├── README.md ├── README_OLD.md ├── code ├── tmp │ ├── matplotlib │ │ ├── README.md │ │ ├── examples │ │ │ └── spx.csv │ │ └── matplotlib-beginner.ipynb │ ├── numpy │ │ ├── README.md │ │ ├── images │ │ │ ├── 1.png │ │ │ ├── 2.png │ │ │ ├── 3.png │ │ │ └── 4.png │ │ └── numpy-beginner.ipynb │ ├── pandas │ │ ├── 10-Minutes-to-pandas │ │ │ ├── .ipynb_checkpoints │ │ │ │ └── 10 Minutes to pandas-checkpoint.ipynb │ │ │ ├── 10 Minutes to pandas.ipynb │ │ │ ├── README.md │ │ │ └── data │ │ │ │ ├── foo.csv │ │ │ │ ├── foo.h5 │ │ │ │ └── foo.xlsx │ │ ├── Pandas_Exercises │ │ │ └── README.md │ │ ├── README.md │ │ └── pandas_beginner │ │ │ ├── .ipynb_checkpoints │ │ │ └── pandas_beginner-checkpoint.ipynb │ │ │ ├── README.md │ │ │ ├── pandas_beginner.ipynb │ │ │ └── student.csv │ ├── seaborn │ │ ├── .ipynb_checkpoints │ │ │ └── Searborn-checkpoint.ipynb │ │ ├── README.md │ │ └── Searborn.ipynb │ ├── 案例 │ │ └── covid19 │ │ │ ├── README.md │ │ │ ├── code │ │ │ ├── .ipynb_checkpoints │ │ │ │ ├── COVID-19_ EDA with recent update on April-checkpoint.ipynb │ │ │ │ └── coronavirus-covid-19-visualization-prediction-checkpoint.ipynb │ │ │ ├── COVID-19_ EDA with recent update on April.ipynb │ │ │ └── coronavirus-covid-19-visualization-prediction.ipynb │ │ │ └── input │ │ │ └── covid19 │ │ │ ├── submission.csv │ │ │ ├── test.csv │ │ │ ├── train.csv │ │ │ ├── usa_states.csv │ │ │ └── usa_states2.csv │ └── 神经网络 │ │ ├── README.md │ │ └── pytorch-tutorial-master │ │ ├── .gitignore │ │ ├── LICENSE │ │ ├── README.md │ │ ├── logo │ │ ├── pytorch_logo.png │ │ └── pytorch_logo_2018.svg │ │ └── tutorials │ │ ├── 01-basics │ │ ├── feedforward_neural_network │ │ │ └── main.py │ │ ├── linear_regression │ │ │ └── main.py │ │ ├── logistic_regression │ │ │ └── main.py │ │ └── pytorch_basics │ │ │ ├── main.ipynb │ │ │ └── main.py │ │ ├── 02-intermediate │ │ ├── bidirectional_recurrent_neural_network │ │ │ └── main.py │ │ ├── convolutional_neural_network │ │ │ └── main.py │ │ ├── deep_residual_network │ │ │ └── main.py │ │ ├── language_model │ │ │ ├── data_utils.py │ │ │ └── main.py │ │ └── recurrent_neural_network │ │ │ └── main.py │ │ ├── 03-advanced │ │ ├── generative_adversarial_network │ │ │ └── main.py │ │ ├── image_captioning │ │ │ ├── README.md │ │ │ ├── build_vocab.py │ │ │ ├── data_loader.py │ │ │ ├── download.sh │ │ │ ├── model.py │ │ │ ├── png │ │ │ │ ├── example.png │ │ │ │ ├── image_captioning.png │ │ │ │ └── model.png │ │ │ ├── requirements.txt │ │ │ ├── resize.py │ │ │ ├── sample.py │ │ │ └── train.py │ │ ├── neural_style_transfer │ │ │ ├── README.md │ │ │ ├── main.py │ │ │ ├── png │ │ │ │ ├── content.png │ │ │ │ ├── neural_style.png │ │ │ │ ├── neural_style2.png │ │ │ │ ├── style.png │ │ │ │ ├── style2.png │ │ │ │ ├── style3.png │ │ │ │ └── style4.png │ │ │ └── requirements.txt │ │ └── variational_autoencoder │ │ │ └── main.py │ │ └── 04-utils │ │ └── tensorboard │ │ ├── README.md │ │ ├── gif │ │ └── tensorboard.gif │ │ ├── logger.py │ │ ├── main.py │ │ └── requirements.txt └── 历年赛题 │ └── 2020年B题--汽油辛烷值建模 │ ├── 2 │ └── MathModeling2020-master │ │ ├── .gitignore │ │ ├── data_processing.py │ │ ├── jrb │ │ ├── data.xlsx │ │ ├── model.py │ │ ├── pca.py │ │ ├── pick.py │ │ ├── requirements.txt │ │ ├── x.xlsx │ │ └── y.xlsx │ │ ├── modeling.py │ │ ├── optimization.py │ │ ├── requirements.txt │ │ ├── scatter.py │ │ ├── statistic.py │ │ └── util.py │ ├── 1(有论文 │ ├── code │ │ ├── 285.csv │ │ ├── 313.csv │ │ ├── PCA_ICA.ipynb │ │ ├── RON_loss.txt │ │ ├── data_analyze.ipynb │ │ ├── first_it.pdf │ │ ├── helloworld.py │ │ ├── ml_pipeline.drawio │ │ ├── opt2.drawio │ │ ├── pipeline.ipynb │ │ ├── processed.csv │ │ ├── raw_data.csv │ │ ├── requirements.txt │ │ ├── second_it.pdf │ │ ├── train_lgbm │ │ │ ├── RON_loss_abnormal.pdf │ │ │ ├── RON_loss_abnormal.png │ │ │ ├── RON_loss_cross-validation.pdf │ │ │ ├── RON_loss_cross-validation.png │ │ │ ├── RON_loss_data.csv │ │ │ ├── RON_loss_model.bz2 │ │ │ ├── S_cross-validation.pdf │ │ │ ├── S_cross-validation.png │ │ │ ├── S_data.csv │ │ │ ├── S_model.bz2 │ │ │ ├── log.txt │ │ │ └── train_lgbm.py │ │ ├── 五题关键代码1.txt │ │ ├── 五题关键代码2.txt │ │ └── 附件一：325个样本数据.xlsx │ ├── paper.pdf │ └── 对汽油辛烷值的多变量预测和优化.pdf │ └── 题目&数据 │ ├── ~$二：样本确定方法.docx │ ├── ~$辛烷值优化建模.docx │ ├── 汽油辛烷值优化建模.docx │ ├── 附件一：325个样本数据.xlsx │ ├── 附件三：285号和313号样本原始数据.xlsx │ ├── 附件二：样本确定方法.docx │ └── 附件四：354个操作变量信息.xlsx ├── 实用工具 ├── README.md └── 不用code的网站.md ├── 建模算法 ├── Dijkstra │ ├── dijkstra.py │ ├── question1.1.ipynb │ ├── question1.2.ipynb │ ├── 附件1：数据集1-终稿.xlsx │ └── 附件2：数据集2-终稿.xlsx ├── logistic_similar │ ├── logstic_similar.md │ └── logstic_similar.py ├── lstm │ ├── data.csv │ ├── data.net_params.pkl │ └── lstm.py ├── pipline │ └── mlFlow.ipynb ├── 可视化 │ └── 可视化以恐怖袭击为例.ipynb ├── 回归 │ └── 线性回归 │ │ ├── .ipynb_checkpoints │ │ ├── boston-checkpoint.ipynb │ │ ├── pytorch_linear_distributed-checkpoint.py │ │ └── 线性回归-checkpoint.md │ │ ├── boston.ipynb │ │ ├── pytorch_linear_distributed.py │ │ └── 线性回归.md ├── 数学建模 四大模型总结 - 百度文库.pdf ├── 灰色关联度分析 │ ├── GRA.py │ ├── wine.csv │ └── 灰色关联度分析.md ├── 约束优化 │ ├── .ipynb_checkpoints │ │ ├── gurobi-checkpoint.ipynb │ │ └── gurobi求解器-checkpoint.md │ ├── gurobi.ipynb │ ├── gurobi求解器.md │ └── 贪心.ipynb ├── 组合赋权法 │ ├── AHP.py │ └── 组合赋权.md ├── 缺失值插补 │ ├── imputer.ipynb │ ├── imputer.py │ └── 插补.md ├── 聚类 │ └── 划分聚类 │ │ ├── wkmeans.md │ │ └── wkmeans.py ├── 降维 │ ├── .ipynb_checkpoints │ │ └── pca_analyze-checkpoint.py │ ├── factor_analyze.py │ ├── pca_analyze.py │ ├── picture │ │ ├── 因子分析法1.png │ │ ├── 因子分析法10.png │ │ ├── 因子分析法2.png │ │ ├── 因子分析法3.png │ │ ├── 因子分析法4.png │ │ ├── 因子分析法5.png │ │ ├── 因子分析法6.png │ │ ├── 因子分析法7.png │ │ ├── 因子分析法8.png │ │ └── 因子分析法9.png │ ├── terrorism.ipynb │ └── 因子分析法.md ├── 集成学习 │ ├── .ipynb_checkpoints │ │ └── classify_regression_visual-checkpoint.ipynb │ ├── classify_regression_visual.ipynb │ └── 集成学习.md └── 预测 │ ├── kaggle预测题 │ └── Rossmann_Store_Sales.ipynb │ ├── ym_num.csv │ ├── 整合移动平均自回归(ARIMA) │ ├── .ipynb_checkpoints │ │ ├── ARIMA-checkpoint │ │ └── ARIMA-checkpoint.ipynb │ ├── ARIMA.ipynb │ ├── ARIMA.md │ └── arima.py │ └── 灰色预测 │ ├── .ipynb_checkpoints │ ├── 灰色预测&线性回归&函数拟合-checkpoint.ipynb │ └── 灰色预测-checkpoint.md │ ├── gm.py │ ├── 灰色预测&线性回归&函数拟合.ipynb │ └── 灰色预测.md ├── 数模之星 ├── D题-上海大学-答辩PPT.pptx ├── D题-上海大学-论文.pdf └── 提交附件 │ └── 代码与中间结果 │ ├── data │ ├── ADMET.xlsx │ ├── ERα_activity.xlsx │ ├── Molecular_Descriptor.xlsx │ └── 分子描述符含义解释.xlsx │ ├── 第一题 │ ├── 20feature.csv │ ├── molecular_des_corr_del.csv │ └── 第一问.ipynb │ ├── 第三题 │ ├── finish_model_1.pkl │ ├── finish_model_2.pkl │ ├── finish_model_3.pkl │ ├── finish_model_4.pkl │ ├── finish_model_5.pkl │ ├── molecular_des_corr_del.csv │ ├── p1_ss.pkl │ ├── 第一个模型.ipynb │ ├── 第三个模型.ipynb │ ├── 第二个模型.ipynb │ ├── 第五个模型.ipynb │ └── 第四个模型.ipynb │ ├── 第二题 │ ├── 2参数优化表.csv │ └── 第二问.ipynb │ ├── 第四题 │ ├── 第四问.ipynb │ ├── 第四问2.ipynb │ └── 迭代最优个体_20_2.csv │ └── 论文图片 │ ├── 278e4ad860135def985fe222adb3b89.png │ ├── 3bf319ce8617dd843b409c818272be0.png │ ├── 6fc4448ec4bd6999fa797a3e9d4dd40.png │ ├── Figure 1.png │ ├── Figure 10.png │ ├── Figure 11.png │ ├── Figure 12.png │ ├── Figure 13.png │ ├── Figure 16.png │ ├── Figure 17.png │ ├── Figure 2.png │ ├── Figure 3.png │ ├── Figure 4.png │ ├── Figure 4_.png │ ├── Figure 7.png │ ├── Figure 8.png │ ├── Figure 9.png │ ├── RT_TRAIN.png │ ├── df_residual.png │ ├── gdbt_residual.png │ ├── knn_residual.png │ ├── png_CYP3A4 Feature importance.png │ ├── png_Caco-2 Feature importance.png │ ├── png_HOB Feature importance.png │ ├── png_MH Feature importance.png │ ├── png_hERG Feature importance.png │ ├── r2_val.png │ ├── rf_residual.png │ ├── stack_residual.png │ └── svr_residual.png ├── 杂七杂八 ├── README.md ├── Scikit-learn使用总结.pdf ├── 建模交流-zxm.pdf ├── 建模交流_sw.pdf ├── 研究生数模国赛报告.pdf └── 谈谈“竞赛套路化”问题.pdf └── 论文模板 ├── 2021数模论文模板 - 备份.doc └── README.md /README.md: -------------------------------------------------------------------------------- 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