第二节--PHP5 的对象模型
/*
+-------------------------------------------------------------------------------+
| = 本文为Haohappy读>
| = 中Classes and Objects一章的笔记
| = 翻译为主+个人心得
| = 为避免可能发生的不必要的麻烦请勿转载,谢谢
| = 欢迎批评指正,希望和所有PHP爱好者共同进步!
| = PHP5研究中心： http://blog.csdn.net/haohappy2004
+-------------------------------------------------------------------------------+
*/
第二节--PHP5 的对象模型
PHP5有一个单重继承的,限制访问的,可以重载的对象模型. 本章稍后会详细讨论的”继承”,包含类间的父-子关系. 另外,PHP支持对属性和方法的限制性访问. 你可以声明成员为private,不允许外部类访问. 最后,PHP允许一个子类从它的父类中重载成员.
//haohappy注:PHP4中没有private,只有public.private对于更好地实现封装很有好处.
PHP5的对象模型把对象看成与任何其它数据类型不同,通过引用来传递. PHP不要求你通过引用(reference)显性传递和返回对象. 在本章的最后将会详细阐述基于句柄的对象模型. 它是PHP5中最重要的新特性.
有了更直接的对象模型,基于句柄的体系有附加的优势: 效率提高, 占用内存少,并且具有更大的灵活性.
在PHP的前几个版本中,脚本默认复制对象.现在PHP5只移动句柄,需要更少的时间. 脚本执行效率的提升是由于避免了不必要的复制. 在对象体系带来复杂性的同时,也带来了执行效率上的收益. 同时,减少复制意味着占用更少的内存,可以留出更多内存给其它操作,这也使效率提高.
//haohappy注:基于句柄,就是说两个对象可以指向同一块内存,既减少了复制动作,又减少对内存的占用.
Zand引擎2具有更大的灵活性. 一个令人高兴的发展是允许析构--在对象销毁之前执行一个类方法. 这对于利用内存也很有好处,让PHP清楚地知道什么时候没有对象的引用,把空出的内存分配到其它用途.
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
The world's most powerful open source MoE model is here, with Chinese capabilities comparable to GPT-4, and the price is only nearly one percent of GPT-4-Turbo
May 07, 2024 pm 04:13 PM
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
KAN, which replaces MLP, has been extended to convolution by open source projects
Jun 01, 2024 pm 10:03 PM
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
Hello, electric Atlas! Boston Dynamics robot comes back to life, 180-degree weird moves scare Musk
Apr 18, 2024 pm 07:58 PM
Boston Dynamics Atlas officially enters the era of electric robots! Yesterday, the hydraulic Atlas just "tearfully" withdrew from the stage of history. Today, Boston Dynamics announced that the electric Atlas is on the job. It seems that in the field of commercial humanoid robots, Boston Dynamics is determined to compete with Tesla. After the new video was released, it had already been viewed by more than one million people in just ten hours. The old people leave and new roles appear. This is a historical necessity. There is no doubt that this year is the explosive year of humanoid robots. Netizens commented: The advancement of robots has made this year's opening ceremony look like a human, and the degree of freedom is far greater than that of humans. But is this really not a horror movie? At the beginning of the video, Atlas is lying calmly on the ground, seemingly on his back. What follows is jaw-dropping
Google is ecstatic: JAX performance surpasses Pytorch and TensorFlow! It may become the fastest choice for GPU inference training
Apr 01, 2024 pm 07:46 PM
The performance of JAX, promoted by Google, has surpassed that of Pytorch and TensorFlow in recent benchmark tests, ranking first in 7 indicators. And the test was not done on the TPU with the best JAX performance. Although among developers, Pytorch is still more popular than Tensorflow. But in the future, perhaps more large models will be trained and run based on the JAX platform. Models Recently, the Keras team benchmarked three backends (TensorFlow, JAX, PyTorch) with the native PyTorch implementation and Keras2 with TensorFlow. First, they select a set of mainstream
AI subverts mathematical research! Fields Medal winner and Chinese-American mathematician led 11 top-ranked papers | Liked by Terence Tao
Apr 09, 2024 am 11:52 AM
AI is indeed changing mathematics. Recently, Tao Zhexuan, who has been paying close attention to this issue, forwarded the latest issue of "Bulletin of the American Mathematical Society" (Bulletin of the American Mathematical Society). Focusing on the topic "Will machines change mathematics?", many mathematicians expressed their opinions. The whole process was full of sparks, hardcore and exciting. The author has a strong lineup, including Fields Medal winner Akshay Venkatesh, Chinese mathematician Zheng Lejun, NYU computer scientist Ernest Davis and many other well-known scholars in the industry. The world of AI has changed dramatically. You know, many of these articles were submitted a year ago.
Tesla robots work in factories, Musk: The degree of freedom of hands will reach 22 this year!
May 06, 2024 pm 04:13 PM
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
FisheyeDetNet: the first target detection algorithm based on fisheye camera
Apr 26, 2024 am 11:37 AM
Target detection is a relatively mature problem in autonomous driving systems, among which pedestrian detection is one of the earliest algorithms to be deployed. Very comprehensive research has been carried out in most papers. However, distance perception using fisheye cameras for surround view is relatively less studied. Due to large radial distortion, standard bounding box representation is difficult to implement in fisheye cameras. To alleviate the above description, we explore extended bounding box, ellipse, and general polygon designs into polar/angular representations and define an instance segmentation mIOU metric to analyze these representations. The proposed model fisheyeDetNet with polygonal shape outperforms other models and simultaneously achieves 49.5% mAP on the Valeo fisheye camera dataset for autonomous driving
Yolov10: Detailed explanation, deployment and application all in one place!
Jun 07, 2024 pm 12:05 PM
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
