void head_insert(DomesticPtr& head, string firstNameD, string lastNameD, string province, float cgpaD, int researchScoreD, int idD)
{
DomesticPtr temp_ptr;
DomesticPtr temp2;
temp_ptr= new DomesticStudent(head, firstNameD, lastNameD, province,cgpaD,researchScoreD,idD);
temp2 = head->getLink();
temp2==temp_ptr;
head=temp_ptr;
if (head->getLink() == NULL)
return;
else
{
bubblesort(head);
}
}
void bubblesort(DomesticStudent* head)
{
int rsd;
int cgpad;
int p;
DomesticPtr tempc, tempd, tempe;
tempd=head;
tempe= head->getLink();
{
while(tempd != NULL)
{
rsd=compareResearchScore(tempd, tempe);
if (rsd==1)
{
tempc=head;
head->next=head;
head=tempc;
}// if
else if (rsd==0)
{
cgpad= compareCGPA(tempe,tempd);
if (cgpad==1)
{
tempc=head;
head->next=head;
head=tempc;
}// if (cgpad[k]>cgpad[k+1])
else if(cgpad==0)
{
p=compareProvince(tempd,tempe);
if(p==1)
{
tempc=head;
head->next=head;
head=tempc;
}// if (p[k]>p[k+1])
}//
}// else if cgpad[k]
}// else if rsd[k]
// }
// }
tempd = tempe;
}
int compareResearchScore(DomesticPtr RSA, DomesticPtr RSB)
{
if (RSB == NULL || RSA==NULL )
{
return 0;
}
if (RSA->researchScoreD==RSB->researchScoreD) //compares if is the same for domesetic students returns value for bubble sort
{
return 0;
}
if (RSA->researchScoreD > RSB->researchScoreD)
{
return 1;
}
if (RSA->researchScoreD< RSB->researchScoreD)
{
return -1;
}
}
I'm trying to to have my linked list sorted every time a new node is inserted. It compiles but every time I try to run the program it is stuck on the point that I am trying to print my list. I have a destructor but no copy constructor or assignment operator.
The head_insert calls the sort function and the sort function calls the compare function to receive an integer output so that it can make a swap. I want to compare research, the cgpa, and then province. Any input would be much appreciated, this is for a project so I wouldn't like any blocks of code but if you could point me in the right direction or multiple directions.
I am trying to implement an A star algorithm with C++ in a game that I am creating and it is not working, I don't really know if there's something I've missed about the code or the algorithm. I've used sets because they are sorted and the return value is a vector with the nodes I've got to visit. I've never used this algorithm before so probably I've got some kind of error.
struct node {
Pos pos;
int f; //the sum of the distance from the goal to succcessor
int g; // the sum of the cost of the current plus the one from the successor
int h; //distance from goal to successor
friend bool operator< (node right, node left) {
return (right.f < left.f);
} };
vector<node> search(Pos inicio,Pos desti){
set<node> opennodes;
vector<node> closednodes;
node inici;
node successor;
inici.pos = inicio;
inici.h = heuristic(inicio,desti);
inici.g = getcost(inicio);
inici.f = inici.g + inici.h;
opennodes.insert(inici);
closednodes.push_back(inici);
while(not opennodes.empty()){
node current = *(opennodes.begin());
opennodes.erase(opennodes.begin());
if(current.pos == desti) cerr<<"encontrao";
Dir direccio;
for(int i = 0; i < 4;++i){
if(i==0){
direccio = LEFT;
}
else if(i==1){
direccio = RIGHT;
}
else if(i==2){
direccio = TOP;
}
else {
direccio = BOTTOM;
}
successor.pos = current.pos + direccio;
if(successor.pos == desti) return closednodes;
if(pos_ok(successor.pos)){
successor.g = current.g + getcost(successor.pos);
successor.h = heuristic(successor.pos,desti);
successor.f = successor.g + successor.h;
node n1 = checkposition(successor.pos, opennodes); //I had to create two checkposition just to know if there's the node in the set or in the vector
node n2 = checkposition2(successor.pos, closednodes);
if (n1.f != -1 and n1.f < successor.f);
else if (n2.f != -1 and n2.f < successor.f);
else opennodes.insert(successor);
}
}
closednodes.push_back(current);
}
return closednodes;
}
So, first:
if(current.pos == desti) cerr<<"encontrao";
Shouldn't there be a break statement here? the cerr function doesn't break your loop, just throws and error message to your stdout.
And the for statement inside your while is always running up to 4, so direccio is always set to BOTTOM.
Other than that, I think the heuristic is fine, the problem is within the code structure, I'd suggest debugging it and posting here your results.
TrieNode and Trie Object:
struct TrieNode {
char nodeChar = NULL;
map<char, TrieNode> children;
TrieNode() {}
TrieNode(char c) { nodeChar = c; }
};
struct Trie {
TrieNode *root = new TrieNode();
typedef pair<char, TrieNode> letter;
typedef map<char, TrieNode>::iterator it;
Trie(vector<string> dictionary) {
for (int i = 0; i < dictionary.size(); i++) {
insert(dictionary[i]);
}
}
void insert(string toInsert) {
TrieNode * curr = root;
int increment = 0;
// while letters still exist within the trie traverse through the trie
while (curr->children.find(toInsert[increment]) != curr->children.end()) { //letter found
curr = &(curr->children.find(toInsert[increment])->second);
increment++;
}
//when it doesn't exist we know that this will be a new branch
for (int i = increment; i < toInsert.length(); i++) {
TrieNode temp(toInsert[i]);
curr->children.insert(letter(toInsert[i], temp));
curr = &(curr->children.find(toInsert[i])->second);
if (i == toInsert.length() - 1) {
temp.nodeChar = NULL;
curr->children.insert(letter(NULL, temp));
}
}
}
vector<string> findPre(string pre) {
vector<string> list;
TrieNode * curr = root;
/*First find if the pre actually exist*/
for (int i = 0; i < pre.length(); i++) {
if (curr->children.find(pre[i]) == curr->children.end()) { //DNE
return list;
}
else {
curr = &(curr->children.find(pre[i])->second);
}
}
/*Now curr is at the end of the prefix, now we will perform a DFS*/
pre = pre.substr(0, pre.length() - 1);
findPre(list, curr, pre);
}
void findPre(vector<string> &list, TrieNode *curr, string prefix) {
if (curr->nodeChar == NULL) {
list.push_back(prefix);
return;
}
else {
prefix += curr->nodeChar;
for (it i = curr->children.begin(); i != curr->children.end(); i++) {
findPre(list, &i->second, prefix);
}
}
}
};
The problem is this function:
void findPre(vector<string> &list, TrieNode *curr, string prefix) {
/*if children of TrieNode contains NULL char, it means this branch up to this point is a complete word*/
if (curr->nodeChar == NULL) {
list.push_back(prefix);
}
else {
prefix += curr->nodeChar;
for (it i = curr->children.begin(); i != curr->children.end(); i++) {
findPre(list, &i->second, prefix);
}
}
}
The purpose is to return all words with the same prefix from a trie using DFS. I manage to retrieve all the necessary strings but I can't exit out of the recursion.
The code completes the last iteration of the if statement and breaks. Visual Studio doesn't return any error code.
The typical end to a recursion is just as you said- return all words. A standard recursion looks something like this:
returnType function(params...){
//Do stuff
if(need to recurse){
return function(next params...);
}else{ //This should be your defined base-case
return base-case;
}
The issue arises in that your recursive function can never return- it can either execute the push_back, or it can call itself again. Neither of these seems to properly exit, so it'll either end quietly (with an inferred return of nothing), or it'll keep recursing.
In your situation, you likely need to store the results from recursion in an intermediate structure like a list or such, and then return that list after iteration (since it's a tree search and ought to check all the children, not return the first one only)
On that note, you seem to be missing part of the point of recursions- they exist to fill a purpose: break down a problem into pieces until those pieces are trivial to solve. Then return that case and build back to a full solution. Any tree-searching must come from this base structure, or you may miss something- like forgetting to return your results.
Check the integrity of your Trie structure. The function appears to be correct. The reason why it wouldn't terminate is if one or more of your leaf nodes doesn't have curr->nodeChar == NULL.
Another case is that any node (leaf or non-leaf) has a garbage child node. This will cause the recursion to break into reading garbage values and no reason to stop. Running in debug mode should break the execution with segmentation fault.
Write another function to test if all leaf-nodes have NULL termination.
EDIT:
After posting the code, the original poster has already pointed out that the problem was that he/she was not returning the list of strings.
Apart from that, there are a few more suggestions I would like to provide based on the code:
How does this while loop terminate if toInsert string is already in the Trie.
You will overrun the toInsert string and read a garbage character.
It will exit after that, but reading beyond your string is a bad way to program.
// while letters still exist within the trie traverse through the trie
while (curr->children.find(toInsert[increment]) != curr->children.end())
{ //letter found
curr = &(curr->children.find(toInsert[increment])->second);
increment++;
}
This can be written as follows:
while (increment < toInsert.length() &&
curr->children.find(toInsert[increment]) != curr->children.end())
Also,
Trie( vector<string> dictionary)
should be
Trie( const vector<string>& dictionary )
because dictionary can be a large object. If you don't pass by reference, it will create a second copy. This is not efficient.
I am a idiot. I forgot to return list on the first findPre() function.
vector<string> findPre(string pre) {
vector<string> list;
TrieNode * curr = root;
/*First find if the pre actually exist*/
for (int i = 0; i < pre.length(); i++) {
if (curr->children.find(pre[i]) == curr->children.end()) { //DNE
return list;
}
else {
curr = &(curr->children.find(pre[i])->second);
}
}
/*Now curr is at the end of the prefix, now we will perform a DFS*/
pre = pre.substr(0, pre.length() - 1);
findPre(list, curr, pre);
return list; //<----- this thing
}
This what i have so far.
void BST::insert(const data& aData)
{
if ( items[root_index].empty )
{
items[root_index].theData = aData;// Get the data.
items[root_index].empty = false;
oldRoot.theData = aData;
}
else
{
if ( aData < items[root_index].theData )
{
leftChild = root_index * 2;
if ( items[leftChild].empty )
{
items[leftChild].theData = aData;
items[leftChild].empty = false;
}
else
{
root_index++;
items[root_index].theData = items[leftChild].theData;
items[root_index].empty = false;
this->insert(aData);
}
}
else if ( items[root_index].theData < aData )
{
rightChild = root_index * 2 + 1;
if ( items[rightChild].empty )
{
items[rightChild].theData = aData;
items[rightChild].empty = false;
}
else
{//this where the problem is for "Z" insertion
root_index++;
items[root_index].theData = items[rightChild].theData;
items[root_index].empty = false;
this->insert(aData);
}
}
else return;
}
items[1].theData = oldRoot.theData;
}
and the constructor:
BST::BST(int capacity) : items(new item[capacity]), size(0),
leftChild(0), rightChild(0), root_index(1)
{
items->empty = true;
maxSize = capacity-1;
}
It doesnt work. I dont know why. I cant seem to write the code to make it balanced. What I have so far is a tree that resembles:
R
/
A
\
F
When inserting, "R", "A", and "F", so when i try to insert "Z" it becomes F's right child. But it should really be the root's right child:
R
/ \
A Z
\
F
Can someone help me make it like that?
I could be missing something, but the code just looks like all kinds of messed up. The root_index++, the recursive call to insert() after you add the data to the tree... You might want to go back to the drawing board on this one.
If you're going for a complete binary search tree (which is the main reason for using an array), you can append new elements to the array and move elements that break the tree order invariants (left descendents < node and node < right descendents) up the tree.
I have an array of a few million numbers.
double* const data = new double (3600000);
I need to iterate through the array and find the range (the largest value in the array minus the smallest value). However, there is a catch. I only want to find the range where the smallest and largest values are within 1,000 samples of each other.
So I need to find the maximum of: range(data + 0, data + 1000), range(data + 1, data + 1001), range(data + 2, data + 1002), ...., range(data + 3599000, data + 3600000).
I hope that makes sense. Basically I could do it like above, but I'm looking for a more efficient algorithm if one exists. I think the above algorithm is O(n), but I feel that it's possible to optimize. An idea I'm playing with is to keep track of the most recent maximum and minimum and how far back they are, then only backtrack when necessary.
I'll be coding this in C++, but a nice algorithm in pseudo code would be just fine. Also, if this number I'm trying to find has a name, I'd love to know what it is.
Thanks.
This type of question belongs to a branch of algorithms called streaming algorithms. It is the study of problems which require not only an O(n) solution but also need to work in a single pass over the data. the data is inputted as a stream to the algorithm, the algorithm can't save all of the data and then and then it is lost forever. the algorithm needs to get some answer about the data, such as for instance the minimum or the median.
Specifically you are looking for a maximum (or more commonly in literature - minimum) in a window over a stream.
Here's a presentation on an article that mentions this problem as a sub problem of what they are trying to get at. it might give you some ideas.
I think the outline of the solution is something like that - maintain the window over the stream where in each step one element is inserted to the window and one is removed from the other side (a sliding window). The items you actually keep in memory aren't all of the 1000 items in the window but a selected representatives which are going to be good candidates for being the minimum (or maximum).
read the article. it's abit complex but after 2-3 reads you can get the hang of it.
The algorithm you describe is really O(N), but i think the constant is too high. Another solution which looks reasonable is to use O(N*log(N)) algorithm the following way:
* create sorted container (std::multiset) of first 1000 numbers
* in loop (j=1, j<(3600000-1000); ++j)
- calculate range
- remove from the set number which is now irrelevant (i.e. in index *j - 1* of the array)
- add to set new relevant number (i.e. in index *j+1000-1* of the array)
I believe it should be faster, because the constant is much lower.
This is a good application of a min-queue - a queue (First-In, First-Out = FIFO) which can simultaneously keep track of the minimum element it contains, with amortized constant-time updates. Of course, a max-queue is basically the same thing.
Once you have this data structure in place, you can consider CurrentMax (of the past 1000 elements) minus CurrentMin, store that as the BestSoFar, and then push a new value and pop the old value, and check again. In this way, keep updating BestSoFar until the final value is the solution to your question. Each single step takes amortized constant time, so the whole thing is linear, and the implementation I know of has a good scalar constant (it's fast).
I don't know of any documentation on min-queue's - this is a data structure I came up with in collaboration with a coworker. You can implement it by internally tracking a binary tree of the least elements within each contiguous sub-sequence of your data. It simplifies the problem that you'll only pop data from one end of the structure.
If you're interested in more details, I can try to provide them. I was thinking of writing this data structure up as a paper for arxiv. Also note that Tarjan and others previously arrived at a more powerful min-deque structure that would work here, but the implementation is much more complex. You can google for "mindeque" to read about Tarjan et al.'s work.
std::multiset<double> range;
double currentmax = 0.0;
for (int i = 0; i < 3600000; ++i)
{
if (i >= 1000)
range.erase(range.find(data[i-1000]));
range.insert(data[i]);
if (i >= 999)
currentmax = max(currentmax, *range.rbegin());
}
Note untested code.
Edit: fixed off-by-one error.
read in the first 1000 numbers.
create a 1000 element linked list which tracks the current 1000 number.
create a 1000 element array of pointers to linked list nodes, 1-1 mapping.
sort the pointer array based on linked list node's values. This will rearrange the array but keep the linked list intact.
you can now calculate the range for the first 1000 numbers by examining the first and last element of the pointer array.
remove the first inserted element, which is either the head or the tail depending on how you made your linked list. Using the node's value perform a binary search on the pointer array to find the to-be-removed node's pointer, and shift the array one over to remove it.
add the 1001th element to the linked list, and insert a pointer to it in the correct position in the array, by performing one step of an insertion sort. This will keep the array sorted.
now you have the min. and max. value of the numbers between 1 and 1001, and can calculate the range using the first and last element of the pointer array.
it should now be obvious what you need to do for the rest of the array.
The algorithm should be O(n) since the delete and insertion is bounded by log(1e3) and everything else takes constant time.
I decided to see what the most efficient algorithm I could think of to solve this problem was using actual code and actual timings. I first created a simple solution, one that tracks the min/max for the previous n entries using a circular buffer, and a test harness to measure the speed. In the simple solution, each data value is compared against the set of min/max values, so that's about window_size * count tests (where window size in the original question is 1000 and count is 3600000).
I then thought about how to make it faster. First off, I created a solution that used a fifo queue to store window_size values and a linked list to store the values in ascending order where each node in the linked list was also a node in the queue. To process a data value, the item at the end of the fifo was removed from the linked list and the queue. The new value was added to the start of the queue and a linear search was used to find the position in the linked list. The min and max values could then be read from the start and end of the linked list. This was quick, but wouldn't scale well with increasing window_size (i.e. linearly).
So I decided to add a binary tree to the system to try to speed up the search part of the algorithm. The final timings for window_size = 1000 and count = 3600000 were:
Simple: 106875
Quite Complex: 1218
Complex: 1219
which was both expected and unexpected. Expected in that using a sorted linked list helped, unexpected in that the overhead of having a self balancing tree didn't offset the advantage of a quicker search. I tried the latter two with an increased window size and found that the were always nearly identical up to a window_size of 100000.
Which all goes to show that theorising about algorithms is one thing, implementing them is something else.
Anyway, for those that are interested, here's the code I wrote (there's quite a bit!):
Range.h:
#include <algorithm>
#include <iostream>
#include <ctime>
using namespace std;
// Callback types.
typedef void (*OutputCallback) (int min, int max);
typedef int (*GeneratorCallback) ();
// Declarations of the test functions.
clock_t Simple (int, int, GeneratorCallback, OutputCallback);
clock_t QuiteComplex (int, int, GeneratorCallback, OutputCallback);
clock_t Complex (int, int, GeneratorCallback, OutputCallback);
main.cpp:
#include "Range.h"
int
checksum;
// This callback is used to get data.
int CreateData ()
{
return rand ();
}
// This callback is used to output the results.
void OutputResults (int min, int max)
{
//cout << min << " - " << max << endl;
checksum += max - min;
}
// The program entry point.
void main ()
{
int
count = 3600000,
window = 1000;
srand (0);
checksum = 0;
std::cout << "Simple: Ticks = " << Simple (count, window, CreateData, OutputResults) << ", checksum = " << checksum << std::endl;
srand (0);
checksum = 0;
std::cout << "Quite Complex: Ticks = " << QuiteComplex (count, window, CreateData, OutputResults) << ", checksum = " << checksum << std::endl;
srand (0);
checksum = 0;
std::cout << "Complex: Ticks = " << Complex (count, window, CreateData, OutputResults) << ", checksum = " << checksum << std::endl;
}
Simple.cpp:
#include "Range.h"
// Function to actually process the data.
// A circular buffer of min/max values for the current window is filled
// and once full, the oldest min/max pair is sent to the output callback
// and replaced with the newest input value. Each value inputted is
// compared against all min/max pairs.
void ProcessData
(
int count,
int window,
GeneratorCallback input,
OutputCallback output,
int *min_buffer,
int *max_buffer
)
{
int
i;
for (i = 0 ; i < window ; ++i)
{
int
value = input ();
min_buffer [i] = max_buffer [i] = value;
for (int j = 0 ; j < i ; ++j)
{
min_buffer [j] = min (min_buffer [j], value);
max_buffer [j] = max (max_buffer [j], value);
}
}
for ( ; i < count ; ++i)
{
int
index = i % window;
output (min_buffer [index], max_buffer [index]);
int
value = input ();
min_buffer [index] = max_buffer [index] = value;
for (int k = (i + 1) % window ; k != index ; k = (k + 1) % window)
{
min_buffer [k] = min (min_buffer [k], value);
max_buffer [k] = max (max_buffer [k], value);
}
}
output (min_buffer [count % window], max_buffer [count % window]);
}
// A simple method of calculating the results.
// Memory management is done here outside of the timing portion.
clock_t Simple
(
int count,
int window,
GeneratorCallback input,
OutputCallback output
)
{
int
*min_buffer = new int [window],
*max_buffer = new int [window];
clock_t
start = clock ();
ProcessData (count, window, input, output, min_buffer, max_buffer);
clock_t
end = clock ();
delete [] max_buffer;
delete [] min_buffer;
return end - start;
}
QuiteComplex.cpp:
#include "Range.h"
template <class T>
class Range
{
private:
// Class Types
// Node Data
// Stores a value and its position in various lists.
struct Node
{
Node
*m_queue_next,
*m_list_greater,
*m_list_lower;
T
m_value;
};
public:
// Constructor
// Allocates memory for the node data and adds all the allocated
// nodes to the unused/free list of nodes.
Range
(
int window_size
) :
m_nodes (new Node [window_size]),
m_queue_tail (m_nodes),
m_queue_head (0),
m_list_min (0),
m_list_max (0),
m_free_list (m_nodes)
{
for (int i = 0 ; i < window_size - 1 ; ++i)
{
m_nodes [i].m_list_lower = &m_nodes [i + 1];
}
m_nodes [window_size - 1].m_list_lower = 0;
}
// Destructor
// Tidy up allocated data.
~Range ()
{
delete [] m_nodes;
}
// Function to add a new value into the data structure.
void AddValue
(
T value
)
{
Node
*node = GetNode ();
// clear links
node->m_queue_next = 0;
// set value of node
node->m_value = value;
// find place to add node into linked list
Node
*search;
for (search = m_list_max ; search ; search = search->m_list_lower)
{
if (search->m_value < value)
{
if (search->m_list_greater)
{
node->m_list_greater = search->m_list_greater;
search->m_list_greater->m_list_lower = node;
}
else
{
m_list_max = node;
}
node->m_list_lower = search;
search->m_list_greater = node;
}
}
if (!search)
{
m_list_min->m_list_lower = node;
node->m_list_greater = m_list_min;
m_list_min = node;
}
}
// Accessor to determine if the first output value is ready for use.
bool RangeAvailable ()
{
return !m_free_list;
}
// Accessor to get the minimum value of all values in the current window.
T Min ()
{
return m_list_min->m_value;
}
// Accessor to get the maximum value of all values in the current window.
T Max ()
{
return m_list_max->m_value;
}
private:
// Function to get a node to store a value into.
// This function gets nodes from one of two places:
// 1. From the unused/free list
// 2. From the end of the fifo queue, this requires removing the node from the list and tree
Node *GetNode ()
{
Node
*node;
if (m_free_list)
{
// get new node from unused/free list and place at head
node = m_free_list;
m_free_list = node->m_list_lower;
if (m_queue_head)
{
m_queue_head->m_queue_next = node;
}
m_queue_head = node;
}
else
{
// get node from tail of queue and place at head
node = m_queue_tail;
m_queue_tail = node->m_queue_next;
m_queue_head->m_queue_next = node;
m_queue_head = node;
// remove node from linked list
if (node->m_list_lower)
{
node->m_list_lower->m_list_greater = node->m_list_greater;
}
else
{
m_list_min = node->m_list_greater;
}
if (node->m_list_greater)
{
node->m_list_greater->m_list_lower = node->m_list_lower;
}
else
{
m_list_max = node->m_list_lower;
}
}
return node;
}
// Member Data.
Node
*m_nodes,
*m_queue_tail,
*m_queue_head,
*m_list_min,
*m_list_max,
*m_free_list;
};
// A reasonable complex but more efficent method of calculating the results.
// Memory management is done here outside of the timing portion.
clock_t QuiteComplex
(
int size,
int window,
GeneratorCallback input,
OutputCallback output
)
{
Range <int>
range (window);
clock_t
start = clock ();
for (int i = 0 ; i < size ; ++i)
{
range.AddValue (input ());
if (range.RangeAvailable ())
{
output (range.Min (), range.Max ());
}
}
clock_t
end = clock ();
return end - start;
}
Complex.cpp:
#include "Range.h"
template <class T>
class Range
{
private:
// Class Types
// Red/Black tree node colours.
enum NodeColour
{
Red,
Black
};
// Node Data
// Stores a value and its position in various lists and trees.
struct Node
{
// Function to get the sibling of a node.
// Because leaves are stored as null pointers, it must be possible
// to get the sibling of a null pointer. If the object is a null pointer
// then the parent pointer is used to determine the sibling.
Node *Sibling
(
Node *parent
)
{
Node
*sibling;
if (this)
{
sibling = m_tree_parent->m_tree_less == this ? m_tree_parent->m_tree_more : m_tree_parent->m_tree_less;
}
else
{
sibling = parent->m_tree_less ? parent->m_tree_less : parent->m_tree_more;
}
return sibling;
}
// Node Members
Node
*m_queue_next,
*m_tree_less,
*m_tree_more,
*m_tree_parent,
*m_list_greater,
*m_list_lower;
NodeColour
m_colour;
T
m_value;
};
public:
// Constructor
// Allocates memory for the node data and adds all the allocated
// nodes to the unused/free list of nodes.
Range
(
int window_size
) :
m_nodes (new Node [window_size]),
m_queue_tail (m_nodes),
m_queue_head (0),
m_tree_root (0),
m_list_min (0),
m_list_max (0),
m_free_list (m_nodes)
{
for (int i = 0 ; i < window_size - 1 ; ++i)
{
m_nodes [i].m_list_lower = &m_nodes [i + 1];
}
m_nodes [window_size - 1].m_list_lower = 0;
}
// Destructor
// Tidy up allocated data.
~Range ()
{
delete [] m_nodes;
}
// Function to add a new value into the data structure.
void AddValue
(
T value
)
{
Node
*node = GetNode ();
// clear links
node->m_queue_next = node->m_tree_more = node->m_tree_less = node->m_tree_parent = 0;
// set value of node
node->m_value = value;
// insert node into tree
if (m_tree_root)
{
InsertNodeIntoTree (node);
BalanceTreeAfterInsertion (node);
}
else
{
m_tree_root = m_list_max = m_list_min = node;
node->m_tree_parent = node->m_list_greater = node->m_list_lower = 0;
}
m_tree_root->m_colour = Black;
}
// Accessor to determine if the first output value is ready for use.
bool RangeAvailable ()
{
return !m_free_list;
}
// Accessor to get the minimum value of all values in the current window.
T Min ()
{
return m_list_min->m_value;
}
// Accessor to get the maximum value of all values in the current window.
T Max ()
{
return m_list_max->m_value;
}
private:
// Function to get a node to store a value into.
// This function gets nodes from one of two places:
// 1. From the unused/free list
// 2. From the end of the fifo queue, this requires removing the node from the list and tree
Node *GetNode ()
{
Node
*node;
if (m_free_list)
{
// get new node from unused/free list and place at head
node = m_free_list;
m_free_list = node->m_list_lower;
if (m_queue_head)
{
m_queue_head->m_queue_next = node;
}
m_queue_head = node;
}
else
{
// get node from tail of queue and place at head
node = m_queue_tail;
m_queue_tail = node->m_queue_next;
m_queue_head->m_queue_next = node;
m_queue_head = node;
// remove node from tree
node = RemoveNodeFromTree (node);
RebalanceTreeAfterDeletion (node);
// remove node from linked list
if (node->m_list_lower)
{
node->m_list_lower->m_list_greater = node->m_list_greater;
}
else
{
m_list_min = node->m_list_greater;
}
if (node->m_list_greater)
{
node->m_list_greater->m_list_lower = node->m_list_lower;
}
else
{
m_list_max = node->m_list_lower;
}
}
return node;
}
// Rebalances the tree after insertion
void BalanceTreeAfterInsertion
(
Node *node
)
{
node->m_colour = Red;
while (node != m_tree_root && node->m_tree_parent->m_colour == Red)
{
if (node->m_tree_parent == node->m_tree_parent->m_tree_parent->m_tree_more)
{
Node
*uncle = node->m_tree_parent->m_tree_parent->m_tree_less;
if (uncle && uncle->m_colour == Red)
{
node->m_tree_parent->m_colour = Black;
uncle->m_colour = Black;
node->m_tree_parent->m_tree_parent->m_colour = Red;
node = node->m_tree_parent->m_tree_parent;
}
else
{
if (node == node->m_tree_parent->m_tree_less)
{
node = node->m_tree_parent;
LeftRotate (node);
}
node->m_tree_parent->m_colour = Black;
node->m_tree_parent->m_tree_parent->m_colour = Red;
RightRotate (node->m_tree_parent->m_tree_parent);
}
}
else
{
Node
*uncle = node->m_tree_parent->m_tree_parent->m_tree_more;
if (uncle && uncle->m_colour == Red)
{
node->m_tree_parent->m_colour = Black;
uncle->m_colour = Black;
node->m_tree_parent->m_tree_parent->m_colour = Red;
node = node->m_tree_parent->m_tree_parent;
}
else
{
if (node == node->m_tree_parent->m_tree_more)
{
node = node->m_tree_parent;
RightRotate (node);
}
node->m_tree_parent->m_colour = Black;
node->m_tree_parent->m_tree_parent->m_colour = Red;
LeftRotate (node->m_tree_parent->m_tree_parent);
}
}
}
}
// Adds a node into the tree and sorted linked list
void InsertNodeIntoTree
(
Node *node
)
{
Node
*parent = 0,
*child = m_tree_root;
bool
greater;
while (child)
{
parent = child;
child = (greater = node->m_value > child->m_value) ? child->m_tree_more : child->m_tree_less;
}
node->m_tree_parent = parent;
if (greater)
{
parent->m_tree_more = node;
// insert node into linked list
if (parent->m_list_greater)
{
parent->m_list_greater->m_list_lower = node;
}
else
{
m_list_max = node;
}
node->m_list_greater = parent->m_list_greater;
node->m_list_lower = parent;
parent->m_list_greater = node;
}
else
{
parent->m_tree_less = node;
// insert node into linked list
if (parent->m_list_lower)
{
parent->m_list_lower->m_list_greater = node;
}
else
{
m_list_min = node;
}
node->m_list_lower = parent->m_list_lower;
node->m_list_greater = parent;
parent->m_list_lower = node;
}
}
// Red/Black tree manipulation routine, used for removing a node
Node *RemoveNodeFromTree
(
Node *node
)
{
if (node->m_tree_less && node->m_tree_more)
{
// the complex case, swap node with a child node
Node
*child;
if (node->m_tree_less)
{
// find largest value in lesser half (node with no greater pointer)
for (child = node->m_tree_less ; child->m_tree_more ; child = child->m_tree_more)
{
}
}
else
{
// find smallest value in greater half (node with no lesser pointer)
for (child = node->m_tree_more ; child->m_tree_less ; child = child->m_tree_less)
{
}
}
swap (child->m_colour, node->m_colour);
if (child->m_tree_parent != node)
{
swap (child->m_tree_less, node->m_tree_less);
swap (child->m_tree_more, node->m_tree_more);
swap (child->m_tree_parent, node->m_tree_parent);
if (!child->m_tree_parent)
{
m_tree_root = child;
}
else
{
if (child->m_tree_parent->m_tree_less == node)
{
child->m_tree_parent->m_tree_less = child;
}
else
{
child->m_tree_parent->m_tree_more = child;
}
}
if (node->m_tree_parent->m_tree_less == child)
{
node->m_tree_parent->m_tree_less = node;
}
else
{
node->m_tree_parent->m_tree_more = node;
}
}
else
{
child->m_tree_parent = node->m_tree_parent;
node->m_tree_parent = child;
Node
*child_less = child->m_tree_less,
*child_more = child->m_tree_more;
if (node->m_tree_less == child)
{
child->m_tree_less = node;
child->m_tree_more = node->m_tree_more;
node->m_tree_less = child_less;
node->m_tree_more = child_more;
}
else
{
child->m_tree_less = node->m_tree_less;
child->m_tree_more = node;
node->m_tree_less = child_less;
node->m_tree_more = child_more;
}
if (!child->m_tree_parent)
{
m_tree_root = child;
}
else
{
if (child->m_tree_parent->m_tree_less == node)
{
child->m_tree_parent->m_tree_less = child;
}
else
{
child->m_tree_parent->m_tree_more = child;
}
}
}
if (child->m_tree_less)
{
child->m_tree_less->m_tree_parent = child;
}
if (child->m_tree_more)
{
child->m_tree_more->m_tree_parent = child;
}
if (node->m_tree_less)
{
node->m_tree_less->m_tree_parent = node;
}
if (node->m_tree_more)
{
node->m_tree_more->m_tree_parent = node;
}
}
Node
*child = node->m_tree_less ? node->m_tree_less : node->m_tree_more;
if (node->m_tree_parent->m_tree_less == node)
{
node->m_tree_parent->m_tree_less = child;
}
else
{
node->m_tree_parent->m_tree_more = child;
}
if (child)
{
child->m_tree_parent = node->m_tree_parent;
}
return node;
}
// Red/Black tree manipulation routine, used for rebalancing a tree after a deletion
void RebalanceTreeAfterDeletion
(
Node *node
)
{
Node
*child = node->m_tree_less ? node->m_tree_less : node->m_tree_more;
if (node->m_colour == Black)
{
if (child && child->m_colour == Red)
{
child->m_colour = Black;
}
else
{
Node
*parent = node->m_tree_parent,
*n = child;
while (parent)
{
Node
*sibling = n->Sibling (parent);
if (sibling && sibling->m_colour == Red)
{
parent->m_colour = Red;
sibling->m_colour = Black;
if (n == parent->m_tree_more)
{
LeftRotate (parent);
}
else
{
RightRotate (parent);
}
}
sibling = n->Sibling (parent);
if (parent->m_colour == Black &&
sibling->m_colour == Black &&
(!sibling->m_tree_more || sibling->m_tree_more->m_colour == Black) &&
(!sibling->m_tree_less || sibling->m_tree_less->m_colour == Black))
{
sibling->m_colour = Red;
n = parent;
parent = n->m_tree_parent;
continue;
}
else
{
if (parent->m_colour == Red &&
sibling->m_colour == Black &&
(!sibling->m_tree_more || sibling->m_tree_more->m_colour == Black) &&
(!sibling->m_tree_less || sibling->m_tree_less->m_colour == Black))
{
sibling->m_colour = Red;
parent->m_colour = Black;
break;
}
else
{
if (n == parent->m_tree_more &&
sibling->m_colour == Black &&
(sibling->m_tree_more && sibling->m_tree_more->m_colour == Red) &&
(!sibling->m_tree_less || sibling->m_tree_less->m_colour == Black))
{
sibling->m_colour = Red;
sibling->m_tree_more->m_colour = Black;
RightRotate (sibling);
}
else
{
if (n == parent->m_tree_less &&
sibling->m_colour == Black &&
(!sibling->m_tree_more || sibling->m_tree_more->m_colour == Black) &&
(sibling->m_tree_less && sibling->m_tree_less->m_colour == Red))
{
sibling->m_colour = Red;
sibling->m_tree_less->m_colour = Black;
LeftRotate (sibling);
}
}
sibling = n->Sibling (parent);
sibling->m_colour = parent->m_colour;
parent->m_colour = Black;
if (n == parent->m_tree_more)
{
sibling->m_tree_less->m_colour = Black;
LeftRotate (parent);
}
else
{
sibling->m_tree_more->m_colour = Black;
RightRotate (parent);
}
break;
}
}
}
}
}
}
// Red/Black tree manipulation routine, used for balancing the tree
void LeftRotate
(
Node *node
)
{
Node
*less = node->m_tree_less;
node->m_tree_less = less->m_tree_more;
if (less->m_tree_more)
{
less->m_tree_more->m_tree_parent = node;
}
less->m_tree_parent = node->m_tree_parent;
if (!node->m_tree_parent)
{
m_tree_root = less;
}
else
{
if (node == node->m_tree_parent->m_tree_more)
{
node->m_tree_parent->m_tree_more = less;
}
else
{
node->m_tree_parent->m_tree_less = less;
}
}
less->m_tree_more = node;
node->m_tree_parent = less;
}
// Red/Black tree manipulation routine, used for balancing the tree
void RightRotate
(
Node *node
)
{
Node
*more = node->m_tree_more;
node->m_tree_more = more->m_tree_less;
if (more->m_tree_less)
{
more->m_tree_less->m_tree_parent = node;
}
more->m_tree_parent = node->m_tree_parent;
if (!node->m_tree_parent)
{
m_tree_root = more;
}
else
{
if (node == node->m_tree_parent->m_tree_less)
{
node->m_tree_parent->m_tree_less = more;
}
else
{
node->m_tree_parent->m_tree_more = more;
}
}
more->m_tree_less = node;
node->m_tree_parent = more;
}
// Member Data.
Node
*m_nodes,
*m_queue_tail,
*m_queue_head,
*m_tree_root,
*m_list_min,
*m_list_max,
*m_free_list;
};
// A complex but more efficent method of calculating the results.
// Memory management is done here outside of the timing portion.
clock_t Complex
(
int count,
int window,
GeneratorCallback input,
OutputCallback output
)
{
Range <int>
range (window);
clock_t
start = clock ();
for (int i = 0 ; i < count ; ++i)
{
range.AddValue (input ());
if (range.RangeAvailable ())
{
output (range.Min (), range.Max ());
}
}
clock_t
end = clock ();
return end - start;
}
Idea of algorithm:
Take the first 1000 values of data and sort them
The last in the sort - the first is range(data + 0, data + 999).
Then remove from the sort pile the first element with the value data[0]
and add the element data[1000]
Now, the last in the sort - the first is range(data + 1, data + 1000).
Repeat until done
// This should run in (DATA_LEN - RANGE_WIDTH)log(RANGE_WIDTH)
#include <set>
#include <algorithm>
using namespace std;
const int DATA_LEN = 3600000;
double* const data = new double (DATA_LEN);
....
....
const int RANGE_WIDTH = 1000;
double range = new double(DATA_LEN - RANGE_WIDTH);
multiset<double> data_set;
data_set.insert(data[i], data[RANGE_WIDTH]);
for (int i = 0 ; i < DATA_LEN - RANGE_WIDTH - 1 ; i++)
{
range[i] = *data_set.end() - *data_set.begin();
multiset<double>::iterator iter = data_set.find(data[i]);
data_set.erase(iter);
data_set.insert(data[i+1]);
}
range[i] = *data_set.end() - *data_set.begin();
// range now holds the values you seek
You should probably check this for off by 1 errors, but the idea is there.