Write the implementation of the function T ComputeMedian() const
that computes the median value in the tree in O(n) time. Assume that the tree is a BST but is not necessarily balanced. Recall that the median of n numbers is defined as follows: If n is odd, the median is x such that the number of values smaller than x is equal to the number of values greater than x. If n is even, then one plus the number of values smaller than x is equal to the number of values greater than x. For example, given the numbers 8, 7, 2, 5, 9, the median is 7, because there are two values smaller than 7 and two values larger than 7. If we add number 3 to the set, the median becomes 5.
Here is the class of binary search tree node:
template <class T>
class BSTNode
{
public:
BSTNode(T& val, BSTNode* left, BSTNode* right);
~BSTNode();
T GetVal();
BSTNode* GetLeft();
BSTNode* GetRight();
private:
T val;
BSTNode* left;
BSTNode* right;
BSTNode* parent; //ONLY INSERT IS READY TO UPDATE THIS MEMBER DATA
int depth, height;
friend class BST<T>;
};
Binary search tree class:
template <class T>
class BST
{
public:
BST();
~BST();
bool Search(T& val);
bool Search(T& val, BSTNode<T>* node);
void Insert(T& val);
bool DeleteNode(T& val);
void BFT(void);
void PreorderDFT(void);
void PreorderDFT(BSTNode<T>* node);
void PostorderDFT(BSTNode<T>* node);
void InorderDFT(BSTNode<T>* node);
void ComputeNodeDepths(void);
void ComputeNodeHeights(void);
bool IsEmpty(void);
void Visit(BSTNode<T>* node);
void Clear(void);
private:
BSTNode<T> *root;
int depth;
int count;
BSTNode<T> *med; // I've added this member data.
void DelSingle(BSTNode<T>*& ptr);
void DelDoubleByCopying(BSTNode<T>* node);
void ComputeDepth(BSTNode<T>* node, BSTNode<T>* parent);
void ComputeHeight(BSTNode<T>* node);
void Clear(BSTNode<T>* node);
};
I know I should count the nodes of the tree first and then do an inorder traversal until I reach (n/2)th node and return it. I just have no clue how.
Median is the middle node of a tree, with an almost equal number of nodes in its left and right subparts. If N is odd, median = ((N / 2) + (N / 2 + 1)) / 2.
To find the median, calculate the mean by adding together the middle values and dividing them by two.
The median-of-medians algorithm is a deterministic linear-time selection algorithm. The algorithm works by dividing a list into sublists and then determines the approximate median in each of the sublists. Then, it takes those medians and puts them into a list and finds the median of that list.
As you mentioned, it is fairly easy to first find the number of nodes, doing any traversal:
findNumNodes(node):
if node == null:
return 0
return findNumNodes(node.left) + findNumNodes(node.right) + 1
Then, with an inorder traversal that aborts when the node number is n/2:
// index is a global variable / class variable, or any other variable that is constant between all calls
index=0
findMedian(node):
if node == null:
return null
cand = findMedian(node.left)
if cand != null:
return cand
if index == n/2:
return node
index = index + 1
return findMedian(node.right)
The idea is that in-order traversal processes nodes in BST in sorted manner. So, since the tree is a BST, the i
th node you process, is the i
th node in order, this is of course also true for i==n/2
, and when you find it is the n/2
th node, you return it.
As a side note, you can add functionality to BST to find i
th element efficiently (O(h)
, where h
is the tree's height), using order statistics trees.
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