Merge branch 'main' of https://github.com/Bin-Cao/Bgolearn

README.md

@@ -7,9 +7,46 @@
 </h1>
 
 
-
 🤝🤝🤝 Please star ⭐️ it for promoting open source projects 🌍 ! Thanks !
 
+## links
+![Screenshot 2023-11-16 at 11 23 35](https://github.com/Bin-Cao/Bgolearn/assets/86995074/cd0d24e4-06db-45f7-b6d6-12750fa8b819)
+
+- https://www.wheelodex.org/projects/bgolearn/
+- https://pypi.tuna.tsinghua.edu.cn/simple/bgolearn/
+- [user count](https://pypistats.org/packages/bgolearn)
+
+
+### for regression
+- 1.Expected Improvement algorith (期望提升函数)
+
+- 2.Expected improvement with “plugin” (有“plugin”的期望提升函数)
+
+- 3.Augmented Expected Improvement (增广期望提升函数)
+
+- 4.Expected Quantile Improvement (期望分位提升函数)
+
+- 5.Reinterpolation Expected Improvement (重插值期望提升函数)
+
+- 6.Upper confidence bound (高斯上确界函数)
+
+- 7.Probability of Improvement (概率提升函数)
+
+- 8.Predictive Entropy Search (预测熵搜索函数)
+
+- 9.Knowledge Gradient (知识梯度函数)
+
+###  for classification
+- 1.Least Confidence (欠信度函数)
+
+- 2.Margin Sampling (边界函数)
+
+- 3.Entropy-based approach (熵索函数)
+
+## Download History （- Nov16，2023）
+![WechatIMG4661](https://github.com/Bin-Cao/Bgolearn/assets/86995074/591e26b4-c8c3-4a17-ae8b-b3bcf9237514)
+
+
 if you have any questions or need help, you are welcome to contact me
 
 Source code: [![](https://img.shields.io/badge/PyPI-caobin-blue)](https://pypi.org/project/Bgolearn/)
@@ -26,13 +63,44 @@ Source code: [![](https://img.shields.io/badge/PyPI-caobin-blue)](https://pypi.o
 ## Package Document / 手册
 see 📒 [Bgolearn](https://bgolearn.netlify.app) (Click to view）
 
+见 📒 [中文说明](https://mp.weixin.qq.com/s/y-i_2ixbtJOv-nEYDu9THg) (Click to view）
 
 Written using Python, which is suitable for operating systems, e.g., Windows/Linux/MAC OS etc.
 
-
+## Template 
+``` javascript
+# import BGOsampling after installation 
+# 安装后, 通过此命令调用BGOsampling类
+import Bgolearn.BGOsampling as BGOS
+
+# import your dataset (Samples have been characterized)
+# 导入研究的数据集(已经表征过的样本)
+data = pd.read_csv('data.csv') 
+# features 
+x = data.iloc[:,:-1]
+# response / target 
+y = data.iloc[:,-1]
+
+# virtual samples which have same feature dimension with x
+# 设计的虚拟样本, 与x具有相同的维度
+vs = pd.read_csv('virtual_data.csv') 
+
+# instantiate class
+# 实例化类 Bgolearn
+Bgolearn = BGOS.Bgolearn() 
+
+# Pass parameters to the function
+# 传入参数
+Mymodel = Bgolearn.fit(data_matrix = x, Measured_response = y, virtual_samples = vs)
+
+# derive the result by EI
+# 通过EI导出结果
+Mymodel.EI()
+```
 
 If you are using this code, please cite:
-+ Zhang Tong-yi, Cao Bin, Wang Yuanhao, Tian Yuan, Sun Sheng. Bayesian global optimization package for material design [2022SR1481726], 2022, Software copyright, GitHub : github.com/Bin-Cao/Bgolearn.
+
+    Zhang Tong-yi, Cao Bin, Wang Yuanhao, Tian Yuan, Sun Sheng. Bayesian global optimization package for material design [2022SR1481726], 2022, Software copyright, GitHub : github.com/Bin-Cao/Bgolearn.
 
 ## Installing / 安装
     pip install Bgolearn 
@@ -50,10 +118,26 @@ Before version 2.0, function building
 
 Bgolearn V2.1.1 Jun 9, 2023. *para noise_std* By default, the built-in Gaussian process model estimates the noise of the input dataset by maximum likelihood, and yields in a more robust model.
 
+
+``` javascript
+from BgoKit import ToolKit
+# vs is the virtual samples
+# score_1,score_2 are output of Bgolearn
+
+Model = ToolKit.MultiOpt(vs,[score_1,score_2])
+Model.BiSearch()
+Model.plot_distribution()
+```
+See : [Link](https://github.com/Bin-Cao/Bgolearn/blob/main/Template/%E4%B8%AD%E6%96%87%E7%A4%BA%E4%BE%8B/%E5%A4%9A%E7%9B%AE%E6%A0%87%E5%AE%9E%E7%8E%B0/%E5%A4%9A%E7%9B%AE%E6%A0%87.ipynb)
+
+
 ``` javascript
 Thank you for choosing Bgolearn for material design. 
 Bgolearn is developed to facilitate the application of machine learning in research.
-Bgolearn is designed for optimizing single-target material properties. 
+Bgolearn is designed for optimizing single-target material properties.
+The BgoKit package is being developed to facilitate multi-task design.
+
+
 If you need to perform multi-target optimization, here are two important reminders:
 
 1. Multi-tasks can be converted into a single task using domain knowledge.