7cart is a base project for building online shops, catalogs or service platforms. 7cart is written with simple code and simple database schema. It is easy to support and fast.
Play with live demo!
Try it!
Why 7cart?
7cart is for building projects that can't be built with the existing e-commerce platforms. It aims to be free of:
- difficult to support code and database
- slow requests
How is 7cart better?
The project database doesn't implement EAV table,but the Entity table with JSONB columns.That gives:
- less relations and simple code
- simpler and faster SQL queries [proof link]
Features
- built in Docker,it can be quickly deployed and run on different OSs;
- built-in Product Filters and Filer Counters
Technology stack
Built as a Single Page Application (SPA),it utilises the latest possible
- Ember.js
- Symfony 4
- PostgreSQL
- PHP 7
Download
Download Zip with project source code.
What do you think of this?
Tell us in the issue tracker
or message to 7cart Facebook page
-
1. Cart树简介¶ Cart模型是一种决策树模型，它即可以用于分类，也可以用于回归，其学习算法分为下面两步： （1）决策树生成：用训练数据生成决策树，生成树尽可能大 （2）决策树剪枝：基于损失函数最小化的剪枝，用验证数据对生成的数据进行剪枝。 分类和回归树模型采用不同的最优化策略。Cart回归树使用平方误差最小化策略，Cart分类生成树采用的基尼指数最小化策略。 Scikit-learn中有两
-
1. 回归决策树构建原理¶ CART 回归树和 CART 分类树的不同之处在于: CART 分类树预测输出的是一个离散值，CART 回归树预测输出的是一个连续值。 CART 分类树使用基尼指数作为划分、构建树的依据，CART 回归树使用平方损失。 分类树使用叶子节点里出现更多次数的类别作为预测类别，回归树则采用叶子节点里均值作为预测输出 CART 回归树构建: Loss(y,f(x))=(f(x