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NL2SQL-web-demo-master

于 2020-06-24 发布
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下载积分: 1 下载次数: 0
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代码说明：

说明：  一个简单的python网站开发 实现nl2sql(A simple Python website development)

文件列表：

NL2SQL-web-demo-master, 0 , 2019-12-28
NL2SQL-web-demo-master\README.md, 164 , 2019-12-28
NL2SQL-web-demo-master\backend, 0 , 2019-12-28
NL2SQL-web-demo-master\backend\data, 0 , 2019-12-28
NL2SQL-web-demo-master\backend\data\test.db, 24453120 , 2019-12-28
NL2SQL-web-demo-master\backend\server.py, 4013 , 2019-12-28
NL2SQL-web-demo-master\backend\sqlnet, 0 , 2019-12-28
NL2SQL-web-demo-master\backend\sqlnet\__init__.py, 0 , 2019-12-28
NL2SQL-web-demo-master\backend\sqlnet\__init__.pyc, 126 , 2019-12-28
NL2SQL-web-demo-master\backend\sqlnet\lib, 0 , 2019-12-28
NL2SQL-web-demo-master\backend\sqlnet\lib\__init__.py, 0 , 2019-12-28
NL2SQL-web-demo-master\backend\sqlnet\lib\__init__.pyc, 130 , 2019-12-28
NL2SQL-web-demo-master\backend\sqlnet\lib\dbengine.py, 2257 , 2019-12-28
NL2SQL-web-demo-master\backend\sqlnet\lib\dbengine.pyc, 2874 , 2019-12-28
NL2SQL-web-demo-master\backend\test.log, 4689572 , 2019-12-28
NL2SQL-web-demo-master\demo.mp4, 2137585 , 2019-12-28
NL2SQL-web-demo-master\demo.png, 203627 , 2019-12-28
NL2SQL-web-demo-master\frontend, 0 , 2019-12-28
NL2SQL-web-demo-master\frontend\.babelrc, 312 , 2019-12-28
NL2SQL-web-demo-master\frontend\.editorconfig, 147 , 2019-12-28
NL2SQL-web-demo-master\frontend\.gitignore, 145 , 2019-12-28
NL2SQL-web-demo-master\frontend\.postcssrc.js, 196 , 2019-12-28
NL2SQL-web-demo-master\frontend\README.md, 782 , 2019-12-28
NL2SQL-web-demo-master\frontend\build, 0 , 2019-12-28
NL2SQL-web-demo-master\frontend\build\build.js, 953 , 2019-12-28
NL2SQL-web-demo-master\frontend\build\check-versions.js, 1257 , 2019-12-28
NL2SQL-web-demo-master\frontend\build\dev-client.js, 245 , 2019-12-28
NL2SQL-web-demo-master\frontend\build\dev-server.js, 2444 , 2019-12-28
NL2SQL-web-demo-master\frontend\build\utils.js, 1949 , 2019-12-28
NL2SQL-web-demo-master\frontend\build\vue-loader.conf.js, 307 , 2019-12-28
NL2SQL-web-demo-master\frontend\build\webpack.base.conf.js, 1306 , 2019-12-28
NL2SQL-web-demo-master\frontend\build\webpack.dev.conf.js, 1225 , 2019-12-28
NL2SQL-web-demo-master\frontend\build\webpack.prod.conf.js, 3941 , 2019-12-28
NL2SQL-web-demo-master\frontend\config, 0 , 2019-12-28
NL2SQL-web-demo-master\frontend\config\dev.env.js, 139 , 2019-12-28
NL2SQL-web-demo-master\frontend\config\index.js, 1437 , 2019-12-28
NL2SQL-web-demo-master\frontend\config\prod.env.js, 48 , 2019-12-28
NL2SQL-web-demo-master\frontend\favicon.ico, 4021 , 2019-12-28
NL2SQL-web-demo-master\frontend\index.html, 1088 , 2019-12-28
NL2SQL-web-demo-master\frontend\package-lock.json, 305131 , 2019-12-28
NL2SQL-web-demo-master\frontend\package.json, 1619 , 2019-12-28
NL2SQL-web-demo-master\frontend\src, 0 , 2019-12-28
NL2SQL-web-demo-master\frontend\src\App.vue, 1211 , 2019-12-28
NL2SQL-web-demo-master\frontend\src\assets, 0 , 2019-12-28
NL2SQL-web-demo-master\frontend\src\assets\logo.png, 4021 , 2019-12-28
NL2SQL-web-demo-master\frontend\src\components, 0 , 2019-12-28
NL2SQL-web-demo-master\frontend\src\components\search.vue, 10716 , 2019-12-28
NL2SQL-web-demo-master\frontend\src\main.js, 349 , 2019-12-28
NL2SQL-web-demo-master\frontend\static, 0 , 2019-12-28
NL2SQL-web-demo-master\frontend\static\.gitkeep, 0 , 2019-12-28
NL2SQL-web-demo-master\frontend\static\logo.png, 9055 , 2019-12-28

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  • 梯度提升树算法实现
    提升方法是将弱学习算法提升为强学习算法的统计学习方法。在分类学习中，提升方法通过反复修改训练数据的权值分布，构建一系列的基本分类器（弱分类器），并将这些基本分类器线性组合，构成强分类器。提升树是建立在决策树上的一种提升方法。针对回归、分类问题，它采用的损失函数不同。对于回归问题，通常使用平方误差损失函数；而对于分类问题，通常使用指数损失函数。代表性的方法主要有AdaBoost算法以及梯度提升树算法（GBDT)。
    2020-12-11下载
    积分：1
  • ReliefF-2
    由于Relief算法比较简单，运行效率高，并且结果也比较令人满意，因此得到广泛应用，但是其局限性在于只能处理两类别数据，因此1994年Kononeill对其进行了扩展，得到了ReliefF作算法，可以处理多类别问题。该算法用于处理目标属性为连续值的回归问题。(Because Relief algorithm is relatively simple, high efficiency, and the results are more satisfactory, so widely used, but its limitation is that only two types of data can be processed, so Kononeill in 1994 to expand it, get ReliefF algorithm , Can handle multiple categories of problems. The algorithm is used to deal with the regression problem that the target attribute is a continuous value.)
    2017-05-28 21:08:48下载
    积分：1
  • tempxgboot
    说明：  气象因素 ：全球海平面气温 二氧化碳浓度 太阳总辐照度 利用气象因素和xgboost预测未来25年气温变化(Meteorological factors: global sea level temperature, carbon dioxide concentration, total solar irradiance Prediction of Temperature Change in the Next 25 Years Using Meteorological Factors and xgboost)
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    python tornado web开源框架最新版3.3 facebook用的哦(The latest version 3.3 of open source framework tornado)
    2014-02-28 10:03:39下载
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    说明：  python实现实时视频流传输和显示，从客户端到服务器端(Python implements real-time video streaming transmission and display from client to server)
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    Intro to Game Design, Pygame范例(Pygame Sample Codes)
    2013-12-05 08:05:01下载
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    Python密度聚类 最近在Science上的一篇基于密度的聚类算法《Clustering by fast search and find of density peaks》引起了大家的关注(在我的博文“论文中的机器学习算法——基于密度峰值的聚类算法”中也进行了中文的描述)。于是我就想了解下基于密度的聚类算法，熟悉下基于密度的聚类算法与基于距离的聚类算法，如K-Means算法之间的区别。 基于密度的聚类算法主要的目标是寻找被低密度区域分离的高密度区域。与基于距离的聚类算法不同的是，基于距离的聚类算法的聚类结果是球状的簇，而基于密度的聚类算法可以发现任意形状的聚类，这对于带有噪音点的数据起着重要的作用。(The main goal of the density based clustering algorithm is to find high density regions separated by low density regions. Different from distance based clustering algorithm, the clustering results based on distance clustering algorithm are spherical clusters, and density based clustering algorithm can detect clustering of arbitrary shapes, which plays an important role in data with noisy points.)
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    2019-03-06 20:12:54下载
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