Numeroasele moduri prin care datele pot fi aranjate, salvate și gestionate în cadrul unui program de calculator sunt denumite structuri de date în Java. Aceste structuri oferă o metodă metodică de manipulare și gestionare a datelor în mod eficient, permițând operațiuni utile precum inserarea, ștergerea, preluarea și traversarea.
Articolul va explora tot ceea ce este legat de structurile de date din Java și îi ajută pe începători să înțeleagă ușor și eficient.
- Ce este Java?
- Ce sunt structurile de date în Java?
- Tipuri de structuri de date în Java
- Avantajele structurilor de date în Java
- Clasificarea structurilor de date
- Întrebări frecvente privind structurile de date în Java
Ce este Java?
Java este un limbaj popular de programare orientat pe obiecte, renumit pentru biblioteca sa standard vastă și libertatea platformei. Oferă o arhitectură solidă pentru crearea de programe care rulează fără recompilare pe diverse platforme. Cunoscuta bibliotecă pentru Java are o gamă largă de sisteme de înregistrări care fac viabil să se ocupe eficient de numeroase tipuri de date.
Ce sunt structurile de date în Java?
Modul în care datele sunt organizate și stocate în memoria unui program de calculator se bazează îndeaproape pe structurile de înregistrări Java. Renumita bibliotecă Java include un tip semnificativ de structuri statistice încorporate. Câteva dintre sistemele de înregistrări care permit programatorilor modalități scurte și simple de a salva și aranja datele includ liste conectate, stive, cozi și matrice. Dezvoltatorii pot efectua rapid operațiuni precum inserarea, ștergerea, căutarea și sortarea, deoarece oferă o serie de mecanisme pentru obținerea accesului la, modificarea și gestionarea datelor. Programatorii Java pot reduce utilizarea memoriei și pot crește considerabil eficiența generală a programelor lor prin utilizarea acestor structuri de date.
Tipuri de structuri de date în Java
Lista structurilor de date din Java este listată mai jos
- Matrice
- ArrayList
- LinkedList
- Grămadă
- Coadă
- HashMap
- HashSet
- TreeSet
- Harta copacului
- Grafic
- Copac
Diagrama de mai jos explică foarte clar tipurile de structuri de date în Java.
Clasificare suplimentară a tipurilor de structuri de date:
Există două tipuri de structuri de date: -
- Structuri de date primitive
- Structuri de date non-primitive
1) Structuri de date primitive: Cunoscute și ca tipuri de date primitive, acestea sunt tipuri de date de bază încorporate în Java. Ei includ:
2) Structuri de date non-primitive: Structurile de înregistrări non-primitive sunt mai complexe și sunt compuse din tipuri primitive de informații. Ele pot fi, în plus, clasificate în două feluri:
Avantajele structurilor de date în Java
1) Matrice:
O matrice este o structură de date de bază și adesea folosită în contextul structurilor de date Java. Oferă o metodă de stocare a unei colecții de dimensiuni fixe de componente de tip identic. Deoarece oferă acces rapid și ușor la elemente în funcție de indexul lor, matricele sunt un instrument crucial pentru gestionarea și organizarea datelor.
Avantaje:
Dezavantaje:
Functii:
Implementare:
Nume de fișier: ArrayExample.java
import java.util.*; public class ArrayExample { public static void main(String[] args) { int[] numbers={10,20,30,40,50}; // Initialize an array of integers System.out.println('Element at index 0:'+numbers[0]); System.out.println('Element at index 2:'+numbers[2]); System.out.println('Element at index 4:'+numbers[4]); int sum=0; for (int i=0;i<numbers.length;i++) { sum+="numbers[i];" } system.out.println('sum of array elements:'+sum); numbers[2]="35;" update an element in the system.out.println('updated at index 2:'+numbers[2]); system.out.println('elements array:'); for (int number:numbers) system.out.println(number); < pre> <p> <strong>Output:</strong> </p> <pre> Element at index 0:10 Element at index 2:30 Element at index 4:50 Sum of array elements:150 Updated element at index 2:35 Elements in the array: 10 20 35 40 50 </pre> <h3>2) ArrayList:</h3> <p>ArrayList in Java is a dynamic data structure that allows for the storage and manipulation of elements. It is part of the Java Collections Framework and is implemented using an array internally.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Dynamic Size:</td> Unlike arrays, ArrayLists can dynamically grow or shrink in size as elements are added or removed. It eliminates the need for manual resizing and allows for handling varying amounts of data conveniently. </tr><tr><td>Easy Element Manipulation:</td> ArrayLists offer methods to add, remove, and modify elements at any position within the list. Its flexibility simplifies common operations such as insertion, deletion, and updating, making element manipulation more efficient. </tr><tr><td>Random Access:</td> ArrayLists support random Access to elements using their index, enabling quick retrieval and modification of elements at specific positions within the list. It facilitates efficient element access and enhances overall performance. </tr><tr><td>Compatibility with Java Collection Framework:</td> ArrayLists implement the List interface, making them compatible with other Collection classes in the Java Collections Framework. Its compatibility allows for seamless integration with various algorithms and operations provided by the framework. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Higher Memory Overhead:</td> ArrayLists require additional memory to maintain their internal structure, resulting in higher memory overhead compared to arrays. It can be a concern when dealing with large collections of elements. </tr><tr><td>Slower Insertion and Deletion:</td> Inserting or deleting elements in the middle of an ArrayList requires shifting elements, which can be time-consuming for large lists. In scenarios where frequent insertion or deletion operations are expected, other data structures like LinkedList may offer better performance. </tr><tr><td>Limited Performance for Search:</td> Searching for an element in an unsorted ArrayList requires iterating over the elements until a match is found. It is a linear search approach that results in slower search performance compared to data structures optimized for searching, such as HashSet or TreeMap. </tr><tr><td>No Primitive Type Support:</td> ArrayLists can only store objects and do not directly support primitive data types like int or char. To store primitive types, wrapper classes like Integer or Character need to be used, leading to potential autoboxing and unboxing overhead. </tr></ol> <p> <strong>Functions:</strong> </p> <ol class="points"> <tr><td>Creating an ArrayList:</td> Declare and initialize an ArrayList using the ArrayList class and specify the element type within the angle brackets. </tr><tr><td>Adding Elements:</td> Use the add method to append elements at the end of the ArrayList. </tr><tr><td>Accessing Elements:</td> Use the get technique to retrieve the price of detail at a selected index. </tr><tr><td>Modifying Elements:</td> Update the cost of detail at a specific index for the usage of the set approach. </tr><tr><td>Finding Size:</td> Use the dimensions method to get the cutting-edge quantity of factors in the ArrayList. </tr><tr><td>Removing Elements:</td> Use the remove approach to delete a detail at a specific index or via providing the object reference. </tr><tr><td>Iterating through the ArrayList:</td> Use loops to iterate over each element in the ArrayList and perform operations on them. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> ArrayListExample.java</p> <pre> import java.util.*; public class ArrayListExample { public static void main(String[] args) { // Create an ArrayList to store integers ArrayList numbers=new ArrayList(List.of(10,20,30,40,50)); //Access and print elements from the ArrayList System.out.println('Element at index 0:'+numbers.get(0)); System.out.println('Element at index 2:'+numbers.get(2)); System.out.println('Element at index 4:'+numbers.get(4)); // Calculate and print the sum of all elements in the ArrayList int sum=numbers.stream().mapToInt(Integer::intValue).sum(); System.out.println('Sum of ArrayList elements:'+sum); // Update an element in the ArrayList numbers.set(2,35); System.out.println('Updated element at index 2:'+numbers.get(2)); // Iterate through the ArrayList using a for-each loop and print the elements System.out.println('Elements in the ArrayList:'); for (int number:numbers) { System.out.println(number); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Element at index 0:10 Element at index 2:30 Element at index 4:50 Sum of ArrayList elements:150 Updated element at index 2:35 Elements in the ArrayList: 10 20 35 40 50 </pre> <h3>3) Linked List:</h3> <p>A linked list is a linear data structure in which elements are stored in separate objects called nodes. A reference link to the following node in the sequence is included in each node's data element. The list's final node links to null, indicating that the list has ended.</p> <p>Unlike arrays, linked lists do not require contiguous memory allocation. Each node in a linked list can be allocated independently, allowing for dynamic memory allocation and efficient insertion and deletion operations.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Dynamic Size:</td> LinkedList can grow or shrink dynamically, making it suitable for varying or unknown data sizes. </tr><tr><td>Efficient Insertion and Deletion:</td> Inserting or deleting elements within a LinkedList is efficient, as it does not require shifting elements. </tr><tr><td>No Contiguous Memory Requirement:</td> LinkedList does not need contiguous memory allocation, making it flexible and suitable for unpredictable memory situations. </tr><tr><td>Easy Modification:</td> LinkedList allows easy modification of elements by changing reference pointers, enabling efficient manipulation. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Slower Random Access:</td> LinkedList has slower random Access as it requires traversing the list to access elements by index. </tr><tr><td>Increased Memory Overhead:</td> LinkedList requires additional memory for references and nodes, increasing memory overhead compared to arrays. </tr><tr><td>Inefficient Search:</td> LinkedList has slower search operations, requiring sequential iteration to find specific elements. </tr></ol> <p> <strong>Functions:</strong> </p> <ol class="points"> <tr><td>Creating a LinkedList:</td> Declare and initialize a LinkedList using the LinkedList class. </tr><tr><td>Adding Elements:</td> Use the add method to append elements at the end of the LinkedList. </tr><tr><td>Accessing Elements:</td> Use the get method to retrieve the value of an element at a specific index. </tr><tr><td>Modifying Elements:</td> Update the value of an element at a particular index using the set method. </tr><tr><td>Removing Elements:</td> Use the remove method to delete an element at a specific index or by providing the object reference. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> LinkedList1.java</p> <pre> import java.util.*; public class LinkedList1 { public static void main(String[] args) { // Create a LinkedList to store integers LinkedList linkedList1 = new LinkedList(); // Add elements to the LinkedList linkedList1.add(10); linkedList1.add(20); linkedList1.add(30); linkedList1.add(40); linkedList1.add(50); // Print the LinkedList System.out.println('LinkedList:'+linkedList1); // Remove an element from the LinkedList linkedList1.removeFirst(); System.out.println('LinkedList after removing first element:'+linkedList1); // Check if an element exists in the LinkedList boolean containsElement=linkedList1.contains(30); System.out.println('LinkedList contains element 30?'+containsElement); // Get the first and last elements of the LinkedList int firstElement=linkedList1.getFirst(); int lastElement=linkedList1.getLast(); System.out.println('First element:'+firstElement); System.out.println('Last element:'+lastElement); // Clear the LinkedList linkedList1.clear(); System.out.println('LinkedList after clearing:'+linkedList1); } } </pre> <p> <strong>Output:</strong> </p> <pre> LinkedList:[10, 20, 30, 40, 50] LinkedList after removing first element:[20, 30, 40, 50] LinkedList contains element 30?true First element:20 Last element:50 LinkedList after clearing:[] </pre> <h3>4) Stack:</h3> <p>The Last-In-First-Out (LIFO) principle dictates that the element that was most recently inserted is also the element that is removed first. A stack is a linear data structure that follows this rule. It employs the commands 'push' and 'pop' to add elements to the stack and, accordingly, remove the top element from the stack. The 'peek' technique additionally enables Access to the top element without removing it.</p> <p> <strong>Features of a stack:</strong> </p> <ol class="points"> <tr><td>LIFO behavior:</td> The last element pushed onto the stack is the first one to be popped out, making it suitable for applications where the order of insertion and removal is important. </tr><tr><td>Limited Access:</td> Stacks typically provide restricted Access to elements. You can only access the topmost element, and to reach other elements, you need to pop the elements above them. </tr><tr><td>Dynamic size:</td> Stacks can be implemented using arrays or linked lists, allowing for a dynamic size. They can grow or shrink as needed during runtime. </tr></ol> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Simplicity:</td> Stacks are easy to understand and implement. </tr><tr><td>Efficiency:</td> Insertion and deletion operations have a time complexity of O(1). </tr><tr><td>Function call management:</td> Stacks efficiently manage function calls and variable storage. </tr><tr><td>Undo/Redo functionality:</td> Stacks enable undo and redo operations in applications. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Limited Access:</td> Access to elements is restricted to the top of the stack. </tr><tr><td>Size restrictions:</td> Stacks may have size limitations depending on the implementation. </tr><tr><td>Not suitable for all scenarios:</td> Stacks are specific to LIFO behavior and may not be appropriate in other cases. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> StackExample.java</p> <pre> import java.util.Stack; public class StackExample { public static void main(String[] args) { // Create a stack Stack stack=new Stack(); // Push elements onto the stack stack.push(10); stack.push(20); stack.push(30); // Print the top element of the stack System.out.println('Top element:'+stack.peek()); // Pop elements from the stack int poppedElement=stack.pop(); System.out.println('Popped element:'+poppedElement); // Check if the stack is empty System.out.println('Is stack empty?'+stack.isEmpty()); // Get the size of the stack System.out.println('Stack size:'+stack.size()); // Iterate over the stack System.out.println('Stack elements:'); for (Integer element:stack) { System.out.println(element); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Top element:30 Popped element:30 Is stack empty?false Stack size:2 Stack elements: 10 20 </pre> <h3>5) Queue:</h3> <p>A queue is a linear data structure in Java that follows the First-In-First-Out (FIFO) principle. It represents a collection of elements where elements are inserted at the rear and removed from the front.</p> <p> <strong>Features:</strong> </p> <ol class="points"> <tr><td>Enqueue:</td> Adding an element to the rear of the queue. </tr><tr><td>Dequeue:</td> Removing an element from the front of the queue. </tr><tr><td>Peek:</td> Retrieve the element at the front of the queue without removing it. </tr><tr><td>Size:</td> Determining the number of elements in the queue. </tr><tr><td>Empty Check:</td> Checking if the queue is empty. </tr></ol> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>FIFO Behavior:</td> Elements are processed in the order of their insertion, ensuring the preservation of the original sequence. </tr><tr><td>Efficient Insertion and Removal:</td> Adding and removing elements from a queue is fast and has a constant time complexity of O(1). </tr><tr><td>Synchronization:</td> Java provides synchronized queue implementations, making them safe for concurrent programming. </tr><tr><td>Standardized Interface:</td> The Queue interface in Java offers a common set of methods, allowing easy interchangeability between different queue implementations. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>No Random Access:</td> Queues do not support direct Access to elements in the middle. Accessing specific positions requires dequeuing preceding elements. </tr><tr><td>Limited Size:</td> Some queue implementations have a fixed size or capacity, leading to overflow or exceptions when exceeding the maximum size. </tr><tr><td>Inefficient Search:</td> Searching for an element in a queue requires dequeuing until a match is found, resulting in a linear search with potentially high time complexity. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> QueueExample.java</p> <pre> import java.util.Stack; import java.util.LinkedList; import java.util.Queue; public class QueueExample { public static void main(String[] args) { // Create a Queue to store integers Queue queue=new LinkedList(); // Enqueue elements to the Queue queue.offer(10); queue.offer(20); queue.offer(30); queue.offer(40); queue.offer(50); //Access and print the front element of the Queue System.out.println('Front element:'+queue.peek()); // Dequeue elements from the Queue and print them while (!queue.isEmpty()) { int element=queue.poll(); System.out.println('Dequeued element:'+element); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Front element:10 Dequeued element:10 Dequeued element:20 Dequeued element:30 Dequeued element:40 Dequeued element:50 </pre> <h3>6) HashMap:</h3> <p>A HashMap is a data structure in Java that provides a way to store and retrieve key-value pairs. It is part of the Java Collections Framework and is implemented based on the hash table data structure.</p> <p> <strong>Functions:</strong> </p> <ul> <tr><td>put(key, value):</td> Inserts the specified key-value pair into the HashMap. </tr><tr><td>get(key):</td> Retrieves the value associated with the specified key. </tr><tr><td>containsKey(key):</td> Checks if the HashMap contains the specified key. </tr><tr><td>containsValue(value):</td> Checks if the HashMap contains the specified value. </tr><tr><td>remove(key):</td> Removes the key-value pair associated with the specified key from the HashMap. </tr><tr><td>size():</td> Returns the number of key-value pairs in the HashMap. </tr><tr><td>isEmpty():</td> Checks if the HashMap is empty. </tr><tr><td>keySet():</td> Returns a Set containing all the keys in the HashMap. </tr><tr><td>values():</td> Returns a Collection containing all the values in the HashMap. </tr><tr><td>clear():</td> Removes all the key-value pairs from the HashMap. </tr></ul> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Efficient Retrieval:</td> HashMap provides fast retrieval of values based on keys with constant-time complexity O(1). </tr><tr><td>Flexible Key-Value Pairing:</td> HashMap allows any non-null object as a key, enabling custom-defined keys for storing and retrieving data. </tr><tr><td>Dynamic Size:</td> HashMap can dynamically grow or shrink in size to handle varying amounts of data. </tr><tr><td>Compatibility with Java Collections Framework:</td> HashMap implements the Map interface, allowing seamless integration with other Collection classes. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Lack of Ordering:</td> HashMap does not preserve the order of elements. Use LinkedHashMap or TreeMap for specific ordering requirements. </tr><tr><td>Increased Memory Overhead:</td> HashMap requires additional memory for hash codes and internal structure compared to simpler data structures. </tr><tr><td>Slower Iteration:</td> Iterating over a HashMap can be slower compared to arrays or lists due to traversing the underlying hash table. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> HashMapExample.java</p> <pre> import java.util.HashMap; public class HashMapExample { public static void main(String[] args) { // Create a HashMap to store String keys and Integer values HashMap hashMap=new HashMap(); // Add key-value pairs to the HashMap hashMap.put('John',25); hashMap.put('Alice',30); hashMap.put('Bob',35); //Access and print values based on keys System.out.println('Age of John:'+hashMap.get('John')); System.out.println('Age of Alice:'+hashMap.get('Alice')); // Check if a key exists in the HashMap System.out.println('Is Bob present?'+hashMap.containsKey('Bob')); // Update the value associated with a key hashMap.put('Alice',32); // Remove a key-value pair from the HashMap hashMap.remove('John'); // Print all key-value pairs in the HashMap System.out.println('Key-Value pairs in the HashMap:'); for (String key : hashMap.keySet()) { System.out.println(key+':'+hashMap.get(key)); } // Check the size of the HashMap System.out.println('Size of the HashMap:'+hashMap.size()); } } </pre> <p> <strong>Output:</strong> </p> <pre> Age of John:25 Age of Alice:30 Is Bob present?true Key-Value pairs in the HashMap: Bob:35 Alice:32 Size of the HashMap:2 </pre> <h3>7) HashSet:</h3> <p>HashSet is a data structure in Java that implements the Set interface and stores elements in a hash table.</p> <p> <strong>Features:</strong> </p> <ol class="points"> <tr><td>Stores unique elements:</td> HashSet does not allow duplicate elements. Each element in the HashSet is unique. </tr><tr><td>Uses hash-based lookup:</td> HashSet uses the hash value of each element to determine its storage location, providing efficient element retrieval. </tr><tr><td>Unordered collection:</td> The elements in a HashSet are not stored in a specific order. The order of elements may change over time. </tr></ol> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Fast element lookup:</td> HashSet provides fast lookup operations, making it efficient to check if an element exists in the set. </tr><tr><td>No duplicate elements:</td> HashSet automatically handles duplicate elements and ensures that each element is unique. </tr><tr><td>Integration with Java Collections Framework:</td> HashSet implements the Set interface, making it compatible with other collection classes in the Java Collections Framework. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>No guaranteed order:</td> HashSet does not maintain the order of elements. If the order of elements is important, HashSet is not suitable. </tr><tr><td>No indexing:</td> HashSet does not provide direct indexing or positional Access to elements. To access elements, you need to iterate over the set. </tr><tr><td>Higher memory overhead:</td> HashSet requires additional memory to store hash values and maintain the hash table structure, resulting in higher memory usage compared to some other data structures. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> HashSetExample.java</p> <pre> import java.util.HashSet; public class HashSetExample { public static void main(String[] args) { // Create a HashSet HashSet set=new HashSet(); // Add elements to the HashSet set.add('Apple'); set.add('Banana'); set.add('Orange'); set.add('Grapes'); set.add('Mango'); // Print the HashSet System.out.println('HashSet:'+set); // Check if an element exists System.out.println('Contains 'Apple':'+set.contains('Apple')); // Remove an element set.remove('Banana'); // Print the updated HashSet System.out.println('Updated HashSet:'+set); // Get the size of the HashSet System.out.println('Size of HashSet:'+set.size()); // Clear the HashSet set.clear(); // Check if the HashSet is empty System.out.println('Is HashSet empty?'+set.isEmpty()); } } </pre> <p> <strong>Output:</strong> </p> <pre> HashSet:[Apple, Grapes, Mango, Orange, Banana] Contains 'Apple':true Updated HashSet:[Apple, Grapes, Mango, Orange] Size of HashSet:4 Is HashSet empty?true </pre> <h3>8) TreeSet:</h3> <p>TreeSet is an implementation of the SortedSet interface in Java that uses a self-balancing binary search tree called a red-black tree to store elements in sorted order.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Sorted Order:</td> TreeSet automatically maintains the elements in a sorted order based on their natural ordering or a custom comparator. It allows for efficient searching and retrieval of elements in ascending or descending order. </tr><tr><td>No Duplicate Elements:</td> TreeSet does not allow duplicate elements. It ensures that each element in the set is unique, which can be useful in scenarios where duplicate values should be avoided. </tr><tr><td>Efficient Operations:</td> TreeSet provides efficient operations like insertion, deletion, and searching. These operations have a time complexity of O(log n), where n is the number of elements in the set. </tr><tr><td>Navigable Set Operations:</td> TreeSet provides additional navigational methods, such as higher(), lower(), ceiling(), and floor(), which allow you to find elements that are greater than, less than, or equal to a given value. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Overhead:</td> TreeSet requires additional memory to store the internal data structure, which can lead to higher memory overhead compared to other set implementations. </tr><tr><td>Slower Insertion and Removal:</td> Insertion and removal operations in TreeSet involve maintaining the sorted order of elements, which may require tree restructuring. It can make these operations slightly slower compared to HashSet or LinkedHashSet. </tr><tr><td>Limited Customization:</td> TreeSet is primarily designed for natural ordering or a single custom comparator. It may need more flexibility for multiple sorting criteria or complex sorting logic. </tr></ol> <p> <strong>Functions:</strong> </p> <ul> <tr><td>add(element):</td> Adds an element to the TreeSet while maintaining the sorted order. </tr><tr><td>remove(element):</td> Removes the specified element from the TreeSet. </tr><tr><td>contains(element):</td> Checks if the TreeSet contains the specified element. </tr><tr><td>size():</td> Returns the number of elements in the TreeSet. </tr><tr><td>first():</td> Returns the first (lowest) element in the TreeSet. </tr><tr><td>last():</td> Returns the last (highest) element in the TreeSet. </tr><tr><td>higher(element):</td> Returns the least element in the TreeSet that is strictly greater than the given element. </tr><tr><td>lower(element):</td> Returns the greatest element in the TreeSet that is strictly less than the given element. </tr></ul> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> TreeSetExample.java</p> <pre> import java.util.TreeSet; public class TreeSetExample { public static void main(String[] args) { // Create a TreeSet TreeSet numbers=new TreeSet(); // Add elements to the TreeSet numbers.add(5); numbers.add(2); numbers.add(8); numbers.add(1); numbers.add(4); // Print the TreeSet System.out.println('Elements in the TreeSet:'+numbers); // Check if an element exists System.out.println('Does TreeSet contain 4?'+numbers.contains(4)); // Remove an element numbers.remove(2); // Print the TreeSet after removal System.out.println('Elements in the TreeSet after removal:'+numbers); // Get the size of the TreeSet System.out.println('Size of the TreeSet:'+numbers.size()); // Get the first and last element System.out.println('First element:'+numbers.first()); System.out.println('Last element:'+numbers.last()); // Iterate over the TreeSet System.out.println('Iterating over the TreeSet:'); for (int number:numbers) { System.out.println(number); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Elements in the TreeSet:[1, 2, 4, 5, 8] Does TreeSet contain 4? true Elements in the TreeSet after removal:[1, 4, 5, 8] Size of the TreeSet:4First element:1 Last element:8 Iterating over the TreeSet: 1 4 5 8 </pre> <h3>9) TreeMap:</h3> <p>TreeMap is a class in Java that implements the Map interface and provides a sorted key-value mapping based on the natural order of the keys or a custom comparator.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Sorted Ordering:</td> TreeMap maintains the keys in sorted order, which allows for efficient searching, retrieval, and range-based operations. </tr><tr><td>Key-Value Mapping:</td> TreeMap stores key-value pairs, enabling efficient lookup and retrieval of values based on the associated keys. </tr><tr><td>Red-Black Tree Implementation:</td> TreeMap uses a balanced binary search tree (Red-Black Tree) internally, ensuring efficient performance even for large datasets. </tr><tr><td>Support for Custom Comparators:</td> TreeMap allows the use of custom comparators to define the sorting order of the keys, providing flexibility in sorting criteria. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Memory Overhead:</td> TreeMap requires additional memory to store the internal tree structure and associated objects, resulting in higher memory usage compared to simpler data structures like HashMap. </tr><tr><td>Slower Insertion and Deletion:</td> Insertion and deletion operations in TreeMap have a time complexity of O(log n) due to the need for tree restructuring, making them slower compared to HashMap or LinkedHashMap. </tr><tr><td>Limited Performance for Unsorted Data:</td> TreeMap performs efficiently for sorted data, but its performance can degrade when dealing with unsorted data or frequent modifications, as it requires maintaining the sorted order. </tr></ol> <p> <strong>Functions:</strong> </p> <ul> <tr><td>put(key, value):</td> Inserts a key-value pair into the TreeMap. </tr><tr><td>get(key):</td> Retrieves the value associated with the specified key. </tr><tr><td>containsKey(key):</td> Checks if the TreeMap contains a specific key. </tr><tr><td>remove(key):</td> Removes the key-value pair associated with the specified key. </tr><tr><td>size():</td> Returns the number of key-value pairs in the TreeMap. </tr><tr><td>keySet():</td> Returns a set of all keys in the TreeMap. </tr><tr><td>values():</td> Returns a collection of all values in the TreeMap. </tr><tr><td>entrySet():</td> Returns a set of key-value pairs in the TreeMap. </tr></ul> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> TreeMapExample.java</p> <pre> import java.util.TreeMap; public class TreeMapExample { public static void main(String[] args) { // Create a TreeMap TreeMap scores=new TreeMap(); // Insert key-value pairs into the TreeMap scores.put('Alice',90); scores.put('Bob',80); scores.put('Charlie',95); scores.put('David',87); scores.put('Eve',92); //Access and print values from the TreeMap System.out.println('Score of Alice:'+scores.get('Alice')); System.out.println('Score of Charlie:'+scores.get('Charlie')); System.out.println('Score of David:'+scores.get('David')); // Update a value in the TreeMap scores.put('Bob',85); // Remove a key-value pair from the TreeMap scores.remove('Eve'); // Iterate through the TreeMap using a for-each loop System.out.println('Scores in the TreeMap:'); for (String name:scores.keySet()) { int score=scores.get(name); System.out.println(name+':'+score); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Score of Alice:90 Score of Charlie:95 Score of David:87 Scores in the TreeMap: Alice:90 Bob:85 Charlie:95 David:87 </pre> <h3>10) Graph:</h3> <p>Graphs are data structure that represents a collection of interconnected nodes or vertices. They are composed of vertices and edges, where vertices represent entities and edges represent the relationships between those entities.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Versatility:</td> Graphs can represent a wide range of real-world scenarios, making them suitable for various applications such as social networks, transportation systems, and computer networks. </tr><tr><td>Relationship Representation:</td> Graphs provide a natural way to represent relationships and connections between entities, allowing for efficient analysis and traversal of these relationships. </tr><tr><td>Efficient Search and Traversal:</td> Graph algorithms like breadth-first search (BFS) and depth-first search (DFS) enable efficient traversal and searching of the graph's vertices and edges. </tr><tr><td>Modeling Complex Relationships:</td> Graphs can model complex relationships, including hierarchical structures, cyclic dependencies, and multiple connections between entities. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Space Complexity:</td> Graphs can consume a significant amount of memory, especially large-scale graphs with many vertices and edges. </tr><tr><td>The complexity of Operations:</td> Certain graph operations, such as finding the shortest path or detecting cycles, can have high time complexity, particularly in dense graphs. </tr><tr><td>Difficulty in Maintenance:</td> Modifying or updating a graph can be complex, as changes in the graph's structure may impact its connectivity and existing algorithms. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> GraphExample.java</p> <pre> import java.util.*; public class GraphExample { private int V; // Number of vertices private List<list> adjacencyList; // Adjacency list representation public GraphExample(int V) { this.V=V; adjacencyList=new ArrayList(V); // Initialize the adjacency list for (int i=0;i<v;i++) { adjacencylist.add(new arraylist()); } function to add an edge between two vertices public void addedge(int source,int destination) adjacencylist.get(source).add(destination); adjacencylist.get(destination).add(source); perform breadth-first search traversal of the graph bfs(int startvertex) boolean[] visited="new" boolean[v]; queue linkedlist(); visited[startvertex]="true;" queue.add(startvertex); while (!queue.isempty()) int currentvertex="queue.poll();" system.out.print(currentvertex+' '); list neighbors="adjacencyList.get(currentVertex);" for (int neighbor:neighbors) if (!visited[neighbor]) visited[neighbor]="true;" queue.add(neighbor); system.out.println(); depth-first dfs(int dfsutil(startvertex,visited); private dfsutil(int vertex,boolean[] visited) visited[vertex]="true;" system.out.print(vertex+' dfsutil(neighbor,visited); static main(string[] args) v="5;" number graphexample graphexample(v); edges graph.addedge(0,1); graph.addedge(0,2); graph.addedge(1,3); graph.addedge(2,3); graph.addedge(2,4); system.out.print('bfs traversal: graph.bfs(0); system.out.print('dfs graph.dfs(0); < pre> <p> <strong>Output:</strong> </p> <pre> BFS traversal: 0 1 2 3 4 DFS traversal: 0 1 3 2 4 </pre> <h3>11) Tree:</h3> <p>A tree is a widely used data structure in computer science that represents a hierarchical structure. It consists of nodes connected by edges, where each node can have zero or more child nodes.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Hierarchical Structure:</td> Trees provide a natural way to represent hierarchical relationships, such as file systems, organization charts, or HTML/XML documents. </tr><tr><td>Efficient Search:</td> Binary search trees enable efficient searching with a time complexity of O(log n), making them suitable for storing and retrieving sorted data. </tr><tr><td>Fast Insertion and Deletion:</td> Tree data structures offer efficient insertion and deletion operations, especially when balanced, such as AVL trees or Red-Black trees. </tr><tr><td>Ordered Iteration:</td> In-order traversal of a binary search tree gives elements in a sorted order, which is helpful for tasks like printing elements in sorted order or finding the next/previous element. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>High Memory Overhead:</td> Trees require additional memory to store node references or pointers, which can result in higher memory usage compared to linear data structures like arrays or lists. </tr><tr><td>Complex Implementation:</td> Implementing and maintaining a tree data structure can be more complex compared to other data structures like arrays or lists, especially for balanced tree variants. </tr><tr><td>Limited Operations:</td> Some tree variants, like binary search trees, do not support efficient operations like finding the kth smallest element or finding the rank of an element. </tr></ol> <p> <strong>Functions:</strong> </p> <ol class="points"> <tr><td>Insertion:</td> Add a new node to the tree. </tr><tr><td>Deletion:</td> Remove a node from the tree. </tr><tr><td>Search:</td> Find a specific node or element in the tree. </tr><tr><td>Traversal:</td> Traverse the tree in different orders, such as in-order, pre-order, or post-order. </tr><tr><td>Height/Depth:</td> Calculate the height or depth of the tree. </tr><tr><td>Balance:</td> Ensure the tree remains balanced to maintain efficient operations. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> TreeExample.java</p> <pre> import java.util.*; class TreeNode { int value; TreeNode left; TreeNode right; public TreeNode(int value) { this.value = value; left = null; right = null; } } public class TreeExample { public static void main(String[] args) { // Create a binary search tree TreeNode root = new TreeNode(50); root.left = new TreeNode(30); root.right = new TreeNode(70); root.left.left = new TreeNode(20); root.left.right = new TreeNode(40); root.right.left = new TreeNode(60); root.right.right = new TreeNode(80); // Perform common operations System.out.println('In-order Traversal:'); inOrderTraversal(root); System.out.println('
Search for value 40: '+search(root, 40)); System.out.println('Search for value 90: '+search(root, 90)); int minValue = findMinValue(root); System.out.println('Minimum value in the tree: '+minValue); int maxValue = findMaxValue(root); System.out.println('Maximum value in the tree: '+maxValue); } // In-order traversal: left subtree, root, right subtree public static void inOrderTraversal(TreeNode node) { if (node != null) { inOrderTraversal(node.left); System.out.print(node.value + ' '); inOrderTraversal(node.right); } } // Search for a value in the tree public static boolean search(TreeNode node, int value) { if (node == null) return false; if (node.value == value) return true; if (value <node.value) return search(node.left, value); else search(node.right, } find the minimum value in tree public static int findminvalue(treenode node) { if (node.left="=" null) node.value; findminvalue(node.left); maximum findmaxvalue(treenode (node.right="=" findmaxvalue(node.right); < pre> <p> <strong>Output:</strong> </p> <pre> In-order Traversal:20 30 40 50 60 70 80 Search for value 40: true Search for value 90: false Minimum value in the tree: 20 Maximum value in the tree: 80 </pre> <hr></node.value)></pre></v;i++)></list></pre></numbers.length;i++)> 2) ArrayList:
ArrayList în Java este o structură de date dinamică care permite stocarea și manipularea elementelor. Face parte din cadrul Java Collections Framework și este implementat folosind o matrice intern.
Avantaje:
Dezavantaje:
Functii:
constructor de corzi
Implementare:
Nume de fișier: ArrayListExample.java
import java.util.*; public class ArrayListExample { public static void main(String[] args) { // Create an ArrayList to store integers ArrayList numbers=new ArrayList(List.of(10,20,30,40,50)); //Access and print elements from the ArrayList System.out.println('Element at index 0:'+numbers.get(0)); System.out.println('Element at index 2:'+numbers.get(2)); System.out.println('Element at index 4:'+numbers.get(4)); // Calculate and print the sum of all elements in the ArrayList int sum=numbers.stream().mapToInt(Integer::intValue).sum(); System.out.println('Sum of ArrayList elements:'+sum); // Update an element in the ArrayList numbers.set(2,35); System.out.println('Updated element at index 2:'+numbers.get(2)); // Iterate through the ArrayList using a for-each loop and print the elements System.out.println('Elements in the ArrayList:'); for (int number:numbers) { System.out.println(number); } } } Ieșire:
Element at index 0:10 Element at index 2:30 Element at index 4:50 Sum of ArrayList elements:150 Updated element at index 2:35 Elements in the ArrayList: 10 20 35 40 50
3) Lista legată:
O listă legată este o structură de date liniară în care elementele sunt stocate în obiecte separate numite noduri. O legătură de referință către următorul nod din secvență este inclusă în elementul de date al fiecărui nod. Nodul final al listei se leagă la null, indicând faptul că lista s-a încheiat.
conversia șirului în json în java
Spre deosebire de matrice, listele legate nu necesită alocarea de memorie contigue. Fiecare nod dintr-o listă legată poate fi alocat independent, permițând alocarea dinamică a memoriei și operațiuni eficiente de inserare și ștergere.
Avantaje:
Dezavantaje:
Functii:
Implementare:
Nume de fișier: LinkedList1.java
import java.util.*; public class LinkedList1 { public static void main(String[] args) { // Create a LinkedList to store integers LinkedList linkedList1 = new LinkedList(); // Add elements to the LinkedList linkedList1.add(10); linkedList1.add(20); linkedList1.add(30); linkedList1.add(40); linkedList1.add(50); // Print the LinkedList System.out.println('LinkedList:'+linkedList1); // Remove an element from the LinkedList linkedList1.removeFirst(); System.out.println('LinkedList after removing first element:'+linkedList1); // Check if an element exists in the LinkedList boolean containsElement=linkedList1.contains(30); System.out.println('LinkedList contains element 30?'+containsElement); // Get the first and last elements of the LinkedList int firstElement=linkedList1.getFirst(); int lastElement=linkedList1.getLast(); System.out.println('First element:'+firstElement); System.out.println('Last element:'+lastElement); // Clear the LinkedList linkedList1.clear(); System.out.println('LinkedList after clearing:'+linkedList1); } } Ieșire:
LinkedList:[10, 20, 30, 40, 50] LinkedList after removing first element:[20, 30, 40, 50] LinkedList contains element 30?true First element:20 Last element:50 LinkedList after clearing:[]
4) Stiva:
Principiul Last-In-First-Out (LIFO) dictează că elementul care a fost introdus cel mai recent este și elementul care este eliminat primul. O stivă este o structură de date liniară care urmează această regulă. Folosește comenzile „push” și „pop” pentru a adăuga elemente la stivă și, în consecință, pentru a elimina elementul de sus din stivă. Tehnica „peek” permite în plus accesul la elementul superior fără a-l îndepărta.
Caracteristicile unei stive:
Avantaje:
Dezavantaje:
Implementare:
Nume de fișier: StackExample.java
import java.util.Stack; public class StackExample { public static void main(String[] args) { // Create a stack Stack stack=new Stack(); // Push elements onto the stack stack.push(10); stack.push(20); stack.push(30); // Print the top element of the stack System.out.println('Top element:'+stack.peek()); // Pop elements from the stack int poppedElement=stack.pop(); System.out.println('Popped element:'+poppedElement); // Check if the stack is empty System.out.println('Is stack empty?'+stack.isEmpty()); // Get the size of the stack System.out.println('Stack size:'+stack.size()); // Iterate over the stack System.out.println('Stack elements:'); for (Integer element:stack) { System.out.println(element); } } } Ieșire:
Top element:30 Popped element:30 Is stack empty?false Stack size:2 Stack elements: 10 20
5) coadă:
O coadă este o structură de date liniară în Java care urmează principiul First-In-First-Out (FIFO). Reprezintă o colecție de elemente în care elementele sunt introduse în spate și îndepărtate din față.
Caracteristici:
Avantaje:
Dezavantaje:
Implementare:
Nume de fișier: QueueExample.java
import java.util.Stack; import java.util.LinkedList; import java.util.Queue; public class QueueExample { public static void main(String[] args) { // Create a Queue to store integers Queue queue=new LinkedList(); // Enqueue elements to the Queue queue.offer(10); queue.offer(20); queue.offer(30); queue.offer(40); queue.offer(50); //Access and print the front element of the Queue System.out.println('Front element:'+queue.peek()); // Dequeue elements from the Queue and print them while (!queue.isEmpty()) { int element=queue.poll(); System.out.println('Dequeued element:'+element); } } } Ieșire:
Front element:10 Dequeued element:10 Dequeued element:20 Dequeued element:30 Dequeued element:40 Dequeued element:50
6) HashMap:
Un HashMap este o structură de date în Java care oferă o modalitate de a stoca și de a prelua perechi cheie-valoare. Face parte din cadrul Java Collections Framework și este implementat pe baza structurii de date a tabelului hash.
Functii:
Avantaje:
Dezavantaje:
Implementare:
Nume de fișier: HashMapExample.java
import java.util.HashMap; public class HashMapExample { public static void main(String[] args) { // Create a HashMap to store String keys and Integer values HashMap hashMap=new HashMap(); // Add key-value pairs to the HashMap hashMap.put('John',25); hashMap.put('Alice',30); hashMap.put('Bob',35); //Access and print values based on keys System.out.println('Age of John:'+hashMap.get('John')); System.out.println('Age of Alice:'+hashMap.get('Alice')); // Check if a key exists in the HashMap System.out.println('Is Bob present?'+hashMap.containsKey('Bob')); // Update the value associated with a key hashMap.put('Alice',32); // Remove a key-value pair from the HashMap hashMap.remove('John'); // Print all key-value pairs in the HashMap System.out.println('Key-Value pairs in the HashMap:'); for (String key : hashMap.keySet()) { System.out.println(key+':'+hashMap.get(key)); } // Check the size of the HashMap System.out.println('Size of the HashMap:'+hashMap.size()); } } Ieșire:
Age of John:25 Age of Alice:30 Is Bob present?true Key-Value pairs in the HashMap: Bob:35 Alice:32 Size of the HashMap:2
7) HashSet:
HashSet este o structură de date în Java care implementează interfața Set și stochează elemente într-un tabel hash.
Caracteristici:
Avantaje:
Dezavantaje:
metoda string în java
Implementare:
Nume de fișier: HashSetExample.java
import java.util.HashSet; public class HashSetExample { public static void main(String[] args) { // Create a HashSet HashSet set=new HashSet(); // Add elements to the HashSet set.add('Apple'); set.add('Banana'); set.add('Orange'); set.add('Grapes'); set.add('Mango'); // Print the HashSet System.out.println('HashSet:'+set); // Check if an element exists System.out.println('Contains 'Apple':'+set.contains('Apple')); // Remove an element set.remove('Banana'); // Print the updated HashSet System.out.println('Updated HashSet:'+set); // Get the size of the HashSet System.out.println('Size of HashSet:'+set.size()); // Clear the HashSet set.clear(); // Check if the HashSet is empty System.out.println('Is HashSet empty?'+set.isEmpty()); } } Ieșire:
HashSet:[Apple, Grapes, Mango, Orange, Banana] Contains 'Apple':true Updated HashSet:[Apple, Grapes, Mango, Orange] Size of HashSet:4 Is HashSet empty?true
8) TreeSet:
TreeSet este o implementare a interfeței SortedSet în Java care utilizează un arbore de căutare binar cu auto-echilibrare numit arbore roșu-negru pentru a stoca elementele în ordine sortată.
Avantaje:
Dezavantaje:
Functii:
Implementare:
Nume de fișier: TreeSetExample.java
import java.util.TreeSet; public class TreeSetExample { public static void main(String[] args) { // Create a TreeSet TreeSet numbers=new TreeSet(); // Add elements to the TreeSet numbers.add(5); numbers.add(2); numbers.add(8); numbers.add(1); numbers.add(4); // Print the TreeSet System.out.println('Elements in the TreeSet:'+numbers); // Check if an element exists System.out.println('Does TreeSet contain 4?'+numbers.contains(4)); // Remove an element numbers.remove(2); // Print the TreeSet after removal System.out.println('Elements in the TreeSet after removal:'+numbers); // Get the size of the TreeSet System.out.println('Size of the TreeSet:'+numbers.size()); // Get the first and last element System.out.println('First element:'+numbers.first()); System.out.println('Last element:'+numbers.last()); // Iterate over the TreeSet System.out.println('Iterating over the TreeSet:'); for (int number:numbers) { System.out.println(number); } } } Ieșire:
Elements in the TreeSet:[1, 2, 4, 5, 8] Does TreeSet contain 4? true Elements in the TreeSet after removal:[1, 4, 5, 8] Size of the TreeSet:4First element:1 Last element:8 Iterating over the TreeSet: 1 4 5 8
9) Harta arborelui:
TreeMap este o clasă în Java care implementează interfața Map și oferă o mapare cheie-valoare sortată pe baza ordinii naturale a cheilor sau a unui comparator personalizat.
Avantaje:
Dezavantaje:
Functii:
Implementare:
Nume de fișier: TreeMapExample.java
import java.util.TreeMap; public class TreeMapExample { public static void main(String[] args) { // Create a TreeMap TreeMap scores=new TreeMap(); // Insert key-value pairs into the TreeMap scores.put('Alice',90); scores.put('Bob',80); scores.put('Charlie',95); scores.put('David',87); scores.put('Eve',92); //Access and print values from the TreeMap System.out.println('Score of Alice:'+scores.get('Alice')); System.out.println('Score of Charlie:'+scores.get('Charlie')); System.out.println('Score of David:'+scores.get('David')); // Update a value in the TreeMap scores.put('Bob',85); // Remove a key-value pair from the TreeMap scores.remove('Eve'); // Iterate through the TreeMap using a for-each loop System.out.println('Scores in the TreeMap:'); for (String name:scores.keySet()) { int score=scores.get(name); System.out.println(name+':'+score); } } } Ieșire:
Score of Alice:90 Score of Charlie:95 Score of David:87 Scores in the TreeMap: Alice:90 Bob:85 Charlie:95 David:87
10) Grafic:
Graficele sunt structuri de date care reprezintă o colecție de noduri sau vârfuri interconectate. Ele sunt compuse din vârfuri și muchii, unde vârfurile reprezintă entități și muchiile reprezintă relațiile dintre acele entități.
Avantaje:
Dezavantaje:
Implementare:
Nume de fișier: GraphExample.java
import java.util.*; public class GraphExample { private int V; // Number of vertices private List<list> adjacencyList; // Adjacency list representation public GraphExample(int V) { this.V=V; adjacencyList=new ArrayList(V); // Initialize the adjacency list for (int i=0;i<v;i++) { adjacencylist.add(new arraylist()); } function to add an edge between two vertices public void addedge(int source,int destination) adjacencylist.get(source).add(destination); adjacencylist.get(destination).add(source); perform breadth-first search traversal of the graph bfs(int startvertex) boolean[] visited="new" boolean[v]; queue linkedlist(); visited[startvertex]="true;" queue.add(startvertex); while (!queue.isempty()) int currentvertex="queue.poll();" system.out.print(currentvertex+\' \'); list neighbors="adjacencyList.get(currentVertex);" for (int neighbor:neighbors) if (!visited[neighbor]) visited[neighbor]="true;" queue.add(neighbor); system.out.println(); depth-first dfs(int dfsutil(startvertex,visited); private dfsutil(int vertex,boolean[] visited) visited[vertex]="true;" system.out.print(vertex+\' dfsutil(neighbor,visited); static main(string[] args) v="5;" number graphexample graphexample(v); edges graph.addedge(0,1); graph.addedge(0,2); graph.addedge(1,3); graph.addedge(2,3); graph.addedge(2,4); system.out.print(\'bfs traversal: graph.bfs(0); system.out.print(\'dfs graph.dfs(0); < pre> <p> <strong>Output:</strong> </p> <pre> BFS traversal: 0 1 2 3 4 DFS traversal: 0 1 3 2 4 </pre> <h3>11) Tree:</h3> <p>A tree is a widely used data structure in computer science that represents a hierarchical structure. It consists of nodes connected by edges, where each node can have zero or more child nodes.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Hierarchical Structure:</td> Trees provide a natural way to represent hierarchical relationships, such as file systems, organization charts, or HTML/XML documents. </tr><tr><td>Efficient Search:</td> Binary search trees enable efficient searching with a time complexity of O(log n), making them suitable for storing and retrieving sorted data. </tr><tr><td>Fast Insertion and Deletion:</td> Tree data structures offer efficient insertion and deletion operations, especially when balanced, such as AVL trees or Red-Black trees. </tr><tr><td>Ordered Iteration:</td> In-order traversal of a binary search tree gives elements in a sorted order, which is helpful for tasks like printing elements in sorted order or finding the next/previous element. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>High Memory Overhead:</td> Trees require additional memory to store node references or pointers, which can result in higher memory usage compared to linear data structures like arrays or lists. </tr><tr><td>Complex Implementation:</td> Implementing and maintaining a tree data structure can be more complex compared to other data structures like arrays or lists, especially for balanced tree variants. </tr><tr><td>Limited Operations:</td> Some tree variants, like binary search trees, do not support efficient operations like finding the kth smallest element or finding the rank of an element. </tr></ol> <p> <strong>Functions:</strong> </p> <ol class="points"> <tr><td>Insertion:</td> Add a new node to the tree. </tr><tr><td>Deletion:</td> Remove a node from the tree. </tr><tr><td>Search:</td> Find a specific node or element in the tree. </tr><tr><td>Traversal:</td> Traverse the tree in different orders, such as in-order, pre-order, or post-order. </tr><tr><td>Height/Depth:</td> Calculate the height or depth of the tree. </tr><tr><td>Balance:</td> Ensure the tree remains balanced to maintain efficient operations. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> TreeExample.java</p> <pre> import java.util.*; class TreeNode { int value; TreeNode left; TreeNode right; public TreeNode(int value) { this.value = value; left = null; right = null; } } public class TreeExample { public static void main(String[] args) { // Create a binary search tree TreeNode root = new TreeNode(50); root.left = new TreeNode(30); root.right = new TreeNode(70); root.left.left = new TreeNode(20); root.left.right = new TreeNode(40); root.right.left = new TreeNode(60); root.right.right = new TreeNode(80); // Perform common operations System.out.println('In-order Traversal:'); inOrderTraversal(root); System.out.println('
Search for value 40: '+search(root, 40)); System.out.println('Search for value 90: '+search(root, 90)); int minValue = findMinValue(root); System.out.println('Minimum value in the tree: '+minValue); int maxValue = findMaxValue(root); System.out.println('Maximum value in the tree: '+maxValue); } // In-order traversal: left subtree, root, right subtree public static void inOrderTraversal(TreeNode node) { if (node != null) { inOrderTraversal(node.left); System.out.print(node.value + ' '); inOrderTraversal(node.right); } } // Search for a value in the tree public static boolean search(TreeNode node, int value) { if (node == null) return false; if (node.value == value) return true; if (value <node.value) return search(node.left, value); else search(node.right, } find the minimum value in tree public static int findminvalue(treenode node) { if (node.left="=" null) node.value; findminvalue(node.left); maximum findmaxvalue(treenode (node.right="=" findmaxvalue(node.right); < pre> <p> <strong>Output:</strong> </p> <pre> In-order Traversal:20 30 40 50 60 70 80 Search for value 40: true Search for value 90: false Minimum value in the tree: 20 Maximum value in the tree: 80 </pre> <hr></node.value)></pre></v;i++)></list> 11) Arborele:
Un arbore este o structură de date utilizată pe scară largă în informatică, care reprezintă o structură ierarhică. Este format din noduri conectate prin muchii, unde fiecare nod poate avea zero sau mai multe noduri copil.
Avantaje:
Dezavantaje:
comparați cu șirurile din java
Functii:
Implementare:
Nume de fișier: TreeExample.java
import java.util.*; class TreeNode { int value; TreeNode left; TreeNode right; public TreeNode(int value) { this.value = value; left = null; right = null; } } public class TreeExample { public static void main(String[] args) { // Create a binary search tree TreeNode root = new TreeNode(50); root.left = new TreeNode(30); root.right = new TreeNode(70); root.left.left = new TreeNode(20); root.left.right = new TreeNode(40); root.right.left = new TreeNode(60); root.right.right = new TreeNode(80); // Perform common operations System.out.println('In-order Traversal:'); inOrderTraversal(root); System.out.println('
Search for value 40: '+search(root, 40)); System.out.println('Search for value 90: '+search(root, 90)); int minValue = findMinValue(root); System.out.println('Minimum value in the tree: '+minValue); int maxValue = findMaxValue(root); System.out.println('Maximum value in the tree: '+maxValue); } // In-order traversal: left subtree, root, right subtree public static void inOrderTraversal(TreeNode node) { if (node != null) { inOrderTraversal(node.left); System.out.print(node.value + ' '); inOrderTraversal(node.right); } } // Search for a value in the tree public static boolean search(TreeNode node, int value) { if (node == null) return false; if (node.value == value) return true; if (value <node.value) return search(node.left, value); else search(node.right, } find the minimum value in tree public static int findminvalue(treenode node) { if (node.left="=" null) node.value; findminvalue(node.left); maximum findmaxvalue(treenode (node.right="=" findmaxvalue(node.right); < pre> <p> <strong>Output:</strong> </p> <pre> In-order Traversal:20 30 40 50 60 70 80 Search for value 40: true Search for value 90: false Minimum value in the tree: 20 Maximum value in the tree: 80 </pre> <hr></node.value)>