numpy实现的周志华《机器学习》书中的算法及其他一些传统机器学习算法.zip

# tinyml 利用numpy实现的一些周志华《机器学习》（西瓜书）一书及 斯坦福cs229课程中的算法，宜配合西瓜书和cs229课件食用。并选择性实现了一些经典算法的简易版本， 如 按照陈天奇的slides实现的XGBRegressor。 ## 已经实现的算法 - **线性模型** - [LinearRegression](/tinyml/linear_model/LinearRegression.py) [线性回归闭式解推导](notes/linear_model/linear_reg_closed_form.pdf) - [LogisticRegression](/tinyml/linear_model/LogisticRegression.py) [逻辑回归相关推导](/notes/linear_model/logistic_regression.pdf) - [SGDRegressor](/tinyml/linear_model/SGDRegressor.py) - [LocallyWeightedLinearRegression](/tinyml/linear_model/LocallyWeightedLinearRegression.py) - **判别分析** - [LDA](/tinyml/discriminant_analysis/LDA.py) - [GDA](/tinyml/discriminant_analysis/GDA.py) - **决策回归树** - [DecisionTreeClassifier](/tinyml/tree/DecisionTreeClassifier.py) - [DecisionTreeRegressor](/tinyml/tree/DecisionTreeRegressor.py) - **支持向量机** - [SVC](/tinyml/svm/SVC.py) - **贝叶斯** - [NaiveBayesClassifier](/tinyml/bayes/NaiveBayesClassifier.py) - **聚类算法** - [KMeans](/tinyml/cluster/KMeans.py) - [LVQ](/tinyml/cluster/LVQ.py) - [GaussianMixture](/tinyml/cluster/GaussianMixture.py) - [DBSCAN](/tinyml/cluster/DBSCAN.py) - [AGNES](/tinyml/cluster/AGNES.py) - **降维算法** - [MDS](/tinyml/dimension_reduction/MDS.py) - [PCA](/tinyml/dimension_reduction/PCA.py) - [KernelPCA](/tinyml/dimension_reduction/KernelPCA.py) - [LLE](/tinyml/dimension_reduction/LLE.py) - [Isomap](/tinyml/dimension_reduction/Isomap.py) - **集成学习** - [AdaBoostClassifier](/tinyml/ensemble/AdaBoostClassifier.py) - [GradientBoostingRegressor](/tinyml/ensemble/GradientBoostingRegressor.py) - [RandomForestRegressor](/tinyml/ensemble/RandomForestRegressor.py) - [XGBRegressor](/tinyml/ensemble/XGBRegressor.py) - **特征选择** - [ReliefFeatureSelection](/tinyml/feature_selection/ReliefFeatureSelection.py) ## 和sklearn实现的比较 - **回归算法结果** [代码](/tinyml/compare/compare_regresssor.py) <table> <tr> <td rowspan="2">Algorithm vs. RMSE</td> <td colspan="2">sklearn-boston</td> </tr> <tr> <td>tinyml</td> <td>sklearn</td> </tr> <tr> <td>LinearRegression</td> <td>27.196</td> <td>27.196</td> </tr> <tr> <td>SGDRegressor</td> <td>27.246</td> <td>27.231</td> </tr> <tr> <td>DecisionTreeRegressor</td> <td>21.887</td> <td>21.761</td> </tr> <tr> <td>RandomForestRegressor</td> <td>21.142</td> <td>21.142</td> </tr> <tr> <td>GradientBoostRegressor</td> <td>16.778</td> <td>16.106</td> </tr> <tr> <td>XGBRegressor</td> <td>20.149</td> <td>15.7</td> </tr> </table> - **分类算法结果** [代码](/tinyml/compare/compare_classification.py) <table> <tr> <td rowspan="2">Algorithm vs. RMSE</td> <td colspan="2">sklearn-breast_cancer</td> </tr> <tr> <td>tinyml</td> <td>sklearn</td> </tr> <tr> <td>NaiveBayes</td> <td>90.64%</td> <td>90.64%</td> </tr> <tr> <td>LogisticRegression</td> <td>92.98%</td> <td>92.98%</td> </tr> <tr> <td>LDA</td> <td>94.15%</td> <td>92.40%</td> </tr> <tr> <td>GDA</td> <td>92.40%</td> <td>93.57%</td> </tr> <tr> <td>SVC</td> <td>86.55%</td> <td>92.98%</td> </tr> <tr> <td>AdaboostClassifier</td> <td>92.40%</td> <td>92.40%</td> </tr> </table> - **聚类算法比较** [代码](/tinyml/compare/compare_clustering.py) - KMeans <div align="center"> <img src="/tinyml/compare/cluster_result/tinyml_KMeans.jpg" height="300px" alt="tinyml KMeans" > <img src="/tinyml/compare/cluster_result/sklearn_KMeans.jpg" height="300px" alt="sklearn KMeans" > </div> - DBSCAN <div align="center"> <img src="/tinyml/compare/cluster_result/tinyml_DBSCAN.jpg" height="300px" alt="tinyml DBSCAN" > <img src="/tinyml/compare/cluster_result/sklearn_DBSCAN.jpg" height="300px" alt="sklearn DBSCAN" > </div> - GMM <div align="center"> <img src="/tinyml/compare/cluster_result/tinyml_GMM.jpg" height="300px" alt="tinyml GMM" > <img src="/tinyml/compare/cluster_result/sklearn_GMM.jpg" height="300px" alt="sklearn GMM" > </div> - AGNES <div align="center"> <img src="/tinyml/compare/cluster_result/tinyml_AGNES.jpg" height="300px" alt="tinyml AGNES" > <img src="/tinyml/compare/cluster_result/sklearn_AGNES.jpg" height="300px" alt="sklearn AGNES" > </div> - **降维算法比较** [代码](/tinyml/compare/compare_dimension_reduction.py) - PCA <div align="center"> <img src="/tinyml/compare/dimension_reduction_result/tinyml_PCA.jpg" height="300px" alt="tinyml PCA" > <img src="/tinyml/compare/dimension_reduction_result/sklearn_PCA.jpg" height="300px" alt="sklearn PCA" > </div> - KernalPCA <div align="center"> <img src="/tinyml/compare/dimension_reduction_result/tinyml_KernalPCA.jpg" height="300px" alt="tinyml KernalPCA" > <img src="/tinyml/compare/dimension_reduction_result/sklearn_KernalPCA.jpg" height="300px" alt="sklearn KernalPCA" > </div> - LLE <div align="center"> <img src="/tinyml/compare/dimension_reduction_result/tinyml_LLE.jpg" height="300px" alt="tinyml LLE" > <img src="/tinyml/compare/dimension_reduction_result/sklearn_LLE.jpg" height="300px" alt="sklearn LLE" > </div> - MDS <div align="center"> <img src="/tinyml/compare/dimension_reduction_result/tinyml_MDS.jpg" height="300px" alt="tinyml MDS" > <img src="/tinyml/compare/dimension_reduction_result/sklearn_MDS.jpg" height="300px" alt="sklearn MDS" > </div> 免责声明： 1.本资源仅供学习和交流使用，不保证其准确性、完整性、及时性或适用性。 2.本资源仅包含一般信息，不构成专业建议。在使用本资源时，请务必自行研究并谨慎决策。 3.我已尽力确保本资源的正确性和合法性，但不对其准确性、完整性和及时性做出保证。 4.本资源不应用于商业用途。 5.在使用本资源的过程中，用户应自行承担所有风险和责任，并遵守相关法律法规。 6.对于因使用本资源而产生的任何损失或损害，我概不负责。 请确保在使用本资源时仔细阅读并遵守以上免责声明。如果您有任何疑问或需要进一步帮助，请联系我。