//Heap Data Member
//Demonstrates an object with a dynamically allocated data member
#include
<iostream>
#include
<string>
using
namespace std;
class
Critter
{
public
:
Critter(
const string& name = "")
{
cout <<
"Constructor called\n";
m_pName =
new string(name);
}
~Critter()
//destructor
{
cout <<
"Destructor called\n";
delete
m_pName;
}
Critter(
const Critter& c) //copy constructor
{
cout <<
"Copy Constructor called\n";
m_pName =
new string;
*m_pName =
c.GetName();
}
Critter
& operator=(const Critter& c) //overloaded assignment operator
{
cout <<
"Overloaded Assignment Operator called\n";
if
(this == &c)
{
return
*this;
}
else
{
*m_pName =
c.GetName();
return
*this;
}
}
string
GetName() const { return *m_pName; }
void
SetName(const string& name = "") { *m_pName = name; }
void
SayHi() const { cout << "Hi, my name is " << GetName() << "\n"; }
private
:
string
* m_pName;
};
void
testDestructor();
void
testCopyConstructor(Critter copy);
void
testAssignmentOp();
int
main()
{
testDestructor();
cout << endl;
Critter
crit("Poochie");
crit.SayHi();
testCopyConstructor(crit);
cout << endl;
testAssignmentOp();
return
0;
}
void
testDestructor()
{
Critter
crit("Rover");
crit.SayHi();
}
//passing object by value invokes its copy constructor
void
testCopyConstructor(Critter copy)
{
copy
.SayHi();
}
void
testAssignmentOp()
{
Critter
crit1("crit1");
Critter
crit2("crit2");
crit1 = crit2;
crit1.SayHi();
crit2.SayHi();
cout <<
"Setting name of crit1 back to 'crit1'\n";
crit1.SetName(
"crit1");
crit1.SayHi();
crit2.SayHi();
Critter
crit("crit");
crit = crit;
}
The time complexity of removing an element from a heap data structure is O(log n), where n is the number of elements in the heap.
A minimum binary heap is a data structure where the parent node is smaller than its children nodes. The main operations of a minimum binary heap are insertion, deletion, and heapify. Insertion adds a new element to the heap, deletion removes the minimum element, and heapify maintains the heap property after an operation.
Following the IMDB - Internet Movie Data Base, Septimus Heap: Magyk will came out in 2010.
selection sort
To efficiently decrease the key value of an element in a heap data structure, you can perform a "decrease key" operation by updating the value of the element and then adjusting the heap structure to maintain the heap property. This typically involves comparing the new key value with the parent node and swapping elements if necessary to restore the heap property.
You should know classes & pointers before heaps. My idea of a heap is a bit like this: You have a class or struct(i.g. Heap_t) with a pointer to another of itself, and some data. Example: class Heap_t{ public: Heap_t(){ Pointer=NULL; } Heap_t * point; int data; }; int main(){ //Variable Heap_t * heap;//This will hold a pointer to the root of the heap Heap_t tmp; //Temporary storage for a piece of the heap before it is added Heap_t * cur; //Temporary storage for the current piece of the heap //Setup the heap tmp.data=0;//Make data something meaningful cur=(Heap_t *) tmp;//Make the root of the heap heap=cur;//Backup the root of the heap //Make the heap big for(int i=1;i<10;i++){ tmp.data=i;//Make the data something meaningfull (*cur).next=(Heap_t *) tmp; //Add tmp to the heap } //Do stuff //Exit return(0); } You could also use a reference or use it without a pointer or a reference. This is a bit more like a linked list, but it is an example.
In the bottom-up heap construction process, a heap is built by starting with individual elements and gradually combining them into a complete heap structure. This is done by repeatedly "heapifying" smaller sub-heaps until the entire heap is formed. The process involves comparing elements and swapping them if necessary to maintain the heap property, which ensures that the parent node is always greater (for a max heap) or smaller (for a min heap) than its children. This method is commonly used in data structures and algorithms to efficiently create and maintain heap structures.
A median heap is a data structure used to efficiently find the median value in a set of numbers. It combines the properties of a min heap and a max heap to quickly access the middle value. This is useful in algorithms that require finding the median, such as sorting algorithms and statistical analysis.
Heap sorting is a comparison-based sorting algorithm that utilizes a binary heap data structure to efficiently sort elements. The process begins by constructing a max heap from the input data, which organizes elements such that the largest value is at the root. The algorithm then repeatedly removes the root (maximum element) and rebuilds the heap, progressively sorting the array in ascending order. Graphically, this can be represented by a series of heap trees and arrays, illustrating the transformation of the heap structure and the sorted output at each stage.
Dijkstra's algorithm can be implemented in Java using a heap data structure to efficiently calculate the shortest path. The heap data structure helps in maintaining the priority queue of vertices based on their distances from the source node. By updating the distances and reorganizing the heap, the algorithm can find the shortest path in a more optimized way compared to using other data structures.
No, a heap is not a type of tree structure. A heap is a specialized tree-based data structure commonly used in computer science for efficient priority queue operations.
heap