Backend Development
PHP Tutorial
Laravel framework study notes (2) Project actual models (Models), laravelmodels_PHP tutorial
Laravel framework study notes (2) Project actual models (Models), laravelmodels_PHP tutorial
The previous article has introduced the establishment of the development environment. This article will focus on actual project development and understand the laravel framework step by step. First, let’s understand the model of laravel framework (Models)
When developing an mvc project, models are the first step.
Let’s start with modeling.
1. Entity relationship diagram,
Since I don’t know any good modeling tools in PHP, here I use vs ado.net entity model data modeling
Let’s start laravel coding. Before coding, you must first configure the database connection. In the app/config/database.php file
'mysql' => array( 'driver' => 'mysql', 'read' => array( 'host' => '127.0.0.1:3306', ), 'write' => array( 'host' => '127.0.0.1:3306' ), 'database' => 'test', 'username' => 'root', 'password' => 'root', 'charset' => 'utf8', 'collation' => 'utf8_unicode_ci', 'prefix' => '', ),
After configuration, you need to use the artisan tool, which is a php command tool in the laravel directory
First you need to create a migration migrate through artisan, which is almost exactly the same as asp.net mvc
Shfit + right-click in the laravel directory to open the command window and enter artisan migrate:make create_XXXX to generate a migration file with a timestamp prefix under the app/database/migrations file
Code:
<?php
use Illuminate\Database\Schema\Blueprint;
use Illuminate\Database\Migrations\Migration;
class CreateTablenameTable extends Migration {
/**
* Run the migrations.
*
* @return void
*/
public function up()
{
}
/**
* Reverse the migrations.
*
* @return void
*/
public function down()
{
}
}Anyone who has experience in entityframework migration here basically finds that it is surprisingly similar.
The next step is to create our entity structure. Laravel’s structure generator can refer to http://www.php.cn/
<?php
use Illuminate\Database\Schema\Blueprint;
use Illuminate\Database\Migrations\Migration;
class CreateTablenameTable extends Migration {
/**
* Run the migrations.
*
* @return void
*/
public function up()
{
Schema::create('posts', function(Blueprint $table) {
$table->increments('id');
$table->unsignedInteger('user_id');
$table->string('title');
$table->string('read_more');
$table->text('content');
$table->unsignedInteger('comment_count');
$table->timestamps();
});
Schema::create('comments', function(Blueprint $table) {
$table->increments('id');
$table->unsignedInteger('post_id');
$table->string('commenter');
$table->string('email');
$table->text('comment');
$table->boolean('approved');
$table->timestamps();
});
Schema::table('users', function (Blueprint $table) {
$table->create();
$table->increments('id');
$table->string('username');
$table->string('password');
$table->string('email');
$table->string('remember_token', 100)->nullable();
$table->timestamps();
});
}
/**
* Reverse the migrations.
*
* @return void
*/
public function down()
{
Schema::drop('posts');
Schema::drop('comments');
Schema::drop('users');
}
}Continue to enter php artisan migrate in the above command window and the migration will be executed
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
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
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
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.
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
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
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
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
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
DualBEV: significantly surpassing BEVFormer and BEVDet4D, open the book!
Mar 21, 2024 pm 05:21 PM
This paper explores the problem of accurately detecting objects from different viewing angles (such as perspective and bird's-eye view) in autonomous driving, especially how to effectively transform features from perspective (PV) to bird's-eye view (BEV) space. Transformation is implemented via the Visual Transformation (VT) module. Existing methods are broadly divided into two strategies: 2D to 3D and 3D to 2D conversion. 2D-to-3D methods improve dense 2D features by predicting depth probabilities, but the inherent uncertainty of depth predictions, especially in distant regions, may introduce inaccuracies. While 3D to 2D methods usually use 3D queries to sample 2D features and learn the attention weights of the correspondence between 3D and 2D features through a Transformer, which increases the computational and deployment time.
