last element , list [ right ] , as a pivot .
// The index of the pivot after partitioning is returned .
// This is exactly the same partition algorithm used by quicksort .
pivotIndex := partition ( list , left , right )
// If the pivotIndex is equal to j, then we found the j-th smallest
// element and it is in the right place ! Yay!
// If the position j is smaller than the pivot index , we know that
// the j-th smallest element must be between left , and pivotIndex -1 , so
// recursively look for the j-th smallest element in that subarray :
if j < pivotIndex
jSmallest ( list , left , pivotIndex -1 , j )
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// Otherwise , the position j must be larger than the pivotIndex ,
// so the j-th smallest element must be between pivotIndex +1 and right .
else if j > pivotIndex
jSmallest ( list , pivotIndex +1 , right , j )
// Otherwise , the pivot ended up at list [j] , and the pivot *is* the
// j-th smallest element and we ’re done .
Notice that there is nothing returned by jSmallest, rather, it is the postcondition that is important.
The postcondition is simply that the element of the subarray specified by left and right that belongs
at index j if the subarray were sorted is placed at index j and that elements between le f t and j − 1 are
smaller than the j-th smallest element and the elements between j + 1 and right are larger than the
j-th smallest element. There are no guarantees on ordering of the elements within these parts of the
subarray except that they are smaller and larger than the the element at index j, respectively. This means
that if you invoke this algorithm with j = (right + le f t)/2 then you will end up with the median element in
the median position of the subarray, all smaller elements to its left (though unordered) and all larger elements to
its right (though unordered), which is just what you need to implement the tree-building algorithm! NOTE: for
this algorithm to work on arrays of NDPoint280 objects you will need an additional parameter d that
specifies which dimension (coordinate) of the points is to be used to compare points. An advantage of
making this algorithm operate on subarrays is that you can use it to build the k-d tree without using
any additional storage — your input is just one array of NDPoint280 objects and you can do all the
work without any additional arrays — just work with the correct subarrays.
You may have noticed that jSmallest uses the partition algorithm partition the elements of the
subarray using a pivot. The pseudocode for the partition algorithm used by the jSmallest algorithm
is given below. Note that in your implementation, you will, again, need to add a parameter d to denote
which dimension of the n-dimensional points should be used for comparison of NDPoint280 objects.
// Partition a subarray using its last element as a pivot .
Algorithm partition ( list , left , right )
list – array of comparable elements
left – lower limit on subarray to be partitioned
right – upper limit on subarray to be partitioned
Precondition : all elements in ’ list ’ are unique ( things get messy otherwise !)
Postcondition : all elements smaller than the pivot appear in the leftmost
part of the subarray , then the pivot element , followed by
the elements larger than the pivot . There is no guarantee
about the ordering of the elements before and after the
pivot .
returns the offset at which the pivot element ended up
pivot = points [ right ]
swapOffset = left
for i = left to right -1
if( points [ i ] <= pivot )
swap points [ i ] and points [ swapOffset ]
swapOffset = swapOffset + 1
swap points [ right ] and points [ swapOffset ]
return swapOffset ; // return the offset where the pivot ended up
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Algoirthm for Building the Tree
An algorithm for building a k-d tree from a set of k-dimensional points is given below. It is slightly
more detailed than the version given in the lecture slides. It uses the jSmallest algorithm presented
above.
Algorithm kdtree ( pointArray , left , right , int depth )
pointArray - array of k - dimensional points
left - offset of start of subarray from which to build a kd - tree
right - offset of end of subarray from which to build a kd - tree
depth - the current depth in the partially built tree - note that the root
of a tree has depth 0 and the $k$ dimensions of the points
are numbered 0 through k -1.
if pointArray is empty
return null ;
else
// Select axis based on depth so that axis cycles through all
// valid values . (k is the dimensionality of the tree )
d = depth mod k ;
medianOffset = ( left + right )/2
// Put the median element in the correct position
// This call assumes you have added the dimension d parameter
// to jSmallest as described earlier .
jSmallest ( pointArray , left , right , d , medianOffset )
// Create node and construct subtrees
node = a new id - tree node
node . item = pointArray [ medianOffset ]
node . leftChild = kdtree ( pointArray , left , medianOffet -1 , depth +1);
node . rightChild = kdtree ( pointArray medianOffset +1 , right , depth +1);
return node ;
Implementing the k-D Tree – What You Must Do
Implement a k-D tree. You must use the NDPoint280 class provided in the lib280.base package of
lib280-asn6 to represent your k-dimensional points. You must design and implement both a node
class (KDNode280.java) and a tree class (KDTree280.java). Other than specific instructions given in
this question, the design of these classes is up to you and you can use as much or as little of lib280 as
you deem appropriate, and you may use whatever private/protected methods you deem necessary.
A portion of the marks for this question will be awarded for the design/modularity/style of the implementation of your class. A portion of the marks for this question will be awarded for acceptable
inline and javadoc commenting.
Your ADT must support the following operations:
• Construct a new (balanced) k-D tree from a set of k-dimensional points (it must work for any
k > 0).
• Perform a range search: given a pair of points (a1, a2, . . . ak
) and (b1, b2, . . . , bk
), ai <= bi
for all
i = 1 . . . k, return all of the points (c1, c2, . . . , ck
) such that a1 ≤ c1 ≤ b1, a2 ≤ c2 ≤ b2, . . . , ak ≤
ck ≤ bk
.
Note: your tree does not have to have operations that insert or remove individual NDPoints.
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In addition, you should write a test program that generates the correctness of your tree. The test
program should consist of two parts:
1. Show that your class can correctly build a k-D tree from a set of points. For k=2, display the
the k-dimensional points that are given as input (use between 8 and 12 elements), followed by a
graphical representation of the built tree (similar to the toStringByLevel() output in the trees
we’ve done previously). Do this again for one other value of k, between 3 and 5 (your choice).
2. For the second of the two trees you displayed in part 1, perform at least three range searches. For
each search, display the query range, execute the range search, and then display the list of points
in the tree that were found to be in range. A sample test program output is given below.
Implementation and Debugging Strategy
In order to implement the tree-building algorithm kdtree you first need to implement jSmallest
which, in turn requires partition. It is strongly suggested that you implement and thoroughly
test partition before trying to implement jSmallest. In turn, throughly test jSmallest before you
implement kdtree. If you don’t do this, I can tell you from experience that it will be a nightmare to
debug. You need to be sure that each algorithm is correct before implementing the algorithms that
depend on it, otherwise, if you run into a bug it will be very hard to determine in which method in
the chain of dependent methods the bug is occurring.
Grading Scheme
• Correctness: 35 points (for node and tree class implementations, and required console output)
• Design: 5 points
• Comments (inline and Javadoc): 6 points
Sample Output
Input 2 D points :
(5.0 , 2.0)
(9.0 , 10.0)
(11.0 , 1.0)
(4.0 , 3.0)
(2.0 , 12.0)
(3.0 , 7.0)
(1.0 , 5.0)
The 2 D tree built from these points is :
4: -
3: (9.0 , 10.0)
4: -
2: (5.0 , 2.0)
4: -
3: (11.0 , 1.0)
4: -
1: (4.0 , 3.0)
4: -
3: (2.0 , 12.0)
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4: -
2: (3.0 , 7.0)
4: -
3: (1.0 , 5.0)
4: -
Input 3 D points :
(1.0 , 12.0 , 1.0)
(18.0 , 1.0 , 2.0)
(2.0 , 12.0 , 16.0)
(7.0 , 3.0 , 3.0)
(3.0 , 7.0 , 5.0)
(16.0 , 4.0 , 4.0)
(4.0 , 6.0 , 1.0)
(5.0 , 5.0 , 17.0)
5: -
4: (5.0 , 5.0 , 17.0)
5: -
3: (16.0 , 4.0 , 4.0)
4: -
2: (7.0 , 3.0 , 3.0)
4: -
3: (18.0 , 1.0 , 2.0)
4: -
1: (4.0 , 6.0 , 1.0)
4: -
3: (1.0 , 12.0 , 1.0)
4: -
2: (2.0 , 12.0 , 16.0)
4: -
3: (3.0 , 7.0 , 5.0)
4: -
Looking for points between (0.0 , 1.0 , 0.0) and (4.0 , 6.0 , 3.0).
Found :
(4.0 , 6.0 , 1.0)
Looking for points between (0.0 , 1.0 , 0.0) and (8.0 , 7.0 , 4.0).
Found :
(7.0 , 3.0 , 3.0)
(4.0 , 6.0 , 1.0)
Looking for points between (0.0 , 1.0 , 0.0) and (17.0 , 9.0 , 10.0).
Found :
(16.0 , 4.0 , 4.0)
(7.0 , 3.0 , 3.0)
(3.0 , 7.0 , 5.0)
(4.0 , 6.0 , 1.0)
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3 Files Provided
lib280-asn6: A copy of lib280 which includes:
• solutions to assignment 5;
• TheTwoThreeTree280 class and related node and position classes in the lib280.tree package
for Question 1.
• Partially completed IterableTwoThreeTree280 class in the in lib280.tree package for Question 1.
• the NDPoint280 class in the lib280.base package for representing n-dimensional points for
question 2;
4 What to Hand In
IterableTwoThreeTree280.java: Your completed B+ Tree of order 3 for Question 1.
KDNode280.java: The node class for your k-D tree from Question 2.
KDTree280.java: Your k-D tree class for Question 2.
a6q2.txt/doc/pdf: The console output from your test program for question 2, cut and paste from the IntelliJ
console window.
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Appendix
Leather
Armor
Potion of
Healing
Vampiric
Blade
+1 Mace
2000
Blue Ioun
Stone
20000
Leather
Armor
10
Plate
Armor
350
Potion of
Healing
100
Vampiric
Blade
12000
Plate Armor Blue Ioun
Stone
Leather
Armor
Potion of
Healing
Vampiric
Blade Plate Armor Blue Ioun
Stone
+1 Mace
2000
Blue Ioun
Stone
20000
Leather
Armor
10
Plate
Armor
350
Potion of
Healing
100
Vampiric
Blade
12000
Figure 1: Top: a 2-3 tree; Bottom: a B+ tree of order 3 containing the same elements. Here the keys are
strings (describing magical items in a fantasy game world) and the data items are integers (representing
the value, in gold pieces, of the object described by the key). Note that the trees are the same except for
the extra linkages of the leaf nodes.
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IterableTwoThreeTree280
#smallest: LinkedLeafTwoThreeNode280
#largest: LinkedLeafTwoThreeNode280
#cursor: LinkedLeafTwoThreeNode280
#prev: LinkedLeafTwoThreeNode280
K,I
TwoThreeTree280
#rootNode: TwoThreeNode280
+height
#createNewLeafNode
#createNewInternalNode(TwoThreeNode, K,
TwoThreeNode, K,
TwoThreeNode)
#find(K)
#giveLeft(TwoThreeNode, TwoThreeNode)
#giveRight(TwoThreeNode, TwoThreeNode)
#stealLeft(TwoThreeNode, TwoThreeNode)
#stealRight(TwoThreeNode, TwoThreeNode)
+toString
+toStringByLevel
K,I
«interface»
Container280
clear
isEmpty
isFull «interface»
KeyedBasicDict280
delete(K)
has(K)
insert(I)
obtain(K)
set(I)
K,I
«interface»
KeyedDict280
deleteItem
search(K)
searchCeilingOf(K)
setItem(I)
K,I
«interface»
KeyedLinearIterator280
K,I
«interface»
CursorSaving280
currentPosition
goPosition(CursorPosition280)
«interface»
KeyedCursor280
itemKey
keyItemPair
K,I
«interface»
LinearIterator280
after
before
goAfter
goBefore
goFirst
goForth
I
«interface»
Cursor280
item
itemExists
I
Figure 2: Class hierarchy for IterableTwoThreeNode280. For methods, only type names of parameters are shown.
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LinkedLeafTwoThreeNode280
next: LinkedLeafTwoThreeNode280
prev: LinkedLeafTwoThreeNode280
+next
+setNext(LinkedTwoThreeNode280)
+prev
+setPrev(LinkedTwoThreeNode280)
K,I
LeafTwoThreeNode280
data : I
InternalTwoThreeNode280 K,I
key1: K
key2: K
leftSubtree: TwoThreeNode280
middleSubtree: TwoThreeNode280
rightSubtree: TwoThreeNode280
K,I
TwoThreeNode280
this is an abstract class
+getData
+getKey1
+getKey2
+getLeftSubtree
+getRightSubtree
+getMiddleSubtree
+isInternal
+isRightChild
+setKey1(K)
+setKey2(K)
+setLeftSubtree(TwoThreeNode280)
+setRightSubtree(TwoThreeNode280)
+setMiddleSubtree(TwoThreeNode280)
K,I
Figure 3: UML Class Hierarchy for 2-3 Tree Nodes in lib280. Every method that might be needed for either
an internal or a leaf node is defined in the common abstract ancestor class TwoThreeTree280 (note: because
it is abstract, it cannot be instantiated). Subclasses InternalTwoThreeNode280 and LeafTwoThreeNode280
contain the data needed for the respective types of nodes, and definitions of each method appropriate to
that type of node. Inherited methods that don’t make sense for a particular type of node (e.g. getData() on
an internal node) are defined to throw exceptions. The actual type of a reference to a TwoThreeNode can
be determined by calling isInternal which is defined by internal nodes to return true and is defined by
leaf nodes to return false. The LinkedLeafTwoThreeNode280 extends the leaf node class to add predecessor
and successor references to maintain the bi-linked list of leaf nodes in the B+ tree of order 3.
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