The differences of Iterator and Iterable interface

本文详细介绍了Java中Iterator和Iterable两个接口的区别。从抽象方法、使用场景及实现类等方面进行对比,帮助读者理解何时及如何使用这些接口。

This post will describe som differences of Iterator and Iterable interface. Introduction with three aspects is given out.

Firstly, relative abstract method.

Iterator interface:

boolean hasNext(); // return true if there are elements to iterate.

E next(); //return the next element of iterator.

void remove(); //remove the last element of iterator.

Iterable interface:

Iterator<E> Iterator();//return an iterator with a group elements of data  type E.

Secondly, when to use Iterable.

1. If you want to reiterate the elements, you must use the Iterable interface.

2. If you want to use the loop foreach, you must use the Iterable interface. e.g., Iterable<E> its; for(E it:its) {}, ArrayList<T> als=new ArrayList<T>(); for(T al:als){}

! foreach only uses the array or the instance of Iterable.

Thirdly, all the classes implement the Iterable interface.

AbstractCollection, AbstractList, AbstractQueue, AbstractSequentialList, AbstractSet, ArrayBlockingQueue, ArrayDeque, ArrayList, AttributeList, BatchUpdateException, BeanContextServicesSupport, BeanContextSupport, ConcurrentLinkedQueue, ConcurrentSkipListSet, CopyOnWriteArrayList, CopyOnWriteArraySet, DataTruncation, DelayQueue, EnumSet, HashSet, JobStateReasons, LinkedBlockingDeque, LinkedBlockingQueue, LinkedHashSet, LinkedList, PriorityBlockingQueue, PriorityQueue, RoleList, RoleUnresolvedList, RowSetWarning, SerialException, ServiceLoader, SQLClientInfoException, SQLDataException, SQLException, SQLFeatureNotSupportedException, SQLIntegrityConstraintViolationException, SQLInvalidAuthorizationSpecException, SQLNonTransientConnectionException, SQLNonTransientException, SQLRecoverableException, SQLSyntaxErrorException, SQLTimeoutException, SQLTransactionRollbackException, SQLTransientConnectionException, SQLTransientException, SQLWarning, Stack, SyncFactoryException, SynchronousQueue, SyncProviderException, TreeSet, Vector

Fourthly, all the classes implement the Iterator interface.

BeanContextSupport.BCSIterator, EventReaderDelegate, Scanner

 

GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) are both advanced natural language processing (NLP) models developed by OpenAI and Google respectively. Although they share some similarities, there are key differences between the two models. 1. Pre-training Objective: GPT is pre-trained using a language modeling objective, where the model is trained to predict the next word in a sequence of words. BERT, on the other hand, is trained using a masked language modeling objective. In this approach, some words in the input sequence are masked, and the model is trained to predict these masked words based on the surrounding context. 2. Transformer Architecture: Both GPT and BERT use the transformer architecture, which is a neural network architecture that is specifically designed for processing sequential data like text. However, GPT uses a unidirectional transformer, which means that it processes the input sequence in a forward direction only. BERT, on the other hand, uses a bidirectional transformer, which allows it to process the input sequence in both forward and backward directions. 3. Fine-tuning: Both models can be fine-tuned on specific NLP tasks, such as text classification, question answering, and text generation. However, GPT is better suited for text generation tasks, while BERT is better suited for tasks that require a deep understanding of the context, such as question answering. 4. Training Data: GPT is trained on a massive corpus of text data, such as web pages, books, and news articles. BERT is trained on a similar corpus of text data, but it also includes labeled data from specific NLP tasks, such as the Stanford Question Answering Dataset (SQuAD). In summary, GPT and BERT are both powerful NLP models, but they have different strengths and weaknesses depending on the task at hand. GPT is better suited for generating coherent and fluent text, while BERT is better suited for tasks that require a deep understanding of the context.
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