PHP 5.0对象模型深度探索之构造和析构_PHP
如果你在一个类中声明一个函数,命名为__construct,这个函数将被当成是一个构造函数并在建立一个对象实例时被执行。清楚地说,__是两个下划线。就像其它任何函数一样,构造函数可能有参数或者默认值. 你可以定义一个类来建立一个对象并将其属性全放在一个语句(statement)中。
你也可以定义一个名为__destruct的函数,PHP将在对象被销毁前调用这个函数. 它称为析构函数.
继承是类的一个强大功能。一个类(子类/派生类)可以继承另一类(父类/基类)的功能. 派生类将包含有基类的所有属性和方法,并可以在派生类中加上其他属性和方法。你也可以覆写基类的方法和属性。就像前文中显示的,你可以用extends关键字来继承一个类。
你可能想知道构造函数是如何被继承的。当它们和其它方法一起被继承时,他们不会在创建对象时被执行。
如果你需要这个功能,你需要用第二章提到的::运算符. 它允许你指向一块命名空间. parent指向父类命名空间,你可以用parent::__construct来调用父类的构造函数。
一些面向对象语言在类之后命名构造函数。PHP的前几个版本也是如此,到现在这种方法仍然有效.也就是:如果你把一个类命名为Animal并且在其中建立一个命名也是Animal的方法,则这个方法就是构造函数.如果一个类的同时拥有__construt构造函数和与类名相同的函数,PHP将把__construct看作构造函数。这使得用以前的PHP版本所写的类仍然可以使用. 但新的脚本(PHP5)应当使用__construct。
PHP的这种新的声明构造函数的方法可以使构造函数有一个独一无二的名称,无论它所在的类的名称是什么。这样你在改变类的名称时,就不需要改变构造函数的名称。
你可能在PHP中给构造函数一个像其它类方法一样的访问方式。访问方式将会影响从一定范围内实例化对象的能力。这允许实现一些固定的设计模式,如Singleton模式。
析构函数,相反于构造函数。PHP调用它们来将一个对象从内存中销毁。默认地,PHP仅仅释放对象属性所占用的内存并销毁对象相关的资源。析构函数允许你在使用一个对象之后执行任意代码来清除内存。
当PHP决定你的脚本不再与对象相关时,析构函数将被调用. 在一个函数的命名空间内,这会发生在函数return的时候. 对于全局变量,这发生于脚本结束的时候. 如果你想明确地销毁一个对象,你可以给指向该对象的变量分配任何其它值. 通常将变量赋值勤为NULL或者调用unset。
下面的例子中,计算从类中实例化的对象的个数. Counter类从构造函数开始增值,在析构函数减值。
一旦你定义了一个类,你可以用new来建立一个这个类的实例. 类的定义是设计图,实例则是放在装配线上的元件. New需要类的名称,并返回该类的一个实例。如果构造函数需要参数,你应当在new后输入参数。
class Counter
{
private static $count = 0;
function __construct()
{
self::$count ;
}
function __destruct()
{
self::$count--;
}
function getCount()
{
return self::$count;
}
}
//建立第一个实例
$c = new Counter();
//输出1
print($c->getCount() . "n");
//建立第二个实例
$c2 = new Counter();
//输出2
print($c->getCount() . "n");
//销毁实例
$c2 = NULL;
//输出1
print($c->getCount() . "n");
?>

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



Imagine an artificial intelligence model that not only has the ability to surpass traditional computing, but also achieves more efficient performance at a lower cost. This is not science fiction, DeepSeek-V2[1], the world’s most powerful open source MoE model is here. DeepSeek-V2 is a powerful mixture of experts (MoE) language model with the characteristics of economical training and efficient inference. It consists of 236B parameters, 21B of which are used to activate each marker. Compared with DeepSeek67B, DeepSeek-V2 has stronger performance, while saving 42.5% of training costs, reducing KV cache by 93.3%, and increasing the maximum generation throughput to 5.76 times. DeepSeek is a company exploring general artificial intelligence

Earlier this month, researchers from MIT and other institutions proposed a very promising alternative to MLP - KAN. KAN outperforms MLP in terms of accuracy and interpretability. And it can outperform MLP running with a larger number of parameters with a very small number of parameters. For example, the authors stated that they used KAN to reproduce DeepMind's results with a smaller network and a higher degree of automation. Specifically, DeepMind's MLP has about 300,000 parameters, while KAN only has about 200 parameters. KAN has a strong mathematical foundation like MLP. MLP is based on the universal approximation theorem, while KAN is based on the Kolmogorov-Arnold representation theorem. As shown in the figure below, KAN has

The latest video of Tesla's robot Optimus is released, and it can already work in the factory. At normal speed, it sorts batteries (Tesla's 4680 batteries) like this: The official also released what it looks like at 20x speed - on a small "workstation", picking and picking and picking: This time it is released One of the highlights of the video is that Optimus completes this work in the factory, completely autonomously, without human intervention throughout the process. And from the perspective of Optimus, it can also pick up and place the crooked battery, focusing on automatic error correction: Regarding Optimus's hand, NVIDIA scientist Jim Fan gave a high evaluation: Optimus's hand is the world's five-fingered robot. One of the most dexterous. Its hands are not only tactile

FP8 and lower floating point quantification precision are no longer the "patent" of H100! Lao Huang wanted everyone to use INT8/INT4, and the Microsoft DeepSpeed team started running FP6 on A100 without official support from NVIDIA. Test results show that the new method TC-FPx's FP6 quantization on A100 is close to or occasionally faster than INT4, and has higher accuracy than the latter. On top of this, there is also end-to-end large model support, which has been open sourced and integrated into deep learning inference frameworks such as DeepSpeed. This result also has an immediate effect on accelerating large models - under this framework, using a single card to run Llama, the throughput is 2.65 times higher than that of dual cards. one

In order to align large language models (LLMs) with human values and intentions, it is critical to learn human feedback to ensure that they are useful, honest, and harmless. In terms of aligning LLM, an effective method is reinforcement learning based on human feedback (RLHF). Although the results of the RLHF method are excellent, there are some optimization challenges involved. This involves training a reward model and then optimizing a policy model to maximize that reward. Recently, some researchers have explored simpler offline algorithms, one of which is direct preference optimization (DPO). DPO learns the policy model directly based on preference data by parameterizing the reward function in RLHF, thus eliminating the need for an explicit reward model. This method is simple and stable

At the forefront of software technology, UIUC Zhang Lingming's group, together with researchers from the BigCode organization, recently announced the StarCoder2-15B-Instruct large code model. This innovative achievement achieved a significant breakthrough in code generation tasks, successfully surpassing CodeLlama-70B-Instruct and reaching the top of the code generation performance list. The unique feature of StarCoder2-15B-Instruct is its pure self-alignment strategy. The entire training process is open, transparent, and completely autonomous and controllable. The model generates thousands of instructions via StarCoder2-15B in response to fine-tuning the StarCoder-15B base model without relying on expensive manual annotation.

Written above & the author’s personal understanding: This paper is dedicated to solving the key challenges of current multi-modal large language models (MLLMs) in autonomous driving applications, that is, the problem of extending MLLMs from 2D understanding to 3D space. This expansion is particularly important as autonomous vehicles (AVs) need to make accurate decisions about 3D environments. 3D spatial understanding is critical for AVs because it directly impacts the vehicle’s ability to make informed decisions, predict future states, and interact safely with the environment. Current multi-modal large language models (such as LLaVA-1.5) can often only handle lower resolution image inputs (e.g.) due to resolution limitations of the visual encoder, limitations of LLM sequence length. However, autonomous driving applications require

1. Introduction Over the past few years, YOLOs have become the dominant paradigm in the field of real-time object detection due to its effective balance between computational cost and detection performance. Researchers have explored YOLO's architectural design, optimization goals, data expansion strategies, etc., and have made significant progress. At the same time, relying on non-maximum suppression (NMS) for post-processing hinders end-to-end deployment of YOLO and adversely affects inference latency. In YOLOs, the design of various components lacks comprehensive and thorough inspection, resulting in significant computational redundancy and limiting the capabilities of the model. It offers suboptimal efficiency, and relatively large potential for performance improvement. In this work, the goal is to further improve the performance efficiency boundary of YOLO from both post-processing and model architecture. to this end
