ThinkPHP 之 自定义模型、连贯操作要点
我来总结一下学了什么~
获取数据主键的方法:$goods_model -> getPk();
实例化数据表两种方法 new Model() ; M()快捷方法 快捷方法只占用一次内存而第一种方法会每次增加一次内存存储量
query()获得查询结果 execute()获得影响行数
D()方法用来获取自定义模型,D()函数的执行顺序为首先查找自定义模型当文件名和类名符合规则后执行自定义模型,若有不符合则查找表名进行实例化,若没有符合表名则返回false
连贯操作
field()为显示范围,where()为取值条件:用数组或对象作为条件,limit()为取值个数,order(para desc)为排序,group()为按照字段分组自动排序,having()另一种取值条件,table(tbname)为跨越表取值,table(db.tbname)为跨越数据库进行取值,定义数组$cont['para']=array('like','%a%');进行生成数组条件控制sql,sum()求和,avg()平均数,count()求个数

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

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

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.

In Go, WebSocket messages can be sent using the gorilla/websocket package. Specific steps: Establish a WebSocket connection. Send a text message: Call WriteMessage(websocket.TextMessage,[]byte("Message")). Send a binary message: call WriteMessage(websocket.BinaryMessage,[]byte{1,2,3}).

The benchmark YOLO series of target detection systems has once again received a major upgrade. Since the release of YOLOv9 in February this year, the baton of the YOLO (YouOnlyLookOnce) series has been passed to the hands of researchers at Tsinghua University. Last weekend, the news of the launch of YOLOv10 attracted the attention of the AI community. It is considered a breakthrough framework in the field of computer vision and is known for its real-time end-to-end object detection capabilities, continuing the legacy of the YOLO series by providing a powerful solution that combines efficiency and accuracy. Paper address: https://arxiv.org/pdf/2405.14458 Project address: https://github.com/THU-MIG/yo

Memory leaks can cause Go program memory to continuously increase by: closing resources that are no longer in use, such as files, network connections, and database connections. Use weak references to prevent memory leaks and target objects for garbage collection when they are no longer strongly referenced. Using go coroutine, the coroutine stack memory will be automatically released when exiting to avoid memory leaks.

70B model, 1000 tokens can be generated in seconds, which translates into nearly 4000 characters! The researchers fine-tuned Llama3 and introduced an acceleration algorithm. Compared with the native version, the speed is 13 times faster! Not only is it fast, its performance on code rewriting tasks even surpasses GPT-4o. This achievement comes from anysphere, the team behind the popular AI programming artifact Cursor, and OpenAI also participated in the investment. You must know that on Groq, a well-known fast inference acceleration framework, the inference speed of 70BLlama3 is only more than 300 tokens per second. With the speed of Cursor, it can be said that it achieves near-instant complete code file editing. Some people call it a good guy, if you put Curs

Last week, amid the internal wave of resignations and external criticism, OpenAI was plagued by internal and external troubles: - The infringement of the widow sister sparked global heated discussions - Employees signing "overlord clauses" were exposed one after another - Netizens listed Ultraman's "seven deadly sins" Rumors refuting: According to leaked information and documents obtained by Vox, OpenAI’s senior leadership, including Altman, was well aware of these equity recovery provisions and signed off on them. In addition, there is a serious and urgent issue facing OpenAI - AI safety. The recent departures of five security-related employees, including two of its most prominent employees, and the dissolution of the "Super Alignment" team have once again put OpenAI's security issues in the spotlight. Fortune magazine reported that OpenA

In Go, you can use regular expressions to match timestamps: compile a regular expression string, such as the one used to match ISO8601 timestamps: ^\d{4}-\d{2}-\d{2}T \d{2}:\d{2}:\d{2}(\.\d+)?(Z|[+-][0-9]{2}:[0-9]{2})$ . Use the regexp.MatchString function to check if a string matches a regular expression.
